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Feature Papers in Chemical Engineering

A special issue of ChemEngineering (ISSN 2305-7084).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 209474

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Laboratory of Separation and Reaction Engineering - Laboratory of Catalysis and Materials (LSRE-LCM), Department of Chemical Engineering, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
Interests: chemical engineering; bioengineering; materials engineering
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Department of Chemical, Paper, and Biomedical Engineering, Miami University, 64 Engineering Building 650 E High Street, Oxford, OH 45056, USA
Interests: thermodynamics; phase-equilibrium; molecular simulation; separation processes
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Special Issue Information

Dear Colleagues,

Chemical engineering combines chemistry, physics, biology, and mathematics with engineering sciences and economics to transform raw materials into useful products in a green and sustainable way. Modern chemical engineering (ChE) can be represented by ChE = M2P2 with M2 for Molecular and Materials Engineering and P2 for Process and Product Engineering. Chemical Engineering spreads over many areas, such as energy systems, environmental, medicine, biotechnology, microelectronics, advanced materials, consumer products, and additive manufacturing.

This Special Issue aims to encourage scientists and engineers to publish your experimental and theoretical results in as much detail as possible. We invite relevant experts and colleagues to contribute feature papers reflecting the latest progress in this research field. Communications, full research papers, and review papers are acceptable formats for the submission of manuscripts.

Prof. Dr. Alírio E. Rodrigues
Dr. Andrew S. Paluch
Guest Editors

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ChemEngineering is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • molecular engineering and materials engineering
  • process engineering
  • product engineering
  • biochemical engineering
  • catalytic engineering
  • chemical reaction engineering
  • computational methods in chemical engineering
  • electrochemical engineering
  • environmental chemical engineering
  • process systems engineering
  • microfluidic engineering and process intensification
  • separation processes
  • surface and interface engineering
  • sustainable process engineering
  • big data and artificial intelligence

Published Papers (58 papers)

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27 pages, 3032 KiB  
Article
Robust Fault Detection in Monitoring Chemical Processes Using Multi-Scale PCA with KD Approach
by K. Ramakrishna Kini, Muddu Madakyaru, Fouzi Harrou, Anoop Kishore Vatti and Ying Sun
ChemEngineering 2024, 8(3), 45; https://doi.org/10.3390/chemengineering8030045 - 25 Apr 2024
Viewed by 1098
Abstract
Effective fault detection in chemical processes is of utmost importance to ensure operational safety, minimize environmental impact, and optimize production efficiency. To enhance the monitoring of chemical processes under noisy conditions, an innovative statistical approach has been introduced in this study. The proposed [...] Read more.
Effective fault detection in chemical processes is of utmost importance to ensure operational safety, minimize environmental impact, and optimize production efficiency. To enhance the monitoring of chemical processes under noisy conditions, an innovative statistical approach has been introduced in this study. The proposed approach, called Multiscale Principal Component Analysis (PCA), combines the dimensionality reduction capabilities of PCA with the noise reduction capabilities of wavelet-based filtering. The integrated approach focuses on extracting features from the multiscale representation, balancing the need to retain important process information while minimizing the impact of noise. For fault detection, the Kantorovich distance (KD)-driven monitoring scheme is employed based on features extracted from Multiscale PCA to efficiently detect anomalies in multivariate data. Moreover, a nonparametric decision threshold is employed through kernel density estimation to enhance the flexibility of the proposed approach. The detection performance of the proposed approach is investigated using data collected from distillation columns and continuously stirred tank reactors (CSTRs) under various noisy conditions. Different types of faults, including bias, intermittent, and drift faults, are considered. The results reveal the superior performance of the proposed multiscale PCA-KD based approach compared to conventional PCA and multiscale PCA-based monitoring methods. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
Show Figures

Figure 1

Figure 1
<p>Multiscale decomposition of a heavy-sine signal using Haar wavelet.</p>
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<p>Multiscale PCA-KD based fault detection strategy.</p>
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<p>A schematic overview of the distillation column process, highlighting structural components, RTD sensors, and the entry conditions for a binary mixture of propane and isobutene.</p>
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<p>Correlation matrix heatmap depicting the Pearson correlation among variables in the fault-free distillation column dataset.</p>
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<p>RadViz visualization illustrating the influence of different factors on (<b>a</b>) ‘Propane’ and (<b>b</b>) ‘Isobutene’ concentrations in the distillation column. Each point on the circular plot represents a data point, and the positioning of points along the circumference reflects the values of various factor.</p>
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<p>Calculation of decomposition depth for (<b>a</b>) SNR = 15 and (<b>b</b>) SNR = 5.</p>
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<p>Intermittent fault monitoring in the DC process by PCA based methods under SNR level of 15: (<b>a</b>) PCA-<math display="inline"><semantics> <msup> <mi>T</mi> <mn>2</mn> </msup> </semantics></math>, (<b>b</b>) PCA-<span class="html-italic">Q</span>, (<b>c</b>) PCA-KD (Red line indicates significance threshold).</p>
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<p>Intermittent fault monitoring in the DC process by MSPCA based methods under SNR level of 15: (<b>a</b>) MSPCA-<math display="inline"><semantics> <msup> <mi>T</mi> <mn>2</mn> </msup> </semantics></math>, (<b>b</b>) MSPCA-<span class="html-italic">Q</span>, (<b>c</b>) MSPCA-KD (Red line indicates significance threshold).</p>
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<p>Intermittent fault monitoring in the DC process by PCA based methods under SNR level of 5: (<b>a</b>) PCA-<math display="inline"><semantics> <msup> <mi>T</mi> <mn>2</mn> </msup> </semantics></math>, (<b>b</b>) PCA-<span class="html-italic">Q</span>, (<b>c</b>) PCA-KD (Red line indicates significance threshold).</p>
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<p>Intermittent fault monitoring in the DC process by MSPCA based methods under SNR level of 5: (<b>a</b>) MSPCA-<math display="inline"><semantics> <msup> <mi>T</mi> <mn>2</mn> </msup> </semantics></math>, (<b>b</b>) MSPCA-<span class="html-italic">Q</span>, (<b>c</b>) MSPCA-KD (Red line indicates significance threshold).</p>
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<p>A schematic of distillation column process.</p>
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<p>Correlation matrix of the fault-free CSTR data.</p>
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<p>Bias fault monitoring by PCA based methods in the CSTR process under SNR level of 15: (<b>a</b>) PCA-<math display="inline"><semantics> <msup> <mi>T</mi> <mn>2</mn> </msup> </semantics></math>, (<b>b</b>) PCA-<span class="html-italic">Q</span>, (<b>c</b>) PCA-KD (Red line indicates significance threshold).</p>
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<p>Bias fault monitoring by MPCA based methods in the CSTR process under SNR level of 15: (<b>a</b>) MSPCA-<math display="inline"><semantics> <msup> <mi>T</mi> <mn>2</mn> </msup> </semantics></math>, (<b>b</b>) MSPCA-<span class="html-italic">Q</span>, and (<b>c</b>) MSPCA-KD (Red line indicates significance threshold).</p>
Full article ">Figure 15
<p>Bias fault monitoring by PCA based methods in the CSTR process under SNR level of 5: (<b>a</b>) PCA-<math display="inline"><semantics> <msup> <mi>T</mi> <mn>2</mn> </msup> </semantics></math>, (<b>b</b>) PCA-<span class="html-italic">Q</span>, (<b>c</b>) PCA-KD (Red line indicates significance threshold).</p>
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<p>Bias fault monitoring by MPCA based methods in the CSTR process under SNR level of 5: (<b>a</b>) MSPCA-<math display="inline"><semantics> <msup> <mi>T</mi> <mn>2</mn> </msup> </semantics></math>, (<b>b</b>) MSPCA-<span class="html-italic">Q</span>, and (<b>c</b>) MSPCA-KD. (Red line indicates significance threshold).</p>
Full article ">
17 pages, 2648 KiB  
Article
An Industrial Control System for Cement Sulfates Content Using a Feedforward and Feedback Mechanism
by Dimitris Tsamatsoulis
ChemEngineering 2024, 8(2), 33; https://doi.org/10.3390/chemengineering8020033 - 7 Mar 2024
Viewed by 1453
Abstract
This study examines the design and long-term implementation of a feedforward and feedback (FF–FB) mechanism in a control system for cement sulfates applied to all types of cement produced in two mills at a production facility. We compared the results with those of [...] Read more.
This study examines the design and long-term implementation of a feedforward and feedback (FF–FB) mechanism in a control system for cement sulfates applied to all types of cement produced in two mills at a production facility. We compared the results with those of a previous controller (SC) that operated in the same unit. The Shewhart charts of the annual SO3 mean values and the nonparametric Mann–Whitney test demonstrate that, for the FF–FB controller, the mean values more effectively approach the SO3 target than the older controller in two out of the three cement types. The s-charts for the annual standard deviation of all cement types and mills indicate that the ratio of the central lines of FF–FB to SC ranges from 0.39 to 0.59, representing a significant improvement. The application of the error propagation technique validates and explains these improvements. The effectiveness of the installed system is due to two main factors. The feedforward (FF) component tracks the set point of SO3 when the mill begins grinding a different type of cement, while the feedback (FB) component effectively attenuates the fluctuations in the sulfates of the raw materials. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
Show Figures

Figure 1

Figure 1
<p>Flowchart of a closed grinding circuit.</p>
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<p>Block diagram of sulfates control.</p>
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<p>Comparison of SC and FF–FB systems for CEM II B-M (P-L) 32.5.</p>
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<p>Control charts of CEM II B-M (P-L) 32.5 produced in CM6: (<b>a</b>) <math display="inline"><semantics> <mover accent="true"> <mi>X</mi> <mo>¯</mo> </mover> </semantics></math>-chart and (<b>b</b>) s-chart.</p>
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<p>Control charts of CEM II A-L 42.5 produced in CM6: (<b>a</b>) <math display="inline"><semantics> <mover accent="true"> <mi>X</mi> <mo>¯</mo> </mover> </semantics></math>-chart and (<b>b</b>) s-chart.</p>
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<p>Control charts of CEM II B-M (P-L) 32.5 produced in CM5: (<b>a</b>) <math display="inline"><semantics> <mover accent="true"> <mi>X</mi> <mo>¯</mo> </mover> </semantics></math>-chart and (<b>b</b>) s-chart.</p>
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<p>Control charts of CEM II A-L 42.5 produced in CM5: (<b>a</b>) <math display="inline"><semantics> <mover accent="true"> <mi>X</mi> <mo>¯</mo> </mover> </semantics></math>-chart and (<b>b</b>) s-chart.</p>
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<p>Control charts of CEM IV B (P-W) 32.5 produced in CM5: (<b>a</b>) <math display="inline"><semantics> <mover accent="true"> <mi>X</mi> <mo>¯</mo> </mover> </semantics></math>-chart and (<b>b</b>) s-chart.</p>
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<p>Frequency distributions of the yearly SO<sub>3</sub> mean values for (<b>a</b>) CEM II B-M (P-L) 32.5 and (<b>b</b>) CEM II A-L 42.5.</p>
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<p><span class="html-italic">y<sub>act</sub></span> and <span class="html-italic">y<sub>calc</sub></span> values for (<b>a</b>) SC and (<b>b</b>) FF–FB implementation.</p>
Full article ">
14 pages, 4498 KiB  
Article
Numerical Simulation of a Valorisation-Oriented Hybrid Process for the Bio-Oil-Related Separation of Acetol and Acetic Acid
by Chavdar Chilev, Farida Lamari and Patrick Langlois
ChemEngineering 2024, 8(1), 5; https://doi.org/10.3390/chemengineering8010005 - 22 Dec 2023
Viewed by 1620
Abstract
Biomass as a whole offers a more diverse potential for valorisation than any other renewable energy source. As one of the stages in the separation of bio-oil involves a liquid mixture of acetol and acetic acid, and as both components are particularly well [...] Read more.
Biomass as a whole offers a more diverse potential for valorisation than any other renewable energy source. As one of the stages in the separation of bio-oil involves a liquid mixture of acetol and acetic acid, and as both components are particularly well suited for valorisation, a hybrid method was developed for their separation with a high purity level through an approach combining liquid–liquid extraction and distillation. In order to design and simulate the flowsheet, the ChemCAD 7.0 simulation software was used. Sensitivity analyses were carried out to investigate the influence of the different parameters in the distillation columns, such as the reflux ratio, the feed stage location, and the vapour/bottom molar flow ratio. The effect of different extractants and of their excess on the separation process, as well as the possibility of regenerating the extractant, was also studied. Tri-n-octylamine was accordingly selected as a separating agent that was fully recycled. The end result for separating an initial 48/52 wt% acetol/acetic acid liquid mixture was acetol with a purity of 99.4 wt% and acetic acid with a purity of 100 wt%. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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Figure 1

Figure 1
<p>Equilibrium data for the A/HAc system.</p>
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<p>Flowsheet for A/HAc separation.</p>
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<p>Acetol amount in the extract as a function of solvent flow rate.</p>
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<p>Acetol concentration in the raffinate as a function of solvent flow rate.</p>
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<p>Equilibrium predicted for the n-decane/A/HAc system.</p>
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<p>Equilibrium predicted for the tri-n-octylamine/A/HAc system.</p>
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<p>Tray liquid concentration profile in column 2.</p>
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<p>Tray temperature profile in column 2.</p>
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<p>Tray concentration profile in column 3.</p>
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<p>Tray temperature profile in column 3.</p>
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31 pages, 6131 KiB  
Article
Adaptive Mesh Refinement Strategies for Cost-Effective Eddy-Resolving Transient Simulations of Spray Dryers
by Jairo Andrés Gutiérrez Suárez, Carlos Humberto Galeano Urueña and Alexánder Gómez Mejía
ChemEngineering 2023, 7(5), 100; https://doi.org/10.3390/chemengineering7050100 - 18 Oct 2023
Viewed by 1867
Abstract
The use of adaptive meshing strategies to perform cost-effective transient simulations of spray drying processes is evaluated. These simulations are often computationally expensive, given the large differences between the characteristic times of the central jet and those of the unsteady flow developed at [...] Read more.
The use of adaptive meshing strategies to perform cost-effective transient simulations of spray drying processes is evaluated. These simulations are often computationally expensive, given the large differences between the characteristic times of the central jet and those of the unsteady flow developed at its periphery. Managing the computational cost through the control of the grid resolution by regions is inadequate in many of these applications since the grid resolution requirements change dynamically within the domain. These conditions are related to the unsteady nature of the flow in both the central jet and the flow recirculation zones. Therefore, the application of adaptive mesh refinement (AMR) strategies is recommended. In this paper, general AMR criteria based on relative errors are evaluated by testing three mesh adaptation criteria: velocity gradient, pressure gradient, and vorticity. This evaluation is performed using a low-cost turbulence model with eddy resolution (DDES) in two different types of drying chambers, in which experimental measurements are available. The use of AMR exerts appreciable effects on decreasing computational costs and contributes to the capture of large eddies in critical regions. The present approach provides an appropriate balance between solution accuracy and computational cost. By using a correct AMR configuration, it is possible to obtain results similar to those obtained on a fixed grid but reducing the computational costs by 3 to 5 times. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Geometry and domain of the two spray drying chambers modeled in this study. (<b>a</b>) Pilot-size co-current drying chamber studied by Benavides-Morán et al. [<a href="#B18-ChemEngineering-07-00100" class="html-bibr">18</a>], Cubillos Varela [<a href="#B37-ChemEngineering-07-00100" class="html-bibr">37</a>], Gutiérrez Suárez [<a href="#B27-ChemEngineering-07-00100" class="html-bibr">27</a>]. (<b>b</b>) Detail of the air disperser region featuring an annular inlet. (<b>c</b>) Defect in the concentricity of the annular entrance reported by Gutiérrez Suárez [<a href="#B27-ChemEngineering-07-00100" class="html-bibr">27</a>]. (<b>d</b>) Semi-industrial-size drying chamber studied by Kieviet et al. [<a href="#B35-ChemEngineering-07-00100" class="html-bibr">35</a>]. (<b>e</b>) Detail of the air disperser region. For both drying chambers, the position of the HWA probe during inlet air flow measurement is denoted by a red asterisk.</p>
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<p>Grid configuration for initial simulation setup (before AMR activation) in both drying chambers: (<b>a</b>,<b>c</b>), grid details in the near-field region of the jet, including the prerefinement, for the pilot-size and semi-industrial-size chambers; (<b>b</b>,<b>d</b>), cross-sectional view of the grid for the pilot-size and semi-industrial-size chambers. With the exception of the elements forming the outlet duct of the semi-industrial dryer, both grids are structured and concentrate the grid elements around the jet discharge and outlet duct regions.</p>
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<p>Axial positions and radial paths sampled in the CFD simulations. (<b>a</b>,<b>b</b>), sampled axial positions in the pilot-size and semi-industrial size spray chambers; (<b>c</b>,<b>d</b>), top view of the pilot-size and semi-industrial-size drying chambers showing the sampled path and some radial positions for reference. The sampled path replicates the movement of the hot-wire probe during the experimental measurements reported by Gutiérrez Suárez [<a href="#B27-ChemEngineering-07-00100" class="html-bibr">27</a>] and by Kieviet and Kerkhof [<a href="#B5-ChemEngineering-07-00100" class="html-bibr">5</a>].</p>
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<p>Computational costs in terms of the total processor hours per characteristic time (<math display="inline"><semantics> <mrow> <mi>P</mi> <mi>H</mi> <mo>/</mo> <msub> <mi>t</mi> <mi mathvariant="normal">c</mi> </msub> </mrow> </semantics></math>) for all studied cases.</p>
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<p>Temporal evolution of mean and <span class="html-italic">rms</span> values of the axial velocity <math display="inline"><semantics> <msub> <mi>U</mi> <mi>x</mi> </msub> </semantics></math> in the central jet and flow recirculation regions. For all cases, the x-axis represents the characteristic time <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mi mathvariant="normal">c</mi> </msub> <mo>.</mo> </mrow> </semantics></math> (<b>a</b>,<b>b</b>): mean and <span class="html-italic">rms</span> <math display="inline"><semantics> <msub> <mi>U</mi> <mi>x</mi> </msub> </semantics></math> for the pilot-size drying chamber; (<b>c</b>,<b>d</b>) mean and <span class="html-italic">rms</span> <math display="inline"><semantics> <msub> <mi>U</mi> <mi>x</mi> </msub> </semantics></math> for the semi-industrial-size drying chamber. The pointed horizontal lines in (<b>c</b>) represent the mean <math display="inline"><semantics> <msub> <mi>U</mi> <mi>x</mi> </msub> </semantics></math> values when the simulation is run from <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mi mathvariant="normal">c</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> to 8.</p>
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<p>Unsteady flow inside the pilot-size spray chamber at different characteristic times (<math display="inline"><semantics> <msub> <mi>t</mi> <mi mathvariant="normal">c</mi> </msub> </semantics></math> = 2.55; 2.95; 3.45) for the base case: (<b>a</b>) 2D cross-section of the contours of velocity magnitude; (<b>b</b>) axial velocity <math display="inline"><semantics> <msub> <mi>U</mi> <mi>x</mi> </msub> </semantics></math> measured at <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>/</mo> <mi>H</mi> <mo>=</mo> <mn>0.367</mn> </mrow> </semantics></math>.</p>
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<p>Effect of the AMR criterion on the <math display="inline"><semantics> <msub> <mi>C</mi> <mi>rel</mi> </msub> </semantics></math> fields and cell level for the pilot-size drying chamber taken at <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mi mathvariant="normal">c</mi> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>: (<b>a</b>,<b>b</b>) cross-section of the <math display="inline"><semantics> <msub> <mi>C</mi> <mi>rel</mi> </msub> </semantics></math> fields for case C taken at <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mi mathvariant="normal">c</mi> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <msub> <mi>C</mi> <mi>rel</mi> </msub> </semantics></math> for cases C1, C2, C3, C2-1, and C2-2; (<b>d</b>) fields of cell level for cases C1, C2, C3, C2-1, C2-2, and C2-3.</p>
Full article ">Figure 7 Cont.
<p>Effect of the AMR criterion on the <math display="inline"><semantics> <msub> <mi>C</mi> <mi>rel</mi> </msub> </semantics></math> fields and cell level for the pilot-size drying chamber taken at <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mi mathvariant="normal">c</mi> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>: (<b>a</b>,<b>b</b>) cross-section of the <math display="inline"><semantics> <msub> <mi>C</mi> <mi>rel</mi> </msub> </semantics></math> fields for case C taken at <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mi mathvariant="normal">c</mi> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <msub> <mi>C</mi> <mi>rel</mi> </msub> </semantics></math> for cases C1, C2, C3, C2-1, and C2-2; (<b>d</b>) fields of cell level for cases C1, C2, C3, C2-1, C2-2, and C2-3.</p>
Full article ">Figure 7 Cont.
<p>Effect of the AMR criterion on the <math display="inline"><semantics> <msub> <mi>C</mi> <mi>rel</mi> </msub> </semantics></math> fields and cell level for the pilot-size drying chamber taken at <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mi mathvariant="normal">c</mi> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>: (<b>a</b>,<b>b</b>) cross-section of the <math display="inline"><semantics> <msub> <mi>C</mi> <mi>rel</mi> </msub> </semantics></math> fields for case C taken at <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mi mathvariant="normal">c</mi> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <msub> <mi>C</mi> <mi>rel</mi> </msub> </semantics></math> for cases C1, C2, C3, C2-1, and C2-2; (<b>d</b>) fields of cell level for cases C1, C2, C3, C2-1, C2-2, and C2-3.</p>
Full article ">Figure 8
<p>Unsteady flow inside the semi-industrial-size spray chamber and grid adaptation at different characteristic times (<math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>4.5</mn> <mo>;</mo> <mn>5.5</mn> <mo>;</mo> <mn>6.5</mn> </mrow> </semantics></math>) for case S2: (<b>a</b>) 2D cross-section showing contours of absolute velocity; (<b>b</b>) 2D cross-section of the relative error <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>rel</mi> </mrow> </msub> </semantics></math> indicating zones subject to refining (<math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mrow> <mi>rel</mi> <mspace width="4.pt"/> </mrow> </msub> <mo>&gt;</mo> <msub> <mi>C</mi> <mi>crit</mi> </msub> </mrow> </semantics></math>) and unrefining (<math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>un</mi> </msub> <mo>&lt;</mo> <msub> <mi>C</mi> <mrow> <mi>rel</mi> <mspace width="4.pt"/> </mrow> </msub> <mo>&lt;</mo> <msub> <mi>C</mi> <mi>crit</mi> </msub> </mrow> </semantics></math>).</p>
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<p>Coherent structures (contours with <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>&gt;</mo> <mn>200,000</mn> </mrow> </semantics></math>) taken at <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>3.23</mn> </mrow> </semantics></math> for different study cases: (<b>a</b>) base case (case C); (<b>b</b>) case C1 (<math display="inline"><semantics> <mrow> <mo>∇</mo> <mi>U</mi> </mrow> </semantics></math> criterion); (<b>c</b>) case C2 (<math display="inline"><semantics> <mrow> <mo>∇</mo> <mi>p</mi> </mrow> </semantics></math> criterion); (<b>d</b>) case C3 (<math display="inline"><semantics> <mi mathvariant="sans-serif">Ω</mi> </semantics></math> criterion).</p>
Full article ">Figure 10
<p>Radial profiles of the effects of the AMR criterion (<math display="inline"><semantics> <mrow> <mo>∇</mo> <mi>U</mi> <mo>,</mo> <mo>∇</mo> <mi>p</mi> <mo>,</mo> <mi mathvariant="sans-serif">Ω</mi> </mrow> </semantics></math>) on the mean axial velocity <math display="inline"><semantics> <msub> <mi>U</mi> <mi>x</mi> </msub> </semantics></math> at different axial positions (<math display="inline"><semantics> <mrow> <mi>x</mi> <mo>/</mo> <mi>H</mi> <mo>=</mo> <mn>0.073</mn> <mo>,</mo> <mn>0.147</mn> <mo>,</mo> <mn>0.22</mn> <mo>,</mo> <mn>0.367</mn> </mrow> </semantics></math>) of the pilot-size spray chamber. Black line: reference case (case C) using a fixed grid; blue, red, and green lines represent simulation data using different AMR criteria; black squares: experimental hot-wire anemometry data from Gutiérrez Suárez [<a href="#B27-ChemEngineering-07-00100" class="html-bibr">27</a>].</p>
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<p>Radial profiles of the mean axial velocity <math display="inline"><semantics> <msub> <mi>U</mi> <mi>x</mi> </msub> </semantics></math> at two axial positions (<math display="inline"><semantics> <mrow> <mi>x</mi> <mo>/</mo> <mi>H</mi> <mo>=</mo> <mn>0.22</mn> <mo>,</mo> <mn>0.294</mn> </mrow> </semantics></math>) for the pilot-size spray chamber. Blue line: reference case (case C) using a fixed grid; red line: CFD data reported in Benavides-Morán et al. [<a href="#B18-ChemEngineering-07-00100" class="html-bibr">18</a>] using a SAS (scale adaptive simulation) turbulence method; black squares: experimental hot-wire anemometry data from Gutiérrez Suárez [<a href="#B27-ChemEngineering-07-00100" class="html-bibr">27</a>].</p>
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<p>Comparison of the radial profiles of resolved turbulence fraction <math display="inline"><semantics> <mi>Γ</mi> </semantics></math> obtained from the base case (C), and adaptive meshing criteria (C1, C2, and C3) at different axial positions.</p>
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<p>Radial profiles of the effect of the AMR threshold and maximum refinement level on the mean axial velocity <math display="inline"><semantics> <msub> <mi>U</mi> <mi>x</mi> </msub> </semantics></math> at different axial positions (<math display="inline"><semantics> <mrow> <mi>x</mi> <mo>/</mo> <mi>H</mi> <mo>=</mo> <mn>0.073</mn> <mo>,</mo> <mn>0.147</mn> <mo>,</mo> <mn>0.22</mn> <mo>,</mo> <mn>0.367</mn> </mrow> </semantics></math>) of the pilot-size spray chamber. Black line: reference case (case C) using a fixed grid; red line: higher threshold value (case 2-1); blue line: higher maximum refinement level (case 2-2); black squares: experimental hot-wire anemometry data from Gutiérrez Suárez [<a href="#B27-ChemEngineering-07-00100" class="html-bibr">27</a>].</p>
Full article ">Figure 14
<p>Radial profiles of the effect of the AMR threshold and maximum refinement level on the resolved fraction <math display="inline"><semantics> <mo>Γ</mo> </semantics></math> at different axial positions (<math display="inline"><semantics> <mrow> <mi>x</mi> <mo>/</mo> <mi>H</mi> <mo>=</mo> <mn>0.073</mn> <mo>,</mo> <mn>0.147</mn> <mo>,</mo> <mn>0.22</mn> <mo>,</mo> <mn>0.367</mn> </mrow> </semantics></math>) of the pilot-size spray chamber. Black line: reference case (case C) using a fixed grid; red line: higher threshold value (case 2-1); blue line: higher maximum refinement level (case 2-2).</p>
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<p>Radial profiles of the modeled turbulent kinetic energy (<math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>RANS</mi> </msub> <mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> for cases C, C2-1, and C2-2 at two different axial positions (<math display="inline"><semantics> <mrow> <mi>x</mi> <mo>/</mo> <mi>H</mi> <mo>=</mo> <mn>0.073</mn> <mo>,</mo> <mn>0.147</mn> </mrow> </semantics></math>) of the pilot-size spray chamber. Black line: reference case (case C) using a fixed grid; blue dotted line: higher threshold value (case 2-1); green line: higher maximum refinement level (case 2-2).</p>
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<p>Radial profiles of mean axial velocity of the air <math display="inline"><semantics> <mrow> <mo>(</mo> <msub> <mi>U</mi> <mi>x</mi> </msub> <mo>)</mo> </mrow> </semantics></math> at different axial positions <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mo>(</mo> <mn>0.3</mn> <mo>,</mo> <mspace width="0.166667em"/> <mn>0.6</mn> <mo>,</mo> <mspace width="0.166667em"/> <mn>1.0</mn> <mo>,</mo> <mspace width="0.166667em"/> <mn>2.0</mn> <mo>)</mo> <mspace width="0.166667em"/> </mrow> </semantics></math>m. Red, blue, and green symbols represent cases S1, S2, and S3; black squares: hot-wire anemometry data from Ref. [<a href="#B5-ChemEngineering-07-00100" class="html-bibr">5</a>]; error bars: interval of axial velocities estimated from measurements by the same author.</p>
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<p>Radial profiles of mean axial velocity of the air <math display="inline"><semantics> <mrow> <mo>(</mo> <msub> <mi>U</mi> <mi>x</mi> </msub> <mo>)</mo> </mrow> </semantics></math> at different axial positions <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mo>(</mo> <mn>1.0</mn> <mo>,</mo> <mspace width="0.166667em"/> <mn>2.0</mn> <mo>)</mo> <mspace width="0.166667em"/> </mrow> </semantics></math>m. The results from case S2 are compared with the CFD results from [<a href="#B19-ChemEngineering-07-00100" class="html-bibr">19</a>,<a href="#B38-ChemEngineering-07-00100" class="html-bibr">38</a>,<a href="#B40-ChemEngineering-07-00100" class="html-bibr">40</a>].</p>
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<p>Radial profiles of maximum and minimum axial velocities <math display="inline"><semantics> <mrow> <mo>(</mo> <msub> <mi>U</mi> <mi>x</mi> </msub> <mo>)</mo> </mrow> </semantics></math> at different axial positions <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mo>(</mo> <mn>0.3</mn> <mo>,</mo> <mspace width="0.166667em"/> <mn>0.6</mn> <mo>,</mo> <mspace width="0.166667em"/> <mn>1.0</mn> <mo>,</mo> <mspace width="0.166667em"/> <mn>2.0</mn> <mo>)</mo> </mrow> </semantics></math> m. Red, blue, and green symbols represent cases S1, S2, and S3; error bars: interval of axial velocities measured by [<a href="#B5-ChemEngineering-07-00100" class="html-bibr">5</a>].</p>
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24 pages, 16615 KiB  
Article
Adsorption of Lead (II) Ions onto Goethite Chitosan Beads: Isotherms, Kinetics, and Mechanism Studies
by Tanawit Sirijaree and Pornsawai Praipipat
ChemEngineering 2023, 7(3), 52; https://doi.org/10.3390/chemengineering7030052 - 1 Jun 2023
Cited by 9 | Viewed by 2399
Abstract
Lead is a highly toxic heavy metal that creates a water pollutant. It can be released from industrial processes, agricultural chemistry, and community wastes, affecting creatures and human health even at a low concentration. As a result, it is advised that lead be [...] Read more.
Lead is a highly toxic heavy metal that creates a water pollutant. It can be released from industrial processes, agricultural chemistry, and community wastes, affecting creatures and human health even at a low concentration. As a result, it is advised that lead be removed before releasing wastewater into the environment. This study synthesized three chitosan bead materials from shrimp shell wastes which were chitosan powder beads (CB), chitosan powder mixed with goethite beads (CFB), and chitosan powder beads coated with goethite (CBF) for removing lead in an aqueous solution. Their surface area, pore volumes, and pore sizes were explored according to Brunauer– Emmett–Teller, and their crystalline formations were investigated using an X-ray diffractometer. Their surface structures were studied using field emission scanning electron microscopy and a focus ion beam, and their chemical compositions were determined using an energy dispersive X-ray spectrometer. Their chemical functional groups were identified via Fourier-transform infrared spectroscopy. In addition, batch experiments were conducted to investigate the effects of several factors on removing lead, and the adsorption isotherm and kinetics were also investigated for determining their adsorption pattern and mechanism. In addition, the desorption experiments were studied to confirm their possible material reusability. The CBF demonstrated the highest surface area and smallest pore size compared with the other materials. In addition, the pore sizes of the CFB and CBF were micropores, whereas those of the CB were mesopores. All materials were semicrystalline structures, and the specific goethite peaks were observed in the CFB and CBF. All materials had spherical shapes with heterogeneous surfaces. Six chemical components of O, C, Ca, N, Cl, and Na were discovered in all materials, and Fe was only found in the CFB and CBF because of the addition of goethite. Five main chemical functional groups of N–H, O–H, C–H, C–O, and –COOH were found in all materials. The optimum conditions of the CB, CFB, and CBF for removing lead were 0.5 g, 16 h, pH 5, 0.5 g, 16 h, pH 5, and 0.4 g, 14 h, pH 5, respectively. The results of the batch experiments demonstrated that the CB, CFB, and CBF were high-efficiency adsorbents for removing lead in solution by more than 95%, whereby the CBF showed the highest lead removal of 99%. The Freundlich isotherm model and pseudo-second-order kinetic model helped to well explain their adsorption pattern and mechanism. The maximum lead adsorption capacities of the CB, CFB, and CBF were 322.58, 333.33, and 344.83 mg/g, respectively. Furthermore, all chitosan materials can be reused for more than three cycles with high lead removal by more than 94%; so, they are potential materials for application in industrial applications. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>The chitosan material synthesis of (<b>a</b>) chitosan powder (CP), (<b>b</b>) chitosan powder beads (CB), (<b>c</b>) chitosan powder mixed with goethite beads (CFB), and (<b>d</b>) chitosan powder beads coated with goethite (CBF).</p>
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<p>The physical characterizations of (<b>a</b>) commercial chitosan standard (STD), (<b>b</b>) chitosan powder (CP), (<b>c</b>) chitosan powder beads (CB), (<b>d</b>) chitosan powder mixed with goethite beads (CFB), and (<b>e</b>) chitosan powder beads coated with goethite (CBF).</p>
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<p>BJH pore size distribution of chitosan powder beads (CB), chitosan powder mixed with goethite beads (CFB), and chitosan powder beads coated with goethite (CBF).</p>
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<p>The XRD patterns of (<b>a</b>) commercial chitosan standard (STD), (<b>b</b>) chitosan powder (CP), (<b>c</b>) chitosan powder beads (CB), (<b>d</b>) chitosan powder mixed with goethite beads (CFB), and (<b>e</b>) chitosan powder beads coated with goethite (CBF).</p>
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<p>The surface structures of (<b>a</b>,<b>b</b>) chitosan powder beads (CB), (<b>c</b>,<b>d</b>) chitosan powder mixed with goethite beads (CFB), and (<b>e</b>,<b>f</b>) chitosan powder beads coated with goethite (CBF).</p>
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<p>FTIR spectra of (<b>a</b>) commercial chitosan standard (STD), (<b>b</b>) chitosan powder (CP), (<b>c</b>) chitosan powder beads (CB), (<b>d</b>) chitosan powder mixed with goethite beads (CFB), and (<b>e</b>) chitosan powder beads coated with goethite (CBF).</p>
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<p>The effects of (<b>a</b>) dose, (<b>b</b>) contact time, (<b>c</b>) pH, and (<b>d</b>) initial lead concentrations according to a series of batch experiments on chitosan powder beads (CB), chitosan powder mixed with goethite beads (CFB), and chitosan powder beads coated with goethite (CBF).</p>
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<p>Effect of pH on lead speciation in aqueous systems at concentration of 50 mg/L.</p>
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<p>The adsorption isotherms of chitosan powder beads (CB), chitosan powder mixed with goethite beads (CFB), and chitosan powder beads coated with goethite (CBF) for (<b>a</b>) linear Langmuir model and (<b>b</b>) linear Freundlich model.</p>
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<p>The adsorption isotherm of adsorption capacity (<span class="html-italic">q</span><sub>e</sub>) vs. lead concentration (<span class="html-italic">C</span><sub>e</sub>).</p>
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<p>The adsorption kinetics of the chitosan powder beads (CB), chitosan powder mixed with goethite beads (CFB), and chitosan powder beads coated with goethite (CBF) on (<b>a</b>) linear pseudo-first-order kinetic model and (<b>b</b>) linear pseudo-second-order kinetic model.</p>
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<p>The adsorption equilibriums of chitosan powder beads (CB), chitosan powder mixed with goethite beads (CFB), and chitosan powder beads coated with goethite (CBF) for lead adsorptions.</p>
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<p>Lead adsorption and desorption of chitosan powder beads (CB), chitosan powder mixed with goethite beads (CFB), and chitosan powder beads coated with goethite (CBF) in three cycles.</p>
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<p>The possible mechanisms of lead adsorption of (<b>a</b>) chitosan powder beads (CB), (<b>b</b>) chitosan powder mixed with goethite beads (CFB), and (<b>c</b>) chitosan powder beads coated with goethite (CBF).</p>
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12 pages, 6061 KiB  
Article
One-Step Crystallization of Gahnite Glass-Ceramics in a Wide Thermal Gradient
by Georgiy Yu. Shakhgildyan, Roman O. Alekseev, Nikita V. Golubev, Vitaliy I. Savinkov, Andrey S. Naumov, Natalia N. Presnyakova and Vladimir N. Sigaev
ChemEngineering 2023, 7(2), 37; https://doi.org/10.3390/chemengineering7020037 - 18 Apr 2023
Cited by 4 | Viewed by 2039
Abstract
The glass crystallization regime plays a crucial role in the fabrication of glass ceramics: it affects both phase composition and microstructure, and thus the properties of the final product. In the search for new glass-ceramic materials, the development of a proper heat-treatment schedule [...] Read more.
The glass crystallization regime plays a crucial role in the fabrication of glass ceramics: it affects both phase composition and microstructure, and thus the properties of the final product. In the search for new glass-ceramic materials, the development of a proper heat-treatment schedule involves the utilization of numerous glass samples that need to be thermally treated and then investigated to determine the values of the target characteristics. In this study, we evaluated the effect of crystallization temperature on the glass structure, phase composition, and hardness of glass ceramics in the ZnO-MgO-Al2O3-SiO2 system containing TiO2 and ZrO2 as nucleators. To maximize the number of heat treatments, we performed polythermal crystallization of the glass in a wide temperature range with the help of a gradient furnace. Using X-ray diffraction, Raman spectroscopy, and transmission electron microscopy, we showed the precipitation of gahnite nanocrystals as the main phase in the bulk of a single glass sample and observed a gradual change in its microstructure, transparency, and hardness. The dependence of Vickers hardness values on heat treatment temperature was found to follow a non-linear trend, revealing the optimal thermal range for glass crystallization. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>(<b>a</b>) The DSC curve, and (<b>b</b>) the dilatometric curve of the initial glass.</p>
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<p>(<b>a</b>) Image of the glass sample after the polythermal crystallization. Designations A–D indicated in the figure denote the zones of further analysis. (<b>b</b>) XRD patterns recorded from the corresponding zones A–D of the treated sample and initial glass.</p>
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<p>Raman spectra recorded from different zones of the glass sample (symbols A–D correspond to <a href="#ChemEngineering-07-00037-f002" class="html-fig">Figure 2</a>a).</p>
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<p>(<b>a</b>,<b>b</b>) HRTEM images of zone A in different areas.</p>
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<p>(<b>a</b>,<b>b</b>) HRTEM images of zone B in different areas. Red lines highlight the interplanar distance.</p>
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<p>TEM images of zone C.</p>
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<p>(<b>a</b>) TEM images of zone D; (<b>b</b>,<b>c</b>) HRTEM images of gahnite nanocrystals with the corresponding interplanar spacings. Red lines highlight the interplanar distance.</p>
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<p>Vickers hardness values along the glass sample after the polythermal crystallization.</p>
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17 pages, 769 KiB  
Article
Correlating Pure Component Properties with MOSCED Solubility Parameters: Enthalpy of Vaporization and Vapor Pressure
by Nick H. Wong, Pratik Dhakal, Sydnee N. Roese and Andrew S. Paluch
ChemEngineering 2023, 7(2), 25; https://doi.org/10.3390/chemengineering7020025 - 18 Mar 2023
Viewed by 1864
Abstract
Tools to predict vapor–liquid phase equilibria are indispensable for the conceptualization and design of separation processes. Modified separation of cohesive energy density (MOSCED) is a solubility-parameter-based method parameterized to make accurate predictions of the limiting activity coefficient. As a solubility-parameter-based method, MOSCED can [...] Read more.
Tools to predict vapor–liquid phase equilibria are indispensable for the conceptualization and design of separation processes. Modified separation of cohesive energy density (MOSCED) is a solubility-parameter-based method parameterized to make accurate predictions of the limiting activity coefficient. As a solubility-parameter-based method, MOSCED can not only make quantitative predictions, but can shed light on the underlying intermolecular interactions. In the present study, we demonstrated the ability of MOSCED to correlate the enthalpy of vaporization and vapor pressure at a specific temperature using multiple linear regression. With this addition, MOSCED is able to predict vapor–liquid phase equilibria in the absence of reference data. This was demonstrated for the prediction of the Henry’s constant and solvation free energy of organic solutes in water, which was found to be superior to mod-UNIFAC. In addition to being able to make phase equilibrium predictions, the ability to correlate the enthalpy of vaporization and vapor pressure offers the opportunity to include additional properties in the regression of the MOSCED parameters. Given this success, we additionally attempted to correlate a wide range of physical properties using a similar expression. While, in some cases, the results were reasonable, they were inferior to the correlations of the enthalpy of vaporization and vapor pressure. Future efforts will be needed to improve the correlations. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Parity plot of <math display="inline"><semantics> <mrow> <mo>ln</mo> <msup> <mi>P</mi> <mi>sat</mi> </msup> </mrow> </semantics></math> calculated using Equation (<a href="#FD17-ChemEngineering-07-00025" class="html-disp-formula">17</a>) at 0, 20, and 40 <math display="inline"><semantics> <msup> <mrow/> <mo>°</mo> </msup> </semantics></math>C, as indicated, versus the reference values. <math display="inline"><semantics> <msup> <mi>P</mi> <mi>sat</mi> </msup> </semantics></math> is in units of bar. The solid line corresponds to the <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>x</mi> </mrow> </semantics></math> line, and the dashed lines correspond to plus or minus the root-mean-squared error (RMS) at 20 <math display="inline"><semantics> <msup> <mrow/> <mo>°</mo> </msup> </semantics></math>C and are drawn as a reference.</p>
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<p>Residual plot of the difference between the reference and calculated (using Equation (<a href="#FD17-ChemEngineering-07-00025" class="html-disp-formula">17</a>)) values of <math display="inline"><semantics> <mrow> <mo>ln</mo> <msup> <mi>P</mi> <mi>sat</mi> </msup> </mrow> </semantics></math> at 0, 20, and 40 <math display="inline"><semantics> <msup> <mrow/> <mo>°</mo> </msup> </semantics></math>C, as indicated, versus the reference values. <math display="inline"><semantics> <msup> <mi>P</mi> <mi>sat</mi> </msup> </semantics></math> is in units of bar. The solid line corresponds to the <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> line and is drawn as a reference.</p>
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<p>Parity plot of the dimensionless <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msup> <mi>H</mi> <mi>vap</mi> </msup> <mo>/</mo> <mfenced separators="" open="(" close=")"> <mi>R</mi> <mi>T</mi> </mfenced> </mrow> </semantics></math> calculated using Equation (<a href="#FD14-ChemEngineering-07-00025" class="html-disp-formula">14</a>) at 0, 20, and 40 <math display="inline"><semantics> <msup> <mrow/> <mo>°</mo> </msup> </semantics></math>C, as indicated, versus the reference values. The solid line corresponds to the <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>x</mi> </mrow> </semantics></math> line, and the dashed lines correspond to plus or minus the root-mean-squared error (RMS) at 20 <math display="inline"><semantics> <msup> <mrow/> <mo>°</mo> </msup> </semantics></math>C and are drawn as a reference.</p>
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<p>Residual plot of the difference between the reference and calculated (using Equation (<a href="#FD14-ChemEngineering-07-00025" class="html-disp-formula">14</a>)) values of the dimensionless <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msup> <mi>H</mi> <mi>vap</mi> </msup> <mo>/</mo> <mfenced separators="" open="(" close=")"> <mi>R</mi> <mi>T</mi> </mfenced> </mrow> </semantics></math> at 0, 20, and 40 <math display="inline"><semantics> <msup> <mrow/> <mo>°</mo> </msup> </semantics></math>C, as indicated, versus the reference values. The solid line corresponds to the <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> line and is drawn as a reference.</p>
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<p>Residual plot of the difference between the reference and calculated log Henry’s constant (<math display="inline"><semantics> <mrow> <mo>ln</mo> <msub> <mi mathvariant="script">H</mi> <mrow> <mn>2</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>, Equation (<a href="#FD3-ChemEngineering-07-00025" class="html-disp-formula">3</a>)) for organic solutes in water, versus the reference values. <math display="inline"><semantics> <msub> <mi mathvariant="script">H</mi> <mrow> <mn>2</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> </semantics></math> is in units of bar. The solid line corresponds to the <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> line and is drawn as a reference. Predictions were made for MOSCED with <math display="inline"><semantics> <mrow> <mo>ln</mo> <msubsup> <mi>P</mi> <mn>2</mn> <mi>sat</mi> </msubsup> </mrow> </semantics></math> computed using the Clausius–Clapeyron equation with a reference temperature of 20 and 40 <math display="inline"><semantics> <msup> <mrow/> <mo>°</mo> </msup> </semantics></math>C, as indicated.</p>
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<p>Residual plot of the difference between the reference and calculated solvation free energy (<math display="inline"><semantics> <mrow> <mo>Δ</mo> <msubsup> <mi>G</mi> <mrow> <mn>2</mn> <mo>,</mo> <mn>1</mn> </mrow> <mi>solv</mi> </msubsup> </mrow> </semantics></math>, Equation (<a href="#FD4-ChemEngineering-07-00025" class="html-disp-formula">4</a>)) for organic solutes in water, versus the reference values. <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msubsup> <mi>G</mi> <mrow> <mn>2</mn> <mo>,</mo> <mn>1</mn> </mrow> <mi>solv</mi> </msubsup> </mrow> </semantics></math> is in units of kJ/mol. The solid line corresponds to the <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> line and is drawn as a reference. Predictions were made for MOSCED with <math display="inline"><semantics> <mrow> <mo>ln</mo> <msubsup> <mi>P</mi> <mn>2</mn> <mi>sat</mi> </msubsup> </mrow> </semantics></math> computed using the Clausius–Clapeyron equation with a reference temperature of 20 <math display="inline"><semantics> <msup> <mrow/> <mo>°</mo> </msup> </semantics></math>C.</p>
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9 pages, 2540 KiB  
Article
Degradation Studies of Air-Exposed Black Phosphorous and Black Arsenic Phosphorous
by Usman O. Abu, Dinushika Vithanage, Ashan Vitharana, Jacek B. Jasinski and Gamini Sumanasekera
ChemEngineering 2023, 7(2), 18; https://doi.org/10.3390/chemengineering7020018 - 3 Mar 2023
Cited by 4 | Viewed by 1454
Abstract
This work investigates the effects of oxygen and humidity on black phosphorous (BP) and black arsenic phosphorous (AsxP1x ) flakes using Raman spectroscopy and in situ electric transport measurements (four-probe resistance and thermoelectric power, TEP). The results [...] Read more.
This work investigates the effects of oxygen and humidity on black phosphorous (BP) and black arsenic phosphorous (AsxP1x ) flakes using Raman spectroscopy and in situ electric transport measurements (four-probe resistance and thermoelectric power, TEP). The results show that the incorporation of arsenic into the lattice of BP renders it more stable, with the degradation times for BP, As0.2P0.8, and As0.4P0.6 being 4, 5, and 11 days, respectively. The P-P Raman peak intensities were determined to decrease with exposure to oxygen and moisture. The TEP measurements confirmed that both BP and AsxP1x are p-type semiconductors with the TEP of As0.4P0.6 stabilizing more slowly than that of BP. In addition, the four-probe resistance of BP and AsxP1x stabilized significantly faster when exposed to air after being degassed in a vacuum. This was attributed to the charge transfer between the oxygen redox potential of air and the Fermi energy (EF) of the semiconductors. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Experimental set-up for electrical transport measurements showing the reactor with provisions for pumping and air exposures and the sample probe and the electrical contacts.</p>
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<p>Raman spectra of unexposed synthesized pristine BP and As<sub>x</sub>P<sub>1−x</sub> alloys with three different chemical compositions.</p>
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<p>Results derived from Raman spectroscopy. (<b>a</b>–<b>c</b>) Degradation maps showing time evolution of Raman spectra upon exposure to ambient environment for BP, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>As</mi> </mrow> <mrow> <mn>0.2</mn> </mrow> </msub> <msub> <mi mathvariant="normal">P</mi> <mrow> <mn>0.8</mn> </mrow> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>As</mi> </mrow> <mrow> <mn>0.6</mn> </mrow> </msub> <msub> <mi mathvariant="normal">P</mi> <mrow> <mn>0.4</mn> </mrow> </msub> </mrow> </semantics></math>, respectively. (<b>d</b>) The total Raman intensity as a function of exposure time for BP (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>As</mi> </mrow> <mn>0</mn> </msub> <msub> <mi mathvariant="normal">P</mi> <mn>1</mn> </msub> </mrow> </semantics></math>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>As</mi> </mrow> <mrow> <mn>0.2</mn> </mrow> </msub> <msub> <mi mathvariant="normal">P</mi> <mrow> <mn>0.8</mn> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>As</mi> </mrow> <mrow> <mn>0.6</mn> </mrow> </msub> <msub> <mi mathvariant="normal">P</mi> <mrow> <mn>0.4</mn> </mrow> </msub> </mrow> </semantics></math>. The initial (t = 0) total intensity for all three samples was normalized to 1.</p>
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<p>Results derived from electrical transport measurements of exposed samples after annealing. (<b>a</b>) Normalized TEP for <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>As</mi> </mrow> <mrow> <mn>0.4</mn> </mrow> </msub> <msub> <mi mathvariant="normal">P</mi> <mrow> <mn>0.6</mn> </mrow> </msub> </mrow> </semantics></math> and BP. (<b>b</b>) Normalized resistance for <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>As</mi> </mrow> <mrow> <mn>0.4</mn> </mrow> </msub> <msub> <mi mathvariant="normal">P</mi> <mrow> <mn>0.6</mn> </mrow> </msub> </mrow> </semantics></math> and BP.</p>
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<p>Band diagrams explaining the electron exchange between BP/<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>As</mi> </mrow> <mi mathvariant="normal">x</mi> </msub> <msub> <mi mathvariant="normal">P</mi> <mrow> <mn>1</mn> <mo>−</mo> <mi mathvariant="normal">x</mi> </mrow> </msub> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>As</mi> </mrow> <mi mathvariant="normal">x</mi> </msub> <msub> <mi mathvariant="normal">P</mi> <mrow> <mn>1</mn> <mo>−</mo> <mi mathvariant="normal">x</mi> </mrow> </msub> </mrow> </semantics></math> material systems and the aqueous redox couple (<b>a</b>) before and (<b>b</b>) after exposure to ambient conditions.</p>
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23 pages, 4769 KiB  
Article
Removal of Ammonium Ions from Aqueous Solutions Using Alkali-Activated Analcime as Sorbent
by Hanna Runtti, Elavarasi Sundhararasu, Janne Pesonen, Sari Tuomikoski, Tao Hu, Ulla Lassi and Teija Kangas
ChemEngineering 2023, 7(1), 5; https://doi.org/10.3390/chemengineering7010005 - 12 Jan 2023
Cited by 4 | Viewed by 2472
Abstract
Five alkali-activated analcime (ANA) sorbents (ANA-MK 1, ANA 2, ANA 3, ANA-MK 4, and ANA-MK 5) were developed for ammonium (NH4+) ion removal. Acid treatment and calcination were used as pre-treatments for analcime, and metakaolin (MK) was used as a [...] Read more.
Five alkali-activated analcime (ANA) sorbents (ANA-MK 1, ANA 2, ANA 3, ANA-MK 4, and ANA-MK 5) were developed for ammonium (NH4+) ion removal. Acid treatment and calcination were used as pre-treatments for analcime, and metakaolin (MK) was used as a blending agent in three sorbents. Sorption experiments were performed to evaluate the effects of sorbent dosage (1–20 g L−1), initial NH4+ ion concentration (5–1000 g L−1), and contact time (1 min–24 h). ANA-MK 1, ANA 2, and ANA-MK 4 were the most efficient sorbents for NH4+ ion removal, with a maximum experimental sorption uptake of 29.79, 26.00, and 22.24 mg g−1, respectively. ANA 3 and ANA-MK 5 demonstrated lower sorption capacities at 7.18 and 12.65 mg g−1, respectively. The results for the sorption of NH4+ ions onto the alkali-activated analcime surfaces were modeled using several isotherms. The Langmuir, Freundlich, Sips, and Bi-Langmuir isotherms were the best isotherm models to represent the studied systems. The results of the kinetic studies showed the maximum NH4+ ion removal percentage of the sorbents was ~80%, except for ANA-MK 5, which had a ~70% removal. Moreover, the pseudo-first-order, pseudo-second-order, and Elovich models were applied to the experimental data. The results showed that the sorption process for ANA-MK 1, ANA 2, ANA 3, and ANA-MK 4 followed the Elovich model, whereas the pseudo-second-order model provided the best correlation for ANA-MK 5. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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Figure 1
<p>Schematic diagram of the alkali- and acid-activated analcime sorbents.</p>
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<p>Schematic diagram of the alkali-activated analcime sorbents.</p>
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<p>XRD patterns of the alkali-activated analcime sorbents. * Fe<sub>2</sub>O<sub>3</sub> (04-010-3230), ● SiO<sub>2</sub> (04-014-7568), □ CaAl<sub>2</sub>Si<sub>2</sub>O<sub>8</sub> (00-041-1486), Δ NaAlSi<sub>3</sub>O<sub>8</sub> (01-083-1609), + CaMg<sub>2</sub>Fe<sub>16</sub>O<sub>27</sub> (00-056-0629), ◊ LiAlSi<sub>2</sub>O<sub>6</sub> (04-020-3038), ○ NaAl(Si<sub>2</sub>O<sub>6</sub>)(H<sub>2</sub>O) (01-074-2219), # Mg(SiO<sub>3</sub>) (01-076-6770), × K<sub>0.05</sub>Na<sub>0.94</sub>Ca<sub>0.01</sub>Al<sub>1.01</sub>Si<sub>2.99</sub>O<sub>8</sub> (04-023-4722).</p>
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<p>FTIR spectra of the alkali-activated analcime sorbents.</p>
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<p>FESEM images of the analcime (<b>a</b>–<b>c</b>), metakaolin (<b>d</b>–<b>f</b>), ANA-MK 1 (<b>g</b>–<b>i</b>), ANA 2 (<b>j</b>–<b>l</b>), ANA 3 (<b>m</b>–<b>o</b>), ANA-MK 4 (<b>p</b>–<b>r</b>) and ANA-MK 5 (<b>s</b>–<b>u</b>).</p>
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<p>Effect of sorbent dosage on the ability of (<b>a</b>) ANA-MK 1, (<b>b</b>) ANA 2, (<b>c</b>) ANA 3, (<b>d</b>) ANA-MK 4, and (<b>e</b>) ANA-MK 5 to remove NH<sub>4</sub><sup>+</sup> ions under two different initial NH<sub>4</sub><sup>+</sup> ion concentrations. The removal percentage of NH<sub>4</sub><sup>+</sup> ions is represented by a solid line, and the sorption capacities (<span class="html-italic">q<sub>e</sub></span>) are represented by a dashed line. Experimental conditions: sorbent dosage: 1–20 g L<sup>−1</sup>; initial pH of the solution: 2.5 (pH was adjusted after the addition of sorbent to the solution); contact time: 24 h; and temperature: 22 °C–23 °C. Analysis was based on the concentrations of NH<sub>4</sub><sup>+</sup>-N.</p>
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<p>Effect of initial concentration on NH<sub>4</sub><sup>+</sup> ion removal onto (<b>a</b>) ANA-MK 1, (<b>b</b>) ANA 2, (<b>c</b>) ANA 3, (<b>d</b>) ANA-MK 4, and (<b>e</b>) ANA-MK 5. The removal percentage for NH<sub>4</sub><sup>+</sup> ions is represented by a solid line, and the final pH values are represented by a dashed line. Experimental conditions: sorbent dosage: 5 g L<sup>−1</sup>; initial pH of the solution: 2.5; contact time: 24 h; and temperature: 22 °C–23 °C. Analysis was based on the concentrations of NH<sub>4</sub><sup>+</sup>-N.</p>
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<p>Isotherms for the sorption of NH<sub>4</sub><sup>+</sup> ion onto (<b>a</b>) ANA-MK 1, (<b>b</b>) ANA 2, (<b>c</b>) ANA 3, (<b>d</b>) ANA-MK 4, and (<b>e</b>) ANA-MK 5. Experimental conditions: sorbent dosage: 5 g L<sup>−1</sup>; initial pH of the solution: 2.5; contact time: 24 h; and temperature: 22 °C–23 °C. Analysis was based on the concentrations of NH<sub>4</sub><sup>+</sup>-N.</p>
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<p>Effect of contact time on NH<sub>4</sub><sup>+</sup> ion removal onto (<b>a</b>) ANA-MK 1, (<b>b</b>) ANA 2, (<b>c</b>) ANA 3, (<b>d</b>) ANA-MK 4, and (<b>e</b>) ANA-MK 5. The removal percentage of NH<sub>4</sub><sup>+</sup> ions is represented by a solid line, and the final pH values are represented by a dashed line. Experimental conditions: sorbent dosage: 5 g L<sup>−1</sup>; the volume of the solution: 0.975 L; <span class="html-italic">C</span><sub>0</sub> (NH<sub>4</sub><sup>+</sup>-N): ~40 mg L<sup>−1</sup>; initial pH of the solution: 2.5; contact time: 24 h; and temperature: 22 °C–23 °C.</p>
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<p>Pseudo-first-order, pseudo-second-order, and Elovich model plots of NH<sub>4</sub><sup>+</sup> ion removal using (<b>a</b>) ANA-MK 1, (<b>b</b>) ANA 2, (<b>c</b>) ANA 3, (<b>d</b>) ANA-MK 4, and (<b>e</b>) ANA-MK 5. Experimental conditions: sorbent dosage: 5 g L<sup>−1</sup>; the volume of solution: 0.975 L; <span class="html-italic">C</span><sub>0</sub> (NH<sub>4</sub><sup>+</sup>-N): ~40 mg L<sup>−1</sup>; initial pH of solution: 2.5; contact time: 24 h; and room temperature: 22 °C–23 °C.</p>
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<p>Weber and Morris intraparticle diffusion model plot for the sorption of NH<sub>4</sub><sup>+</sup> ions onto the alkali-activated analcime sorbents, namely, (<b>a</b>) ANA-MK 1, (<b>b</b>) ANA 2, (<b>c</b>) ANA 3, (<b>d</b>) ANA-MK 4, and (<b>e</b>) ANA-MK 5. Experimental conditions: concentration of the model NH<sub>4</sub><sup>+</sup> ion solution, <span class="html-italic">C</span><sub>0</sub> (NH<sub>4</sub><sup>+</sup>-N): ~40 mg L<sup>−1</sup>; initial pH: 2.5; sorbent dosage: 5 g L<sup>−1</sup>; contact time: 24 h; and room temperature: 22 °C–23 °C.</p>
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21 pages, 2667 KiB  
Article
CFD-Simulation of Isobutane Dehydrogenation for a Fluidized Bed Reactor
by Sergei A. Solovev, Olga V. Soloveva, Giyjaz E. Bekmukhamedov, Svetlana R. Egorova and Alexander A. Lamberov
ChemEngineering 2022, 6(6), 98; https://doi.org/10.3390/chemengineering6060098 - 19 Dec 2022
Cited by 1 | Viewed by 2222
Abstract
In the present study, a mathematical model of the isobutane dehydrogenation process for a laboratory reactor with a diameter of 2.8 cm and a height of 70 cm was created using CFD methods. A two-fluid model was selected as a model for the [...] Read more.
In the present study, a mathematical model of the isobutane dehydrogenation process for a laboratory reactor with a diameter of 2.8 cm and a height of 70 cm was created using CFD methods. A two-fluid model was selected as a model for the fluidization simulation, when the gas and solid granular phases were considered as continuous. The model of chemical kinetics considers three reactions that make the main contribution to the products mass fraction at the reactor outlet: the reaction of catalytic dehydrogenation of isobutane to isobutylene, the reaction of thermal cracking of isobutylene with the formation of methane and propylene, and the reaction of catalytic hydrogenation of propylene. The model was verified in a series of experimental studies. Experimental studies and numerical simulations were carried out for the process parameters: gas velocity 0.008, 0.012 and 0.016 m/s, gas temperature 550, 575, 600 and 625 °C, and catalyst mass 75, 100 and 125 g. The optimal process temperature was 575 °C, where the yield of isobutylene averaged 47.6% of the mass. As the temperature decreased, the yield of isobutylene decreased to 40.1% by weight on average. With an increase in temperature, the yield of isobutylene increased to 52.8% by weight on average, and the total yield of products of side reactions increased to 20% by weight on average. Changes in the gas velocity and catalyst mass had an insignificant effect on the values of the yield of isobutylene, but significantly affected the values of the yield of the by-products. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Laboratory reactor: (<b>a</b>) experimental reactor scheme; (<b>b</b>) 3D model of computational domain.</p>
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<p>Mesh: (<b>a</b>) graphical example; (<b>b</b>) definition of optimal <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>x</mi> </mrow> </semantics></math>.</p>
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<p>Solid phase volume fraction for the case of a catalyst mass of 100 g, a gas velocity of 0.016 m/s and a temperature of 575 °C.</p>
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<p>The mass fraction of reaction products for the case of catalyst mass of 100 g, a gas velocity of 0.016 m/s, and temperature of 575 °C: (<b>a</b>) C<sub>4</sub>H<sub>10</sub> and C<sub>4</sub>H<sub>8</sub>; (<b>b</b>) H<sub>2</sub>, CH<sub>4</sub>, C<sub>4</sub>H<sub>8</sub> and C<sub>4</sub>H<sub>6</sub>.</p>
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<p>The mass fraction of isobutane dehydrogenation reaction products for the case of a catalyst mass of 100 g, a gas velocity of 0.016 m/s, and temperature of 575 °C: (<b>a</b>) C<sub>4</sub>H<sub>10</sub>; (<b>b</b>) C<sub>4</sub>H<sub>8</sub>; and (<b>c</b>) H<sub>2</sub>.</p>
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<p>Mass fraction of side reaction products for the case of a catalyst mass of 100 g, a gas velocity of 0.016 m/s, and temperature of 575 °C: (<b>a</b>) CH<sub>4</sub>; (<b>b</b>) C<sub>3</sub>H<sub>6</sub>; and (<b>c</b>) C<sub>3</sub>H<sub>8</sub>.</p>
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<p>The mass fraction of reaction products for the case of catalyst mass of 75 g: (<b>a</b>) C<sub>4</sub>H<sub>10</sub> and C<sub>4</sub>H<sub>8</sub> at <span class="html-italic">v</span> = 0.008 m/s; (<b>b</b>) H<sub>2</sub>, CH<sub>4</sub>, C<sub>3</sub>H<sub>8</sub>, C<sub>3</sub>H<sub>6</sub> at <span class="html-italic">v</span> = 0.008 m/s; (<b>c</b>) C<sub>4</sub>H<sub>10</sub> and C<sub>4</sub>H<sub>8</sub> at <span class="html-italic">v</span> = 0.012 m/s; (<b>d</b>) H<sub>2</sub>, CH<sub>4</sub>, C<sub>3</sub>H<sub>8</sub>, C<sub>3</sub>H<sub>6</sub> at <span class="html-italic">v</span> = 0.012 m/s; (<b>e</b>) C<sub>4</sub>H<sub>10</sub> and C<sub>4</sub>H<sub>8</sub> at <span class="html-italic">v</span> = 0.016 m/s; and (<b>f</b>) H<sub>2</sub>, CH<sub>4</sub>, C<sub>3</sub>H<sub>8</sub>, C<sub>3</sub>H<sub>6</sub> at <span class="html-italic">v</span> = 0.016 m/s.</p>
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<p>The mass fraction of reaction products for the case of catalyst mass of 100 g: (<b>a</b>) C<sub>4</sub>H<sub>10</sub> and C<sub>4</sub>H<sub>8</sub> at <span class="html-italic">v</span> = 0.008 m/s; (<b>b</b>) H<sub>2</sub>, CH<sub>4</sub>, C<sub>3</sub>H<sub>8</sub>, C<sub>3</sub>H<sub>6</sub> at <span class="html-italic">v</span> = 0.008 m/s; (<b>c</b>) C<sub>4</sub>H<sub>10</sub> and C<sub>4</sub>H<sub>8</sub> at <span class="html-italic">v</span> = 0.012 m/s; (<b>d</b>) H<sub>2</sub>, CH<sub>4</sub>, C<sub>3</sub>H<sub>8</sub>, C<sub>3</sub>H<sub>6</sub> at <span class="html-italic">v</span> = 0.012 m/s; (<b>e</b>) C<sub>4</sub>H<sub>10</sub> and C<sub>4</sub>H<sub>8</sub> at <span class="html-italic">v</span> = 0.016 m/s; and (<b>f</b>) H<sub>2</sub>, CH<sub>4</sub>, C<sub>3</sub>H<sub>8</sub>, C<sub>3</sub>H<sub>6</sub> at <span class="html-italic">v</span> = 0.016 m/s.</p>
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<p>The mass fraction of reaction products for the case of catalyst mass of 125 g: (<b>a</b>) C<sub>4</sub>H<sub>10</sub> and C<sub>4</sub>H<sub>8</sub> at <span class="html-italic">v</span> = 0.008 m/s; (<b>b</b>) H<sub>2</sub>, CH<sub>4</sub>, C<sub>3</sub>H<sub>8</sub>, C<sub>3</sub>H<sub>6</sub> at <span class="html-italic">v</span> = 0.008 m/s; (<b>c</b>) C<sub>4</sub>H<sub>10</sub> and C<sub>4</sub>H<sub>8</sub> at <span class="html-italic">v</span> = 0.012 m/s; (<b>d</b>) H<sub>2</sub>, CH<sub>4</sub>, C<sub>3</sub>H<sub>8</sub>, C<sub>3</sub>H<sub>6</sub> at <span class="html-italic">v</span> = 0.012 m/s; (<b>e</b>) C<sub>4</sub>H<sub>10</sub> and C<sub>4</sub>H<sub>8</sub> at <span class="html-italic">v</span> = 0.016 m/s; and (<b>f</b>) H<sub>2</sub>, CH<sub>4</sub>, C<sub>3</sub>H<sub>8</sub>, C<sub>3</sub>H<sub>6</sub> at <span class="html-italic">v</span> = 0.016 m/s.</p>
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6 pages, 1599 KiB  
Communication
Fe Atom—Mixed Edges Fractal Graphene via DFT Calculation
by Lobna Aloui, Thierry Dintzer and Izabela Janowska
ChemEngineering 2022, 6(5), 79; https://doi.org/10.3390/chemengineering6050079 - 8 Oct 2022
Cited by 1 | Viewed by 1885
Abstract
The stability of small fractal graphene models with two different symmetries and Fe atoms at their mixed edges is addressed by density functional theory (DFT) calculations. Four kinds of edge configurations and Fe atom localizations are determined depending on the model. The edges [...] Read more.
The stability of small fractal graphene models with two different symmetries and Fe atoms at their mixed edges is addressed by density functional theory (DFT) calculations. Four kinds of edge configurations and Fe atom localizations are determined depending on the model. The edges have mixed configuration, the zig-zag and “intra-zig-zag” in symmetrical structures and armchair and zig-zag type in the architectures with rotational symmetry. The rotational symmetry graphene exhibits slightly higher stability per carbon atom compared to the symmetrical model, while the localization of Fe atoms is more favorable at armchair and “inversed zigzag” than at zigzag type carbon termination. Larger graphene structures with rotational symmetry were observed previously via experimental cutting of graphene with Fe nanoparticles (NPs). Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>The models of graphene formed from coronene growing in a fractal manner with two symmetries: (<b>a</b>) symmetric and (<b>b</b>) rotational symmetry (19.1°) [<a href="#B23-ChemEngineering-06-00079" class="html-bibr">23</a>] (The models are drawn by ChemDraw; all C atoms have sp<sup>2</sup> hybridization). The TEM micrograph shows jaggy non-integer dimension graphene edges with rotational symmetry.</p>
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<p>Schemes of the two systems studied using DFT method: (<b>a</b>) Mix (1) as a mixture of Zig-zag and intra-Zig-zag edges with 46 atoms of carbon, and (<b>b</b>) Mix (2) as a mixture of Zig-zag and Armchair with 44 atoms of carbon. The free and fixed atoms were configurated as shown via grey coloring. The total energy and energy per C atom calculated for two graphene models.</p>
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<p>Schema of four systems including Fe-M(1) (<b>a</b>) and Fe-M(2) (<b>b</b>) with different positions of the Fe atoms studied with DFT method and the corresponding energy variation curves.</p>
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<p>Schema of Fe/intra-Zig-zag system with different d distance. (d<sub>1</sub>, d<sub>2</sub>) Energy-distance d curve with two minima in the system.</p>
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16 pages, 4456 KiB  
Article
Efficiency of Mechanochemical Ball Milling Technique in the Preparation of Fe/TiO2 Photocatalysts
by Shabnam Taghipour, King-Lun Yeung and Behzad Ataie-Ashtiani
ChemEngineering 2022, 6(5), 77; https://doi.org/10.3390/chemengineering6050077 - 2 Oct 2022
Cited by 5 | Viewed by 2423
Abstract
Rapid population growth and widespread industrialization are the main contributing factors to the increasing contamination of the world’s diminishing freshwater resources. This work investigates Fe/TiO2 as an efficient and sustainable photocatalyst for treating organic micropollutants in water. The photocatalysts prepared by these [...] Read more.
Rapid population growth and widespread industrialization are the main contributing factors to the increasing contamination of the world’s diminishing freshwater resources. This work investigates Fe/TiO2 as an efficient and sustainable photocatalyst for treating organic micropollutants in water. The photocatalysts prepared by these mechanochemical methods used a high-energy ball milling technique to manipulate Fe/TiO2’s structural, optical, and catalytic properties for the photo-oxidation of 2,4-Dichlorophenol (2,4-DCP). Doping with iron effectively reduced the band gap of rutile TiO2 from 3 to 2.22 eV. By reducing the ball/powder ratio from 34 to 7, the removal efficiency of 2,4-DCP increased from 65.2 to 84.7%. Measuring the TOC indicated 63.5 and 49.4% mineralization by Fe/TiO2-7 and rutile TiO2, respectively, after 24 h. The energy yields for the Fe/TiO2 and rutile TiO2 were 0.13 and 0.06 g 2,4-DCP/kW h, respectively. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>A photograph of the photoreactor setup for the photocatalytic oxidation of the 2,4-DCP.</p>
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<p>X-ray diffraction patterns of the rutile TiO<sub>2</sub> (Rutile), ball-milled TiO<sub>2</sub> (Rutile-7), and Fe/TiO<sub>2</sub> (B/P = 7, 17, and 34) prepared by mechanochemical ball-milling at 400 rpm for 10 h.</p>
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<p>Micro-Raman spectra of the rutile TiO<sub>2</sub> (Rutile), ball-milled TiO<sub>2</sub> (Rutile-7), and Fe/TiO<sub>2</sub> (B/P = 7, 17, and 34) prepared by mechanochemical ball-milling at 400 rpm for 10 h.</p>
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<p>Photographs of (<b>a</b>) rutile TiO<sub>2</sub> and (<b>b</b>) ball-milled Fe/TiO<sub>2</sub>-7.</p>
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<p>SEM images of (<b>a</b>) pristine TiO<sub>2</sub>, (<b>b</b>) Fe/TiO<sub>2</sub>-34, (<b>c</b>) Fe/TiO<sub>2</sub>-17, and (<b>d</b>) Fe/TiO<sub>2</sub>-7.</p>
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<p>Nitrogen physisorption isotherms for ball-milled Fe/TiO<sub>2</sub>-7.</p>
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<p>UV-Vis diffuse reflectance spectra of the (<b>a</b>) bare rutile TiO<sub>2</sub> and (<b>b</b>) Fe/TiO<sub>2</sub>-7, with their corresponding Tauc plot insets.</p>
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<p>Electronic structure of the rutile TiO<sub>2</sub> and Fe/TiO<sub>2</sub>-7 from UV-Vis DRS and XPS measurements.</p>
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<p>Deconvolution and XPS spectra curve fitting for the (<b>a</b>) O 1 s, (<b>b</b>) Ti 2p, and (<b>c</b>) Fe 2p of TiO<sub>2</sub> and Fe/TiO<sub>2</sub>-7.</p>
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<p>Plots of the 2,4–DCP concentrations during photocatalytic oxidation reactions over the rutile TiO<sub>2</sub>, Fe/TiO<sub>2</sub>-34, Fe/TiO<sub>2</sub>-17, and Fe/TiO<sub>2</sub>-7 under visible light irradiation. Note: [2,4–DCP]<sub>0</sub> = 4.08 mg/L, [TOC]<sub>0</sub> = 9.47 mg/L, catalyst loading = 1 g/L, room temperature, no pH adjustment.</p>
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<p>Schematic display of the electronic band structure of Fe/TiO<sub>2</sub>-7.</p>
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<p>Linear-log plot ln(<span class="html-italic">C</span><sub>0</sub>) versus reaction time (h) for the degradation kinetic after 24 h reaction using (<b>a</b>) rutile and (<b>b</b>) Fe/TiO<sub>2</sub>-7 photocatalysts.</p>
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<p>Plots of TOC removal during photocatalytic oxidation reactions over TiO<sub>2</sub> and Fe/TiO<sub>2</sub>-7 under visible light irradiation. Note: [2,4-DCP]<sub>0</sub> = 4.08 mg/L, [TOC]<sub>0</sub> = 9.47 mg/L, catalyst loading = 1 g/L, room temperature, no pH adjustment.</p>
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11 pages, 3156 KiB  
Article
Supercritical CO2 Assisted Electrospray to Produce Poly(lactic-co-glycolic Acid) Nanoparticles
by Elena Barbero-Colmenar, Mariangela Guastaferro, Lucia Baldino, Stefano Cardea and Ernesto Reverchon
ChemEngineering 2022, 6(5), 66; https://doi.org/10.3390/chemengineering6050066 - 1 Sep 2022
Cited by 4 | Viewed by 2278
Abstract
This work proposes an improvement of the traditional electrospraying process, in which supercritical carbon dioxide (SC-CO2) is used to produce poly(lactic-co-glycolic acid) (PLGA) nanoparticles. The experiments were performed at different PLGA concentrations (1, 3 and 5% w/w), applied [...] Read more.
This work proposes an improvement of the traditional electrospraying process, in which supercritical carbon dioxide (SC-CO2) is used to produce poly(lactic-co-glycolic acid) (PLGA) nanoparticles. The experiments were performed at different PLGA concentrations (1, 3 and 5% w/w), applied voltages (10 and 30 kV) and operating pressures (80, 120 and 140 bar). It was found that working at 140 bar and 30 kV, spherical nanoparticles, with mean diameters of 101 ± 13 nm and 151 ± 45 nm, were obtained, when solutions at 1% w/w and 3% w/w PLGA were electrosprayed, respectively. Increasing PLGA concentration up to 5% w/w, a mixture of fibers and particles was observed, indicating the transition to the electrospinning regime. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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Figure 1

Figure 1
<p>Representation of the setup used for PLGA nanoparticles production by supercritical CO<sub>2</sub> assisted electrospray.</p>
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<p>Real images of the spraying zone formed by the injector system elements and the collector used for nanoparticles deposit. The inset images show the detail of the injector used for these experiments.</p>
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<p>PSDs of samples produced by SC-CO<sub>2</sub> assisted electrospray at P = 80 bar (black), P = 120 bar (red) and P = 140 bar (blue); V = 30 kV and PLGA concentrations: (<b>a</b>) 1% <span class="html-italic">w</span>/<span class="html-italic">w</span> and (<b>b</b>) 3% <span class="html-italic">w</span>/<span class="html-italic">w</span>.</p>
Full article ">Figure 4
<p>SEM images of nanoparticles obtained from: (<b>a</b>) 1% <span class="html-italic">w</span>/<span class="html-italic">w</span> PLGA solution at 120 bar and (<b>b</b>) 3% <span class="html-italic">w</span>/<span class="html-italic">w</span> PLGA solution at 140 bar. Applied voltage 30 kV. Scale bars 1 µm.</p>
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<p>SEM image of 5% <span class="html-italic">w</span>/<span class="html-italic">w</span> PLGA fibers/particles obtained from the solid residue produced at 140 bar. Applied voltage 30 kV. Scale bar 20 µm.</p>
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<p>PSDs of samples produced by SC-CO<sub>2</sub> assisted electrospray at 3% <span class="html-italic">w</span>/<span class="html-italic">w</span> PLGA and 10 kV (red) and 30 kV (black), when operating at: (<b>a</b>) 80 bar and (<b>b</b>) 140 bar.</p>
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16 pages, 7505 KiB  
Article
Experimental Study and Numerical Simulation of Hydrodynamic Parameters of Tangential Swirlers
by Nikolai A. Voinov, Alexander S. Frolov, Anastasiya V. Bogatkova and Denis A. Zemtsov
ChemEngineering 2022, 6(4), 48; https://doi.org/10.3390/chemengineering6040048 - 27 Jun 2022
Viewed by 1856
Abstract
This paper presents and patents new profiled- and annular-channel tangential swirlers with 1.8–3 times less hydraulic drag coefficient compared to swirlers with straight channel walls at the same flow rate, respectively. The results of numerical simulation of the gas velocity and pressure profiles [...] Read more.
This paper presents and patents new profiled- and annular-channel tangential swirlers with 1.8–3 times less hydraulic drag coefficient compared to swirlers with straight channel walls at the same flow rate, respectively. The results of numerical simulation of the gas velocity and pressure profiles for tangential swirler channels of different structures are presented. The modelling was carried out with the help of OpenFOAM software using the k-ε turbulence model. It is found that the shape of the velocity profile at the channel inlet has a decisive influence on the swirler drag coefficient. The greatest contribution to the total drag coefficient of the tangential swirler is made by the pressure drop at the channel inlet compared to the pressure drop at the channel wall and the channel outlet. The experimental dependencies of the tangential swirlers’ drag coefficient on the Reynolds number with a gas criterion of 2000–20,000 and the following structural channel parameters: width 1, 2–9 mm, height 1, 5–10 mm, number 5–45 units, inclination angle 0–45° are presented. The experimental data were compared with the modelling calculations and the convergence of data was achieved. The generalized dependence for the measurement of the hydraulic drag coefficient of three types of tangential swirlers considering the effect made by the geometric parameters (flow rate, width and height of the channel, wall inclination angle) on the pressure drop has been determined; it can be useful at the unit design stage as it allows for reducing the calculation time of the swirler parameters. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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Figure 1

Figure 1
<p>Swirler structure. (<b>a</b>) Channels, (<b>b</b>–<b>d</b>): 1—tray deck; 2—steam channel; 3—cover; 4—tangential swirler.</p>
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<p>Swirler models with straight (<b>a</b>), profiled (<b>b</b>), and annular channels (<b>c</b>).</p>
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<p>The solid-state model for the straight (<b>a</b>), profiled (<b>b</b>) and annular channels (<b>c</b>), pressure and velocity reading lines during simulation (<b>d</b>).</p>
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<p>Dependence of the resistance coefficient value on the Reynolds number (<b>a</b>) for the considered swirler types and distribution of the design pressure drop (<b>b</b>). Experimental data (1–3): 1—Swirler No. 3 (according to <a href="#ChemEngineering-06-00048-t001" class="html-table">Table 1</a>) at channel length <span class="html-italic">l<sub>chan</sub></span> = 0.022 (m); 2—No. 15 at <span class="html-italic">l<sub>chan</sub></span> = 0.025 (m); 3—No. 18 at <span class="html-italic">l<sub>chan</sub></span> = 0.022 (m). (<b>b</b>) Calculation data at velocity in channel <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>u</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>4 (m/s).</p>
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<p>Dependence of the resistance coefficient on the Reynolds number for swirlers with straight (<b>a</b>), profiled (<b>b</b>), and annular (<b>c</b>) channel walls. Experimental points (according to <a href="#ChemEngineering-06-00048-t001" class="html-table">Table 1</a>): (<b>a</b>) 1—No. 2 at <span class="html-italic">l<sub>w</sub></span> = 0.004 (m); 2—No. 5 at <span class="html-italic">l<sub>w</sub></span> = 0.012 (m); 3—No. 6 at <span class="html-italic">l<sub>w</sub></span> = 0.022 (m); 4—No. 9; 5—No. 1; 6—No. 7; 7—No. 8; (<b>b</b>) (1–5): 1—No. 12; 2—No. 13; 3—No. 14; 4—No. 15; 5—No. 16; (<b>c</b>) 1—No. 17 at <span class="html-italic">l<sub>chan</sub></span> = 0.022 (m); 2—No. 18 at <span class="html-italic">l<sub>chan</sub></span> = 0.022 (m); 3—No. 19 at <span class="html-italic">l<sub>chan</sub></span> = 0.020 (m).</p>
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<p>Dependence of the straight channel resistance coefficient (according to <a href="#ChemEngineering-06-00048-t001" class="html-table">Table 1</a>) on Reynolds number (<b>a</b>) and channel length (<b>b</b>). (<b>a</b>) Experimental points (1–3): 1—No. 4 at channel length <span class="html-italic">l<sub>chan</sub></span> = 0.002 (m); 2—No. 5 at <span class="html-italic">l<sub>chan</sub></span> = 0.007 (m); 3—No. 6 <span class="html-italic">l<sub>chan</sub></span> = 0.022 (m); (<b>b</b>) Experimental points at channel width <span class="html-italic">b</span> = 0.005 (m) for the swirler channel profiles. Lines—calculation based on simulation results.</p>
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<p>Dependence of the resistance coefficient on the Reynolds number with straight channels (<b>a</b>) and slope angle α (<b>b</b>): Experimental points at channel width <span class="html-italic">b</span> = 0.003 (m) and channel length <span class="html-italic">l<sub>w</sub></span> = 0.004 (m) by different channel wall slope angle α.</p>
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<p>Change in total velocity <span class="html-italic">u<sub>p</sub></span> for swirlers with straight channel walls along the reading line at the channel inlet <span class="html-italic">L<sub>in</sub></span> and outlet <span class="html-italic">L<sub>out</sub></span> at <span class="html-italic">b</span> = 0.003 (m), <span class="html-italic">h</span> = 0.008 (m), <math display="inline"><semantics> <mover accent="true"> <mi>u</mi> <mo>¯</mo> </mover> </semantics></math> = 14 (m/s): (<b>a</b>) <span class="html-italic">α</span> = 26°; (<b>b</b>) <span class="html-italic">α</span> = 45°; (<b>c</b>) <span class="html-italic">α</span> = 90°. Lines (1–3) for reading by channel height h: 1—<span class="html-italic">h</span> = 0.004 (m); 2—<span class="html-italic">h</span> = 0; 3—<span class="html-italic">h</span> = 0.008 (m).</p>
Full article ">Figure 8 Cont.
<p>Change in total velocity <span class="html-italic">u<sub>p</sub></span> for swirlers with straight channel walls along the reading line at the channel inlet <span class="html-italic">L<sub>in</sub></span> and outlet <span class="html-italic">L<sub>out</sub></span> at <span class="html-italic">b</span> = 0.003 (m), <span class="html-italic">h</span> = 0.008 (m), <math display="inline"><semantics> <mover accent="true"> <mi>u</mi> <mo>¯</mo> </mover> </semantics></math> = 14 (m/s): (<b>a</b>) <span class="html-italic">α</span> = 26°; (<b>b</b>) <span class="html-italic">α</span> = 45°; (<b>c</b>) <span class="html-italic">α</span> = 90°. Lines (1–3) for reading by channel height h: 1—<span class="html-italic">h</span> = 0.004 (m); 2—<span class="html-italic">h</span> = 0; 3—<span class="html-italic">h</span> = 0.008 (m).</p>
Full article ">Figure 9
<p>Distribution of the total velocity <span class="html-italic">u<sub>p</sub></span> and its components along the coordinate axes <span class="html-italic">u<sub>x</sub></span><sub>,</sub> <span class="html-italic">u<sub>y</sub></span>, and <span class="html-italic">u<sub>z</sub></span> at channel wall slope angle α = 26° (<b>a</b>) and α = 45° (<b>b</b>): Straight channel walls.</p>
Full article ">Figure 10
<p>Distribution of pressure drop in straight (<b>a</b>,<b>b</b>) and profiled (<b>c</b>,<b>d</b>) swirler channels at <span class="html-italic">h</span> = 0.080 (m) (<b>a</b>,<b>c</b>) and <span class="html-italic">h</span> = 0.008 (m) (<b>b</b>,<b>d</b>) at <math display="inline"><semantics> <mover accent="true"> <mi>u</mi> <mo>¯</mo> </mover> </semantics></math> = 14 (m/s), <span class="html-italic">b</span> = 0.003 (m), and <span class="html-italic">n</span> = 40. Lines (1–3), according to <a href="#ChemEngineering-06-00048-f008" class="html-fig">Figure 8</a>.</p>
Full article ">Figure 11
<p>Dependence of the resistance coefficient value on the swirler channel width. Experimental data <span class="html-italic">Re</span> = 2200–5000 (1–3): 1—channel width <span class="html-italic">b</span> = 0.0015–0.004 (m); 2—<span class="html-italic">b</span> = 0.0015–0.008 (m); 3—<span class="html-italic">b</span> = 0.003–0.004 (m). Dashed lines: experimental data by <span class="html-italic">Re</span> = 2100–3500.</p>
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<p>Share of the head loss of the total pressure drop at different swirler channel widths and at <span class="html-italic">h</span> = 0.008 (m): (<b>a</b>) straight channels <span class="html-italic">n</span> = 32 (pcs); (<b>b</b>) profile channels <span class="html-italic">n</span> = 40 (pcs); (<b>c</b>) annular channels <span class="html-italic">n</span> = 8 (pcs).</p>
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<p>Full inlet velocity curves for straight (<b>a</b>–<b>c</b>), profiled (<b>d</b>–<b>f</b>), and annular (<b>g</b>–<b>i</b>) channels at different swirler channel widths at <span class="html-italic">Re</span> = 2200: (<b>a</b>,<b>d</b>,<b>g</b>) <span class="html-italic">b</span> = 0.0015 (m); (<b>b</b>,<b>e</b>,<b>h</b>) 0.003 (m); (<b>c</b>,<b>i</b>) 0.006 (m); (<b>f</b>) 0.009 (m). Lines (1–3), according to <a href="#ChemEngineering-06-00048-f008" class="html-fig">Figure 8</a>.</p>
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<p>Velocity distribution at annular channel inlet. Lines (1–4): 1—<span class="html-italic">u<sub>p</sub></span>; 2—<span class="html-italic">u<sub>x</sub></span>; 3—<span class="html-italic">u<sub>y</sub></span>; 4—<span class="html-italic">u<sub>z</sub></span>.</p>
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<p>Distribution of tangential velocity <span class="html-italic">u<sub>y</sub></span>; reference lines for different channel lengths (1–4): 1—<span class="html-italic">l<sub>chan</sub></span> = 0 (inlet), 2—<span class="html-italic">l<sub>chan</sub></span> = 0.007 (m), 3—<span class="html-italic">l<sub>chan</sub></span> = 0.014 (m); 4—<span class="html-italic">l<sub>chan</sub></span> = 0.021 (m).</p>
Full article ">Figure 16
<p>Dependence of the resistance coefficient <span class="html-italic">ξ</span> on <math display="inline"><semantics> <mrow> <msup> <mi>α</mi> <mrow> <mo>−</mo> <mn>0.71</mn> </mrow> </msup> <mo>·</mo> <mi>R</mi> <msup> <mi>e</mi> <mrow> <mo>−</mo> <mi mathvariant="normal">m</mi> </mrow> </msup> <mo>·</mo> <msubsup> <mi>l</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>a</mi> <mi>n</mi> </mrow> <mrow> <mn>0.19</mn> </mrow> </msubsup> <mo>·</mo> <msup> <mi>b</mi> <mrow> <mn>0.6</mn> </mrow> </msup> <mo>·</mo> <msup> <mi>h</mi> <mrow> <mo>−</mo> <mi mathvariant="normal">q</mi> </mrow> </msup> </mrow> </semantics></math> (right side of Equation (8)) for swirlers with straight parallel (<b>a</b>), profiled (<b>b</b>), and annular channels (<b>c</b>). Experimental points (according to <a href="#ChemEngineering-06-00048-t001" class="html-table">Table 1</a>): (<b>a</b>) 1—No. 2 at <span class="html-italic">l<sub>w</sub></span> = 0.004 (m); 2—No. 5 at <span class="html-italic">l<sub>w</sub></span> = 0.012 (m). 3—No. 6 at <span class="html-italic">l<sub>w</sub></span> = 0.022 (m); 4—No. 7; 5—No. 10; 6—No. 11; (<b>b</b>) (1–5): 1—No. 12; 2—No. 13; 3—No. 14; 4—No. 15; 5—No. 16; (<b>c</b>) 1—No. 17 at <span class="html-italic">l<sub>chan</sub></span> = 0.022 (m); 2—No. 18 at <span class="html-italic">l<sub>chan</sub></span> = 0.022 (m); 3—No. 19 at <span class="html-italic">l<sub>chan</sub></span> = 0.020 (m).</p>
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16 pages, 8603 KiB  
Article
Evaluation of VLEs for Binaries of Five Compounds Involved in the Production Processes of Cyclohexanone
by Adriel Sosa, Juan Ortega, Luis Fernández, Arturo Romero, Aurora Santos and David Lorenzo
ChemEngineering 2022, 6(3), 42; https://doi.org/10.3390/chemengineering6030042 - 27 May 2022
Cited by 1 | Viewed by 2367
Abstract
In an attempt to evaluate the separation of certain impurities that arise in some stages of the production of cyclohexanone, this work analyzed the possibility of removing five of these substances via rectification. Due to the scarcity of experimental vapor–liquid equilibrium data for [...] Read more.
In an attempt to evaluate the separation of certain impurities that arise in some stages of the production of cyclohexanone, this work analyzed the possibility of removing five of these substances via rectification. Due to the scarcity of experimental vapor–liquid equilibrium data for most of the solutions in the effluent of the global process, prior knowledge of their behavior is required. In this work, two predictive models, UNIFAC and COSMO-RS, were used to determine a priori the possibility of obtaining, by distillation, the individual components of seven of the binaries formed by the combination of these five compounds. Since both procedures described quasi-ideal behavior for all the chosen solutions, the results are considered as an approximation, owing to the special nature of the studied systems. The results and characteristics of each system are discussed separately. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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Figure 1

Figure 1
<p>ε-caprolactam produced from cyclohexanone via the oxime by a reaction with hydroxylamine and Beckmann transposition.</p>
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<p>Simplified diagram representing the production stages of cyclohexanone, indicating some of the compounds participating in each stream. In blue, the five compounds whose separation is proposed in this work are presented.</p>
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<p>(<b>a</b>) Interaction between functional groups that give rise to the mixing effects following the GCM, UNIFAC. (<b>b</b>) Interaction of hexagonal surface elements that generate <span class="html-italic">μ</span><sub>S</sub> by means of the COSMO-RS model.</p>
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<p>Iso-<span class="html-italic">p</span> VLE at 4 kPa (<b>a</b>,<b>b</b>) and at 26.7 kPa (<b>c</b>,<b>d</b>) of cyclohexanone + 2-methylcyclohexanone [<a href="#B3-ChemEngineering-06-00042" class="html-bibr">3</a>]; (----) UNIFAC-DM; (<span style="color:#3333FF">- - -</span>) COSMO-RS. (●) <span class="html-italic">T</span>,x,y (a,c)/<span class="html-italic">γ</span><sub>i</sub> (<b>b</b>,<b>d</b>); (¯) <span class="html-italic">(y</span>−<span class="html-italic">x</span>), <span class="html-italic">x</span>; (σ) <span class="html-italic">g</span><sup>E</sup>/<span class="html-italic">RT</span>.</p>
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<p>Iso-<span class="html-italic">p</span> VLE at 4 kPa (<b>a</b>,<b>b</b>) and 26.66 kPa (<b>c</b>,<b>d</b>) of the cyclohexanone + cyclohexanol system [<a href="#B3-ChemEngineering-06-00042" class="html-bibr">3</a>]; (----) UNIFAC-DM; (<span style="color:#3333FF">- - -</span>) COSMO-RS. (●) <span class="html-italic">T</span>,x,y (a,c)/<span class="html-italic">γ</span><sub>i</sub> (<b>b</b>,<b>d</b>); (¯) <span class="html-italic">(y</span>−<span class="html-italic">x</span>), <span class="html-italic">x</span>; (σ) <span class="html-italic">g</span><sup>E</sup>/<span class="html-italic">RT</span>.</p>
Full article ">Figure 5 Cont.
<p>Iso-<span class="html-italic">p</span> VLE at 4 kPa (<b>a</b>,<b>b</b>) and 26.66 kPa (<b>c</b>,<b>d</b>) of the cyclohexanone + cyclohexanol system [<a href="#B3-ChemEngineering-06-00042" class="html-bibr">3</a>]; (----) UNIFAC-DM; (<span style="color:#3333FF">- - -</span>) COSMO-RS. (●) <span class="html-italic">T</span>,x,y (a,c)/<span class="html-italic">γ</span><sub>i</sub> (<b>b</b>,<b>d</b>); (¯) <span class="html-italic">(y</span>−<span class="html-italic">x</span>), <span class="html-italic">x</span>; (σ) <span class="html-italic">g</span><sup>E</sup>/<span class="html-italic">RT</span>.</p>
Full article ">Figure 6
<p>Estimations of VLE at 101.32 kPa of cyclohexanone + 2-methylcyclohexanone. (----) UNIFAC-DM; (<span style="color:#3333FF">- - -</span>) COSMO-RS. (<b>a</b>) <span class="html-italic">T</span>,x,y and <span class="html-italic">(y</span>−<span class="html-italic">x</span>), <span class="html-italic">x</span> (<b>b</b>) <span class="html-italic">γ</span><sub>i</sub> vs <span class="html-italic">x</span> and <span class="html-italic">g</span><sup>E</sup>/<span class="html-italic">RT</span> vs <span class="html-italic">x</span>.</p>
Full article ">Figure 7
<p>Estimations of VLE at 101.32 kPa of cyclohexanone + cyclohexanol. (----) UNIFAC-DM; (<span style="color:#3333FF">- - -</span>) COSMO-RS. (<b>a</b>) <span class="html-italic">T</span>,x,y and <span class="html-italic">(y</span>−<span class="html-italic">x</span>), <span class="html-italic">x</span> (<b>b</b>) <span class="html-italic">γ</span><sub>i</sub> vs <span class="html-italic">x</span> and <span class="html-italic">g</span><sup>E</sup>/<span class="html-italic">RT</span> vs <span class="html-italic">x</span>.</p>
Full article ">Figure 8
<p>Estimations of VLE at 101.32 kPa of: (<b>a</b>,<b>b</b>), cyclohexanone + 2-cyclohexen-1-ol, and (<b>c</b>,<b>d</b>), cyclohexanone + 3-cyclohexen-1-ol. (—) UNIFAC-DM; (<span style="color:blue">- - -</span>) COSMO-RS.</p>
Full article ">Figure 9
<p>Estimations of VLE at 101.32 kPa of 2-methylcyclohexanone + cyclohexanol, (----) UNIFAC-DM; (<span style="color:blue">- - -</span>) COSMO-RS. (<b>a</b>) <span class="html-italic">T</span>,x,y and <span class="html-italic">(y</span>−<span class="html-italic">x</span>), <span class="html-italic">x</span> (<b>b</b>) <span class="html-italic">γ</span><sub>i</sub> vs <span class="html-italic">x</span> and <span class="html-italic">g</span><sup>E</sup>/<span class="html-italic">RT</span> vs <span class="html-italic">x</span>.</p>
Full article ">Figure 10
<p>VLE at 101.32 kPa of: (<b>a</b>,<b>b</b>) cyclohexanol + 2-cyclohexen-1-ol, and (<b>c</b>,<b>d</b>) cyclohexanol + 3-cyclohexen-1-ol. (----) UNIFAC-DM; (<span style="color:blue">- - -</span>) COSMO-RS.</p>
Full article ">Figure 10 Cont.
<p>VLE at 101.32 kPa of: (<b>a</b>,<b>b</b>) cyclohexanol + 2-cyclohexen-1-ol, and (<b>c</b>,<b>d</b>) cyclohexanol + 3-cyclohexen-1-ol. (----) UNIFAC-DM; (<span style="color:blue">- - -</span>) COSMO-RS.</p>
Full article ">Figure 11
<p>Minimum (----) and maximum limits (----) of the relative volatility estimated with UNIFAC (continuous) and COSMO-RS (dashed lines). Distillation not recommended, <span class="html-fig-inline" id="ChemEngineering-06-00042-i001"> <img alt="Chemengineering 06 00042 i001" src="/ChemEngineering/ChemEngineering-06-00042/article_deploy/html/images/ChemEngineering-06-00042-i001.png"/></span>complex separation <span class="html-fig-inline" id="ChemEngineering-06-00042-i002"> <img alt="Chemengineering 06 00042 i002" src="/ChemEngineering/ChemEngineering-06-00042/article_deploy/html/images/ChemEngineering-06-00042-i002.png"/></span>.</p>
Full article ">Figure 12
<p>Plot of vapor pressure of the compounds: cyclohexanone (−−−−); cyclo- hexanol (• • •); 2-methylcyclohexanone (− ⋅ −); 2-cyclohexen-1-ol (– – –), in reduced coordinates.</p>
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16 pages, 1141 KiB  
Article
Development of a Bioactive Sauce: Effect of the Packaging and Storage Conditions
by Cecilia G. Giménez, María Victoria Traffano-Schiffo, Sonia C. Sgroppo and Carola A. Sosa
ChemEngineering 2022, 6(3), 34; https://doi.org/10.3390/chemengineering6030034 - 26 Apr 2022
Cited by 4 | Viewed by 3628
Abstract
Consumers’ interest in a high-quality healthy diet is creating a growing trend in the food industry, focusing on the design and development of new products rich in bioactive compounds. This work involves the formulation of a vegetable sauce obtained from a mixture of [...] Read more.
Consumers’ interest in a high-quality healthy diet is creating a growing trend in the food industry, focusing on the design and development of new products rich in bioactive compounds. This work involves the formulation of a vegetable sauce obtained from a mixture of pumpkin and pepper, the study of the evolution of bioactive compounds, quality and sensory parameters during storage at 4 and 25 °C, the influence of the packaging materials (PVC, PE/PA, and PS), and the migration degree. Antioxidant activity, polyphenols, carotenoids, and brown pigments contents were studied at 25 °C. Overall migration of the containers and the evolution of the physicochemical parameters and sensory attributes of the sauce were analyzed. All plastic materials showed an overall migration lower than the limit of EU and Mercosur Regulations. PVC better preserved polyphenols, antioxidant activity, and carotenoids until 50, 10, and 30 days, respectively, and lower development of brown pigments was observed. Higher storage temperatures favored undesirable changes in sensory attributes before 50 days of storage. PVC can be used to achieve greater conservation of the sensory attributes of sauce, regardless of the storage temperature. It could be considered the best material to preserve the bioactive properties and sensory attributes of the sauce until 30 days. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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Figure 1
<p>Diagram of the sauce preparation.</p>
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<p>Overall migration in simulant B at 25 °C. For a certain plastic material, bars with the same letter (a–b) indicate no significant differences (<span class="html-italic">p</span> &gt; 0.05). Different numbers (1–3) indicate significant differences between different containers at the same time (<span class="html-italic">p</span> &lt; 0.05). PVC = polyvinyl chloride, PE/PA = polyamide/polyamide alloy, PS = polystyrene.</p>
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<p>(<b>a</b>) Mass fraction of total polyphenolic content (TPC). (<b>b</b>) Ratio of the percentage of inhibition of DPPH chromophoric radical. (<b>c</b>) Mass fraction of total carotenoid content (TCC) in the sauce during storage at 25 °C.</p>
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<p>Brown pigment generation in the sauces packed in PS, PVC, and PE/PA during storage (expressed as a proportionality variable). For a certain plastic material, bars with the same letter (a–e) indicate no significant differences (<span class="html-italic">p</span> &gt; 0.05). Different numbers (1–3) indicate significant differences between the containers at the same time (<span class="html-italic">p</span> &lt; 0.05). PVC = polyvinyl chloride, PE/PA = polyamide/polyamide alloy, PS = polystyrene.</p>
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<p>QDA analysis of sauces packaged in PVC, PE/PA, and PS for 50 days at 4 and 25 °C, with (<b>a</b>) aroma, (<b>b</b>) color, (<b>c</b>) spreadability, (<b>d</b>) lumpiness, and (<b>e</b>) flavor attributes.</p>
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21 pages, 5288 KiB  
Article
Intensification of Heat and Mass Transfer in a Diabatic Column with Vortex Trays
by Nikolai A. Voinov, Anastasiya V. Bogatkova and Denis A. Zemtsov
ChemEngineering 2022, 6(2), 29; https://doi.org/10.3390/chemengineering6020029 - 12 Apr 2022
Viewed by 2597
Abstract
We used vortex contact devices that we developed and investigated to make a new design of an alcohol diabatic distillation column with heat exchange pipes (as the reflux condenser) passing through concentrating section trays. In the column, ascending vapors partially condensed on the [...] Read more.
We used vortex contact devices that we developed and investigated to make a new design of an alcohol diabatic distillation column with heat exchange pipes (as the reflux condenser) passing through concentrating section trays. In the column, ascending vapors partially condensed on the surface of vertically installed heat exchange tubes, forming a reflux. The reflux was then mixed with the draining liquid flow in the vortex contact devices placed on the trays. Heat was removed from the column through the boiling of the draining water film along the inner surface of the heat exchange pipes. We compared both diabatic and adiabatic columns fitted with the developed vortex contact devices on the trays. The proposed innovative contact system allows increasing productivity, reducing column dimensions and steam- and heat-transfer medium consumption, and increasing separation efficiency. Dependences for calculating the gas content, hydraulic resistance, and interphase surface required for designing the vortex contact devices of the proposed unit trays are presented. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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Figure 1
<p>Diagrams of direct-flow vortex trays with tangential swirlers (<b>a</b>) and axial ones (<b>b</b>,<b>c</b>). 1—tray deck; 2—vortex contact device; 3—swirler, 4—overflow device; <span class="html-fig-inline" id="ChemEngineering-06-00029-i001"> <img alt="Chemengineering 06 00029 i001" src="/ChemEngineering/ChemEngineering-06-00029/article_deploy/html/images/ChemEngineering-06-00029-i001.png"/></span>—liquid; <span class="html-fig-inline" id="ChemEngineering-06-00029-i002"> <img alt="Chemengineering 06 00029 i002" src="/ChemEngineering/ChemEngineering-06-00029/article_deploy/html/images/ChemEngineering-06-00029-i002.png"/></span>—steam.</p>
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<p>Diagrams of vortex trays with tangential swirlers: vortex chamber (<b>a</b>,<b>c</b>), vortex tray (<b>b</b>). 1—tray deck; 2—shell; 3—swirler; 4—overflow device; <span class="html-fig-inline" id="ChemEngineering-06-00029-i001"> <img alt="Chemengineering 06 00029 i001" src="/ChemEngineering/ChemEngineering-06-00029/article_deploy/html/images/ChemEngineering-06-00029-i001.png"/></span>—liquid; <span class="html-fig-inline" id="ChemEngineering-06-00029-i002"> <img alt="Chemengineering 06 00029 i002" src="/ChemEngineering/ChemEngineering-06-00029/article_deploy/html/images/ChemEngineering-06-00029-i002.png"/></span>—steam.</p>
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<p>Diagrams of the built-in dephlegmators in the column in the form of coils (<b>a</b>) and pipes (<b>b</b>). 1—housing, 2—swirler, 3—dephlegmator, 4—drain bar, 5—overflow device; <span class="html-fig-inline" id="ChemEngineering-06-00029-i001"> <img alt="Chemengineering 06 00029 i001" src="/ChemEngineering/ChemEngineering-06-00029/article_deploy/html/images/ChemEngineering-06-00029-i001.png"/></span>—liquid; <span class="html-fig-inline" id="ChemEngineering-06-00029-i002"> <img alt="Chemengineering 06 00029 i002" src="/ChemEngineering/ChemEngineering-06-00029/article_deploy/html/images/ChemEngineering-06-00029-i002.png"/></span>—steam.</p>
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<p>Diagrams of the vortex contact devices and steam movements in the swirlers with profiled channels (<b>a</b>) and annular channels (<b>b</b>,<b>c</b>). (<b>a</b>) 1—cylindrical insert 2—swirler; 3—bottom; 4—cover; 5—hydraulic lock cup; 6—overflow device; 7—drain bar; 8—sealing device; 9—steam channel. (<b>b</b>) and (<b>c</b>) 1—tray deck; 2—steam channel; 3—cover; 4—tangential swirler; 5 and 6—inner and outer annular swirler channels; <span class="html-fig-inline" id="ChemEngineering-06-00029-i002"> <img alt="Chemengineering 06 00029 i002" src="/ChemEngineering/ChemEngineering-06-00029/article_deploy/html/images/ChemEngineering-06-00029-i002.png"/></span>—gas.</p>
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<p>Experimental unit set-up. 1—still; 2—shell; 3—tray; 4—contact vortex device; 5—dephlegmator; 6—condenser; 7—heat exchanger; 8—vacuum pump; 9—storage containers; 10—drain bar with overflow device; 11—inspection window; 12—samplers; 13—connecting piece for heat-transfer medium supply; 14—liquid distributor; 15—upper tray condenser; 16—chamber for heat-transfer medium injection. <span class="html-fig-inline" id="ChemEngineering-06-00029-i001"> <img alt="Chemengineering 06 00029 i001" src="/ChemEngineering/ChemEngineering-06-00029/article_deploy/html/images/ChemEngineering-06-00029-i001.png"/></span>—heat-transfer medium; <span class="html-fig-inline" id="ChemEngineering-06-00029-i002"> <img alt="Chemengineering 06 00029 i002" src="/ChemEngineering/ChemEngineering-06-00029/article_deploy/html/images/ChemEngineering-06-00029-i002.png"/></span>—steam; <span class="html-fig-inline" id="ChemEngineering-06-00029-i003"> <img alt="Chemengineering 06 00029 i003" src="/ChemEngineering/ChemEngineering-06-00029/article_deploy/html/images/ChemEngineering-06-00029-i003.png"/></span>—reflux (condensate).</p>
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<p>Dependence of the magnitude of the resistance coefficient on the gas Reynolds number. At <span class="html-italic">b</span> = 0.0035 m, <span class="html-italic">h</span> = 0.008 m, <span class="html-italic">n</span> = 40 pcs, <span class="html-italic">R</span><sub>out</sub> = 0.085 m, <span class="html-italic">l</span> = 0.022 m, α = 26°; Experimental points (1–3): for different channel wall profiles.</p>
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<p>Change in pressure drop on the trays with vortex devices as a function of the gas flow rate (<b>a</b>) and the gas velocity in the channels (<b>b</b>). (<b>a</b>) Experimental points for the annular swirler shown in <a href="#ChemEngineering-06-00029-f004" class="html-fig">Figure 4</a>c, for <span class="html-italic">R</span><sub>out</sub> = 0.103 m, <span class="html-italic">b</span> = 0.005 m, <span class="html-italic">b</span><sub>in</sub> = 0.001 m, <span class="html-italic">h</span> = 0.008 m, <span class="html-italic">n</span> = 16 pcs (1–4): for different liquid column height H<sub>0,</sub> (<b>b</b>) Experimental points for the swirler shown in <a href="#ChemEngineering-06-00029-f004" class="html-fig">Figure 4</a>b, for <span class="html-italic">R</span><sub>out</sub> = 0.075 m, <span class="html-italic">b</span> = 0.005 m, <span class="html-italic">h</span> = 0.003 m, <span class="html-italic">n</span> = 40 pcs (1–2): for different liquid column height H<sub>0</sub>. The dashed lines represent dry tray resistance.</p>
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<p>Structure of gas–liquid mixture on vortex tray: (<b>a</b>) jet mode; (<b>b</b>) bubble mode; (<b>c</b>) bubble-annular mode, <span class="html-italic">D<sub>G-L</sub></span>—gas–liquid diameter, m; <span class="html-italic">Dt</span>—diameter tray, m.</p>
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<p>Change in the magnitude of the aerated liquid layer on the tray from the gas flow rate (<b>a</b>) and the average velocity of liquid movement on the tray from the tray diameter (<b>b</b>). (<b>a</b>) The tray with a contact device, shown in <a href="#ChemEngineering-06-00029-f004" class="html-fig">Figure 4</a>c, at <span class="html-italic">R</span><sub>out</sub> = 0.103 m, <span class="html-italic">h</span> = 0.008 m, <span class="html-italic">b</span> = 0.005 m, <span class="html-italic">b</span><sub>in</sub> = 0.001 m, <span class="html-italic">n</span> = 16 pcs. Experimental points (1–3): for different liquid column height H<sub>0</sub>, (<b>b</b>) Experimental points (1–2): for different gas velocity <span class="html-italic">u<sub>G</sub></span> in the swirler channels.</p>
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<p>Dependence of the critical velocity on the liquid weight in the device (<b>a</b>) and the liquid weight on the vortex tray on the gas velocity in the swirler channels (<b>b</b>). Experimental data for the tray with the device are presented in <a href="#ChemEngineering-06-00029-f004" class="html-fig">Figure 4</a>a at <span class="html-italic">R</span><sub>out</sub> = 0.075 m, <span class="html-italic">b</span> = 0.005 m, <span class="html-italic">h</span> = 0.003 m, <span class="html-italic">n</span> = 40 pcs. Experimental points (1–5): for different distance from the wall to the end of the drain bar <span class="html-italic">δ</span>.</p>
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<p>Dependence of the gas content (<b>a</b>) on the ratio of gas velocities and the interphase surface on the gas energy dissipation (<b>b</b>). (<b>a</b>) The tray with contact devices shown in <a href="#ChemEngineering-06-00029-f004" class="html-fig">Figure 4</a>b, at <span class="html-italic">n</span> = 8 pcs, <span class="html-italic">h</span> = 0.008 m, <span class="html-italic">R</span><sub>out</sub> = 0.103 m, <span class="html-italic">l</span><sub>chan</sub> = 0.022 m; <span class="html-italic">b</span> = 0.003 m. Experimental points (1–4): for different mass of liquid on the tray M. (<b>b</b>) The tray with contact devices shown in <a href="#ChemEngineering-06-00029-f004" class="html-fig">Figure 4</a>b,c. Experimental points (1–3): 1—<span class="html-italic">R</span><sub>out</sub> = 0.092 m, <span class="html-italic">l</span> = 0.022 m; <span class="html-italic">b</span> = 0.005 m, <span class="html-italic">b</span><sub>in</sub> = 0.001 m, <span class="html-italic">n</span> = 16, 2—<span class="html-italic">R</span><sub>out</sub> = 0.063 m, <span class="html-italic">l</span> = 0.011 m; <span class="html-italic">b</span> = 0.001 m, <span class="html-italic">n</span> = 8 pcs. A dashed line represents a transition from the bubble-annular mode to annular.</p>
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<p>Change of the <span class="html-italic">β<sub>V</sub>/V × Q<sub>L</sub></span> dimensionless parameter from the centrifugal Reynolds criterion. Experimental a (1–5) for the tray with contact devices are presented in <a href="#ChemEngineering-06-00029-f004" class="html-fig">Figure 4</a>c at <span class="html-italic">Q<sub>L</sub></span> = 0.8 × 10<sup>−4</sup>—6.1 × 10<sup>−4</sup> m<sup>3</sup>/s, <span class="html-italic">R</span><sub>out</sub> = 0.092 m, <span class="html-italic">l</span> = 0.022 m; <span class="html-italic">b</span> = 0.005 m, <span class="html-italic">b</span><sub>in</sub> = 0.001 m, <span class="html-italic">n</span> = 16 pcs: 1—<span class="html-italic">H</span><sub>0</sub> = 0.034 m; 2—<span class="html-italic">H</span><sub>0</sub> = 0.055 m; 3—<span class="html-italic">H</span><sub>0</sub> = 0.07 m; 4—<span class="html-italic">H</span><sub>0</sub> = 0.085 m; 5—<span class="html-italic">H</span><sub>0</sub> = 0.1 m. Experimental data (6–8) for the tray with contact devices are shown in <a href="#ChemEngineering-06-00029-f004" class="html-fig">Figure 4</a>a at <span class="html-italic">H</span><sub>0</sub> = 0.03 m, <span class="html-italic">δ</span> = 5 … 9 mm: 6—<span class="html-italic">Q<sub>L</sub></span> = 3.6 × 10<sup>−6</sup> m<sup>3</sup>/s; 7—<span class="html-italic">Q<sub>L</sub></span> = 13.6 × 10<sup>−6</sup> m<sup>3</sup>/s; 8—<span class="html-italic">Q<sub>L</sub></span> = 17.8 × 10<sup>−6</sup> m<sup>3</sup>/s. The dashed line shows a change in modes on the tray.</p>
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<p>Dependence of the tray efficiency on the ratio of flow rates (<b>a</b>) and the gas velocity in the swirler channels (<b>b</b>). Experimental points for the annular swirlers at <span class="html-italic">h</span> = 0.008 m, <span class="html-italic">Q<sub>L</sub></span> = 0.000275–0.000725 m<sup>3</sup>/s (1–3): 1—swirler shown in <a href="#ChemEngineering-06-00029-f004" class="html-fig">Figure 4</a>c, at <span class="html-italic">R</span><sub>out</sub> = 0.092 m, <span class="html-italic">l</span> = 0.022 m, <span class="html-italic">b</span> = 0.005 m, <span class="html-italic">b</span><sub>in</sub> = 0.001 m, <span class="html-italic">n</span> = 16 pcs; 2—swirler shown in <a href="#ChemEngineering-06-00029-f004" class="html-fig">Figure 4</a>b, at <span class="html-italic">R</span><sub>out</sub> = 0.103 m, <span class="html-italic">l</span><sub>chan</sub> = 0.011 m; <span class="html-italic">b</span> = 0.005 m, <span class="html-italic">n</span> = 8 pcs; 3—swirler shown in <a href="#ChemEngineering-06-00029-f004" class="html-fig">Figure 4</a>a, <span class="html-italic">l</span> = 0.022 m; <span class="html-italic">b</span> = 0.004 m, <span class="html-italic">R</span><sub>out</sub> = 0.065 m, <span class="html-italic">n</span> = 8 pcs.</p>
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<p>Change in the efficiency of the <span class="html-italic">E</span> vortex tray from ethanol concentration in the <span class="html-italic">x</span> liquid at <span class="html-italic">u<sub>vap</sub></span> = 15–25 m/s. Experimental points (1–4): 1—adiabatic rectification; 2–4—diabatic rectification; 2—<span class="html-italic">Q<sub>vap</sub>/Q<sub>con</sub></span> = 25; 3—4; 4—1.8.</p>
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<p>Dependence of the <span class="html-italic">y<sub>ex</sub>/y<sub>ent</sub></span> concentration ratio in the vapor mixture on the <span class="html-italic">Q<sub>vap</sub>/Q<sub>con</sub></span> ratio. Experimental points: (1–4): for different ethanol concentration in the liquid on the tray <span class="html-italic">x</span>.</p>
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<p>Diagram of the diabatic alcohol column. 1—exhaustive column; 2—concentrating column; 3 and 4—heat-transfer medium outlet and inlet chamber; 5—steam pipe connector; 6—tray; 7—vortex contact device; 8—dephlegmator heat exchange pipes; 9—gas drain connecting piece; 10—heat-transfer medium injection connecting piece; 11—heat-transfer medium supply connecting piece; 12—liquid film pipe connector; 13—lower tray; 14—vortex devices; 15—overflows; 16—hydraulic lock cup; 17—cylindrical insert; 18, 19—connecting pieces, 20—cavity. <span class="html-fig-inline" id="ChemEngineering-06-00029-i001"> <img alt="Chemengineering 06 00029 i001" src="/ChemEngineering/ChemEngineering-06-00029/article_deploy/html/images/ChemEngineering-06-00029-i001.png"/></span>—heat-transfer medium; <span class="html-fig-inline" id="ChemEngineering-06-00029-i002"> <img alt="Chemengineering 06 00029 i002" src="/ChemEngineering/ChemEngineering-06-00029/article_deploy/html/images/ChemEngineering-06-00029-i002.png"/></span>—steam; <span class="html-fig-inline" id="ChemEngineering-06-00029-i003"> <img alt="Chemengineering 06 00029 i003" src="/ChemEngineering/ChemEngineering-06-00029/article_deploy/html/images/ChemEngineering-06-00029-i003.png"/></span>—working mixture.</p>
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13 pages, 2845 KiB  
Article
Sensitivity Control of Hydroquinone and Catechol at Poly(Brilliant Cresyl Blue)-Modified GCE by Varying Activation Conditions of the GCE: An Experimental and Computational Study
by Sharifa Faraezi, Md Sharif Khan, Ferzana Zaman Monira, Abdullah Al Mamun, Tania Akter, Mohammad Al Mamun, Mohammad Mahbub Rabbani, Jamal Uddin and A. J. Saleh Ahammad
ChemEngineering 2022, 6(2), 27; https://doi.org/10.3390/chemengineering6020027 - 28 Mar 2022
Cited by 3 | Viewed by 3301
Abstract
The poly(brilliant cresyl blue) (PBCB)-modified activated glassy carbon electrode (AGCE) shows the catalytic activity toward the oxidation of hydroquinone (HQ) and catechol (CT). The modified electrode can also separate the oxidation peaks of HQ and CT in their mixture, which is not possible [...] Read more.
The poly(brilliant cresyl blue) (PBCB)-modified activated glassy carbon electrode (AGCE) shows the catalytic activity toward the oxidation of hydroquinone (HQ) and catechol (CT). The modified electrode can also separate the oxidation peaks of HQ and CT in their mixture, which is not possible with bare GCE. These properties of the modified electrode can be utilized to fabricate an electrochemical sensor for sensitive and simultaneous detection of HQ and CT. In this study, an attempt is made to control the sensitivity of the modified electrodes. This can be accomplished by simply changing the activation condition of the GCE during electropolymerization. GCE can be activated via one-step (applying only oxidation potential) and two-step (applying both oxidation and reduction potential) processes. When we change the activation condition from onestep to twosteps, a clear enhancement inpeak currents of HQ and CT is observed. This helps us to fabricate a highly sensitive electrochemical sensor for the simultaneous detection of HQ and CT. The molecular dynamics (MD) simulation is carried out to explain the experimental data. The MD simulations provide the insight adsorption phenomena to clarify the reasons for higher signals of CT over HQ due to having meta-position –OH group in its structure. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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Figure 1
<p>CVs for the electropolymerization of BCB onto AGCE in two different activation ways: one-step activation, PBCB1 (<b>A</b>) and two-step activation PBCB2 (<b>B</b>) in 0.1 MPBS (pH 7.0).</p>
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<p>CVs (<b>A</b>) and EIS (<b>B</b>) spectra of 5.0 mM K<sub>3</sub>[Fe(CN)<sub>6</sub>] in 1.0 M KCl at GCE (a), PBCB1(b), and PBCB2 (c).</p>
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<p>CVs of 0.50 mM HQ (<b>A</b>) and CT (<b>B</b>) in PBS (pH = 7.0) at GCE (a), PBCB1(b), and PBCB2 (c).</p>
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<p>CVs (<b>A</b>) and DPVs (<b>B</b>) for the mixture solution of HQ (0.50 mM) and CT (0.50 mM) in PBS (pH = 7.0) at GCE (a), PBCB1 (b), and PBCB2 (c).</p>
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<p>(<b>a</b>) Simulated number distribution of the HQ (dotted line) and CT (solid line) on PBCB1 (red) and PBCB2 (blue); (<b>b</b>) surface charge distribution of activation-1 (red) and activation-2 (blue); (<b>c</b>) diffusion coefficient of PBCB polymers on the activated surface of PBCB1 (red) and PBCB2 (blue).</p>
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<p>(<b>a</b>) Simulated radial distribution function between the H of the OH group and N of PBCB (blue) and O of PBCB (red) in HQ (dotted line) and CT (solid line); (<b>b</b>) interaction energy between OH of HQ and N (red), O (green), and OH of CT and N (black) and O (blue); (<b>c</b>) simulated snapshots of CT adsorption on PBCB (right) and HQ adsorption on PBCB (left), H (white), O (red), N (blue), C of CT and HQ (black), C of PBCB (gray); (<b>d</b>) diffusion coefficient of HQ (dotted line) and CT (solid line) on the surface of PBCB1 (red) and PBCB2 (blue).</p>
Full article ">Figure 7
<p>DPVs for PBCB2 in solution of different concentrations (a-k: 1, 5, 10, 20, 50, 75, 125, 175, 225, 300, and 350 μM) of HQ containing 150 μMCT (<b>A</b>) and different concentrations (a–k: 1, 5, 10, 20, 50, 75, 125, 175, 225, 300, and 350 μM) of CT containing 150 μM HQ (<b>B</b>). Insets show the calibration plots of HQ and CT.</p>
Full article ">Scheme 1
<p>Schematic representation of the modification of PBCB1 and PBCB2 for the simultaneous determination of HQ and CT.</p>
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9 pages, 1972 KiB  
Article
Heterogeneous Photodegradation for the Abatement of Recalcitrant COD in Synthetic Tanning Wastewater
by Maria Toscanesi, Vincenzo Russo, Antonio Medici, Antonella Giarra, Maryam Hmoudah, Martino Di Serio and Marco Trifuoggi
ChemEngineering 2022, 6(2), 25; https://doi.org/10.3390/chemengineering6020025 - 21 Mar 2022
Cited by 2 | Viewed by 2718
Abstract
Tannery wastewater is considered one of the most contaminated and problematic wastes since it consists of considerable amounts of organic and inorganic compounds. These contaminants result in high chemical oxygen demand (COD), biochemical oxygen demand (BOD), and total suspended solids (TSS). In this [...] Read more.
Tannery wastewater is considered one of the most contaminated and problematic wastes since it consists of considerable amounts of organic and inorganic compounds. These contaminants result in high chemical oxygen demand (COD), biochemical oxygen demand (BOD), and total suspended solids (TSS). In this work, the heterogeneous photodegradation of recalcitrant COD in wastewater from the tanning industry was investigated, in particular the recalcitrant COD due to the presence of vegetable tannins extracted from mimosa and chestnut and from synthetic tannins based on 4,4′ dihydroxy phenyl sulfone. TiO2 Aeroxide P-25 was employed to study the photodegradation of model molecules in batch conditions under different parameters, namely initial concentration of COD, temperature, and catalyst dose. The maximum COD abatement reached was 60%. Additionally, preliminary kinetic investigation was conducted to derive the main kinetic parameters that can be useful for process scale-up. It was found to be independent of the temperature value but linearly dependent on both catalyst loading and the initial COD value. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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Figure 1
<p>COD/COD<sub>0</sub> profile vs. reaction times for experiments conducted at different temperatures. The tests were conducted by fixing the following operation conditions: COD<sub>0</sub> = 200 mg/L, <span class="html-italic">ρ<sub>B</sub></span> = 0.5 g/L, <span class="html-italic">Q<sub>air</sub></span> = 1.0 × 10<sup>−6</sup> m<sup>3</sup>/s, and <span class="html-italic">v</span> = 500 rpm.</p>
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<p>Effect of the catalyst loading on the photodegradation of tanning. The experiments were carried out by fixing the following operation conditions: COD<sub>0</sub> = 200 mg/L, <span class="html-italic">T</span> = 298 K, <span class="html-italic">Q<sub>air</sub></span> = 1.0 × 10<sup>−6</sup> m<sup>3</sup>/s, and <span class="html-italic">v</span> = 500 rpm.</p>
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<p>COD/COD<sub>0</sub> profile vs. reaction times for experiments carried out at different initial concentrations of COD. The experiments were conducted fixing <span class="html-italic">ρ<sub>B</sub></span> = 0.5 g/L, T = 298 K, <span class="html-italic">Q<sub>air</sub></span> = 1.0 × 10<sup>−6</sup> m<sup>3</sup>/s, and <span class="html-italic">v</span> = 500 rpm.</p>
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<p>Arrhenius plot obtained using the experiments conducted at different temperatures.</p>
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<p>Effect of the catalyst loading on (<b>A</b>) the observed reaction rate, <span class="html-italic">r<sub>obs</sub></span>, and (<b>B</b>) the COD plateau value, COD<sub>F</sub>.</p>
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<p>The effect of <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mi>B</mi> </msub> </mrow> </semantics></math> on (<b>A</b>) the observed reaction rate constant, <span class="html-italic">k<sub>obs</sub></span>, and (<b>B</b>) the COD removal.</p>
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<p>Effect of the initial COD value on (<b>A</b>) the observed reaction rate, <span class="html-italic">r<sub>obs</sub></span>, and (<b>B</b>) the COD plateau value, COD<sub>F</sub>.</p>
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25 pages, 3318 KiB  
Article
Three-Dimensional CFD Model Development and Validation for Once-Through Steam Generator (OTSG): Coupling Combustion, Heat Transfer and Steam Generation
by Ehsan Askari Mahvelati, Mario Forcinito, Laurent Fitschy and Arthur Maesen
ChemEngineering 2022, 6(2), 23; https://doi.org/10.3390/chemengineering6020023 - 14 Mar 2022
Cited by 4 | Viewed by 3453
Abstract
The current research studies the coupled combustion inside the furnace and the steam generation inside the radiant and convection tubes through a typical Once-Through Steam Generator (OTSG). A 3-D CFD model coupling the combustion and the two-phase flow was developed to model the [...] Read more.
The current research studies the coupled combustion inside the furnace and the steam generation inside the radiant and convection tubes through a typical Once-Through Steam Generator (OTSG). A 3-D CFD model coupling the combustion and the two-phase flow was developed to model the entire system of OTSG. Once the combustion simulation was converged, the results were compared to field data showing a convincing agreement. The CFD analysis provides the detailed flow behavior inside the combustion chamber and the stack, as well as the two-phase flow steam generation process in the radiant and convective sections. The flame shape and orientation, the velocity, the species, and the temperature distribution at the various parts of the furnace, as well as the steam generation and the steam distribution inside the pipes were investigated using the developed CFD model Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Schematic of the OTSG showing the burner, the radiant and the convection sections.</p>
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<p>Tubes and flow arrangement in the convection section.</p>
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<p>Coupling algorithm between the CFD models.</p>
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<p>Degree of coupling between combustion and steam generation.</p>
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<p>Burner type 1 schematic.</p>
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<p>Burner type 2 schematic.</p>
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<p>OTSG geometry model for Fireside.</p>
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<p>Mesh-close up of the interior mesh of fuel gas ring showing the burner tips.</p>
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<p>Fireside mesh grid for the convection-stack section.</p>
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<p>Mesh grid for the convection tubes.</p>
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<p>Composition of combustion products.</p>
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<p>Velocity contour in a vertical cut in the middle of OTSG.</p>
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<p>Temperature contour in a vertical cut in the middle of OTSG.</p>
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<p>Flow streamlines in a vertical cut in the middle of OTSG.</p>
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<p>Temperature distribution in the convection box with and without fins on Second Tubes.</p>
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<p>Flue gas streamlines with drop pressure contour profile on the convention box with (<b>a</b>) and without fins (<b>b</b>) on Second Tubes.</p>
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<p>Duty distribution within OTSG volume.</p>
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<p>Temperature contour profile on burner tile.</p>
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<p>Flame visualization in the radiant section.</p>
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<p>CO concentration distribution in the convection section-[CO] volume average = 6.84 [ppm].</p>
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<p>Wall radiative heat flux over the radiant tubes.</p>
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<p>Zones definition to identify the local CFF in the radiant section.</p>
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<p>Temperature contours within Second Tubes in the convection box–whole pipes.</p>
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<p>Vapor phase fraction contour within First Tubes in the convection box on a vertical cut.</p>
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<p>Vapor phase fraction contour within First Tubes in the convention box-whole pipes.</p>
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<p>Vapor phase fraction contour within the radiant tubes.</p>
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<p>Vapor phase fraction contour within the radiant tubes-whole tubes.</p>
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<p>Gas volume fraction distribution through First Tubes in the convection section.</p>
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<p>Temperature distribution through First Tubes in the convection section.</p>
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9 pages, 2021 KiB  
Article
A First Approach towards Adsorption-Oriented Physics-Informed Neural Networks: Monoclonal Antibody Adsorption Performance on an Ion-Exchange Column as a Case Study
by Vinicius V. Santana, Marlon S. Gama, Jose M. Loureiro, Alírio E. Rodrigues, Ana M. Ribeiro, Frederico W. Tavares, Amaro G. Barreto, Jr. and Idelfonso B. R. Nogueira
ChemEngineering 2022, 6(2), 21; https://doi.org/10.3390/chemengineering6020021 - 1 Mar 2022
Cited by 10 | Viewed by 3443
Abstract
Adsorption systems are characterized by challenging behavior to simulate any numerical method. A novel field of study emerged within the numerical method in the last two years: the physics-informed neural network (PINNs), the application of artificial intelligence to solve partial differential equations. This [...] Read more.
Adsorption systems are characterized by challenging behavior to simulate any numerical method. A novel field of study emerged within the numerical method in the last two years: the physics-informed neural network (PINNs), the application of artificial intelligence to solve partial differential equations. This is a complete new standpoint for solving engineering first-principle models, which up to that date was not explored in the field of adsorption systems. Therefore, this work proposed the evaluation of PINN to address the numerical solutions of a fixed-bed column where a monoclonal antibody is purified. The PINNs solution is compared with a traditional numerical method. The results show the accuracy of the proposed PINNs when compared with the numerical method. This points towards the potential of this technique to address complex numerical problems found in chemical engineering. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Schematical representation of the antibody adsorption by ion-exchange.</p>
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<p>Physics-informed neural network building process.</p>
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<p>Train loss history for numerical adsorption experiment.</p>
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<p>MoL and PINNs solution’s comparison for adsorption experiment.</p>
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14 pages, 1552 KiB  
Article
Reliability of Biodegradation Measurements for Inhibitive Industrial Wastewaters
by Hanna Prokkola, Anne Heponiemi, Janne Pesonen, Toivo Kuokkanen and Ulla Lassi
ChemEngineering 2022, 6(1), 15; https://doi.org/10.3390/chemengineering6010015 - 3 Feb 2022
Cited by 3 | Viewed by 3098
Abstract
Industrial wastewaters may contain toxic or highly inhibitive compounds, which makes the measurement of biological oxygen demand (BOD) challenging. Due to the high concentration of organic compounds within them, industrial wastewater samples must be diluted to perform BOD measurements. This study focused on [...] Read more.
Industrial wastewaters may contain toxic or highly inhibitive compounds, which makes the measurement of biological oxygen demand (BOD) challenging. Due to the high concentration of organic compounds within them, industrial wastewater samples must be diluted to perform BOD measurements. This study focused on determining the reliability of wastewater BOD measurement using two different types of industrial wastewater, namely pharmaceutical wastewater containing a total organic carbon (TOC) value of 34,000 mg(C)/L and industrial paper manufacturing wastewater containing a corresponding TOC value of 30 mg(C)/L. Both manometric respirometry and the closed-bottle method were used in the study, and the results were compared. It was found that the dilution wastewaters containing inhibitive compounds affected BOD values, which increased due to the decreased inhibiting effect of wastewater pollutants. Therefore, the correct BOD for effluents should be measured from undiluted samples, while the diluted value is appropriate for determining the maximum value for biodegradable organic material in the effluent. The accuracy of the results from the blank samples was also examined, and it was found that the readings of these were different to those from the samples. Therefore, the blank value that must be subtracted may differ depending on the sample. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>BOD value of the raw WWP1 sample measured with BOD OxiTop for 28 days.</p>
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<p>Graph of an undiluted sample with one replicate of WWP2 measured with BOD OxiTop for 28 days.</p>
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<p>BOD graph of the blank sample prepared for WWP1.</p>
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<p>BOD graph of the blank sample prepared for WWP2.</p>
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20 pages, 6037 KiB  
Article
Numerical Simulation of Heat and Mass Transfer in an Open-Cell Foam Catalyst on Example of the Acetylene Hydrogenation Reaction
by Sergei A. Solovev, Olga V. Soloveva, Irina G. Akhmetova, Yuri V. Vankov and Daniel L. Paluku
ChemEngineering 2022, 6(1), 11; https://doi.org/10.3390/chemengineering6010011 - 1 Feb 2022
Cited by 18 | Viewed by 3421
Abstract
In the present work, based on numerical simulation, a comparative analysis of the flow of a chemically reacting gas flow through a catalyst is performed using the example of selective hydrogenation of acetylene in a wide range of flow temperatures variation. Catalyst models [...] Read more.
In the present work, based on numerical simulation, a comparative analysis of the flow of a chemically reacting gas flow through a catalyst is performed using the example of selective hydrogenation of acetylene in a wide range of flow temperatures variation. Catalyst models are based on open-cell foam material. A comparison is also made with calculations and experimental data for a granular catalyst. The porosity and cell diameter were chosen as variable parameters for the porous catalyst. The results of numerical studies were obtained in the form of component concentration fields of the gas mixture, vector fields of gas movement, values of conversion, and selectivity of the reaction under study. The parameters of the porous material of the catalyst are determined for the maximum efficiency of the process under study. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Experimental setup: (<b>a</b>) Reactor scheme. (<b>b</b>) Catalyst sample.</p>
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<p>Approximation of an open-cell foam structure by a Kelvin cell model: (<b>a</b>) Geometry of a Kelvin unit cell. (<b>b</b>) Model of a porous structure based on Kelvin cells.</p>
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<p>Cell models with different parameter values of <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>f</mi> </msub> </mrow> </semantics></math>: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>f</mi> </msub> </mrow> </semantics></math> = 0.05 mm. (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>f</mi> </msub> </mrow> </semantics></math> = 0.15 mm. (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>f</mi> </msub> </mrow> </semantics></math> = 0.25 mm.</p>
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<p>The parameters of the constructed models of open-cell foam structure: (<b>a</b>) Surface area referred to the occupied volume (m<sup>2</sup>/m<sup>3</sup>). (<b>b</b>) Porosity. (<b>c</b>) Comparison with literature results [<a href="#B44-ChemEngineering-06-00011" class="html-bibr">44</a>,<a href="#B47-ChemEngineering-06-00011" class="html-bibr">47</a>,<a href="#B48-ChemEngineering-06-00011" class="html-bibr">48</a>].</p>
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<p>The computational domain and boundary conditions.</p>
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<p>Constructed mesh example.</p>
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<p>Model verification with experimental data: (<b>a</b>) Conversion. (<b>b</b>) Selectivity.</p>
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<p>Dependence of the pressure drop on the geometric parameters of the cell. Comparison of the results of these studies with the results of literary correlations [<a href="#B37-ChemEngineering-06-00011" class="html-bibr">37</a>,<a href="#B50-ChemEngineering-06-00011" class="html-bibr">50</a>,<a href="#B51-ChemEngineering-06-00011" class="html-bibr">51</a>,<a href="#B52-ChemEngineering-06-00011" class="html-bibr">52</a>].</p>
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<p>Gas velocity in a porous medium for different values of <span class="html-italic">d<sub>c</sub></span> and <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>f</mi> </msub> </mrow> </semantics></math>: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>c</mi> </msub> </mrow> </semantics></math> = 2.0 mm. (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>c</mi> </msub> </mrow> </semantics></math> = 2.5 mm. (<b>c</b>) <span class="html-italic">d<sub>c</sub></span> = 3.0 mm.</p>
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<p>Parameters of gas movement in a porous medium: (<b>a</b>) Average gas velocity <math display="inline"><semantics> <mrow> <mrow> <mrow> <mi>v</mi> </mrow> <mo>/</mo> <mrow> <msub> <mi>v</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> </mrow> </mrow> </mrow> </semantics></math>. (<b>b</b>) Average time of gas contact with the catalyst surface <math display="inline"><semantics> <mrow> <mrow> <mi>t</mi> <mo>/</mo> <mrow> <msub> <mi>t</mi> <mn>0</mn> </msub> </mrow> </mrow> </mrow> </semantics></math>.</p>
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<p>The mass content of the C<sub>2</sub>H<sub>2</sub> component in the porous medium for different values <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>c</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>f</mi> </msub> </mrow> </semantics></math> at <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math> = 45 °C: (<b>a</b>) <span class="html-italic">d<sub>c</sub></span> = 2.0 mm. (<b>b</b>) <span class="html-italic">d<sub>c</sub></span> = 2.5 mm. (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>c</mi> </msub> </mrow> </semantics></math> = 3.0 mm.</p>
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<p>Results of conversion and selectivity calculation: (<b>a</b>) Conversion for <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>c</mi> </msub> </mrow> </semantics></math> = 2.0 mm. (<b>b</b>) Selectivity for <span class="html-italic">d<sub>c</sub></span> = 2.0 mm. (<b>c</b>) Conversion for <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>c</mi> </msub> </mrow> </semantics></math> = 2.5 mm. (<b>d</b>) Selectivity for <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>c</mi> </msub> </mrow> </semantics></math> = 2.5 mm. (<b>e</b>) Conversion for <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>c</mi> </msub> </mrow> </semantics></math> = 3.0 mm. (<b>f</b>) Selectivity for <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>c</mi> </msub> </mrow> </semantics></math> = 3.0 mm.</p>
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<p>Conversion depending on the fiber diameter <span class="html-italic">d<sub>f</sub></span>.</p>
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<p>Conversion depending on the catalyst surface area.</p>
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14 pages, 2693 KiB  
Article
Prediction of B20 Storage Tank Precipitate Removal Based on Biodiesel Monoglyceride Content
by Misri Gozan, Imam Paryanto, Muhammad Arif Darmawan, Muhammad Sahlan, Heri Hermansyah, Eriawan Rismana, Alfan Danny Arbianto, Tirto Prakoso, Mohamed Kheireddine Aroua and Patrick Cognet
ChemEngineering 2022, 6(1), 7; https://doi.org/10.3390/chemengineering6010007 - 13 Jan 2022
Viewed by 2840
Abstract
Precipitate in B20 fuel stored in storage tanks can accumulate at the bottom level of the tank and affect the fuel filter, clogging in the fuel distribution and engine system. This study examines the precipitate formation prediction in B20 fuel based on the [...] Read more.
Precipitate in B20 fuel stored in storage tanks can accumulate at the bottom level of the tank and affect the fuel filter, clogging in the fuel distribution and engine system. This study examines the precipitate formation prediction in B20 fuel based on the monoglyceride content in biodiesel. This research used a modified CSFT method of ASTM D7501 for the precipitation test. Monopalmitin was added to biodiesel with a variation of monoglyceride content. Each biodiesel sample was then blended with petroleum diesel fuel to produce two groups of samples. Each sample was separately soaked in the cooling chamber at constant and room temperature for 21 days. The bottom layer of each B20 fuel sample stored in the measuring cylinder was then pipetted and filtered, washed with petro-ether, vacuum-dried, and weighed for a constant amount of precipitate retained on the filter. The simulation results show that the ratios between the amount of collected precipitate at the bottom layer of the 2-liter measuring cylinder and the total amount of collected precipitate for the 2-liter measuring cylinder increased with the monoglyceride content biodiesel. This ratio was used to predict the amount of accumulated sludge for a given volume of B20 fuel loaded into the storage tank. This study shows the effect of monoglyceride content on the precipitation behaviour in the storage tank concerning general tank storage dimension parameters and B20 loading frequency. This approach can be applied to estimate the sludge removal frequency for biodiesel storage. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>B20 fuel storage tank with variation in storage capacity (VT), dimensions (H/D ratio), discharge piping level (h), the volume of minimum fuel stock (Vmin), and ‘fresh’ B20 fuel loaded into the tank (VL).</p>
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<p>Flowchart for the precipitation tests for sludge removal prediction in the B20 storage tank.</p>
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<p>Schedule simulation for sludge removal from the storage tank of B20 fuel blended from B100 with monoglyceride content of 0.437%-mass. The tank volume is (<b>a</b>) 50, (<b>b</b>) 100, and (<b>c</b>) 200 m<sup>3</sup>.</p>
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<p>Schedule simulation for sludge removal from the storage tank of B20 fuel blended from B100 with monoglyceride content of 0.437%-mass. The tank volume is (<b>a</b>) 50, (<b>b</b>) 100, and (<b>c</b>) 200 m<sup>3</sup>.</p>
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<p>Schedule simulation for sludge removal from the storage tank of B20 fuel blended from B100 with monoglyceride content of 0.623%-mass. The tank volume is (<b>a</b>) 50, (<b>b</b>) 100, and (<b>c</b>) 200 m<sup>3</sup>.</p>
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<p>Schedule simulation for sludge removal from the storage tank of B20 fuel blended from B100 with monoglyceride content of 0.623%-mass. The tank volume is (<b>a</b>) 50, (<b>b</b>) 100, and (<b>c</b>) 200 m<sup>3</sup>.</p>
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<p>Schedule simulation for sludge removal from the storage tank of B20 fuel from the market. The tank volume is (<b>a</b>) 50, (<b>b</b>) 100, and (<b>c</b>) 200 m<sup>3</sup>.</p>
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<p>Schedule simulation for sludge removal from the storage tank of B20 fuel from the market. The tank volume is (<b>a</b>) 50, (<b>b</b>) 100, and (<b>c</b>) 200 m<sup>3</sup>.</p>
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9 pages, 2171 KiB  
Article
Experimental Study on Absorption Behavior and Efficiency of Brine in Hazardous Gas Absorption Treatment
by Ga-young Jung, Seul-gi Lee, Jun-seo Lee and Byung-chol Ma
ChemEngineering 2022, 6(1), 4; https://doi.org/10.3390/chemengineering6010004 - 4 Jan 2022
Cited by 1 | Viewed by 2408
Abstract
There have been studies recently on bubble-column scrubbers with low cost and high efficiency for the absorption and treatment of hazardous gases in the event of a chemical spill. Bubble columns are vulnerable to freezing at temperatures below zero because the absorbents generally [...] Read more.
There have been studies recently on bubble-column scrubbers with low cost and high efficiency for the absorption and treatment of hazardous gases in the event of a chemical spill. Bubble columns are vulnerable to freezing at temperatures below zero because the absorbents generally do not circulate. To address this issue, this study focused on the applicability, absorbed amount, and performance of brine as an absorbent. Under three different temperatures, i.e., −5 °C, −8 °C and −10 °C we examined brine (NaCl, CaCl2, and MgCl2) by varying the concentration required at each temperature. Following the experiments, CaCl2 brine was determined as the optimal brine for its absorption performance and affordability. Based on the experimental results, the absorption performance for ammonia, ethylene oxide, and methylamine, which are hazardous and water-soluble gases among accident preparedness substances (APS), was tested by using ASEPN PLUS. Our results suggested although the efficiency dropped by about 5% to 25% when brine was used as an absorbent, it can be used at the low temperatures because the gas solubility increased with decreasing temperature. Therefore, if brine, as an alternative, is used at temperatures about 15 °C, it can operate efficiently and stably without deterioration in the absorption performance. Given our experimental results and design data on the absorbed amount and absorbent replacement period for major hazardous gases are utilized to prevent bubble columns from freezing, it can be commercially used for small and medium-sized enterprises because it can help reduce installation and operation costs. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Experimental Setup and Instrumentation.</p>
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<p>Absorbed amount of CO<sub>2</sub> in Solution A over time.</p>
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<p>Absorbed amount of CO<sub>2</sub> in Solution B over time.</p>
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<p>Absorbed amount of CO<sub>2</sub> in Solution C over time.</p>
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<p>Solubility of Carbon dioxide in CaCl<sub>2</sub> Brine (ASPEN results).</p>
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<p>Solubility of Ammonia in CaCl<sub>2</sub> Brine (Aspen results).</p>
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<p>Solubility of Ethylene oxide in CaCl<sub>2</sub> Brine (Aspen results).</p>
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<p>Solubility of Methylamine in CaCl<sub>2</sub> Brine (Aspen results).</p>
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21 pages, 2911 KiB  
Article
Evaluation of the Heat Produced by the Hydrothermal Liquefaction of Wet Food Processing Residues and Model Compounds
by Morgane Briand, Geert Haarlemmer, Anne Roubaud and Pascal Fongarland
ChemEngineering 2022, 6(1), 2; https://doi.org/10.3390/chemengineering6010002 - 4 Jan 2022
Cited by 5 | Viewed by 3198
Abstract
Hydrothermal liquefaction has proven itself as a promising pathway to the valorisation of low-value wet food residues. The chemistry is complex and many questions remain about the underlying mechanism of the transformation. Little is known about the heat of reaction, or even the [...] Read more.
Hydrothermal liquefaction has proven itself as a promising pathway to the valorisation of low-value wet food residues. The chemistry is complex and many questions remain about the underlying mechanism of the transformation. Little is known about the heat of reaction, or even the thermal effects, of the hydrothermal liquefaction of real biomass and its constituents. This paper explores different methods to evaluate the heat released during the liquefaction of blackcurrant pomace and brewers’ spent grains. Some model compounds have also been evaluated, such as lignin, cellulose and glutamic acid. Exothermic behaviour was observed for blackcurrant pomace and brewers’ spent grains. Results obtained in a continuous reactor are similar to those obtained in a batch reactor. The heat release has been estimated between 1 MJ/kg and 3 MJ/kg for blackcurrant pomace and brewers’ spent grains, respectively. Liquefaction of cellulose and glucose also exhibit exothermic behaviour, while the transformation of lignin and glutamic acid present a slightly endothermic behaviour. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Batch Reactor (0.6 L, PARR).</p>
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<p>Continuous reactor (HYDROLIQ).</p>
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<p>Recovery of products from batch hydrothermal liquefaction experiments.</p>
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<p>Average curve on power response to temperature ramp controlled for batch experiments on blackcurrant pomace (BCP), brewers’ spent grains (BSG) and water as the reference.</p>
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<p>Van Krevelen Diagram for BCP (○) and BSG (◊) (0 min).</p>
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<p>Applied power profile and temperature response of blackcurrant pomace compared to water.</p>
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<p>Applied power profile and temperature response of cellulose and glucose compared to water.</p>
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<p>Temperature measurement on a continuous reactor with BCP.</p>
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<p>Temperature measurement on a continuous reactor with BSG.</p>
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<p>Relation between the heat release and the CO<sub>2</sub> produced.</p>
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17 pages, 1022 KiB  
Article
Predicted Mutual Solubilities in Water + C5-C12 Hydrocarbon Systems. Results at 298 K
by Marian Góral and Paweł Oracz
ChemEngineering 2021, 5(4), 89; https://doi.org/10.3390/chemengineering5040089 - 16 Dec 2021
Cited by 1 | Viewed by 2656
Abstract
Mutual solubilities of water with n-alkanes, cycloalkanes, iso-alkanes (branched alkanes), alkenes, alkynes, alkadienes, and alkylbenzenes were calculated at 298 K for 153 systems not yet measured. Recommended data for 64 systems reported in the literature were compared with the predicted values. The [...] Read more.
Mutual solubilities of water with n-alkanes, cycloalkanes, iso-alkanes (branched alkanes), alkenes, alkynes, alkadienes, and alkylbenzenes were calculated at 298 K for 153 systems not yet measured. Recommended data for 64 systems reported in the literature were compared with the predicted values. The solubility of the hydrocarbons in water was calculated with a thermodynamically based equation, which depends on specific properties of the hydrocarbon. The concentration in the second coexisting liquid phase (water in hydrocarbon) was calculated using liquid-liquid equilibrium with an equation of state, which takes into account the self-association of water and co-association of water with π-bonds of the hydrocarbons. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Liquid-liquid equilibrium in benzene (1) + water (2) system under saturated vapor pressure; experimental mole fractions of benzene at various temperatures taken from different literature sources. Dashed line corresponds to the three-phase critical temperature.</p>
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<p>Solubility of benzene (1) in water (2), natural logarithm of mole fraction of benzene (1) in water (2), ln <span class="html-italic">x</span><sub>1</sub>, vs. temperature, <span class="html-italic">T</span>.</p>
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<p>ln <span class="html-italic">x</span><sub>min</sub> used in Equation (3) vs. excluded volume of the corresponding alkane, <span class="html-italic">b</span> (<span class="html-italic">b</span><sub>h</sub>). Where symbols represent: □, <span class="html-italic">n-</span>alkanes; ○, cycloalkanes; ∆, isoalkanes. The alkanes are listed below in increasing order of <span class="html-italic">b</span> within each group: cyclopentane, cyclohexane, methylcyclopentane, cycloheptane, methylcyclohexane, cyclooctane, ethylcyclohexane, propylcyclopentane, pentylcyclopentane, pentane, hexane, heptane, octane, nonane, decane, undecane, 2,2- and 2,3-dimethylbutane, 3-methylpentane, 2-methylpentane, 3,3-, 2,3-, 2,2- and 2,4-dimethylpentane, 3-methylhexane, 2-methylhexane, 2,3,4-trimethylpentane, 2,2,4-trimethylpentane and 3-methylheptane.</p>
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14 pages, 3507 KiB  
Article
Hydrothermal Synthesis of Biphasic Calcium Phosphate from Cuttlebone Assisted by the Biosurfactant L-rhamnose Monohydrate for Biomedical Materials
by Thamonwan Tattanon, Premjit Arpornmaeklong, Sarute Ummartyotin and Thirawudh Pongprayoon
ChemEngineering 2021, 5(4), 88; https://doi.org/10.3390/chemengineering5040088 - 16 Dec 2021
Cited by 8 | Viewed by 3013
Abstract
The motivation of this research work is to develop novel medical material from cuttlebone (calcium source) by L-rhamnose monohydrate (biosurfactant) for aged people. The process can be synthesized biphasic calcium phosphate which is eco-friendly to environment. One of the most important aspects for [...] Read more.
The motivation of this research work is to develop novel medical material from cuttlebone (calcium source) by L-rhamnose monohydrate (biosurfactant) for aged people. The process can be synthesized biphasic calcium phosphate which is eco-friendly to environment. One of the most important aspects for this work is to use cuttlebone as a naturally occurring calcium source from a local beach in Thailand. It usually contains 90% calcium carbonate. The objective of this research work is to synthesize the biphasic calcium phosphate by hydrothermal reaction. Critical micelle concentrations (CMCs) of 10, 20, 100, 500 and 1000 of L-rhamnose monohydrate were used to control particle size and shape. XRD revealed a mixture of β-tricalcium phosphate and hydroxyapatite powder. SEM reported that the size of particles can be effectively controlled by the addition of L-rhamnose monohydrate, and with the addition of surfactant, size uniformity was achieved. The cytotoxicity test was reported to be in the range of 70–75%. It was remarkable to note that biphasic calcium phosphate synthesized from cuttlebone with the aid of L-rhamnose monohydrate will be considered an excellent candidate as a scaffold material. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Schematic diagram of biphasic calcium phosphate synthesized by a hydrothermal reaction using L-rhamnose monohydrate as a biosurfactant.</p>
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<p>FTIR spectra of biphasic calcium phosphate prepared by cuttlebone with variation of the CMC of L-rhamnose monohydrate biosurfactant: (<b>a</b>) no CMC, (<b>b</b>) 10 CMC, (<b>c</b>) 20 CMC, (<b>d</b>) 100 CMC, (<b>e</b>) 500 CMC and (<b>f</b>) 1000 CMC.</p>
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<p>XRD pattern of biphasic calcium phosphate prepared by cuttlebone with variation of the CMC of L-rhamnose monohydrate biosurfactant: (<b>a</b>) no CMC, (<b>b</b>) 10 CMC, (<b>c</b>) 20 CMC, (<b>d</b>) 100 CMC, (<b>e</b>) 500 CMC and (<b>f</b>) 1000 CMC.</p>
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<p>Morphological properties of biphasic calcium phosphate prepared by cuttlebone with variation of the CMC of L-rhamnose monohydrate biosurfactant: (<b>a</b>) no CMC, (<b>b</b>) 10 CMC, (<b>c</b>) 20 CMC, (<b>d</b>) 100 CMC, (<b>e</b>) 500 CMC and (<b>f</b>) 1000 CMC.</p>
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<p>Particle size analysis of biphasic calcium phosphate prepared by cuttlebone with variation of the CMC of L-rhamnose monohydrate biosurfactant: (<b>a</b>) no CMC, (<b>b</b>) 10 CMC, (<b>c</b>) 20 CMC, (<b>d</b>) 100 CMC, (<b>e</b>) 500 CMC and (<b>f</b>) 1000 CMC.</p>
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<p>Size diameter analysis by dynamic light scattering (DLS) of biphasic calcium phosphate prepared by cuttlebone with variation of the CMC of L-rhamnose monohydrate biosurfactant: (<b>a</b>) no CMC, (<b>b</b>) 10 CMC, (<b>c</b>) 20 CMC, (<b>d</b>) 100 CMC, (<b>e</b>) 500 CMC and (<b>f</b>) 1000 CMC.</p>
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18 pages, 2206 KiB  
Article
Raman Calibration Models for Chemical Species Determination in CO2-Loaded Aqueous MEA Solutions Using PLS and ANN Techniques
by Ahmad Syukri Hanafiah, Abdulhalim Shah Maulud, Muhammad Zubair Shahid, Humbul Suleman and Azizul Buang
ChemEngineering 2021, 5(4), 87; https://doi.org/10.3390/chemengineering5040087 - 14 Dec 2021
Viewed by 2897
Abstract
The improvement in energy efficiency is recognized as one of the significant parameters for achieving our net-zero emissions target by 2050. One exciting area for development is conventional carbon capture technologies. Current amine absorption-based systems for carbon capture operate at suboptimal conditions resulting [...] Read more.
The improvement in energy efficiency is recognized as one of the significant parameters for achieving our net-zero emissions target by 2050. One exciting area for development is conventional carbon capture technologies. Current amine absorption-based systems for carbon capture operate at suboptimal conditions resulting in an efficiency loss, causing a high operational expenditure. Knowledge of qualitative and quantitative speciation of CO2-loaded alkanolamine systems and their interactions can improve the equipment design and define optimal operating conditions. This work investigates the potential of Raman spectroscopy as an in situ monitoring tool for determining chemical species concentration in the CO2-loaded aqueous monoethanolamine (MEA) solutions. Experimental information on chemical speciation and vapour-liquid equilibrium was collected at a range of process parameters. Then, partial least squares (PLS) regression and an artificial neural network (ANN) were applied separately to develop two Raman species calibration models where the Kent–Eisenberg model correlated the species concentrations. The data were paired and randomly distributed into calibration and test datasets. A quantitative analysis based on the coefficient of determination (R2) and root mean squared error (RMSE) was performed to select the optimal model parameters for the PLS and ANN approach. The R2 values of above 0.90 are observed for both cases indicating that both regression techniques can satisfactorily predict species concentration. ANN models are slightly more accurate than PLS. However, PLS (being a white box model) allows the analysis of spectral variables using a weight plot. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Experimental setup to collect vapour-liquid equilibrium (VLE) data and Raman spectrum.</p>
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<p>Scree plot for partial least squares (PLS)-based CO<sub>2</sub> loading Raman calibration model.</p>
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<p>Plot of mean squared error (MSE) values after each iteration during training.</p>
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<p>Plots of Raman spectra captured from unloaded and CO<sub>2</sub>-loaded aqueous monoethanolamine (MEA) solution. Each are for (<b>a</b>) combined plot, (<b>b</b>) unloaded MEA, and (<b>c</b>) loaded MEA.</p>
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<p>Weight plots from bicarbonate calibration model (4 molar MEA) for (<b>a</b>) PLS Component 1 and (<b>b</b>) PLS Component 2.</p>
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<p>Regression plots from 3 molar MEA calibration models developed using PLS and ANN techniques.</p>
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13 pages, 2735 KiB  
Article
Features of the Chemical Interaction of 2-Furaldehyde and 1,3,5-Trihydroxybenzene in an Alkaline Medium to Obtain a Plasticizing Additive
by Valentina Anatolyevna Poluektova, Natalia Igorevna Cherkashina, Sergey Aleksandrovich Starchenko and Dmitriy Sergeevich Romanyuk
ChemEngineering 2021, 5(4), 84; https://doi.org/10.3390/chemengineering5040084 - 8 Dec 2021
Viewed by 2214
Abstract
The paper presents data on the study of the polycondensation of 2-furaldehyde and 1,3,5-trihydroxybenzene in an alkaline medium to obtain a plasticizing additive. Results are presented on the study of the products of the separate interaction of 1,3,5-trioxybenzene and 2-furaldehyde with NaOH, and [...] Read more.
The paper presents data on the study of the polycondensation of 2-furaldehyde and 1,3,5-trihydroxybenzene in an alkaline medium to obtain a plasticizing additive. Results are presented on the study of the products of the separate interaction of 1,3,5-trioxybenzene and 2-furaldehyde with NaOH, and the joint polycondensation of 1,3,5-trioxybenzene with 2-furaldehyde with NaOH by UV spectroscopy. The structure of the product of the interaction of 1,3,5-trioxybenzene with 2-furaldehyde in an alkaline medium was studied by IR spectroscopy. Stronger C–H bonds appear in the IR spectrum and stretching vibrations of the C = O group are not observed, which confirms the production of a new compound. The optimal dosage of the developed plasticizing additive has been established as 0.3% of the cement mass (calculated on dry matter). The developed plasticizing additive can significantly reduce the water-cement ratio while maintaining the strength characteristics of cement compositions. In addition, when using the additive, the strength characteristics are significantly increased with a reduced water-cement ratio. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Structural formula: (<b>a</b>) 1,3,5-trioxybenzene; (<b>b</b>) 2-furaldehyde.</p>
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<p>Disproportionation reaction of 2-furaldehyde (Cannizzaro reaction).</p>
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<p>UV spectra of 1,3,5-trihydroxybenzene: (<b>a</b>) initial; (<b>b</b>) after interaction with NaOH at a temperature of 70 °C and holding for 24 h.</p>
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<p>UV spectra of 2-furaldehyde: (<b>a</b>) control solution; (<b>b</b>) interaction with NaOH.</p>
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<p>UV spectrum of the synthesized oligomer: (<b>a</b>) with instant addition of the condensing agent; (<b>b</b>) with smooth introduction of the condensing agent.</p>
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<p>IR spectrum of 2-furaldehyde.</p>
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<p>IR spectrum of 1,3,5-trihydroxybenzene.</p>
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<p>IR spectrum of the product of the interaction of 1,3,5-trioxybenzene with 2-furaldehyde in an alkaline medium.</p>
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<p>Liquid chromatogram of a mixture of 1,3,5-trioxybenzene and 2-furaldehyde in water (<b>a</b>) and the reaction mixture after 20 min (<b>b</b>) and 40 min (<b>c</b>): 1-peak of 1,3,5-trioxybenzene; 2-peak of 2-furaldehyde; 3-mono derivatives of phloroglucinol and furfural; 4-oligomers.</p>
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<p><sup>1</sup>H-NMR spectra (<b>a</b>) a mechanical mixture of 1,3,5-trioxybenzene and 2-furaldehyde; (<b>b</b>) the product of the interaction of 1,3,5-trioxybenzene with 2-furaldehyde in an alkaline medium: 1-protons of the aldehyde group of 2-furaldehyde; 2-protons of the furan ring; 3-protons of OH groups of 1,3,5-trioxybenzene; 4-protons of aromatic rings of 1,3,5-trioxybenzene; 5-methine protons (CH); 6-OH groups.</p>
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<p>Scheme of the joint condensation of 1,3,5-trioxybenzene with 2-furaldehyde in an alkaline medium.</p>
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<p>Schematic process for determining the plasticizing ability of additives for cement systems.</p>
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<p>Dependence of the diameter of the mini-cone spreading on the dosage of 1,3,5-trihydroxybenzene-2-furaldehyde.</p>
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15 pages, 1828 KiB  
Article
Life Cycle Assessment of Solid Recovered Fuel Gasification in the State of Qatar
by Ahmad Mohamed S. H. Al-Moftah, Richard Marsh and Julian Steer
ChemEngineering 2021, 5(4), 81; https://doi.org/10.3390/chemengineering5040081 - 19 Nov 2021
Cited by 9 | Viewed by 3680
Abstract
Gas products from gasified solid recovered fuel (SRF) have been proposed as a replacement for natural gas to produce electricity in future power generation systems. In this work, the life cycle assessment (LCA) of SRF air gasification to energy was conducted using the [...] Read more.
Gas products from gasified solid recovered fuel (SRF) have been proposed as a replacement for natural gas to produce electricity in future power generation systems. In this work, the life cycle assessment (LCA) of SRF air gasification to energy was conducted using the Recipe2016 model considering five environmental impact categories and four scenarios in Qatar. The current situation of municipal solid waste (MSW) handling in Qatar is landfill with composting. The results show that using SRF gasification can reduce the environmental impact of MSW landfills and reliance on natural gas in electricity generation. Using SRF gasification on the selected five environmental impact categories—climate change, terrestrial acidification, marine ecotoxicity, water depletion and fossil resource depletion—returned significant reductions in environmental degradation. The LCA of the SRF gasification for the main four categories in the four scenarios gave varying results. The introduction of the SRF gasification reduced climate change-causing emissions by 41.3% because of production of renewable electricity. A reduction in water depletion and fossil resource depletion of 100 times were achieved. However, the use of solar technology and SRF gasification to generate electricity reduced the impact of climate change to almost zero emissions. Terrestrial acidification showed little to no change in all three scenarios investigated. This study was compared with the previous work from the literature and showed that on a nominal 10 kg MSW processing basis, 5 kg CO2 equivalent emissions were produced for the landfilling scenarios. While the previous studies reported that 8 kg CO2 produced per 10 kg MSW is processed for the same scenario. The findings indicate that introducing SRF gasification in solid waste management and electricity generation in Qatar has the potential to reduce greenhouse gas (GHG) emission load and related social, economic, political and environmental costs. In addition, the adoption of the SRF gasification in the country will contribute to Qatar’s national vision 2030 by reducing landfills and produce sustainable energy. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Qatar energy information showing interactive energy consumption (<a href="https://www.enerdata.net/search/node/qatar%20renewable/" target="_blank">https://www.enerdata.net/search/node/qatar%20renewable/</a>, accessed on 6 November 2021).</p>
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<p>Qatar natural gas domestic consumption during 1990–2020 periods (<a href="https://www.enerdata.net/search/node/qatar%20renewable/" target="_blank">https://www.enerdata.net/search/node/qatar%20renewable/</a>, accessed on 6 November 2021).</p>
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<p>Energy and mass flows diagram for Scenario 2.</p>
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<p>Five environmental impact categories for which non-zero values were recorded as a result of the LCA.</p>
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<p>Comparison of climate change impact category (global warming potential) of all the scenarios considered in this study.</p>
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<p>A Comparison of water depletion of all the scenarios considered in this study.</p>
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<p>A comparison of fossil depletion of all four scenarios considered in this study.</p>
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15 pages, 10577 KiB  
Article
Investigation of Duplex Brass Membranes with Metallography, Permeability and Treatments: Work-Hardening, Annealing and Quenching
by Sofia Kavafaki, George Bomis, Kyriaki Drakaki, Athanasios Varoutoglou, Konstantinos Kiourtzidis, George Z. Kyzas and Athanasios C. Mitropoulos
ChemEngineering 2021, 5(4), 76; https://doi.org/10.3390/chemengineering5040076 - 3 Nov 2021
Viewed by 3618
Abstract
This paper consists of the fabrication and investigation of metal membranes and the study of their behaviour and applications in gas separation processes. The scope is to produce and characterize the porous crystal structure of brass alloy (standardization: DIN 17660) membranes and measure [...] Read more.
This paper consists of the fabrication and investigation of metal membranes and the study of their behaviour and applications in gas separation processes. The scope is to produce and characterize the porous crystal structure of brass alloy (standardization: DIN 17660) membranes and measure their permeability with helium as a penetrant medium. Another part of this study is to alter the brass alloy’s structure throughout metallurgical treatments and investigate how the permeability is allied to the structure’s alteration. This work merges the knowledge and technology of inorganic porous materials science in metallurgy. The novelty of the current research resides in the process to alternate the brass alloy structure throughout metallurgical treatments and how it is allied to the permeability of the membrane, which is of interest to be investigated. The results of the research are analysed and compared conducting the final inferences. All metallurgical treatments resulted in low permeability values when compared to a non-treated specimen. Specifically, the drop in permeance ranged from 76 up to 99.56%. It is noted that consecutive treatments contributed to even further decreases. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Brass alloy images from SEM after EDM: (<b>a</b>) magnification ×1000; (<b>b</b>) magnification ×1500; (<b>c</b>) magnification ×1900; (<b>d</b>) magnification ×3700.</p>
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<p>SEM image of brass alloy; magnification ×1500.</p>
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<p>Specimen 1: Image from optical microscope after metallography of non-treated alloy brass: (<b>a</b>) magnification ×500; (<b>b</b>) magnification ×1000.</p>
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<p>Specimen 2: Image from optical microscope after metallography of work-hardened brass alloy: (<b>a</b>) magnification ×500; (<b>b</b>) magnification ×1000.</p>
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<p>Specimen 3: Image from optical microscope after metallography of work-hardened in steps brass alloy: (<b>a</b>) magnification ×500; (<b>b</b>) magnification ×1000.</p>
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<p>Specimen 4: Image from optical microscope after metallography of work-hardened and annealed brass alloy: (<b>a</b>) magnification ×500; (<b>b</b>) magnification ×1000.</p>
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<p>Specimen 5: Image from optical microscope after metallography of air stream quenched heated at 850 °C brass alloy: (<b>a</b>) magnification ×500; (<b>b</b>) magnification ×1000.</p>
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<p>Mercury intrusion porosimetry results.</p>
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<p>Substrate front view.</p>
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<p>Permeance values (in GPU units) of metal membranes.</p>
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<p>Permeance values (in m<sup>3</sup><sub>stp</sub> m s<sup>−1</sup> m<sup>−2</sup> Pa<sup>−1</sup> units) of metal membranes.</p>
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<p>Permeation values (in barrer units) of metal membranes.</p>
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<p>Permeation values (in m<sup>3</sup><sub>stp</sub> m s<sup>−1</sup> m<sup>−2</sup> Pa<sup>−1</sup> units) of metal membranes.</p>
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13 pages, 3078 KiB  
Article
Effects of Constant Electric Field on Biodegradation of Phenol by Free and Immobilized Cells of Bradyrhizobium japonicum 273
by Evgenia Vasileva, Tsvetomila Parvanova-Mancheva, Venko Beschkov, Zlatka Alexieva, Maria Gerginova and Nadejda Peneva
ChemEngineering 2021, 5(4), 75; https://doi.org/10.3390/chemengineering5040075 - 2 Nov 2021
Cited by 3 | Viewed by 2373
Abstract
It is shown that bacteria Bradyrhizobium japonicum 273 were capable of degrading phenol at moderate concentrations either in a free cell culture or by immobilized cells on granulated activated carbon particles. The amount of degraded phenol was greater in an immobilized cell preparation [...] Read more.
It is shown that bacteria Bradyrhizobium japonicum 273 were capable of degrading phenol at moderate concentrations either in a free cell culture or by immobilized cells on granulated activated carbon particles. The amount of degraded phenol was greater in an immobilized cell preparation than in a free culture. The application of a constant electric field during cultivation led to enhanced phenol biodegradation in a free culture and in immobilized cells on granulated activated carbon. The highest phenol removal efficiency was observed for an anode potential of 1.0 V/S.H.E. The effect was better pronounced in a free culture. The enzyme activities of free cells for phenol oxidation and benzene ring cleavage were very sensitive to the anode potential in the first two steps of the metabolic pathway of phenol biodegradation catalyzed by phenol hydroxylase—catechol-1,2-dioxygenase and catechol-2,3-dioxygenase. It was observed that at an anode potential of 0.8 V/S.H.E., the meta-pathway of cleavage of the benzene ring catalyzed by catechol-2,3-dioxygenase became competitive with the ortho-pathway, catalyzed by catechol-1,2-dioxygenase. The obtained results showed that the positive effect of constant electric field on phenol biodegradation was rather due to electric stimulation of enzyme activity than electrochemical anode oxidation. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Time profile for control experiment with initial phenol concentration as 0.06 g dm<sup>−3</sup>.</p>
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<p>Time profiles for an experiment with free cells and the application of a constant electric field: (<b>A</b>) anode potential 1.0 V/S.H.E; (<b>B</b>) the current measured at anode potential 1.0 V/S.H.E. Initial phenol concentration, 0.06 gdm<sup>−3</sup>.</p>
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<p>Time profiles for an experiment with free cells and the application of a constant electric field: (<b>A</b>) anode potential 1.0 V/S.H.E; (<b>B</b>) the current measured at anode potential 1.0 V/S.H.E. Initial phenol concentration, 0.06 gdm<sup>−3</sup>.</p>
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<p>SEM image of free cell samples for SEM analysis: (<b>A</b>) control experiment; (<b>B</b>) with the constant electric field (0.8 V).</p>
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<p>Time profile for experiment with immobilized bacteria without electric field application. Initial phenol concentration, 0.06 g dm<sup>−3</sup>.</p>
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<p>SEM image of the activated carbon surface with attached bacterial cells.</p>
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<p>Experiment with immobilized cells with a constant electric field at anode potential 1.0 V/S.H.E. Initial phenol concentration 0.06 g dm<sup>−3</sup>: (<b>A</b>) time profiles for added phenol and phenol concentration; (<b>B</b>) the electric current in immobilized cell culture.</p>
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<p>Experiment with immobilized cells with a constant electric field at anode potential 1.0 V/S.H.E. Initial phenol concentration 0.06 g dm<sup>−3</sup>: (<b>A</b>) time profiles for added phenol and phenol concentration; (<b>B</b>) the electric current in immobilized cell culture.</p>
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17 pages, 3899 KiB  
Article
Amino Ethers of Ortho-Phosphoric Acid as Extragents for Ethanol Dehydration
by Alexander V. Klinov, Alexander V. Malygin, Alina R. Khairullina, Alisa R. Davletbaeva, Oleg O. Sazonov, Ivan P. Anashkin and Ilsiya M. Davletbaeva
ChemEngineering 2021, 5(4), 71; https://doi.org/10.3390/chemengineering5040071 - 21 Oct 2021
Viewed by 3105
Abstract
Amino ethers of ortho-phosphoric acid prepared using triethanolamine; ortho-phosphoric acid; polyoxyethylene glycol, diethylene glycol, triethylene glycol and glycerol (AEPA-DEG/TEG/Gl) were investigated as extractants for the separation of aqueous ethanol solutions by extractive distillation. Using the method of open evaporation, the influence [...] Read more.
Amino ethers of ortho-phosphoric acid prepared using triethanolamine; ortho-phosphoric acid; polyoxyethylene glycol, diethylene glycol, triethylene glycol and glycerol (AEPA-DEG/TEG/Gl) were investigated as extractants for the separation of aqueous ethanol solutions by extractive distillation. Using the method of open evaporation, the influence of the molecular structure of AEPA-DEG/TEG/Gl on the conditions of vapor–liquid equilibrium in ethanol–water solutions was studied. It has been shown that the addition of AEPA-DEG/TEG/Gl removes the azeotropic point. At the same time, the observed effect turned out to be significantly higher in comparison with the use of pure glycerol or glycols for these purposes. The UNIFAC model was used to calculate the activity coefficients in a three-component ethanol–water–AEPA-DEG/TEG/Gl mixture. Within the framework of this model, a division of AEPA-DEG/TEG/Gl molecules into group components is proposed. Previously unknown parameters of the groups PO–CH, PO–CH2, PO–OCH2, PO–NHCH2, PO–OH, and PO–H2O were determined from our own and published experimental data. The concentration dependences of the density and dynamic viscosity of AEPA-Gl aqueous solutions have been experimentally measured. Experimental studies of the extractive distillation of ethanol–water using AEPA-Gl as an extractant have been carried out in a column with bubble cap plates and a packing, and its high efficiency has been established. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Residual curves (geometric figures are experimental data) for the ethanol–water–AEPA-(PEG/DEG/TEG/Gl) mixture, [AEPA-(PEG/DEG/TEG/Gl)] = 60 wt.%. Solid line is calculated data for the binary ethanol–water mixture. Dashed line is the calculated data for the ethanol–water–AEPA-Gl mixture. P/L<sub>0</sub> is the distillate rate.</p>
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<p>Comparison of the experimental (geometric figures) and calculated data (lines) of the activity coefficients for the TBP–hexane mixture [<a href="#B50-ChemEngineering-05-00071" class="html-bibr">50</a>].</p>
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<p>Boiling points of ethanol solutions of AEPA-Gl. Geometric figures are experimental data, continuous line is calculated data.</p>
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<p>Boiling points of AEPA-Gl and AEPA-DEG aqueous solutions. Geometric figures are experimental data ((<b>a</b>) circles: AEPA-Gl, (<b>b</b>) squares: AEPA-DEG) and lines are calculated data.</p>
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<p>Residual curves for the ethanol–water–AEPA-Gl mixture at [AEPA-Gl] = 20 wt.% (geometric figures are experimental data, continuous line is calculated data). Dotted lines are calculated values for the ethanol–water mixture.</p>
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<p>Curves of residual concentrations for the ethanol–water–AEPA-DEG mixture at [AEPA-DEG] = 20 wt.% (geometric figures are experimental data, continuous line is calculated data). Dashed lines are calculated data for the ethanol–water mixture.</p>
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<p>The coefficients of the relative volatility depending on the concentration of the extractant at the azeotropic point of the ethanol–water mixture (ethanol: 95.6 wt.%).</p>
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<p>Scheme of the experimental distillation column: 1—electric heater; 2—cube-evaporator; 3—diopter with attachment; 4—unit with a packing; 5—unit with plates; 6—distillate sampling unit; 7—gear pump; 8—condenser; 9—a tank with an extractant; 10—cube temperature sensor; 11—distillate vapor temperature sensor; 12 and 13—cooling water temperature sensors; 14—cooling water flow sensor.</p>
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<p>Synthesis of amino ethers of <span class="html-italic">ortho</span>-phosphoric acid based on glycerol (AEPA-Gl).</p>
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17 pages, 2907 KiB  
Article
Development of a Dynamic Modeling Approach to Simulate a Segmented Distillation Column for Flexible Operation
by Bastian Bruns, Henrik Fasel, Marcus Grünewald and Julia Riese
ChemEngineering 2021, 5(4), 66; https://doi.org/10.3390/chemengineering5040066 - 1 Oct 2021
Cited by 5 | Viewed by 3955
Abstract
The need for flexible process equipment has increased over the past decade in the chemical industry. However, process equipment such as distillation columns have limitations that significantly restrict flexible operation. We investigate a segmented tray column designed to allow flexible operation. The design [...] Read more.
The need for flexible process equipment has increased over the past decade in the chemical industry. However, process equipment such as distillation columns have limitations that significantly restrict flexible operation. We investigate a segmented tray column designed to allow flexible operation. The design consists of radial trays connected at the downcomer of each tray. Each segment can be operated separately, but depending on the capacity of the feed stream, additional segments can be activated or deactivated. The connection between the trays aims to transfer liquid from one stationary segment to the adjacent inactive segment, thereby reducing the time required for the start-up process. In a case study on the separation of methanol and water, we perform dynamic simulations to assess the reduction in the start-up time of inactive segments. The results confirm the advantages over standard tray designs. The segmented distillation column is a step towards improving the flexibility of separation operations. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Schematic of the tray design in the segmented distillation column.</p>
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<p>Schematic representation of a tray in the segmented column, divided into downcomer and equilibrium stage.</p>
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<p>Structure of the segmented column model.</p>
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<p>Visualization of the assumptions in the case study.</p>
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<p>Assumptions for the start-up procedure of the segmented column for cases C1–C4.</p>
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<p>Temperature profiles during start-up of a segment for selected stages and the reboiler for BC<sub>segmented</sub> (black) and case C1 (red).</p>
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<p>Comparison of the overall MX-functions for all cases in the segmented column (<b>left</b>) and zoomed in for cases C1–C4 (<b>right</b>).</p>
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<p>Overall MX-function of the active segment during activation of the adjacent segment.</p>
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<p>MX-functions at the top (solid line) and bottom (dashed line) of the inactive segment for cases C2 and C3.</p>
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<p>Comparison of absolute start-up times of the distillate for the activation of an additional distillation column with standard geometry and an additional segment of the segmented distillation column.</p>
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17 pages, 3328 KiB  
Article
Effect of the Carrier on the Coprecipitation of Curcumin through Supercritical-Assisted Atomization
by Iolanda De Marco and Paola Franco
ChemEngineering 2021, 5(3), 59; https://doi.org/10.3390/chemengineering5030059 - 8 Sep 2021
Cited by 8 | Viewed by 2072
Abstract
In this paper, composite systems containing curcumin (CUR) were prepared through supercritical-assisted atomization (SAA), using different carriers. Curcumin is particularly interesting in the pharmaceutical and nutraceutical fields for its antioxidant, antitumoral, and anti-inflammatory properties. However, its therapeutic effect on human health is restricted [...] Read more.
In this paper, composite systems containing curcumin (CUR) were prepared through supercritical-assisted atomization (SAA), using different carriers. Curcumin is particularly interesting in the pharmaceutical and nutraceutical fields for its antioxidant, antitumoral, and anti-inflammatory properties. However, its therapeutic effect on human health is restricted by its poor water solubility and low dissolution rate, limiting its absorption after its oral administration. To increase the dissolution rate and then the bioavailability of the active compound, CUR was coprecipitated with polymeric, i.e., polyvinylpyrrolidone (PVP) and dextran (DXT), and not polymeric, i.e., hydroxypropyl-β-cyclodextrin (HP-β-CD), carriers. The effects of some operating parameters, namely the concentration of solutes in solution and the active compound/carrier ratio, on the morphology and the particle size distribution of the powders were investigated. Submicrometric particles were produced with all the carriers. Under the best operating conditions, the mean diameters ± standard deviation were equal to 0.69 ± 0.20 μm, 0.40 ± 0.13 μm, and 0.81 ± 0.25 μm for PVP/CUR, DXT/CUR, and HP-β-CD/CUR, respectively. CUR dissolution rates from coprecipitated particles were significantly increased in the case of all the carriers. Therefore, the results are exciting from a pharmaceutical and nutraceutical point of view, to produce supplements containing curcumin, but assuring a high dissolution rate and bioavailability and, consequently, a more effective therapeutic effect. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>A sketch of the SAA laboratory plant. S1, S2, and S3: CO<sub>2</sub> supply, nitrogen supply, and liquid solution supply; P1 and P2: pumps; RB: refrigerating bath; HB: heating bath; M: manometer; HE: heat exchanger; S: saturator; PC: precipitation chamber; DM: dry test meter.</p>
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<p>PVP/CUR particles produced by SAA from EtOH at 10 mg/mL. Effect of the carrier/API ratio: (<b>a</b>) 10/1 <span class="html-italic">w</span>/<span class="html-italic">w</span>; (<b>b</b>) 6/1 <span class="html-italic">w</span>/<span class="html-italic">w</span>; (<b>c</b>) 3/1 <span class="html-italic">w</span>/<span class="html-italic">w</span>; (<b>d</b>) particle size distribution of the powders obtained under different conditions.</p>
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<p>DXT/CUR particles produced by SAA from EtOH/H<sub>2</sub>O 70/30 at 5 mg/mL. Effect of the API/carrier ratio: (<b>a</b>) 10/1 <span class="html-italic">w</span>/<span class="html-italic">w</span>; (<b>b</b>) 6/1 <span class="html-italic">w</span>/<span class="html-italic">w</span>.</p>
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<p>HP-β-CD/CUR particles produced by SAA from EtOH/H<sub>2</sub>O 70/30 at 6/1 <span class="html-italic">w</span>/<span class="html-italic">w</span> ratio. Effect of total concentration in the liquid solution: (<b>a</b>) 5 mg/mL; (<b>b</b>) 10 mg/mL; (<b>c</b>) 20 mg/mL; (<b>d</b>) particle size distribution of the powders obtained in correspondence of the different concentrations.</p>
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<p>FT-IR spectra of unprocessed, physical mixtures and SAA processed carrier/CUR powders: (<b>a</b>) PVP; (<b>b</b>) DEX; (<b>c</b>) HP-β-CD.</p>
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<p>FT-IR spectra of unprocessed, physical mixtures and SAA processed carrier/CUR powders: (<b>a</b>) PVP; (<b>b</b>) DEX; (<b>c</b>) HP-β-CD.</p>
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<p>Diffractograms of unprocessed carriers and CUR and SAA processed carrier/CUR powders: (<b>a</b>) PVP; (<b>b</b>) DEX; (<b>c</b>) HP-β-CD.</p>
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<p>Dissolution profiles of CUR in PBS from powders coprecipitated with different polymeric and not polymeric carriers: (<b>a</b>) PVP; (<b>b</b>) DEX; (<b>c</b>) HP-β-CD.</p>
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<p>Dissolution profiles of CUR in PBS from powders coprecipitated with different polymeric and not polymeric carriers: (<b>a</b>) PVP; (<b>b</b>) DEX; (<b>c</b>) HP-β-CD.</p>
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13 pages, 3537 KiB  
Article
Impact Strength of Hybrid Epoxy–Basalt Composites Modified with Mineral and Natural Fillers
by Danuta Matykiewicz, Mateusz Barczewski, Marwan Suleiman Mousa, Mavinkere Rangappa Sanjay and Suchart Siengchin
ChemEngineering 2021, 5(3), 56; https://doi.org/10.3390/chemengineering5030056 - 31 Aug 2021
Cited by 20 | Viewed by 2797
Abstract
The aim of this study was to evaluate the influence of mineral and natural additives (2.5; 5; 10 wt.%) on the impact strength of epoxy–basalt composites. Three types of filler were used to modify the epoxy matrix: basalt powder (BP), basalt microfiber (BF) [...] Read more.
The aim of this study was to evaluate the influence of mineral and natural additives (2.5; 5; 10 wt.%) on the impact strength of epoxy–basalt composites. Three types of filler were used to modify the epoxy matrix: basalt powder (BP), basalt microfiber (BF) and sunflower husk ash (SA). The impact strength and the maximum force were determined for the materials. The results of the conducted research confirm that the addition of a powder fillers to the epoxy matrix of basalt fiber reinforced composites is an effective method of improving their impact characteristic. The introduction of fillers to epoxy resin allowed to improve the impact properties of all tested groups of laminates. Moreover, in all cases, the introduction of the filler increased the maximum force needed to damage the composite sample and their hardness. For the modified materials, an increase in impact strength was recorded, respectively: by 44% for composites with BP, by 7.5% for composites with BF and by 2.5% for composites with SA. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Particle size distribution of sunflower husk ash.</p>
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<p>SEM images of the fillers: (<b>a</b>) basalt powder, (<b>b</b>) basalt micro fiber, (<b>c</b>) sunflower husk ash.</p>
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<p>Images of the damaged area of composites modified with basalt powder after the impact test.</p>
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<p>The load–displacement curves of the investigated composites with basalt powder.</p>
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<p>Images of the damaged area of composites modified with basalt micro fiber after the impact test.</p>
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<p>The load–displacement curves of the investigated composites with basalt microfiber.</p>
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<p>Images of the damaged area of composites modified with sunflower husk ash after the impact test.</p>
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<p>The load–displacement curves of the investigated composites with sunflower husk ash.</p>
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11 pages, 8203 KiB  
Article
Effects of Pore Connectivity on the Sorption of Fluids in Nanoporous Material: Ethane and CO2 Sorption in Silicalite
by Siddharth Gautam and David R. Cole
ChemEngineering 2021, 5(3), 55; https://doi.org/10.3390/chemengineering5030055 - 30 Aug 2021
Cited by 5 | Viewed by 4113
Abstract
Adsorption of fluids in nanoporous materials is important for several applications including gas storage and catalysis. The pore network in natural, as well as engineered, materials can exhibit different degrees of connectivity between pores. While this might have important implications for the sorption [...] Read more.
Adsorption of fluids in nanoporous materials is important for several applications including gas storage and catalysis. The pore network in natural, as well as engineered, materials can exhibit different degrees of connectivity between pores. While this might have important implications for the sorption of fluids, the effects of pore connectivity are seldom addressed in the studies of fluid sorption. We have carried out Monte Carlo simulations of the sorption of ethane and CO2 in silicalite, a nanoporous material characterized by sub-nanometer pores of different geometries (straight and zigzag channel like pores), with varied degrees of pore connectivity. The variation in pore connectivity is achieved by selectively blocking some pores by loading them with methane molecules that are treated as a part of the rigid nanoporous matrix in the simulations. Normalized to the pore space available for adsorption, the magnitude of sorption increases with a decrease in pore connectivity. The increased adsorption in the systems where pore connections are removed by blocking them is because of additional, albeit weaker, adsorption sites provided by the blocker molecules. By selectively blocking all straight or zigzag channels, we find differences in the absorption behavior of guest molecules in these channels. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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Graphical abstract

Graphical abstract
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<p>Simulation snapshots of CO<sub>2</sub> adsorption in the system S2Z2 at 100 atm in the crystallographic planes a–c (<b>a</b>) and a–b (<b>b</b>). The straight channels in the silicalite samples are highlighted in pink. Half of the straight and zigzag channels are blocked by stuffing them with methane molecules, which are shown as yellow spheres.</p>
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<p>Schematic showing the definition of different systems. Each vertical line in magenta shows free straight channels while blue horizontal lines show free zigzag channel. The blocked channels are marked by an absence of the corresponding line. S4Z4 corresponds to the unmodified silicalite with no pore blocked with methane.</p>
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<p>Adsorption isotherms of CO<sub>2</sub> in silicalite from GCMC simulations of (<b>a</b>) S-majority, (<b>b</b>) Z-majority, and (<b>c</b>) half-blocked models as included in <a href="#ChemEngineering-05-00055-t001" class="html-table">Table 1</a>.</p>
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<p>Adsorption isotherms of ethane in silicalite from GCMC simulations of (<b>a</b>) S-majority, (<b>b</b>) Z-majority, and (<b>c</b>) half-blocked models as included in <a href="#ChemEngineering-05-00055-t001" class="html-table">Table 1</a>.</p>
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<p>Number of fluid molecules (<b>a</b>) CO<sub>2</sub> and (<b>b</b>) ethane adsorbed in systems with half of the pore space in the unmodified silicalite blocked/free (systems in the portion of <a href="#ChemEngineering-05-00055-t001" class="html-table">Table 1</a> highlighted in yellow), as a function of the percentage of straight channels that are open. The <span class="html-italic">X</span>-axis can also be read as the percentage of open zigzag channels, decreasing from 100 (left-most data point) to 0 (rightmost data point).</p>
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<p>Amount of fluid adsorbed, (<b>a</b>) CO<sub>2</sub> and (<b>b</b>) ethane, normalized to the pore volume available in the unmodified silicalite (S4Z4) as a function of the number of pore connections. Bars on symbols signify the extent of uncertainty in the calculations.</p>
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<p>Radial distribution functions of the fluid-fluid and fluid-substrate pairs for (<b>a</b>) CO<sub>2</sub> and (<b>b</b>) Ethane adsorbed in S2Z2 at 100 atm.</p>
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<p>Distribution of angles between the axis of the straight channels and the molecular axis of (<b>a</b>) CO<sub>2</sub> and (<b>b</b>) ethane molecules adsorbed in the half-blocked systems (yellow portion of <a href="#ChemEngineering-05-00055-t001" class="html-table">Table 1</a>) at a pressure of 100 atm. The schematic in inset of (<b>a</b>) shows the position of methane molecules blocking the approach to zigzag or straight channels in the systems S4Z0 and S0Z4. A part of a typical straight channel is shown as a cylinder here, and the methane molecules are shown as yellow discs.</p>
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<p>Distribution of the center of mass of CO<sub>2</sub> molecules adsorbed in (<b>a</b>) zigzag and (<b>b</b>) straight channels of unmodified silicalite (S4Z4) and silicalite with all straight (S0Z4) and zigzag (S4Z0) channels blocked. The intensity corresponds to the number of fluid molecules found at a location in 200 configurations of the system.</p>
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<p>Distribution of the center of mass of ethane molecules adsorbed in (<b>a</b>) zigzag and (<b>b</b>) straight channels of unmodified silicalite (S4Z4) and silicalite with all straight (S0Z4) and zigzag (S4Z0) channels blocked. The intensity corresponds to the number of fluid molecules found at a location in 200 configurations of the system.</p>
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16 pages, 4019 KiB  
Article
Thermal Decomposition Kinetic Study of Non-Recyclable Paper and Plastic Waste by Thermogravimetric Analysis
by Ahmad Mohamed S. H. Al-Moftah, Richard Marsh and Julian Steer
ChemEngineering 2021, 5(3), 54; https://doi.org/10.3390/chemengineering5030054 - 30 Aug 2021
Cited by 12 | Viewed by 3248
Abstract
The global net emissions of the Kyoto Protocol greenhouse gases (GHG), such as carbon dioxide (CO2), fluorinated gases, methane (CH4), and nitrous oxide (N2O), remain substantially high, despite concerted efforts to reduce them. Thermal treatment of solid [...] Read more.
The global net emissions of the Kyoto Protocol greenhouse gases (GHG), such as carbon dioxide (CO2), fluorinated gases, methane (CH4), and nitrous oxide (N2O), remain substantially high, despite concerted efforts to reduce them. Thermal treatment of solid waste contributes at least 2.8–4% of the GHG in part due to increased generation of municipal solid waste (MSW) and inefficient treatment processes, such as incineration and landfill. Thermal treatment processes, such as gasification and pyrolysis, are valuable ways to convert solid materials, such as wastes into syngas, liquids, and chars, for power generation, fuels, or for the bioremediation of soils. Subcoal™ is a commercial product based on paper and plastics from the source segregated waste that is not readily recyclable and that would otherwise potentially find its way in to landfills. This paper looks at the kinetic parameters associated with this product in pyrolysis, gasification, and combustion conditions for consideration as a fuel for power generation or as a reductant in the blast furnace ironmaking process. Thermogravimetric Analysis (TGA) in Nitrogen (N2), CO2, and in air, was used to measure and compare the reaction kinetics. The activation energy (Ea) and pre-exponential factor A were measured at different heating rates using non-isothermal Ozawa Flynn Wall and (OFW) and Kissinger-Akahira-Sonuse (KAS) model-free techniques. The TGA curves showed that the thermal degradation of Subcoal™ comprises three main processes: dehydration, devolatilization, and char and ash formation. In addition, the heating rate drifts the devolatilization temperature to a higher value. Likewise, the derivative thermogravimetry (DTG) results stated that Tm degradation increased as the heating rate increased. Substantial variance in Ea was noted between the four stages of thermal decomposition of Subcoal™ on both methods. The Ea for gasification reached 200.2 ± 33.6 kJ/mol by OFW and 179.0 ± 31.9 kJ/mol by KAS. Pyrolysis registered Ea values of 161.7 ± 24.7 kJ/mol by OFW and 142.6 ± 23.5 kJ/mol by KAS. Combustion returned the lowest Ea values for both OFW (76.74 ± 15.4 kJ/mol) and KAS (71.0 ± 4.4 kJ/mol). The low Ea values in combustion indicate shorter reaction time for Subcoal™ degradation compared to gasification and pyrolysis. Generally, TGA kinetics analysis using KAS and OFW methods show good consistency in evaluating Arrhenius constants. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>The effect of the heating rate in N<sub>2</sub>: (<b>a</b>) conversion degree and (<b>b</b>) DTG weight loss.</p>
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<p>The effect of the heating rate in CO<sub>2</sub>: (<b>a</b>) conversion degree and (<b>b</b>) DTG weight loss.</p>
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<p>The effect of the heating rate in air: (<b>a</b>) conversion degree and (<b>b</b>) DTG weight loss.</p>
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<p>The approximated curves of OFW method of pyrolysis (<b>a</b>), gasification (<b>b</b>), and combustion (<b>c</b>) of Subcoal™ PAF for different values of conversion at heating rates of 5, 10, 15, and 20 °C/min.</p>
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<p>The approximated curves of OFW method of pyrolysis (<b>a</b>), gasification (<b>b</b>), and combustion (<b>c</b>) of Subcoal™ PAF for different values of conversion at heating rates of 5, 10, 15, and 20 °C/min.</p>
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<p>KAS diagrams of pyrolysis (<b>a</b>), gasification (<b>b</b>), and combustion (<b>c</b>) of Subcoal™ PAF for given values of conversion at heating rates of 5, 10, 15, and 20 °C/min.</p>
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<p>KAS diagrams of pyrolysis (<b>a</b>), gasification (<b>b</b>), and combustion (<b>c</b>) of Subcoal™ PAF for given values of conversion at heating rates of 5, 10, 15, and 20 °C/min.</p>
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<p>Compression plots of <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>a</mi> </msub> </mrow> </semantics></math> as a function of conversion <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi>X</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>: (<b>a</b>) OFW and (<b>b</b>) KAS.</p>
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18 pages, 5320 KiB  
Article
Performance Evaluation of the Electric Machine Cooling System Employing Nanofluid as an Advanced Coolant
by Ali Deriszadeh and Filippo de Monte
ChemEngineering 2021, 5(3), 53; https://doi.org/10.3390/chemengineering5030053 - 28 Aug 2021
Cited by 4 | Viewed by 4914
Abstract
In this paper, the overall performance of an electric machine cooling system was examined in terms of heat transfer and fluid flow. The structure of the cooling system was based on the cooling jacket method. The cooling jacket contains spiral channels surrounding the [...] Read more.
In this paper, the overall performance of an electric machine cooling system was examined in terms of heat transfer and fluid flow. The structure of the cooling system was based on the cooling jacket method. The cooling jacket contains spiral channels surrounding the stator and end-windings of the electric machine. Al2O3-water nanofluid is used inside the channels as the cooling fluid. The concentration of nanoparticles and the geometric structure of the cooling system have special effects on both aspects of heat transfer and fluid flow. Therefore, in this paper, the overall performance of the cooling system was evaluated by considering these effects. This study compared the importance of heat transfer and fluid flow performances on the overall performance of the cooling system. Numerical analyses were performed by 3D computational fluid dynamics and 3D fluid motion analysis. The analyses were carried out based on the 3D finite element method using the pressure-based solver of the Ansys Fluent software in steady mode. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Schematic of the cooling system: (<b>a</b>) meshed, and with (<b>b</b>) 4 channel turns, (<b>c</b>) 6 channel turns, and (<b>d</b>) 8 channel turns.</p>
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<p>Contour plot of heat transfer coefficient versus volume fraction and Reynolds number for nanofluids flowing in cooling channels with 8 turns—results were obtained from 20 simulations.</p>
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<p>Relative heat transfer coefficient changes for nanofluids with 2%, 4%, and 5% volume fraction with respect to water.</p>
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<p>Contour plot of heat transfer coefficient versus channel turns number and Reynolds number for a cooling system using water as the coolant—results were obtained from 12 simulations.</p>
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<p>Relative increase in heat transfer coefficient for channels with turns numbers of 6 and 8 with respect to 4.</p>
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<p>Heat transfer coefficient versus Reynolds number, nanoparticle volume fraction, and channel turns number: (<b>a</b>) 4 turns, (<b>b</b>) 6 turns, and (<b>c</b>) 8 turns—results for each subfigure were obtained from 20 simulations.</p>
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<p>Variation in the pressure drop at different Reynolds numbers for nanofluids with different nanoparticle concentrations flowing in channels with 8 turns—results were obtained from 20 simulations.</p>
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<p>Relative increase in pressure drop with respect to water.</p>
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<p>Variations in pressure drop at different Reynolds numbers for channel turns numbers of 4, 6, and 8 for a cooling system using water as the coolant—results were obtained from 12 simulations.</p>
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<p>Relative increase in heat transfer coefficient for channel turns numbers of 6 and 8 with respect to 4.</p>
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<p>Pressure drop versus Reynolds number, nanoparticle volume fraction, and channel turns number: (<b>a</b>) 4 turns, (<b>b</b>) 6 turns, and (<b>c</b>) 8 turns—results for each subfigure were obtained from 20 simulations.</p>
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<p>Pressure drop versus Reynolds number, nanoparticle volume fraction, and channel turns number: (<b>a</b>) 4 turns, (<b>b</b>) 6 turns, and (<b>c</b>) 8 turns—results for each subfigure were obtained from 20 simulations.</p>
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<p>Numerator value changes of PEC versus the denominator value changes for nanoparticle concentrations of 2%, 4%, and 5%.</p>
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<p>PEC values (<b>a</b>) for <span class="html-italic">φ = 2%,</span> (<b>b</b>) for <span class="html-italic">φ = 4%</span>, and (<b>c</b>) for <span class="html-italic">φ = 5%</span>.</p>
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<p>PEC values (<b>a</b>) for <span class="html-italic">φ = 2%,</span> (<b>b</b>) for <span class="html-italic">φ = 4%</span>, and (<b>c</b>) for <span class="html-italic">φ = 5%</span>.</p>
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10 pages, 1421 KiB  
Article
Kinetic Study of the Ultrasound Effect on Acid Brown 83 Dye Degradation by Hydrogen Peroxide Oxidation Processes
by Gerardo León, Beatriz Miguel, Laura Manzanares, María Isabel Saavedra and María Amelia Guzmán
ChemEngineering 2021, 5(3), 52; https://doi.org/10.3390/chemengineering5030052 - 27 Aug 2021
Cited by 4 | Viewed by 2275
Abstract
The effect of ultrasound on the degradation of the dye Acid Brown 83 by seven different degradation methods (blank test using only ultrasound, hydrogen peroxide in a neutral medium, hydrogen peroxide in a sulfuric acid medium and hydrogen peroxide in a sulfuric acid [...] Read more.
The effect of ultrasound on the degradation of the dye Acid Brown 83 by seven different degradation methods (blank test using only ultrasound, hydrogen peroxide in a neutral medium, hydrogen peroxide in a sulfuric acid medium and hydrogen peroxide in a sulfuric acid medium in the presence of Fe(II), both without and with ultrasonic irradiation) is studied in this paper. The effectiveness of these methods is compared by analyzing the degradation percentages of the dye and its initial degradation rate. The application of ultrasound leads to a significant increase in the efficiency of any of the degradation method studied. Kinetic study of Acid Brown 83 degradation by the above-mentioned methods is carried out by using four kinetic models (first order, second order, Behnajady and pseudo-first order). The pseudo-first order model is the one that best fits the experimental data in all the used degradation methods. Although when the degradation is performed in the presence of Fe(II), the Behnajady model presents correlation coefficients slightly higher than those of the pseudo-first order, the maximum experimental conversions obtained fit much better in all cases to the pseudo first order model. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Chemical structure of Acid Brown 83.</p>
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<p>Schematic representation of the ultrasonic experimental setup.</p>
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<p>Variation of Acid Brown 83 concentration with time in different degradation methods.</p>
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<p>Degradation percentages and initial degradation rates of Acid Brown 83 by different degradation methods.</p>
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<p>Kinetics plots of the four studied kinetic models (<b>a</b>) first order; (<b>b</b>) second order; (<b>c</b>) Behnajady model; (<b>d</b>) pseudo-first order model.</p>
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<p>Relationship between experimental maximum conversions (obtained at sixty minutes) and models (pseudo-first order and Behnajady) maximum conversions.</p>
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10 pages, 1146 KiB  
Article
Promising Isotope Effect in Pd77Ag23 for Hydrogen Separation
by Francesco Trequattrini, Oriele Palumbo, Silvano Tosti, Alessia Santucci and Annalisa Paolone
ChemEngineering 2021, 5(3), 51; https://doi.org/10.3390/chemengineering5030051 - 27 Aug 2021
Cited by 4 | Viewed by 2260
Abstract
Pd–Ag alloys are largely used as hydrogen separation membranes and, as a consequence, the Pd–Ag–H system has been intensively studied. On the contrary, fewer information is available for the Pd–Ag–D system; thus, the aim of this work is to improve the knowledge of [...] Read more.
Pd–Ag alloys are largely used as hydrogen separation membranes and, as a consequence, the Pd–Ag–H system has been intensively studied. On the contrary, fewer information is available for the Pd–Ag–D system; thus, the aim of this work is to improve the knowledge of the isotope effect on the commercial Pd77Ag23 alloy, especially for temperature above 200 °C. In particular, deuterium absorption measurements are carried out in the Pd77Ag23 alloy in the temperature range between 79 and 400 °C and in the pressure range between 10−2 and 16 bar. In this exploited pressure (p) and composition (c) range, above 300 °C the pc isotherms display the typical shape of materials where only a solid solution of deuterium is present while at lower temperatures these curves seem to be better described by the coexistence of a solid solution and a deuteride in a large composition range. The obtained results are compared and discussed with the ones previously measured with the lightest hydrogen isotope. Such a comparison shows that the Pd77Ag23 alloy exhibits a clear inverse isotope effect, as the equilibrium pressure of the Pd–Ag–D system is higher than in Pd–Ag–H by a factor of ≈2 and the solubility of deuterium is about one half of that of hydrogen. In addition, the absorption measurements were used to assess the deuteration enthalpy that below 300 °C is ΔHdeut = 31.9 ± 0.3 kJ/mol, while for temperatures higher than 300 °C, ΔHdeut increases to 43 ± 1 kJ/mol. Additionally, in this case a comparison with the lighter isotope is given and both deuteration enthalpy values result lower than those reported for hydrogenation. The results described in this paper are of practical interest for applications operating above 200 °C, such as membranes or packing column, in which Pd77Ag23 has to interact with a gas stream containing both hydrogen isotopes. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Absorption pressure–composition isotherms measured at selected temperatures for Pd<sub>77</sub>Ag<sub>23</sub> with D<sub>2</sub> (<b>left</b>) and H<sub>2</sub> (<b>right</b>) gas.</p>
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<p>Van’t Hoff plot for Pd<sub>77</sub>Ag<sub>23</sub> at fixed D/M (the number of deuterium atoms per atom of metal) and best fit lines to calculate the hydrogenation enthalpy.</p>
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22 pages, 31794 KiB  
Article
Optimizing the Control System of Clinker Cooling: Process Modeling and Controller Tuning
by Dimitris Tsamatsoulis
ChemEngineering 2021, 5(3), 50; https://doi.org/10.3390/chemengineering5030050 - 19 Aug 2021
Cited by 7 | Viewed by 3603
Abstract
This paper aims to present efficient efforts to optimize the proportional-integral-differential (PID) controller of clinker cooling in grate coolers, which have a fixed grate and at least two moving ones. The process model contains three transfer functions between the speed of the moving [...] Read more.
This paper aims to present efficient efforts to optimize the proportional-integral-differential (PID) controller of clinker cooling in grate coolers, which have a fixed grate and at least two moving ones. The process model contains three transfer functions between the speed of the moving grate and the pressures of the static and moving grates. The developed software achieves the identification of the model parameters using industrial data and by implementing non-linear regression methods. The design of the PID controller follows a loop-shaping technique, imposing as a constraint the maximum sensitivity, Ms, of the open-loop transfer function and providing a set of PIDs that satisfy a range of Ms. A simulator determines the optimal PID sets among those calculated at the design step using the integral of absolute error (IAE) as a performance criterion. The combination of a robustness constraint with a performance criterion, Ms and IAE respectively, leads to an area of controllers with Ms belonging to the range of 1.2 to 1.35. The IAE is between 4.2% and 4.8%, depending on the set-point value. PID sets located near the middle of this area can be chosen and implemented in the cooler’s routine operation. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Simplified representation of (<b>a</b>) rotary kiln installation; (<b>b</b>) grate cooler.</p>
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<p>Data extraction and processing.</p>
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<p>Feedback control loop.</p>
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<p>Typical data sets extracted from the SQL database.</p>
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<p>Calculated vs. actual values for the models: (<b>a</b>) <span class="html-italic">N<sub>p</sub></span> = 6, <span class="html-italic">N<sub>g</sub></span> = 6; (<b>b</b>) <span class="html-italic">N<sub>p</sub></span> = 4, <span class="html-italic">N<sub>g</sub></span> = 4.</p>
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<p>Nyquist curve of the open-loop transfer function for <span class="html-italic">M<sub>s</sub></span> = 1.3 and <span class="html-italic">k<sub>D</sub></span> = 1.</p>
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<p>Function between (<b>a</b>) <span class="html-italic">k<sub>P</sub></span> and <span class="html-italic">M<sub>s</sub></span>, <span class="html-italic">k<sub>D</sub></span>; (<b>b</b>) <span class="html-italic">k<sub>I</sub></span> and <span class="html-italic">M<sub>s</sub></span>, <span class="html-italic">k<sub>D</sub></span>.</p>
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<p>Function between pressure of moving grate, grate speed, and pressure of static grate for (<b>a</b>) <span class="html-italic">M<sub>s</sub></span> = 1.1, <span class="html-italic">k<sub>D</sub></span> = 0; (<b>b</b>) <span class="html-italic">M<sub>s</sub></span> = 1.3, <span class="html-italic">k<sub>D</sub></span> = 0.5; and (<b>c</b>) <span class="html-italic">M<sub>s</sub></span> = 1.65, <span class="html-italic">k<sub>D</sub></span> = 0.</p>
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<p><span class="html-italic">IAE<sub>Aver</sub></span> as a function of <span class="html-italic">M<sub>s</sub></span>, <span class="html-italic">k<sub>D</sub></span> for <span class="html-italic">SP<sub>a</sub></span> = 400 mm H<sub>2</sub>O.</p>
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<p>Optimal PID controllers for <span class="html-italic">SP<sub>a</sub></span>: (<b>a</b>) 325 mm H<sub>2</sub>O; (<b>b</b>) 350 mm H<sub>2</sub>O; (<b>c</b>) 375 mm H<sub>2</sub>O; (<b>d</b>) 400 mm H<sub>2</sub>O; and (<b>e</b>) common area of all <span class="html-italic">SP<sub>a</sub></span>.</p>
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<p>IAE distributions for <span class="html-italic">SP<sub>a</sub></span>: (<b>a</b>) 400 mm H<sub>2</sub>O; (<b>b</b>) 375 mm H<sub>2</sub>O.</p>
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11 pages, 4052 KiB  
Article
In Silico Study of the Influence of Various Substrates on the Electronic Properties and Electrical Conductivity of Mono- and Bilayer Films of Armchair Single-Walled Carbon Nanotubes
by Michael M. Slepchenkov, Alexander A. Petrunin and Olga E. Glukhova
ChemEngineering 2021, 5(3), 48; https://doi.org/10.3390/chemengineering5030048 - 9 Aug 2021
Cited by 1 | Viewed by 1959
Abstract
We investigate electronic and electro-physical properties of mono- and bilayer armchair single-walled carbon nanotube (SWCNT) films located on substrates of different types, including substrates in the form of crystalline silicon dioxide (SiO2) films with P42/mnm and P3121 [...] Read more.
We investigate electronic and electro-physical properties of mono- and bilayer armchair single-walled carbon nanotube (SWCNT) films located on substrates of different types, including substrates in the form of crystalline silicon dioxide (SiO2) films with P42/mnm and P3121 space symmetry groups. The SWCNT films interact with substrate only by van der Waals forces. The densities of electronic states (DOS) and the electron transmission functions are calculated for SWCNT films with various substrates. The electrical conductivity of SWCNT films is calculated based on the electron transmission function. It is found that the substrate plays an important role in the formation of DOS of the SWCNT films, and the surface topology determines the degree and nature of the mutual influence of the nanotube and the substrate. It is shown that the substrate affects the electronic properties of monolayer films, changing the electrical resistance value from 2% to 17%. However, the substrate has practically no effect on the electrical conductivity and resistance of the bilayer film in both directions of current transfer. In this case, the values of the resistances of the bilayer film in both directions of current transfer approach the value of ~6.4 kΩ, which is the lowest for individual SWCNT. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Atomistic models of mono- and bilayer films: (<b>a</b>) fragments of mono- and bilayer films based on SWCNTs (5,5); (<b>b</b>) super-cells of monolayer films; (<b>c</b>) super-cell of bilayer film.</p>
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<p>DOS of monolayer films: (<b>a</b>) on the P4<sub>2</sub>/mnm substrate with the (100) surface; (<b>b</b>) on the P3<sub>1</sub>21 substrate with the surface (110).</p>
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<p>Electronic characteristics of a bilayer film on a P3<sub>1</sub>21 substrate: (<b>a</b>) DOS; (<b>b</b>) charge distribution over atoms.</p>
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<p>Integral transmission functions and 2D maps of transmission functions of monolayer films.</p>
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<p>Integral transmission functions and 2D maps of transmission functions of bilayer film.</p>
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14 pages, 2557 KiB  
Article
Production of Sustainable Biochemicals by Means of Esterification Reaction and Heterogeneous Acid Catalysts
by Rosa Vitiello, Francesco Taddeo, Vincenzo Russo, Rosa Turco, Antonio Buonerba, Alfonso Grassi, Martino Di Serio and Riccardo Tesser
ChemEngineering 2021, 5(3), 46; https://doi.org/10.3390/chemengineering5030046 - 7 Aug 2021
Cited by 8 | Viewed by 3530
Abstract
In recent years, the use of renewable raw materials for the production of chemicals has been the subject of different studies. In particular, the interest of the present study was the use of oleins, mixtures of free fatty acids (FFAs), and oleic acid [...] Read more.
In recent years, the use of renewable raw materials for the production of chemicals has been the subject of different studies. In particular, the interest of the present study was the use of oleins, mixtures of free fatty acids (FFAs), and oleic acid to produce bio-based components for lubricants formulations and the investigation of the performance of a styrene-divinylbenzene acid resin (sPSB-SA) in the esterification reaction of fatty acids. This resin has shown good activity as a heterogeneous catalyst and high stability at elevated temperatures (180 °C). It was tested in the esterification reaction of oleic acid with 1,3-propanediol and of oleic acid with glycerol. In particular, the esterification reactions were performed in a steel stirred batch reactor and a PBR loop reactor. Tests were conducted varying the reaction conditions, such as alcohol type, temperature, reaction time, and catalysts, both homogeneous and heterogeneous ones. From the obtained results, acid resin (both in reticulated and not-reticulated form) showed high activity in esterification reaction of oleic acid with 1,3-propanediol and of oleic acid with glycerol and good resistance to the deactivation; thus, they can be considered promising candidates for future applications in continuous devices. Viscosity tests were performed, underlining the good properties of the obtained products as lubricant bases. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Batch reactor configuration.</p>
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<p>PBR loop reactor configuration.</p>
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<p>(<b>a</b>) Oleic acid/1,3-propanediol blank tests, runs 1–4, (<b>b</b>) oleic acid/glycerol blank tests, runs 5–8.</p>
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<p>(<b>a</b>) Operative conditions: 1,3-propanediol/oleic acid = 1/2; catalyst amount = 1.8 × 10<sup>−3</sup> meqH<sup>+</sup>; <span class="html-italic">T</span> = 120 °C; time = 180 min. (<b>b</b>) Operative conditions: glycerol/oleic acid = 1/3; catalyst amount = 1.8 × 10<sup>−3</sup> meqH<sup>+</sup>; <span class="html-italic">T</span> = 120 °C; time = 180 min.</p>
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<p>Operative conditions: molar ratio 1,3-propanediol/oleic acid = 1/2; catalyst amount = 1.8 × 10<sup>−3</sup> meqH<sup>+</sup>; <span class="html-italic">T</span> = 180 °C; time = 360 min.</p>
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<p>Operative conditions: molar ratio glycerol/oleic acid = 1/3; catalyst amount = 1.8 × 10<sup>−3</sup> meq H<sup>+</sup>; <span class="html-italic">T</span> = 180 °C; time = 360 min.</p>
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<p>Operative conditions: oleic acid/1,3-propanediol = 1 considering the functional groups; catalyst amount = 1.8 × 10<sup>−3</sup> meqH<sup>+</sup>; <span class="html-italic">T</span> = 180 °C; time = 180 min.</p>
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9 pages, 664 KiB  
Communication
2D Model of Transfer Processes for Water Boiling Flow in Microchannel
by Valery A. Danilov, Christian Hofmann and Gunther Kolb
ChemEngineering 2021, 5(3), 42; https://doi.org/10.3390/chemengineering5030042 - 2 Aug 2021
Cited by 2 | Viewed by 2516
Abstract
The modeling of transfer processes is a step in the generalization and interpretation of experimental data on heat transfer. The developed two-dimensional model is based on a homogeneous mixture model for boiling water flow in a microchannel with a new evaporation submodel. The [...] Read more.
The modeling of transfer processes is a step in the generalization and interpretation of experimental data on heat transfer. The developed two-dimensional model is based on a homogeneous mixture model for boiling water flow in a microchannel with a new evaporation submodel. The outcome of the simulation is the distribution of velocity, void fraction and temperature profiles in the microchannel. The predicted temperature profile is consistent with the experimental literature data. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Two-dimensional model of the transfer processes in the microchannel. D<sub>1</sub>—channel domain. D<sub>2</sub>—solid domain. Inlet section z = 0. Outlet section z = L. Boundary conditions are listed in <a href="#ChemEngineering-05-00042-t002" class="html-table">Table 2</a>.</p>
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<p>Longitudinal (<b>a</b>) and transverse (<b>b</b>) temperature profiles predicted by a 2D model of transfer processes during heat transfer under boiling conditions in the microchannel. <span class="html-italic">δ</span><span class="html-italic">T</span> = <span class="html-italic">T</span> − <span class="html-italic">T<sub>sat</sub></span>. D<sub>1</sub>–channel domain. D<sub>2</sub>–solid domain. Longitudinal section y = d<sub>h</sub>/2. Transverse section z = L/2. Points correspond to the experimentally measured temperature reported by Díaz and Schmidt [<a href="#B25-ChemEngineering-05-00042" class="html-bibr">25</a>].</p>
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<p>Transverse velocity (<b>a</b>) and void fraction (<b>b</b>) profiles in the microchannel. Boiling heat transfer in the microchannel. Transverse section z = L/2.</p>
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<p>Transverse specific surface area (<b>a</b>) and mixture density (<b>b</b>) profiles in the microchannel. Boiling heat transfer in the microchannel. Transverse section z = L/2.</p>
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9 pages, 1524 KiB  
Article
MoS2-Cysteine Nanofiltration Membrane for Lead Removal
by Jaewon Jang, Sang-Soo Chee, Yesol Kang and Suhun Kim
ChemEngineering 2021, 5(3), 41; https://doi.org/10.3390/chemengineering5030041 - 1 Aug 2021
Cited by 9 | Viewed by 3399
Abstract
To overcome the limitations of polymers, such as the trade-off relationship between water permeance and solute rejection, as well as the difficulty of functionalization, research on nanomaterials is being actively conducted. One of the representative nanomaterials is graphene, which has a two-dimensional shape [...] Read more.
To overcome the limitations of polymers, such as the trade-off relationship between water permeance and solute rejection, as well as the difficulty of functionalization, research on nanomaterials is being actively conducted. One of the representative nanomaterials is graphene, which has a two-dimensional shape and chemical tunability. Graphene is usually used in the form of graphene oxide in the water treatment field because it has advantages such as high water permeance and functionality on its surface. However, there is a problem in that it lacks physical stability under water-contacted conditions due to the high hydrophilicity. To overcome this problem, MoS2, which has a similar shape to graphene and hydrophobicity, can be a new option. In this study, bulk MoS2 was dispersed in a mixed solvent of acetone/isopropyl alcohol, and MoS2 nanosheet was obtained by applying sonic energy to exfoliate. In addition, Cysteine was functionalized in MoS2 with a mild reaction. When the nanofiltration (NF) performance of the membrane was compared under various conditions, the composite membrane incorporated by Cysteine 10 wt % (vs. MoS2) showed the best NF performances. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>(<b>a</b>) XRD patterns and (<b>b</b>) UV-Vis spectra of the bulk MoS<sub>2</sub> and exfoliated MoS<sub>2</sub> nanosheet.</p>
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<p>FTIR spectra of the MoS<sub>2</sub> nanosheet, Cysteine, and Cysteine-functionalized MoS<sub>2</sub>.</p>
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<p>Mo 3d XPS core-level spectra of the (<b>a</b>) MoS<sub>2</sub> nanosheet and (<b>b</b>) MoS<sub>2</sub>-Cys nanosheet. S 2p XPS core-level spectra of the (<b>c</b>) MoS<sub>2</sub> nanosheet and (<b>d</b>) MoS<sub>2</sub>-Cys nanosheet.</p>
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<p>Water permeance and Pb<sup>2+</sup> ion rejection of the MoS<sub>2</sub> nanosheet membrane according to (<b>a</b>) MoS<sub>2</sub> layer thickness and (<b>b</b>) applied pressure (the error bars were calculated based on the repeated experiments).</p>
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<p>NF performance MoS<sub>2</sub>-based membrane according to Cysteine concentrations.</p>
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<p>A procedure of the MoS<sub>2</sub> exfoliation and Cys-functionalized MoS<sub>2</sub> synthesis.</p>
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11 pages, 1323 KiB  
Article
Optimization of Exopolysaccharide (EPS) Production by Rhodotorula mucilaginosa sp. GUMS16
by Oseweuba Valentine Okoro, Amir Reza Gholipour, Faezeh Sedighi, Amin Shavandi and Masoud Hamidi
ChemEngineering 2021, 5(3), 39; https://doi.org/10.3390/chemengineering5030039 - 21 Jul 2021
Cited by 14 | Viewed by 4288
Abstract
Exopolysaccharides (EPSs) are important biopolymers with diverse applications such as gelling compounds in food and cosmetic industries and as bio-flocculants in pollution remediation and bioplastics production. This research focuses on enhancing crude EPS production from Rhodotorula mucilaginosa sp. GUMS16 using the central composite [...] Read more.
Exopolysaccharides (EPSs) are important biopolymers with diverse applications such as gelling compounds in food and cosmetic industries and as bio-flocculants in pollution remediation and bioplastics production. This research focuses on enhancing crude EPS production from Rhodotorula mucilaginosa sp. GUMS16 using the central composite design method in which five levels of process variables of sucrose, pH, and ammonium sulfate were investigated with sucrose and ammonium sulfate serving as carbon and nitrogen sources during microbial incubation. The optimal crude EPS production of 13.48 g/100 mL was achieved at 1 g/100 mL of sucrose concentration, 14.73 g/100 mL of ammonium sulfate at pH 5. Variations in ammonium sulfate concentrations (1.27–14.73 g/100 mL) presented the most significant effects on the crude EPS yield, while changes in sucrose concentrations (1–5 g/100 mL) constituted the least important process variable influencing the EPS yield. The Rhodotorula mucilaginosa sp. GUMS16 may have the potential for large-scale production of EPS for food and biomedical applications. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Exopolysaccharide extraction and recovery process from <span class="html-italic">Rhodotorula mucilaginosa</span> sp. GUMS16.</p>
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<p>Surface plot highlighting the effect of process variables on the crude EPS yield (EPS, g/100 mL). (<b>a</b>) denotes the 3D surface plot showing variations in crude EPS yield as pH and sucrose concentration changes at constant ammonium sulfate concentration of 8 g/ 100 mL. (<b>b</b>) denotes the 3D surface plot showing variations in crude EPS yield as sucrose concentration and ammonium sulfate concentration changes at a constant pH of 4. (<b>c</b>) denotes the 3D surface plot showing variations in crude EPS yield as pH and ammonium sulfate concentration changes at a constant sucrose concentration of 3 g/ 100 mL.</p>
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27 pages, 7543 KiB  
Article
Intraparticle Model for Non-Uniform Active Phase Distribution Catalysts in a Batch Reactor
by Emiliano Salucci, Vincenzo Russo, Tapio Salmi, Martino Di Serio and Riccardo Tesser
ChemEngineering 2021, 5(3), 38; https://doi.org/10.3390/chemengineering5030038 - 19 Jul 2021
Cited by 1 | Viewed by 2936
Abstract
The study and the understanding of the importance of the morphological properties of heterogeneous catalysts can pave the way for important improvements in the performance of catalytic systems. Non-uniform active phase distribution catalysts are normally adopted for consecutive reactions to improve the selectivity [...] Read more.
The study and the understanding of the importance of the morphological properties of heterogeneous catalysts can pave the way for important improvements in the performance of catalytic systems. Non-uniform active phase distribution catalysts are normally adopted for consecutive reactions to improve the selectivity to the desired intermediate product. Attributes on which minor attention is paid, such as the distribution and thickness of the active phase, can be decisive in the final rationale of the catalyst synthesis strategy. Starting from a previous work, where a single non-uniform active phase model for catalyst particles was developed, a key step to control the entire system is to include the bulk-phase equations and related transport phenomena. For this purpose, this work proposes a modeling approach of a biphasic reactive system in a batch reactor in the presence of three different kinds of catalytic particles (egg shell, egg white, and egg yolk) whose distinction lies in the localization of the active zone. The reactive network consists of a couple of reactions in series, which take place exclusively on the solid surface, and the intermediate component is the main product of interest. To reveal the influence related to the type of catalyst, an extensive parametric study was conducted, varying several structural coefficients to highlight the changes in the intraparticle and bulk concentration profiles of the different chemical species. The main results can be considered of wide interest for the chemical reaction engineering community, as it was demonstrated that mass and heat transfer limitations affect the catalyst performance. For the chosen system, the egg shell catalyst normally led to better catalytic performances. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Intraparticle profiles for ES catalyst. Calculated profiles for: <span class="html-italic">cA</span> (<b>a</b>), <span class="html-italic">cB</span> (<b>b</b>), <span class="html-italic">cC</span> (<b>c</b>) and <span class="html-italic">Ts</span> (<b>d</b>). Color-bar of each plot is located on the right-hand-side.</p>
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<p>Intraparticle profiles for ES catalyst. Calculated profiles for: <span class="html-italic">cA</span> (<b>a</b>), <span class="html-italic">cB</span> (<b>b</b>), <span class="html-italic">cC</span> (<b>c</b>) and <span class="html-italic">Ts</span> (<b>d</b>). Color-bar of each plot is located on the right-hand-side.</p>
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<p>Intraparticle profiles for EW catalyst. Calculated profiles for: <span class="html-italic">cA</span> (<b>a</b>)<span class="html-italic">, cB</span> (<b>b</b>)<span class="html-italic">, cC</span> (<b>c</b>) and <span class="html-italic">Ts</span> (<b>d</b>). Color-bar of each plot is located on the right-hand-side.</p>
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<p>Intraparticle profiles for EY catalyst. Calculated profiles for: <span class="html-italic">cA</span> (<b>a</b>), <span class="html-italic">cB</span> (<b>b</b>), <span class="html-italic">cC</span> (<b>c</b>), and <span class="html-italic">Ts</span> (<b>d</b>). Color-bar of each plot is located on the right-hand-side.</p>
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<p>(<b>a</b>) Bulk profiles over time for ES, EW, and EY. Calculated profiles for: <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profiles over time for EY, EW, and EY. Calculated profiles for: <span class="html-italic">T<sub>l</sub>.</span> (<b>c</b>) Catalytic effectiveness factor profiles over time for ES, EW, and EY.</p>
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<p>ES Δ<span class="html-italic">rH<sub>1</sub></span> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>EW Δ<span class="html-italic">rH<sub>1</sub></span> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>EY Δ<span class="html-italic">rH</span><sub>1</sub> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>ES Δ<span class="html-italic">rH</span><sub>2</sub> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>EW Δ<span class="html-italic">rH</span><sub>2</sub> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>EY Δ<span class="html-italic">rH</span><sub>2</sub> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>ES <span class="html-italic">k<sub>Cref</sub></span><sub>1</sub> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>EW <span class="html-italic">k<sub>Cref</sub></span><sub>1</sub> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>EY <span class="html-italic">k<sub>Cref</sub></span><sub>1</sub> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>ES <span class="html-italic">k<sub>Cref</sub></span><sub>2</sub> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>EW <span class="html-italic">k<sub>Cref</sub></span><sub>2</sub> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>EY <span class="html-italic">k<sub>Cref</sub></span><sub>2</sub> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>ES <span class="html-italic">D<sub>eff,A</sub></span> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>EW <span class="html-italic">D<sub>eff,A</sub></span> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>EY <span class="html-italic">D<sub>eff,A</sub></span> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>ES <span class="html-italic">R<sub>P</sub></span> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>EW <span class="html-italic">R<sub>P</sub></span> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>EY <span class="html-italic">R<sub>P</sub></span> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>ES <span class="html-italic">ρ<sub>P</sub></span> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>EW <span class="html-italic">ρ<sub>P</sub></span> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>EY <span class="html-italic">ρ<sub>P</sub></span> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>ES <span class="html-italic">c<sub>A</sub></span><sub>0,l</sub> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>EW <span class="html-italic">c<sub>A</sub></span><sub>0,l</sub> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>EY <span class="html-italic">c<sub>A</sub></span><sub>0,l</sub> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>ES <span class="html-italic">k<sub>m</sub></span> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>EW <span class="html-italic">k<sub>m</sub></span> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>EY <span class="html-italic">k<sub>m</sub></span> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>ES <span class="html-italic">UA</span> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>EW <span class="html-italic">UA</span> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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<p>EY <span class="html-italic">UA</span> investigation. (<b>a</b>) Bulk profiles over time of <span class="html-italic">X<sub>A</sub></span> and <span class="html-italic">S<sub>B</sub></span>. (<b>b</b>) Bulk profile of <span class="html-italic">T<sub>l</sub></span> over time.</p>
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Review

Jump to: Research

48 pages, 21855 KiB  
Review
Arsenic in Water: Understanding the Chemistry, Health Implications, Quantification and Removal Strategies
by Muhammad Murtaza Chaudhary, Saqib Hussain, Chenyu Du, Barbara R. Conway and Muhammad Usman Ghori
ChemEngineering 2024, 8(4), 78; https://doi.org/10.3390/chemengineering8040078 - 1 Aug 2024
Viewed by 1087
Abstract
Arsenic, the 20th most common element in Earth’s crust and historically regarded as the King of Poisons, occurs naturally in two oxidation states, Arsenate (V) and Arsenite (III), and is prevalent worldwide through natural and anthropogenic means. The cations of the metalloid exhibit [...] Read more.
Arsenic, the 20th most common element in Earth’s crust and historically regarded as the King of Poisons, occurs naturally in two oxidation states, Arsenate (V) and Arsenite (III), and is prevalent worldwide through natural and anthropogenic means. The cations of the metalloid exhibit unique chemical behaviour in water and are found to be components of approximately 245 natural minerals, making its occurrence in drinking water a compelling challenge, especially in groundwater. This comprehensive review collates information regarding the prevalence of arsenic contamination in water worldwide and its impact on human health, its chemical behaviour, methods for detection and quantification, and treatment strategies. A comprehensive search was conducted, and the selection of eligible studies was carried out using the PRISMA (the preferred reporting items for systematic reviews and meta-analyses) guidelines. Essential characteristics of eligible research studies were extracted based on geographical areas, origins, concentration levels and the magnitude of populations vulnerable to arsenic contamination in groundwater sources. Arsenic contamination of water affects over 100 countries including Canada, the United States, Pakistan, China, India, Brazil and Bangladesh, where hydrogeological conditions favour prevalence and groundwater is the primary water source for food preparation, irrigation of food crops and drinking water. This leads to human exposure through absorption, ingestion and inhalation, causing numerous health disorders affecting nearly all systems within the human body, with acute and chronic toxicity including cancers. The presence of arsenic in water poses a considerable challenge to humanity, prompting scientists to devise diverse mitigation approaches categorized as (a) oxidation processes, (b) precipitation methods, (c) membrane technologies, (d) adsorption and ion exchange methods, and (e) social interventions. This comprehensive review is expected to be a valuable source for professionals in the water industry, public management, and policymaking, aiding their ongoing and future research and development efforts. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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Figure 1
<p>Chemical mapped risk assessment of arsenic in drinking water/global probability map of arsenic contamination under different hydrogeological conditions in groundwater, (<b>a</b>) reducing groundwater conditions, and (<b>b</b>) high pH/oxidizing conditions, adapted with permission from [<a href="#B14-ChemEngineering-08-00078" class="html-bibr">14</a>], publisher ACS Publications.</p>
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<p>Eh–pH diagram of [As]–H<sub>2</sub>O at 1 ATM pressure and 25 °C temperature, adapted with permission from [<a href="#B9-ChemEngineering-08-00078" class="html-bibr">9</a>], published by WHO.</p>
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<p>Distribution of speciation of arsenite, arsenate and organic arsenical in NaCl as a function of pH under experimental conditions (temperature: 25 °C, pressure: 1 ATM). Reproduced with permission from [<a href="#B10-ChemEngineering-08-00078" class="html-bibr">10</a>], publisher Elsevier B.V.</p>
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<p>The figure depicts the human body systems affected by health implications from As absorption and accumulation.</p>
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<p>Chronic and acute [As]-poisoning-implicated diseases, adapted with permission from [<a href="#B101-ChemEngineering-08-00078" class="html-bibr">101</a>], publisher Springer-Verlag GmbH Germany.</p>
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<p>Overview of As removal strategies from water.</p>
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<p>Illustration describing the coagulation, flocculation and sedimentation of As in water, adapted with permission from [<a href="#B206-ChemEngineering-08-00078" class="html-bibr">206</a>], publisher ACS Publications.</p>
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<p>Pore size classification of different membrane technologies with characteristic dimensions of various foulants, adapted with permission from [<a href="#B233-ChemEngineering-08-00078" class="html-bibr">233</a>], publisher MDPI Publications.</p>
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<p>Schematic describing the adsorption process, adapted with permission from [<a href="#B253-ChemEngineering-08-00078" class="html-bibr">253</a>], publisher MDPI Publications.</p>
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<p>Classification of sorbents adapted with permission from [<a href="#B269-ChemEngineering-08-00078" class="html-bibr">269</a>], publisher MDPI. ZVI = zero valent ion; LDH = layered double hydroxides and GFH = granular ferric hydroxide.</p>
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24 pages, 6981 KiB  
Review
Modelling of Fuel Cells and Related Energy Conversion Systems
by Ilenia Rossetti
ChemEngineering 2022, 6(3), 32; https://doi.org/10.3390/chemengineering6030032 - 20 Apr 2022
Cited by 10 | Viewed by 3543
Abstract
Heat and power cogeneration plants based on fuel cells are interesting systems for energy- conversion at low environmental impact. Various fuel cells have been proposed, of which proton-exchange membrane fuel cells (PEMFC) and solid oxide fuel cells (SOFC) are the most frequently used. [...] Read more.
Heat and power cogeneration plants based on fuel cells are interesting systems for energy- conversion at low environmental impact. Various fuel cells have been proposed, of which proton-exchange membrane fuel cells (PEMFC) and solid oxide fuel cells (SOFC) are the most frequently used. However, experimental testing rigs are expensive, and the development of commercial systems is time consuming if based on fully experimental activities. Furthermore, tight control of the operation of fuel cells is compulsory to avoid damage, and such control must be based on accurate models, able to predict cell behaviour and prevent stresses and shutdown. Additionally, when used for mobile applications, intrinsically dynamic operation is needed. Some selected examples of steady-state, dynamic and fluid-dynamic modelling of different types of fuel cells are here proposed, mainly dealing with PEMFC and SOFC types. The general ideas behind the thermodynamic, kinetic and transport description are discussed, with some examples of models derived for single cells, stacks and integrated power cogeneration units. This review can be considered an introductory picture of the modelling methods for these devices, to underline the different approaches and the key aspects to be taken into account. Examples of different scales and multi-scale modelling are also provided. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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Figure 1
<p>Modelling items in SOFCs. Reproduced from [<a href="#B5-ChemEngineering-06-00032" class="html-bibr">5</a>] under the Creative Commons Licence.</p>
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<p>Time, frequency and length scales for the phenomena underpinning SOFC modelling.</p>
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<p>Equivalent circuit of the simulated PEMFC, (<b>a</b>) simplified, (<b>b</b>) detailed. Reproduced from [<a href="#B19-ChemEngineering-06-00032" class="html-bibr">19</a>] under the Creative Commons Licence.</p>
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<p>Equivalent circuit of the simulated PEMFC, (<b>a</b>) simplified, (<b>b</b>) detailed. Reproduced from [<a href="#B19-ChemEngineering-06-00032" class="html-bibr">19</a>] under the Creative Commons Licence.</p>
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<p>Profiles of voltage (<b>a</b>) and O<sub>2</sub> utilisation (<b>b</b>) vs. time. Reproduced from [<a href="#B19-ChemEngineering-06-00032" class="html-bibr">19</a>] under the Creative Commons Licence.</p>
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<p>(<b>a</b>) PEMFC scheme and (<b>b</b>) blocks for connection to the grid. Reproduced from [<a href="#B19-ChemEngineering-06-00032" class="html-bibr">19</a>] under the Creative Commons Licence.</p>
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<p>(<b>a</b>) PEMFC scheme and (<b>b</b>) blocks for connection to the grid. Reproduced from [<a href="#B19-ChemEngineering-06-00032" class="html-bibr">19</a>] under the Creative Commons Licence.</p>
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<p>Polarisation curve of a PEMFC. Reproduced from [<a href="#B20-ChemEngineering-06-00032" class="html-bibr">20</a>] under the Creative Commons Licence.</p>
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<p>Synopsis of the model of a PEMFC. Reprinted with permission from ref. [<a href="#B31-ChemEngineering-06-00032" class="html-bibr">31</a>]. Copyright 2021, Elsevier.</p>
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<p>Conceptual sketch of fault detection and isolation (FDI). (<b>a</b>) classical model-based FDI based on parity equations with output errors, residual binarisation, and fault signature matrix; (<b>b</b>) data-driven FDI; (<b>c</b>) hybrid approach. Reproduced from [<a href="#B39-ChemEngineering-06-00032" class="html-bibr">39</a>] under the Creative Commons Licence.</p>
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<p>Block diagram for the development of failure models. Reprinted with permission from ref. [<a href="#B43-ChemEngineering-06-00032" class="html-bibr">43</a>]. Copyright 2021, Elsevier.</p>
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<p>Principal current density-voltage relationship for a SOFC, including deactivation. Reprinted with permission from ref. [<a href="#B43-ChemEngineering-06-00032" class="html-bibr">43</a>]. Copyright 2021, Elsevier.</p>
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<p>Failure probability function in comparison to that from [<a href="#B44-ChemEngineering-06-00032" class="html-bibr">44</a>]. Reprinted with permission from ref. [<a href="#B43-ChemEngineering-06-00032" class="html-bibr">43</a>]. Copyright 2021, Elsevier.</p>
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<p>(<b>a</b>) current density and (<b>b</b>) power density variation with temperature and flowrates for a button SOFC (0.6 V). Reprinted with permission from ref. [<a href="#B54-ChemEngineering-06-00032" class="html-bibr">54</a>]. Copyright 2021, Elsevier.</p>
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22 pages, 1781 KiB  
Review
Key Points of Advanced Oxidation Processes (AOPs) for Wastewater, Organic Pollutants and Pharmaceutical Waste Treatment: A Mini Review
by Pavlos K. Pandis, Charalampia Kalogirou, Eirini Kanellou, Christos Vaitsis, Maria G. Savvidou, Georgia Sourkouni, Antonis A. Zorpas and Christos Argirusis
ChemEngineering 2022, 6(1), 8; https://doi.org/10.3390/chemengineering6010008 - 18 Jan 2022
Cited by 153 | Viewed by 17067
Abstract
Advanced oxidation procedures (AOPs) refer to a variety of technical procedures that produce OH radicals to sufficiently oxidize wastewater, organic pollutant streams, and toxic effluents from industrial, hospital, pharmaceutical and municipal wastes. Through the implementation of such procedures, the (post) treatment of such [...] Read more.
Advanced oxidation procedures (AOPs) refer to a variety of technical procedures that produce OH radicals to sufficiently oxidize wastewater, organic pollutant streams, and toxic effluents from industrial, hospital, pharmaceutical and municipal wastes. Through the implementation of such procedures, the (post) treatment of such waste effluents leads to products that are more susceptible to bioremediation, are less toxic and possess less pollutant load. The basic mechanism produces free OH radicals and other reactive species such as superoxide anions, hydrogen peroxide, etc. A basic classification of AOPs is presented in this short review, analyzing the processes of UV/H2O2, Fenton and photo-Fenton, ozone-based (O3) processes, photocatalysis and sonolysis from chemical and equipment points of view to clarify the nature of the reactive species in each AOP and their advantages. Finally, combined AOP implementations are favored through the literature as an efficient solution in addressing the issue of global environmental waste management. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Carbamazepine degradation under UV light upon different H<sub>2</sub>O<sub>2</sub> concentrations (adapted from [<a href="#B42-ChemEngineering-06-00008" class="html-bibr">42</a>]).</p>
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<p>pH effect on methyl orange degradation in the Fenton process (adapted and edited from [<a href="#B57-ChemEngineering-06-00008" class="html-bibr">57</a>,<a href="#B58-ChemEngineering-06-00008" class="html-bibr">58</a>]).</p>
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<p>Electro-Fenton schematic (adapted from [<a href="#B63-ChemEngineering-06-00008" class="html-bibr">63</a>]).</p>
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<p>Indicative mechanism of photocatalysis on TiO<sub>2</sub> (adapted from [<a href="#B78-ChemEngineering-06-00008" class="html-bibr">78</a>]).</p>
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<p>(<b>a</b>) Gas–liquid interface before bubble collapse, and (<b>b</b>) sonolysis of butyric acid after bubble collapse. P, products or intermediates (adapted from [<a href="#B110-ChemEngineering-06-00008" class="html-bibr">110</a>]).</p>
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<p>Overview of the mechanisms in AOP.</p>
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22 pages, 3976 KiB  
Review
An Overview of Natural Polymers as Reinforcing Agents for 3D Printing
by Beatrice Sabbatini, Alessandra Cambriani, Marco Cespi, Giovanni Filippo Palmieri, Diego Romano Perinelli and Giulia Bonacucina
ChemEngineering 2021, 5(4), 78; https://doi.org/10.3390/chemengineering5040078 - 8 Nov 2021
Cited by 23 | Viewed by 6898
Abstract
Three-dimensional (3D) printing, or additive manufacturing, is a group of innovative technologies that are increasingly employed for the production of 3D objects in different fields, including pharmaceutics, engineering, agri-food and medicines. The most processed materials by 3D printing techniques (e.g., fused deposition modelling, [...] Read more.
Three-dimensional (3D) printing, or additive manufacturing, is a group of innovative technologies that are increasingly employed for the production of 3D objects in different fields, including pharmaceutics, engineering, agri-food and medicines. The most processed materials by 3D printing techniques (e.g., fused deposition modelling, FDM; selective laser sintering, SLS; stereolithography, SLA) are polymeric materials since they offer chemical resistance, are low cost and have easy processability. However, one main drawback of using these materials alone (e.g., polylactic acid, PLA) in the manufacturing process is related to the poor mechanical and tensile properties of the final product. To overcome these limitations, fillers can be added to the polymeric matrix during the manufacturing to act as reinforcing agents. These include inorganic or organic materials such as glass, carbon fibers, silicon, ceramic or metals. One emerging approach is the employment of natural polymers (polysaccharides and proteins) as reinforcing agents, which are extracted from plants or obtained from biomasses or agricultural/industrial wastes. The advantages of using these natural materials as fillers for 3D printing are related to their availability together with the possibility of producing printed specimens with a smaller environmental impact and higher biodegradability. Therefore, they represent a “green option” for 3D printing processing, and many studies have been published in the last year to evaluate their ability to improve the mechanical properties of 3D printed objects. The present review provides an overview of the recent literature regarding natural polymers as reinforcing agents for 3D printing. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Graphical schemes of 3D printing techniques. (<b>A</b>). Fused deposition method (FDM) (<b>B</b>). Stereolithography (SLA) (<b>C</b>). Digital light processing (DLP) (<b>D</b>). Selective laser sintering (SLS). Reprinted with permission from ref. [<a href="#B2-ChemEngineering-05-00078" class="html-bibr">2</a>]. Copyright 2018 Springer Nature.</p>
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<p>Scheme reporting the classification of the natural polymers discussed in the review as reinforcing agents for 3D printing.</p>
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<p>Pectin reinforced with the carboxylated cellulose nanofibrils for the production of bio-based inks for 3D printing of scaffolds. Reprinted with permission from ref. [<a href="#B33-ChemEngineering-05-00078" class="html-bibr">33</a>]. Copyright 2019 Elsevier.</p>
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<p>Preparation of 3D printed scaffolds using fused deposition method (FDM) from galactomannan/PLA composite filaments. Reprinted with permission from ref. [<a href="#B43-ChemEngineering-05-00078" class="html-bibr">43</a>]. Copyright 2018 Elsevier.</p>
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<p>Schematic illustration of preparation and characterization of sustainable composites produced from PLA and flax/jute fibers. Reprinted from [<a href="#B79-ChemEngineering-05-00078" class="html-bibr">79</a>].</p>
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<p>Three-dimensional printing of PLA composite scaffolds reinforced with keratin and chitosan. Reprinted with permission from ref. [<a href="#B108-ChemEngineering-05-00078" class="html-bibr">108</a>]. Copyright 2020 Elsevier.</p>
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26 pages, 11093 KiB  
Review
Cleavage via Selective Catalytic Oxidation of Lignin or Lignin Model Compounds into Functional Chemicals
by Xiu-Zhi Wei, Jianguo Liu and Longlong Ma
ChemEngineering 2021, 5(4), 74; https://doi.org/10.3390/chemengineering5040074 - 1 Nov 2021
Cited by 2 | Viewed by 4464
Abstract
Lignin, a complex aromatic polymer with different types of methoxylated phenylpropanoid connections, enables the sustainable supply of value-added chemicals and biofuels through its use as a feedstock. Despite the development of numerous methodologies that upgrade lignin to high-value chemicals such as drugs and [...] Read more.
Lignin, a complex aromatic polymer with different types of methoxylated phenylpropanoid connections, enables the sustainable supply of value-added chemicals and biofuels through its use as a feedstock. Despite the development of numerous methodologies that upgrade lignin to high-value chemicals such as drugs and organic synthesis intermediates, the variety of valuable products obtained from lignin is still very limited, mainly delivering hydrocarbons and oxygenates. Using selective oxidation and activation cleavage of lignin, we can obtain value-added aromatics, including phenols, aldehydes, ketones, and carboxylic acid. However, biorefineries will demand a broad spectrum of fine chemicals in the future, not just simple chemicals like aldehydes and ketones containing simple C = O groups. In particular, most n-containing aromatics, which have found important applications in materials science, agro-chemistry, and medicinal chemistry, such as amide, aniline, and nitrogen heterocyclic compounds, are obtained through n-containing reagents mediating the oxidation cleavage in lignin. This tutorial review provides updates on recent advances in different classes of chemicals from the catalytic oxidation system in lignin depolymerization, which also introduces those functionalized products through a conventional synthesis method. A comparison with traditional synthetic strategies reveals the feasibility of the lignin model and real lignin utilization. Promising applications of functionalized compounds in synthetic transformation, drugs, dyes, and textiles are also discussed. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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Graphical abstract

Graphical abstract
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<p>General classification of nitrogen-containing functionalized compounds in the organic chemistry field.</p>
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<p>Different substituted amines transform oxidized lignin models to various types of aromatic amides.</p>
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<p>Different <span class="html-italic">n</span>-containing reagents transforming oxidized lignin models to various types of <span class="html-italic">n</span>-substituted aromatics, including nitrogen heterocyclic compounds and nitriles.</p>
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<p>Selective oxidation and <span class="html-italic">n</span>-reagent-participating activation cleavage of lignin linkages to yield anilines.</p>
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<p>The general methods of synthesizing esters in organic synthesis.</p>
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<p>The general route from lignin oxidation (green) to DEM production (red).</p>
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<p>Oxidation of phenolic lignin model compounds with different catalysts to produce corresponding quinones and other compounds.</p>
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<p>Oxidation of lignin or lignin model compounds by different oxidative strategies to obtain corresponding aromatic acids and other compounds.</p>
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<p>Oxidation of lignin or lignin model compounds by different oxidative strategies to obtain corresponding non-aromatic acids.</p>
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<p>Distribution and composition of main products after catalytic oxidation simultaneous to/followed by depolymerization of kinds of lignins.</p>
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<p>Distribution and composition of main products after catalytic oxidation simultaneous to/followed by depolymerization of kinds of lignins.</p>
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<p>Oxidation of β-O-4 lignin model compounds by pre-oxidation followed by redox strategies to obtain corresponding phenols and ketones.</p>
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30 pages, 12221 KiB  
Review
Recent Advances on Quinazoline Derivatives: A Potential Bioactive Scaffold in Medicinal Chemistry
by Ram Karan, Pooja Agarwal, Mukty Sinha and Neelima Mahato
ChemEngineering 2021, 5(4), 73; https://doi.org/10.3390/chemengineering5040073 - 26 Oct 2021
Cited by 55 | Viewed by 7257
Abstract
This paper intended to explore and discover recent therapeutic agents in the area of medicinal chemistry for the treatment of various diseases. Heterocyclic compounds represent an important group of biologically active compounds. In the last few years, heterocyclic compounds having quinazoline moiety have [...] Read more.
This paper intended to explore and discover recent therapeutic agents in the area of medicinal chemistry for the treatment of various diseases. Heterocyclic compounds represent an important group of biologically active compounds. In the last few years, heterocyclic compounds having quinazoline moiety have drawn immense attention owing to their significant biological activities. A diverse range of molecules having quinazoline moiety are reported to show a broad range of medicinal activities like antifungal, antiviral, antidiabetic, anticancer, anti-inflammatory, antibacterial, antioxidant and other activities. This study accelerates the designing process to generate a greater number of biologically active candidates. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>2-quinazolinone (<b>a</b>) and 4-quinazolinone (<b>b</b>).</p>
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<p>Synthesis of 3,4 dihydro-4-oxaquinazoline.</p>
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<p>Synthesis of 2-methyl-3-phenylquinazolin-4(3H)-one.</p>
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<p>Synthesis of quinazolin-4(3H)-one.</p>
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<p>Synthesis of 2-methyl-5-nitroquinazolin-4(3H)-one.</p>
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<p>Synthesis of quinazoline-2,4(1H,3H)-dione.</p>
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<p>Synthesis of 2-phenylquinazolin-4(3H)-one.</p>
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24 pages, 3532 KiB  
Review
Graphene Oxide Synthesis, Properties and Characterization Techniques: A Comprehensive Review
by Dimitrios G. Trikkaliotis, Achilleas K. Christoforidis, Athanasios C. Mitropoulos and George Z. Kyzas
ChemEngineering 2021, 5(3), 64; https://doi.org/10.3390/chemengineering5030064 - 17 Sep 2021
Cited by 38 | Viewed by 9296
Abstract
The unique properties of graphene oxide (GO) have attracted the attention of the research community and cost-effective routes for its production are studied. The type and percentage of the oxygen groups that decorate a GO sheet are dependent on the synthesis path, and [...] Read more.
The unique properties of graphene oxide (GO) have attracted the attention of the research community and cost-effective routes for its production are studied. The type and percentage of the oxygen groups that decorate a GO sheet are dependent on the synthesis path, and this path specifies the carbon content of the sheet. The chemical reduction of GO results in reduced graphene oxide (rGO) while the removal of the oxygen groups is also achievable with thermal processes (tpGO). This review article introduces the reader to the carbon allotropes, provides information about graphene which is the backbone of GO and focuses on GO synthesis and properties. The last part covers some characterization techniques of GO (XRD, FTIR, AFM, SEM-EDS, N2 porosimetry and UV-Vis) with a view to the fundamental principles of each technique. Some critical aspects arise for GO synthesized and characterized from our group. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>Graphite structure and van der Waals interactions between graphite layers [<a href="#B23-ChemEngineering-05-00064" class="html-bibr">23</a>].</p>
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<p>Graphene’s common production methods in a scale of 0–3. G refers to quality, S to scalability, P to purity, Y to yield of each route, and C to production cost (low value <math display="inline"><semantics> <mo>⇔</mo> </semantics></math> high cost).</p>
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<p>Thermal CVD growth of graphene [<a href="#B44-ChemEngineering-05-00064" class="html-bibr">44</a>].</p>
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<p>Synthesis routes of GO [<a href="#B53-ChemEngineering-05-00064" class="html-bibr">53</a>].</p>
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<p>The proposed structural models of GO [<a href="#B60-ChemEngineering-05-00064" class="html-bibr">60</a>].</p>
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<p>The in-phase and out-of-phase carbon p orbitals (<b>A</b>,<b>B</b>), the energy diagram and transitions between n, π and π* orbitals (<b>C</b>), and a typical UV-Vis absorption spectrum of GO (<b>D</b>) [<a href="#B55-ChemEngineering-05-00064" class="html-bibr">55</a>].</p>
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<p>Graphene Young’s modulus as a function of the matrix modulus [<a href="#B35-ChemEngineering-05-00064" class="html-bibr">35</a>].</p>
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<p>Synthesis of rGO from pristine graphite [<a href="#B84-ChemEngineering-05-00064" class="html-bibr">84</a>].</p>
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26 pages, 1082 KiB  
Review
Water Purification of Classical and Emerging Organic Pollutants: An Extensive Review
by Simona Somma, Ernesto Reverchon and Lucia Baldino
ChemEngineering 2021, 5(3), 47; https://doi.org/10.3390/chemengineering5030047 - 7 Aug 2021
Cited by 49 | Viewed by 6955
Abstract
The main techniques used for organic pollutant removal from water are adsorption, reductive and oxidative processes, phytoremediation, bioremediation, separation by membranes and liquid–liquid extraction. In this review, strengths and weaknesses of the different purification techniques are discussed, with particular attention to the newest [...] Read more.
The main techniques used for organic pollutant removal from water are adsorption, reductive and oxidative processes, phytoremediation, bioremediation, separation by membranes and liquid–liquid extraction. In this review, strengths and weaknesses of the different purification techniques are discussed, with particular attention to the newest results published in the scientific literature. This study highlighted that adsorption is the most frequently used method for water purification, since it can balance high organic pollutants removal efficiency, it has the possibility to treat a large quantity of water in semi-continuous way and has acceptable costs. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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<p>European water consumption in 2015.</p>
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<p>Number of papers found in SCOPUS, published in the period of 2012–2020, concerning the removal of classical and emerging organic pollutants from water.</p>
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<p>Main categories of water pollutants.</p>
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29 pages, 453 KiB  
Review
A Review on Gas-Liquid Mass Transfer Coefficients in Packed-Bed Columns
by Domenico Flagiello, Arianna Parisi, Amedeo Lancia and Francesco Di Natale
ChemEngineering 2021, 5(3), 43; https://doi.org/10.3390/chemengineering5030043 - 2 Aug 2021
Cited by 23 | Viewed by 11713
Abstract
This review provides a thorough analysis of the most famous mass transfer models for random and structured packed-bed columns used in absorption/stripping and distillation processes, providing a detailed description of the equations to calculate the mass transfer parameters, i.e., gas-side coefficient per unit [...] Read more.
This review provides a thorough analysis of the most famous mass transfer models for random and structured packed-bed columns used in absorption/stripping and distillation processes, providing a detailed description of the equations to calculate the mass transfer parameters, i.e., gas-side coefficient per unit surface ky [kmol·m−2·s−1], liquid-side coefficient per unit surface kx [kmol·m−2·s−1], interfacial packing area ae [m2·m−3], which constitute the ingredients to assess the mass transfer rate of packed-bed columns. The models have been reported in the original form provided by the authors together with the geometric and model fitting parameters published in several papers to allow their adaptation to packings different from those covered in the original papers. Although the work is focused on a collection of carefully described and ready-to-use equations, we have tried to underline the criticalities behind these models, which mostly rely on the assessment of fluid-dynamics parameters such as liquid film thickness, liquid hold-up and interfacial area, or the real liquid paths or any mal-distributions flow. To this end, the paper reviewed novel experimental and simulation approaches aimed to better describe the gas-liquid multiphase flow dynamics in packed-bed column, e.g., by using optical technologies (tomography) or CFD simulations. While the results of these studies may not be easily extended to full-scale columns, the improved estimation of the main fluid-dynamic parameters will provide a more accurate modelling correlation of liquid-gas mass transfer phenomena in packed columns. Full article
(This article belongs to the Special Issue Feature Papers in Chemical Engineering)
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