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ChemEngineering
Volume 8
Issue 3
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ChemEngineering, Volume 8, Issue 3 (June 2024) – 19 articles

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14 pages, 7311 KiB  
Article
Synthesis of AgCoCuFeNi High Entropy Alloy Nanoparticles by Hydrogen Reduction-Assisted Ultrasonic Spray Pyrolysis
by Srecko Stopic, Ayadjenou Humphrey Hounsinou, Tatjana Volkov Husovic, Elif Emil-Kaya and Bernd Friedrich
ChemEngineering 2024, 8(3), 63; https://doi.org/10.3390/chemengineering8030063 - 18 Jun 2024
Viewed by 935
Abstract
Because of their high mixing entropies, multi-component alloys can exhibit enhanced catalytic activity compared to traditional catalysts in various chemical reactions, including hydrogenation, oxidation, and reduction processes. In this work, new AgCoCuFeNi high entropy alloy nanoparticles were synthesized by the hydrogen reduction-assisted ultrasonic [...] Read more.
Because of their high mixing entropies, multi-component alloys can exhibit enhanced catalytic activity compared to traditional catalysts in various chemical reactions, including hydrogenation, oxidation, and reduction processes. In this work, new AgCoCuFeNi high entropy alloy nanoparticles were synthesized by the hydrogen reduction-assisted ultrasonic spray pyrolysis method. The aim was to investigate the effects of processing parameters (reaction temperature, precursor solution concentration, and residence time) on the microstructure, composition, and crystallinity of the high entropy alloy nanoparticles. The characterization was performed with scanning electron microscope, energy-dispersive X-ray spectroscopy, and X-ray diffraction. The syntheses performed at 600, 700, 800, and 900 °C, resulted in smaller and smoother spherical particles with a near-equiatomic elemental composition as the temperature increased to 900 °C. With 0.25, 0.1, and 0.05 M precursor solutions, narrower size distribution and uniform AgCoCuFeNi nanoparticles were produced by reducing the solution concentration to 0.05 M. A near-equiatomic elemental composition was only obtained at 0.25 and 0.05 M. Increasing the residence time from 5.3 to 23.8 s resulted in an unclear particle microstructure. None of the five metal elements were formed in the large tubular reactor. X-ray diffraction revealed that various crystal phase structures were obtained in the synthesized AgCoCuFeNi particles. Full article
(This article belongs to the Special Issue Process Intensification for Chemical Engineering and Processing)
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<p>Schematic view of the USP-HR apparatus used for the AgCoCuFeNi high entropy nanoparticle synthesis.</p> Full article ">Figure 2
<p>SEM analysis of AgCoCuFeNi particles at (<b>a</b>) 600 °C, (<b>b</b>) 700 °C, (<b>c</b>) 800 °C, (<b>d</b>) 900 °C.</p> Full article ">Figure 3
<p>Mean diameters of AgCoCuFeNi particles at (<b>a</b>) 600 °C, (<b>b</b>) 700 °C, (<b>c</b>) 800 °C, (<b>d</b>) 900 °C.</p> Full article ">Figure 4
<p>EDS spectra for AgCoCuFeNi particles at (<b>a</b>) 600 °C, (<b>b</b>) 700 °C, (<b>c</b>) 800 °C, (<b>d</b>) 900 °C.</p> Full article ">Figure 5
<p>XRD patterns of AgCoCuFeNi particles.</p> Full article ">Figure 6
<p>AgCoCuFeNi samples with magnetic properties.</p> Full article ">Figure 7
<p>SEM analysis of AgCoCuFeNi particles at (<b>a</b>) 0.25 mol/L, (<b>b</b>) 0.1 mol/L, (<b>c</b>) 0.05 mol/L.</p> Full article ">Figure 8
<p>Mean diameters of AgCoCuFeNi particles at (<b>a</b>) 0.25, (<b>b</b>) 0.1, (<b>c</b>) 0.05 mol/L.</p> Full article ">Figure 9
<p>SEM analysis of AgCoCuFeNi particles with residence time of (<b>a</b>) 5.3 s, (<b>b</b>) 23.8 s.</p> Full article ">Figure 10
<p>AgCoCuFeNi particles synthesized with residence time of (<b>a</b>) 5.3 s, (<b>b</b>) 23.8 s.</p> Full article ">Figure 11
<p>EDS spectra for AgCoCuFeNi particles with residence time of (<b>a</b>) 5.3 s, (<b>b</b>) 23.8 s.</p> Full article ">
24 pages, 4637 KiB  
Article
Biogas Cleaning via Vacuum Swing Adsorption Using a Calcium Metal–Organic Framework Adsorbent: A Multiscale Simulation Study
by Madison Lasich, Victoria T. Adeleke and Kaniki Tumba
ChemEngineering 2024, 8(3), 62; https://doi.org/10.3390/chemengineering8030062 - 14 Jun 2024
Viewed by 1198
Abstract
Purifying biogas can enhance the performance of distributed smart grid systems while potentially yielding clean feedstock for downstream usage such as steam reforming. Recently, a novel anion-pillared metal–organic framework (MOF) was reported in the literature that shows good capacity to separate acetylene from [...] Read more.
Purifying biogas can enhance the performance of distributed smart grid systems while potentially yielding clean feedstock for downstream usage such as steam reforming. Recently, a novel anion-pillared metal–organic framework (MOF) was reported in the literature that shows good capacity to separate acetylene from carbon dioxide. The present study assesses the usefulness of this adsorbent for separating a typical biogas mixture (consisting of methane, nitrogen, oxygen, hydrogen, carbon dioxide, and hydrogen sulphide) using a multiscale approach. This approach couples atomistic Monte Carlo simulations in the grand canonical ensemble with the batch equilibrium modelling of a pressure swing adsorption system. The metal–organic framework displays selectivity at low pressures for carbon dioxide and especially hydrogen sulphide. An analysis of adsorption isotherm models coupled with statistical distributions of surface–gas interaction energies determined that both CH4 and CO2 exhibited Langmuir-type adsorption, while H2S displayed Langmuir-type behaviour at low pressures, with increasing adsorption site heterogeneity at high pressures. Batch equilibrium modelling of a vacuum swing adsorption system to purify a CH4/CO2 feedstock demonstrated that such a system can be incorporated into a solar biogas reforming process since the target purity of 93–94 mol-% methane for incorporation into the process was readily achievable. Full article
(This article belongs to the Special Issue Green and Sustainable Separation and Purification Technologies)
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<p>Unit cell of the CaMOF used in this study. Atoms are colour-coded according to the element: white = hydrogen, grey = carbon, red = oxygen, blue = nitrogen, and green = calcium.</p> Full article ">Figure 2
<p>Pure species adsorption isotherms for biogas constituents in CaMOF. Where they are not visible, the error bars (representing the standard deviation) are smaller than the symbols. The lines are guides for the eye. The open symbols represent experimental data [<a href="#B30-ChemEngineering-08-00062" class="html-bibr">30</a>] and the dashed line series represents an adjustment of the adsorption isotherms using a simple empirical factor, as outlined in the text.</p> Full article ">Figure 3
<p>Diffusivity plotted as a function of the molar mass of each gas species.</p> Full article ">Figure 4
<p>Pure species isosteric heats of adsorption (h<sub>ad</sub>) in the Ca-MOF adsorbent.</p> Full article ">Figure 5
<p>Mixture adsorption isotherms for biogas constituents in Ca-MOF for biogas composed of 30 mol-% CO<sub>2</sub>, 14.99 mol-% N<sub>2</sub>, 50 mol-% CH<sub>4</sub>, 3 mol-% O<sub>2</sub>, 0.01 mol-% H<sub>2</sub>S, and 2 mol-% H<sub>2</sub>. Where they are not visible, the error bars (representing the standard deviation) are smaller than the symbols. The lines are guides for the eye.</p> Full article ">Figure 6
<p>Mixture adsorption isotherms for biogas constituents in Ca-MOF for biogas composed of 30 mol-% CO<sub>2</sub>, 14.9 mol-% N<sub>2</sub>, 50 mol-% CH<sub>4</sub>, 3 mol-% O<sub>2</sub>, 0.1 mol-% H<sub>2</sub>S, and 2 mol-% H<sub>2</sub>. Where they are not visible, the error bars (representing the standard deviation) are smaller than the symbols. The lines are guides for the eye.</p> Full article ">Figure 7
<p>Density field of H<sub>2</sub>S adsorbed onto the unit cell of the Ca-MOF at 298 K and 99.22 kPa. Atoms are colour-coded according to the element: white = hydrogen, grey = carbon, red = oxygen, blue = nitrogen, and green = calcium. Hydrogen sulphide-containing regions are indicated in black.</p> Full article ">Figure 8
<p>Energy distribution for H<sub>2</sub>S adsorbed in CaMOF. <span class="html-italic">E</span> is the potential energy derived from configurations sampled during the grand canonical Monte Carlo simulations, and <span class="html-italic">p</span>(<span class="html-italic">E</span>) is the probability of occurrence for each energy value. The lines are guides for the eye.</p> Full article ">Figure 9
<p>Energy distribution for CO<sub>2</sub> adsorbed in CaMOF. <span class="html-italic">E</span> is the potential energy derived from configurations sampled during the grand canonical Monte Carlo simulations, and <span class="html-italic">p</span>(<span class="html-italic">E</span>) is the probability of occurrence for each energy value. The lines are guides for the eye.</p> Full article ">Figure 10
<p>Energy distribution for CH<sub>4</sub> adsorbed in CaMOF. <span class="html-italic">E</span> is the potential energy derived from configurations sampled during the grand canonical Monte Carlo simulations, and <span class="html-italic">p</span>(<span class="html-italic">E</span>) is the probability of occurrence for each energy value. The lines are guides for the eye.</p> Full article ">Figure 11
<p>Flow sheet of a solar-driven biogas reforming process [<a href="#B96-ChemEngineering-08-00062" class="html-bibr">96</a>].</p> Full article ">Figure 12
<p>Heat map of CO<sub>2</sub> composition (in mol-%) in the outlet stream from the VSA system as a function of bed mass and operating pressure ratio.</p> Full article ">Figure 13
<p>Heat map of CO<sub>2</sub> composition (in mol-%) in the outlet stream from the VSA system as a function of bed mass and operating pressure ratio, when a correction factor is incorporated to account for deviations between experiments and COMPASS forcefield predictions. Details are provided in the text in <a href="#sec3dot1-ChemEngineering-08-00062" class="html-sec">Section 3.1</a>.</p> Full article ">
25 pages, 4608 KiB  
Review
Synergistic Innovations: Organometallic Frameworks on Graphene Oxide for Sustainable Eco-Energy Solutions
by Ganeshraja Ayyakannu Sundaram, Ahmed F. M. EL-Mahdy, Phuong V. Pham, Selvaraj Kunjiappan and Alagarsamy Santhana Krishna Kumar
ChemEngineering 2024, 8(3), 61; https://doi.org/10.3390/chemengineering8030061 - 12 Jun 2024
Viewed by 834
Abstract
Combining organometallic frameworks with graphene oxide presents a fresh strategy to enhance the electrochemical capabilities of supercapacitors, contributing to the advancement of sustainable energy solutions. Continued refinement of materials and device design holds promise for broader applications across energy storage and conversion systems. [...] Read more.
Combining organometallic frameworks with graphene oxide presents a fresh strategy to enhance the electrochemical capabilities of supercapacitors, contributing to the advancement of sustainable energy solutions. Continued refinement of materials and device design holds promise for broader applications across energy storage and conversion systems. This featured application underscores the inventive utilization of organometallic frameworks on graphene oxide, shedding light on the creation of superior energy storage devices for eco-friendly solutions. This review article delves into the synergistic advancements resulting from the fusion of organometallic frameworks with graphene oxide, offering a thorough exploration of their utility in sustainable eco-energy solutions. This review encompasses various facets, including synthesis methodologies, amplified catalytic performances, and structural elucidations. Through collaborative efforts, notable progressions in photocatalysis, photovoltaics, and energy storage are showcased, illustrating the transformative potential of these hybrids in reshaping solar energy conversion and storage technologies. Moreover, the environmentally conscious features of organometallic–graphene oxide hybrids are underscored through their contributions to environmental remediation, addressing challenges in pollutant elimination, water purification, and air quality enhancement. The intricate structural characteristics of these hybrids are expounded upon to highlight their role in tailoring material properties for specific eco-energy applications. Despite promising advancements, challenges such as scalability and stability are candidly addressed, offering a pragmatic view of the current research landscape. The manuscript concludes by providing insights into prospective research avenues, guiding the scientific community towards surmounting hurdles and fully leveraging the potential of organometallic–graphene oxide hybrids for a sustainable and energy-efficient future. Full article
(This article belongs to the Collection Green and Environmentally Sustainable Chemical Processes)
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<p>Schematic diagram of factors affecting the growth of a metal–organic framework (MOF) or organometallic framework (OMF) in the presence of graphene oxide (GO) and the applications of oriented OMF/GO nanocomposites. Reprinted from Ref. [<a href="#B35-ChemEngineering-08-00061" class="html-bibr">35</a>].</p> Full article ">Figure 2
<p>Various methods for synthesis of OMF/GO composite.</p> Full article ">Figure 3
<p>Highlights key applications of organometallic–graphene oxide composites.</p> Full article ">Figure 4
<p>Illustrates the generation of hydroxyl radicals through sunlight irradiation using an organometallic framework–graphene oxide composite.</p> Full article ">Figure 5
<p>Synthesis of palladium complex 2, immobilization onto rGO (2-rGO), and synthesis of free palladium NPs (2-NPs) and supported onto rGO (2-rGO-NPs). Reprinted from Ref. [<a href="#B154-ChemEngineering-08-00061" class="html-bibr">154</a>].</p> Full article ">Figure 6
<p>(<b>a</b>) Recycling of 2-rGO (0.5 mol %) in the hydrogenation of 1-phenyl-1-butyne with molecular H<sub>2</sub>. First run at 1.5 h because of the induction time and next runs at 1 h. HRTEM image after run 1 (<b>b</b>), run 5 (<b>e</b>), and run 10 (<b>g</b>) and the corresponding XPS spectra of runs 1 (<b>c</b>), 5 (<b>f</b>), and 10 (<b>h</b>). Blue line corresponds to the Pd(II) core-level peaks 3d and red line corresponds to the core-level peaks 3d of Pd(0). Particle size distribution histograms for run 1 (<b>d</b>) and run 10 (<b>i</b>). Reprinted from Ref. [<a href="#B154-ChemEngineering-08-00061" class="html-bibr">154</a>].</p> Full article ">Figure 7
<p>Schematic illustration of different steps for synthesis of in situ synthesized Cu<sub>2</sub>ZnSnS<sub>4</sub> (I-CZTS)/GO composites. Reprinted from Ref. [<a href="#B165-ChemEngineering-08-00061" class="html-bibr">165</a>].</p> Full article ">Figure 8
<p>Schematic of HER on the I-CZTS/GO composite. Reprinted from Ref. [<a href="#B165-ChemEngineering-08-00061" class="html-bibr">165</a>].</p> Full article ">Figure 9
<p>(<b>a</b>) Schematic depiction of nMOF/graphene supercapacitors, and (<b>b</b>) nMOF 867 along with the stack capacitance of nMOF-867/graphene, activated carbon, and pristine nMOF-867. Reprinted from Ref. [<a href="#B189-ChemEngineering-08-00061" class="html-bibr">189</a>].</p> Full article ">
18 pages, 3257 KiB  
Article
Effects of Mineral Elements and Annealing on the Physicochemical Properties of Native Potato Starch
by Johanna A. Thomann, Michael Polhuis, Alessia Lasorsa, Hero J. Heeres and André Heeres
ChemEngineering 2024, 8(3), 60; https://doi.org/10.3390/chemengineering8030060 - 10 Jun 2024
Viewed by 796
Abstract
Native potato starch is an excellent carrier of minerals due to its inherent ion exchange capacity. Mineral enrichment not only changes the nutritional value but also influences starch pasting and swelling properties. Hydrothermal treatments like annealing constitute a straightforward and green way to [...] Read more.
Native potato starch is an excellent carrier of minerals due to its inherent ion exchange capacity. Mineral enrichment not only changes the nutritional value but also influences starch pasting and swelling properties. Hydrothermal treatments like annealing constitute a straightforward and green way to tune functional properties. Here, novel combinations of mineral enrichment and annealing were studied. Ion exchange was readily achieved by suspending starch in a salt solution at room temperature over 3 h and confirmed by ICP-OES. Annealing at 50 °C for 24 h using demineralized water or salt solutions strongly affected pasting, thermal, and swelling properties. The obtained XRD and DSC results support a more ordered structure with relative crystallinity increasing from initially 41.7% to 44.4% and gelatinization onset temperature increasing from 60.39 to 65.94 J/g. Solid-state NMR spectroscopy revealed no detectable changes after annealing. Total digestible starch content decreased after annealing from 8.89 to 7.86 g/100 g. During both ion exchange at room temperature and annealing, monovalent cations promoted swelling and peak viscosity, and divalent cations suppressed peak viscosity through ionic crosslinking. The presented combination allows fine-tuning of pasting behavior, potentially enabling requirements of respective food applications to be met while offering an alternative to chemically modified starches. Full article
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<p>X-ray diffractograms of native potato starch (NPS) (above in blue) and annealed native potato starch (ANN NPS) (below in orange). The crystalline type remains B-type after modification with characteristic peaks at 5°, 17°, and 22–24° 2θ.</p> Full article ">Figure 2
<p>The 1D <sup>13</sup>C CP/MAS NMR spectra of native potato starch before (left, blue spectrum) and after annealing (right, orange spectrum). The corresponding ordered sub-spectra were derived by subtraction of the spectrum for the amorphous phase (drum-dried native potato starch) by scaling the intensity of the amorphous spectrum so that the resultant intensity was zero at around 84 ppm.</p> Full article ">Figure 3
<p>Thermograms of endothermic transition of native potato starch (NPS) before (blue) and after annealing (ANN NPS) (orange).</p> Full article ">Figure 4
<p>RVA pasting profiles for native potato starch (NPS) before and after enrichment with Na, K, Mg, or Ca.</p> Full article ">Figure 5
<p>RVA profile of native potato starch NPS before (blue) and after annealing ANN NPS (orange) for 24 h at 50 °C.</p> Full article ">Figure 6
<p>RVA profiles of mineral-enriched starch samples: (<b>a</b>) native potato starch and annealed native potato starch with and without monovalent cations; (<b>b</b>) native potato starch and annealed native potato starch with and without divalent cations.</p> Full article ">Figure 7
<p>Swelling power and solubility of annealed native potato starch without and with ion enrichment with Mg, Ca, Na, and K, measured in triplicate at 70 °C: (<b>a</b>) ion-exchanged native potato starches at room temperature including control (NPS); (<b>b</b>) annealed native potato starches with and without cation enrichment as indicated. (a, b, c, d are values for swelling power or solubility with the same letter are not significantly different).</p> Full article ">
23 pages, 11017 KiB  
Article
Environmental Win–Win Management: Using Aluminum-Based Solid Waste for Synozol Red-KHL Dye Oxidation
by Manasik M. Nour, Zahraa A. Elsayed and Maha A. Tony
ChemEngineering 2024, 8(3), 59; https://doi.org/10.3390/chemengineering8030059 - 7 Jun 2024
Viewed by 1060
Abstract
The awareness of the concept of the “Circular Economy” is motivating scientists to convert drinking water treatment plant by-products, which are based on aluminum waste, into a valorized material for wastewater treatment. Alum sludge from a local waterworks plant in Egypt was collected [...] Read more.
The awareness of the concept of the “Circular Economy” is motivating scientists to convert drinking water treatment plant by-products, which are based on aluminum waste, into a valorized material for wastewater treatment. Alum sludge from a local waterworks plant in Egypt was collected and dewatered using chitosan-coated magnetic nanoparticles. The role of the conditioned sludge in wastewater treatment was then examined. Chitosan (Ch) augmented with magnetite nanoparticles (MNs), labeled as ChMNs, was prepared by means of a simple co-precipitation route with mixing ratios of 1:1, 2:1, and 3:1 of chitosan and magnetite nanoparticles to form the ChMN catalyst. The ChMNs were shown to beneficially enhance alum sludge conditioning and dewaterability. The conditioned and dried aluminum-based sludge (AS) loaded with ChMNs was then used as a source of Fenton’s catalyst for Synozol Red-KHL textile dyeing wastewater. The characteristics of the AS-ChMN sample were investigated using Fourier transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), and scanning electron microscopy (SEM). The photocatalytic activity of the AS-ChMN composite was assessed by examining its diffuse reflectance spectra (DRS). Response surface methodological analysis was applied to optimize the operational parameters in order to reduce the use of chemicals and improve dye oxidation to form a complete (99%) dye oxidation strategy. The experiments demonstrated that the optimal operating parameters included doses of 1.5 g/L and 420 mg/L for AS-ChMNs and hydrogen peroxide, respectively, as a source of Fenton’s reaction at a working pH of 3.5. Kinetic and thermodynamic analyses for potential full-scale applications were conducted, showing the reaction to be exothermic and spontaneous in nature and following second-order reaction kinetics. Hence, the novelty of this work lies in the introduction of conditioned and dewatered alum sludge waste as a photocatalyst for textile dye effluent oxidation, which could be considered a “win–win” strategy. Full article
(This article belongs to the Special Issue Chemical Engineering in Wastewater Treatment)
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<p>A graphical illustration of the experimental technique.</p> Full article ">Figure 2
<p>SEM micrographs of various ChMN composite materials with different chitosan–magnetite ratios of (<b>a</b>) 1:1, (<b>b</b>) 2:1, and (<b>c</b>) 3:1 and (<b>d</b>) alum sludge.</p> Full article ">Figure 3
<p>SEM-EDX analysis results of area scan of AS-ChMN composite material (<b>a</b>) before and (<b>b</b>) after dye oxidation.</p> Full article ">Figure 4
<p>TEM morphology of AS-ChMN composite material.</p> Full article ">Figure 5
<p>XRD pattern of synthesized AS-ChMN catalyst.</p> Full article ">Figure 6
<p>FTIR analysis results for AS-ChMN composite material (a) before and (b) after dye oxidation.</p> Full article ">Figure 7
<p>BET surface area obtained from N<sub>2</sub> adsorption–desorption isotherms for AS-ChMN composite.</p> Full article ">Figure 8
<p>Plots of (<b>a</b>) the UV–Vis DRS absorption spectrum and (<b>b</b>) the corresponding Kubelka–Munk plot for the indirect band gap energy of the AS-ChMN composite.</p> Full article ">Figure 9
<p>The reaction times of the various oxidative systems.</p> Full article ">Figure 10
<p>The effects of the AS-ChMN concentration on the Fenton-like reaction performance.</p> Full article ">Figure 11
<p>The effects of H<sub>2</sub>O<sub>2</sub> loading on the Fenton oxidation system.</p> Full article ">Figure 12
<p>The effect of pH on the Fenton oxidation reaction.</p> Full article ">Figure 13
<p>The effect of temperature on the Fenton oxidation reaction.</p> Full article ">Figure 14
<p>The activity of recycled catalysts in Synozol Red-KHL oxidation.</p> Full article ">Figure 15
<p>Factorial model design for optimal Synozol Red-KHL oxidation: 3D surface and 2D contour plots of the (Y) response and the interacting effects of (<b>a</b>) the pH and H<sub>2</sub>O<sub>2</sub> dose, (<b>b</b>) the pH and AS-ChMN catalyst dose, and (<b>c</b>) the H<sub>2</sub>O<sub>2</sub> dose and AS-ChMN catalyst.</p> Full article ">
25 pages, 5119 KiB  
Article
Using Excel Solver’s Parameter Function in Predicting and Interpretation for Kinetic Adsorption Model via Batch Sorption: Selection and Statistical Analysis for Basic Dye Removal onto a Novel Magnetic Nanosorbent
by Akkharaphong Wongphat, Surachai Wongcharee, Nuttapon Chaiduangsri, Kowit Suwannahong, Torpong Kreetachat, Saksit Imman, Nopparat Suriyachai, Sukanya Hongthong, Panarat Phadee, Preut Thanarat and Javier Rioyo
ChemEngineering 2024, 8(3), 58; https://doi.org/10.3390/chemengineering8030058 - 6 Jun 2024
Cited by 2 | Viewed by 1309
Abstract
Magnetic nanosorbents efficiently capture substances, particularly basic dyes, and can be easily recovered using a magnetic field in water treatment. Adsorption is a cost-effective and highly efficient method for basic dye removal. This study compared eight nonlinear kinetic adsorption models using Microsoft Excel [...] Read more.
Magnetic nanosorbents efficiently capture substances, particularly basic dyes, and can be easily recovered using a magnetic field in water treatment. Adsorption is a cost-effective and highly efficient method for basic dye removal. This study compared eight nonlinear kinetic adsorption models using Microsoft Excel 2023, which provided a detailed analysis and statistical results comparable to advanced programs like MATLAB and OriginPro. The Fractal Like-Pseudo First Order (FL-PFO) model showed the best fit for the kinetic adsorption model, closely predicting experimental data at 33.09 mg g−1. This suggests that the diffusion rate of basic dye within the magnetic nanosorbent pores is a crucial factor. The statistical parameters confirmed the suitability of these kinetic adsorption models for describing the observed behavior. Overall, Microsoft Excel emerged as a reliable tool for predicting adsorption behavior using various kinetic models for basic dye removal, offering a wide range of functions for diverse applications, including environmental monitoring and modeling. Corrected Akaike’s information criterion was used to determine the optimal model. It found the lowest AICcorrected value of about −3.8479 for the FL-PFO kinetic model, while the Elovich kinetic adsorption model had the highest AICcorrected value of 29.6605. This indicates that the FL-PFO kinetic model effectively correlated the kinetic data. It can be concluded that Microsoft Excel’s accessibility, familiarity, and broad range of capabilities make it a valuable resource for many aspects of environmental engineering. Full article
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<p>Laboratory experimental steps for kinetic adsorption studies.</p> Full article ">Figure 2
<p>Steps for using Microsoft Excel Solver’s spreadsheet-based program as a tool for predicting the experimental data of basic dye loaded onto the magnetic nanosorbent.</p> Full article ">Figure 3
<p>Steps of the Microsoft Excel Solver Function;: (<b>a</b>) plotted graph of the experimental dataset, (<b>b</b>) Solver parameter, (<b>c</b>) plotted graph after running the algorithm, and (<b>d</b>) Solver results.</p> Full article ">Figure 4
<p>The plausible mechanism of Methylene Blue (MB)/basic dye adsorption onto the magnetic nanosorbent material.</p> Full article ">Figure 5
<p>Overall mean AIC<sub>corrected</sub> for the kinetic adsorption models.</p> Full article ">Figure 6
<p>Experimental data and predicted data plotted using the results of Microsoft Excel.</p> Full article ">
20 pages, 2204 KiB  
Article
Partial Replacement of Carbon Black with Graphene in Tire Compounds: Transport Properties, Thermal Stability and Dynamic Mechanical Analysis
by Krishna Prasad Rajan, Aravinthan Gopanna, Mohammed Rafic, Rajesh Theravalappil and Selvin P. Thomas
ChemEngineering 2024, 8(3), 57; https://doi.org/10.3390/chemengineering8030057 - 5 Jun 2024
Viewed by 1021
Abstract
In this study, natural rubber (NR)/polybutadiene rubber (PB) blend-based composites were prepared using graphene as a partial replacement for carbon black (CB) in different parts per hundred rubber (phr) percentages. In a previous study, the vulcanization characteristics, viscoelastic behavior, and static mechanical properties [...] Read more.
In this study, natural rubber (NR)/polybutadiene rubber (PB) blend-based composites were prepared using graphene as a partial replacement for carbon black (CB) in different parts per hundred rubber (phr) percentages. In a previous study, the vulcanization characteristics, viscoelastic behavior, and static mechanical properties were reported, and the compound labeled as compound 2 (with 2.5 phr of graphene and 52.5 phr of carbon black) showed optimum properties. Herein, we report the dynamic mechanical properties and the transport properties of the formulations to establish further characterization of the compounds. Three different organic solvents comprising benzene, toluene, and xylene were employed to analyze the sorption characteristics. The obtained data were also modeled with different theoretical predictions. The dynamic mechanical properties showed that certain compounds can be considered to be green tire formulations, as there were appreciable changes in the tanδ values at different temperatures (−25 °C to 60 °C). The thermogravimetric analysis showed that compound 2, with 2.5 phr of graphene, has a higher t50 value among the studied formulations, which indicates higher thermal stability than the base compound. The partial replacement of 2.5 phr of graphene in place of carbon black (total 55 phr) led to appreciable improvements in terms of thermal stability, transport properties, and dynamic mechanical properties. Full article
(This article belongs to the Special Issue Engineering of Carbon-Based Nano/Micromaterials)
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<p>Crosslink density and molecular weight between crosslinks (<span class="html-italic">M<sub>c</sub></span>) vs. graphene content.</p> Full article ">Figure 2
<p>Sorption curves for various compounds in (<b>a</b>) benzene, (<b>b</b>) toluene, and (<b>c</b>) xylene.</p> Full article ">Figure 3
<p>Equilibrium solvent uptake vs. graphene content in the elastomer composites.</p> Full article ">Figure 4
<p>Plots of log <span class="html-italic">Q<sub>t</sub></span>/<span class="html-italic">Q</span><sub>∞</sub> against log t for (<b>a</b>) samples in benzene, (<b>b</b>) samples in toluene, and (<b>c</b>) samples in xylene.</p> Full article ">Figure 5
<p>Higuchi model applied to the linear portion of experimental data for compound <b>2</b> in (<b>a</b>) benzene, (<b>b</b>) toluene, and (<b>c</b>) xylene.</p> Full article ">Figure 5 Cont.
<p>Higuchi model applied to the linear portion of experimental data for compound <b>2</b> in (<b>a</b>) benzene, (<b>b</b>) toluene, and (<b>c</b>) xylene.</p> Full article ">Figure 6
<p>The Korsemeyer–Peppas model and Peppas–Sahlin equation applied to the experimental data for compound <b>2</b> in (<b>a</b>) benzene, (<b>b</b>) toluene, and (<b>c</b>) xylene.</p> Full article ">Figure 7
<p>(<b>a</b>) The storage modulus against temperature curves and (<b>b</b>) tan delta against temperature curves.</p> Full article ">Figure 8
<p>Thermograms of the elastomer compounds from TGA.</p> Full article ">
13 pages, 1429 KiB  
Review
An Arsenic Removal Technology and Its Application in Arsenic-Containing Copper
by Xiaowei Tang and Yuehui He
ChemEngineering 2024, 8(3), 56; https://doi.org/10.3390/chemengineering8030056 - 3 Jun 2024
Viewed by 609
Abstract
The usage of copper (Cu) ores containing low or no arsenic (As) has reduced, and Cu ores containing high levels of As have emerged as vital mineral resources for Cu extraction and processing. The quality of the Cu ores has decreased from 1.6% [...] Read more.
The usage of copper (Cu) ores containing low or no arsenic (As) has reduced, and Cu ores containing high levels of As have emerged as vital mineral resources for Cu extraction and processing. The quality of the Cu ores has decreased from 1.6% to approximately 1.0%. The proportion of As to Cu in 15% of Cu resources currently reaches 1:5. However, during the extraction and processing of Cu ores, As presents significant environmental harm. Hence, safely and effectively removing As is paramount in Cu smelting and processing, holding substantial importance in fostering environmentally sustainable practices within the Cu extraction and processing industry. This article consolidates the resource distribution of As-containing Cu (ACC) ores, comprehensively and systematically evaluates the present advancements in extracting techniques for these minerals, and identifies the challenges inherent in pyrometallurgical and wet processes for treating ACC deposits. Pyrometallurgy is a simple primary roasting technique and has widespread applicability in the treatment of various ACC minerals. Its disadvantages are the emission of exhaust gas and the high treatment costs associated with it. The wet arsenic removal method boasts advantages including minimal air pollution and a high resource recovery rate, significantly aiding in Cu concentrate recovery; its major drawback is the production of As-containing wastewater. The hydrometallurgical removal of As from ACC mines involves extracting As through leaching. Recently, biometallurgy has presented innovative solutions using specialized microorganisms to bioleach or bioabsorb As, but large-scale industrial applications still lack specific practical implementation. This review explores the underlying causes of the challenges encountered in processing ACC minerals. Additionally, it highlights pyrometallurgical roasting coupled with high-temperature filtration as a pivotal advancement in the extraction and processing of ACC ores. Full article
(This article belongs to the Topic Advances in Chemistry and Chemical Engineering)
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<p>Changes in copper grade worldwide from 2000 to 2021.</p> Full article ">Figure 2
<p>Geochemical cycle of arsenic.</p> Full article ">Figure 3
<p>Thermodynamic equilibrium diagram for As-O-S system.</p> Full article ">Figure 4
<p>Schematic diagram of the El Indio flow.</p> Full article ">
19 pages, 7032 KiB  
Article
Synergistic Effect of Co and Ni Co-Existence on Catalytic Decomposition of Ammonia to Hydrogen—Effect of Catalytic Support and Mg-Al Oxide Matrix
by Andrzej Kowalczyk, Małgorzata Rutkowska, Sylwia Gnyla, Michał Pacia and Lucjan Chmielarz
ChemEngineering 2024, 8(3), 55; https://doi.org/10.3390/chemengineering8030055 - 24 May 2024
Viewed by 1067
Abstract
Hydrotalcite-derived mixed metal oxides containing Co and Ni and containing these metals supported on MgO and Al2O3 were prepared and tested as catalysts for the decomposition of ammonia to hydrogen and nitrogen. The obtained samples were characterised in terms of [...] Read more.
Hydrotalcite-derived mixed metal oxides containing Co and Ni and containing these metals supported on MgO and Al2O3 were prepared and tested as catalysts for the decomposition of ammonia to hydrogen and nitrogen. The obtained samples were characterised in terms of chemical composition (ICP-OES), structure (XRD), textural parameters (low-temperature N2 adsorption–desorption, SEM), form and aggregation of transition-metal species (UV-Vis DRS), reducibility (H2-TPR) and surface acidity (NH3-TPD). The catalytic efficiency of the tested systems strongly depends on the support used. Generally, the alumina-based catalyst operated at lower temperatures compared to transition metals deposited on MgO. For both series of catalysts, a synergistic effect of the co-existence of cobalt and nickel on the catalytic efficiency was observed. The best catalytic results were obtained for hydrotalcite-derived catalysts; however, in the case of these catalysts, an increase in the Al/Mg ratio resulted in a further increase in catalytic activity in the decomposition of ammonia. Full article
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<p>XRD diffraction patterns of catalysts supported on γ-Al<sub>2</sub>O<sub>3</sub> (<b>A</b>) and MgO (<b>B</b>). Samples reduced in hydrogen at 800 °C are marked ‘R’.</p> Full article ">Figure 2
<p>XRD diffraction patterns of the non-calcined samples of the HT40 (<b>A</b>) and HT20 series (<b>B</b>). Diffractograms recorded for calcined samples of the HT40 (<b>C</b>) and HT20 (<b>D</b>) series. Samples reduced in hydrogen at 800 °C are marked ‘R’.</p> Full article ">Figure 3
<p>SEM images of the samples supported on MgO (CoNi-M) and γ-Al<sub>2</sub>O<sub>3</sub> (CoNi-A) (white arrows indicate metal oxide aggregates on the support surface) as well as hydrotalcite-derived catalysts of HT20 (CoNi-HT20) and HT40 (Co-HT40, CoNi-HT40 and Ni-HT40) series.</p> Full article ">Figure 4
<p>UV-VIS-DR spectra of the samples supported on γ-Al<sub>2</sub>O<sub>3</sub> (<b>A</b>), and MgO (<b>B</b>) as well as hydrotalcite-derived catalysts of HT20 (<b>C</b>) and HT40 (<b>D</b>) series.</p> Full article ">Figure 5
<p>TPR profiles of the samples supported on γ-Al<sub>2</sub>O<sub>3</sub> (<b>A</b>), and MgO (<b>B</b>) as well as for HT20 (<b>C</b>) and HT40 (<b>D</b>) series of the samples.</p> Full article ">Figure 6
<p>NH<sub>3</sub>-TPD profiles of the catalyst supports. Values of chemisorbed ammonia concentrations are shown in parentheses.</p> Full article ">Figure 7
<p>Results of catalytic NH<sub>3</sub> decomposition with error bars determined by the propagation error method over catalysts supported on MgO (<b>A</b>) and γ-Al<sub>2</sub>O<sub>3</sub> (<b>B</b>), catalysts of HT20 (<b>C</b>) and HT40 (<b>D</b>) series. Dotted lines represent ammonia conversion of 90%.</p> Full article ">Figure 8
<p>Isothermal catalytic stability test at 500 °C, with error bars determined by the propagation-error method.</p> Full article ">
20 pages, 3532 KiB  
Article
Experimental Investigation of Liquid Holdup in a Co-Current Gas–Liquid Upflow Moving Packed Bed Reactor with Porous Catalyst Using Gamma-Ray Densitometry
by Ali Toukan, Ahmed Jasim, Vineet Alexander, Hamza AlBazzaz and Muthanna Al-Dahhan
ChemEngineering 2024, 8(3), 54; https://doi.org/10.3390/chemengineering8030054 - 23 May 2024
Viewed by 913
Abstract
This study explores the dynamics of liquid holdup in a lab-scale co-current two-phase upflow moving packed bed reactor, specifically examining how superficial gas velocity influences the line average external liquid holdup at a fixed superficial liquid velocity. Utilizing gamma-ray densitometry (GRD) for precise [...] Read more.
This study explores the dynamics of liquid holdup in a lab-scale co-current two-phase upflow moving packed bed reactor, specifically examining how superficial gas velocity influences the line average external liquid holdup at a fixed superficial liquid velocity. Utilizing gamma-ray densitometry (GRD) for precise measurements, this research extends to determining line average internal porosity within catalyst particles. Conducted with an air–water system within a bed packed with 3 mm porous particles, the study presents a novel methodology using Beer–Lambert’s law to calculate liquid, gas, and solid holdups and catalyst porosity that is equivalent to the internal liquid holdup that fills the catalyst pores. Findings reveal a decrease in liquid holdup corresponding with increased superficial gas velocity across axial and radial locations, with a notable transition from bubbly to pulse flow regime at a critical velocity of 3.8 cm/sec. Additionally, the lower sections of the packed bed exhibited higher external liquid holdup compared to the middle sections at varied gas velocities. The liquid holdup distribution appeared uniform at lower flow rates, whereas higher flow rates favored the middle sections. Full article
(This article belongs to the Special Issue Advances in Hydrotreating Catalyst Synthesis for Fuel and Chemical Production Processes)
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<p>Experimental setup: (<b>a</b>) schematic representation, (<b>b</b>) photo of the setup, (<b>c</b>) lab-scale TBR, and (<b>d</b>) internals.</p> Full article ">Figure 2
<p>(<b>a</b>) Schematic diagram of GRD showing the arrangement of source, the packed bed, the collimators, and the detector, and (<b>b</b>) radial scanning points.</p> Full article ">Figure 3
<p>Diameter profile of external catalyst bed void space (εβ).</p> Full article ">Figure 4
<p>Radial distribution of line average internal liquid holdup which is equivalent to the volume of the porosity of the catalysts with respect to the bed volume.</p> Full article ">Figure 5
<p>Radial distribution of solid holdup (εs).</p> Full article ">Figure 6
<p>Liquid holdup (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math>) at (r/R = 0), center.</p> Full article ">Figure 7
<p>Liquid holdup (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math>) at (r/R = 0.5), right side.</p> Full article ">Figure 8
<p>Liquid holdup (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math>) at (r/R = −0.5), left side.</p> Full article ">Figure 9
<p>Liquid holdup (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math>) at (r/R = 0.9), right side of the packed bed.</p> Full article ">Figure 10
<p>Liquid holdup (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math>) at (r/R = −0.9), left side of the packed bed.</p> Full article ">Figure 11
<p>Effect of superficial gas velocity (Ug) on the liquid holdup.</p> Full article ">Figure 12
<p>Effect of superficial gas velocity (Ug) on the liquid holdup at middle (Z/D = 1) of the packed bed at Ul = 0.017 cm/s.</p> Full article ">
44 pages, 4198 KiB  
Review
Inkjet Printing with (Semi)conductive Conjugated Polymers: A Review
by Daniil A. Lukyanov and Oleg V. Levin
ChemEngineering 2024, 8(3), 53; https://doi.org/10.3390/chemengineering8030053 - 8 May 2024
Viewed by 1379
Abstract
Functional inkjet printing is an emerging manufacturing technology for the production of various planar elements and electronic devices. This technology offers affordable freeform and highly customizable production of thin film micron-scale elements on various substrates. Functional inkjet printing employs various inks based on [...] Read more.
Functional inkjet printing is an emerging manufacturing technology for the production of various planar elements and electronic devices. This technology offers affordable freeform and highly customizable production of thin film micron-scale elements on various substrates. Functional inkjet printing employs various inks based on organic and inorganic materials with diverse functional properties, and among them, conjugated polymers are of particular interest due to their electrical, photophysical, and electrochemical properties. This paper provides an overview of inkjet printing with conjugated (semi)conductive polymers, including the fundamentals of the technology and its scope, limitations, and main challenges. Specific attention is drawn to the synthesis and chemistry of these polymers in connection with the patterning and functional properties of the inks composed thereof. Practical aspects of this technology are also highlighted, namely the manufacturing capabilities of the technology and particular applications for the fabrication of various electronic elements and devices. Full article
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<p>(<b>a</b>) Schematic illustration of various functional printing techniques; figure adopted from [<a href="#B7-ChemEngineering-08-00053" class="html-bibr">7</a>]; (<b>b</b>) performance comparison radar plot for these techniques.</p> Full article ">Figure 2
<p>Vertical device fabrication on a pre-patterned substrate; (<b>a</b>–<b>e</b>) side view scheme, (<b>f</b>–<b>j</b>) top view microphotographs, (<b>k</b>–<b>o</b>) cross section scheme; reprinted with permission from [<a href="#B44-ChemEngineering-08-00053" class="html-bibr">44</a>].</p> Full article ">Figure 3
<p>Variations of the metal-catalyzed coupling for the synthesis of conjugated polymers.</p> Full article ">Figure 4
<p>(<b>a</b>) Ziegler–Natta polyacetylene synthesis; (<b>b</b>) Wittig poly(arylenevinylene) synthesis.</p> Full article ">Figure 5
<p>Oxidative polymerization of benzene and five-membered heterocycles.</p> Full article ">Figure 6
<p>Structure of (<b>a</b>) PEDOT:PSS and (<b>b</b>) P3HT.</p> Full article ">Figure 7
<p>Structure of linear PANI base and salt.</p> Full article ">Figure 8
<p>Scheme for the Horner–Wadsworth–Emmons synthesis of MEH-PPV.</p> Full article ">Figure 9
<p>Structures of P(NDI2OD-T2), PC12TV12T, PDVT-8, and F8BT.</p> Full article ">Figure 10
<p>Structures of the PPy, PFO, and PDA.</p> Full article ">Figure 11
<p>Schematic representation of the IJP of a bottom gate FET, with permission from [<a href="#B117-ChemEngineering-08-00053" class="html-bibr">117</a>].</p> Full article ">Figure 12
<p>Schematic representation of (<b>a</b>) electrolyte-based transistor construction and action of the (<b>b</b>) electrolyte-gated and (<b>c</b>) electrochemical transistor; reprinted with permission from [<a href="#B172-ChemEngineering-08-00053" class="html-bibr">172</a>].</p> Full article ">Figure 13
<p>Device structure of full-color AMPLED display pixel and full color image on this display; adopted with permission from [<a href="#B252-ChemEngineering-08-00053" class="html-bibr">252</a>].</p> Full article ">Figure 14
<p>Schematic representation of (<b>a</b>) heterojunction morphologies and (<b>b</b>) the structure of the thin-film photovoltaic element.</p> Full article ">
21 pages, 6252 KiB  
Article
A Cold Flow Model of Interconnected Slurry Bubble Columns for Sorption-Enhanced Fischer–Tropsch Synthesis
by Wiebke Asbahr, Robin Lamparter and Reinhard Rauch
ChemEngineering 2024, 8(3), 52; https://doi.org/10.3390/chemengineering8030052 - 8 May 2024
Viewed by 1099
Abstract
For technical application with continuous operation of sorption-enhanced (SE) reactions, e.g., Fischer–Tropsch, a special reactor concept is required. SE processes are promising due to the negative effects of water on conversion and catalyst. The reactor concept of two interconnected slurry bubble columns combines [...] Read more.
For technical application with continuous operation of sorption-enhanced (SE) reactions, e.g., Fischer–Tropsch, a special reactor concept is required. SE processes are promising due to the negative effects of water on conversion and catalyst. The reactor concept of two interconnected slurry bubble columns combines the reaction with in situ water removal in the first, and sorbent regeneration in the second column with continuous exchange of slurry between the two. The liquid circulation rate (LCR) between the columns is studied in a cold flow model, measured by an ultrasonic sensor. The effects of different operating and geometric parameters, e.g., superficial gas velocity, liquid level and tube diameter on gas holdup and LCR are discussed and modelled via artificial intelligence methods, i.e., extremely randomized trees and neural networks. It was found that the LCR strongly depends on the gas holdup. The maximum of 4.28 L min−1 was reached with the highest exit, widest tube and highest superficial gas velocity of 0.15 m s−1. The influence of liquid level above the exit was marginal but water quality has to be considered. Both models offer predictions of the LCR with errors < 6%. With an extension of the models, particle circulation can be studied in the future. Full article
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<p>New reactor concept of interconnected slurry bubble columns for sorption-enhanced (SE) Fischer–Tropsch (FT) synthesis.</p> Full article ">Figure 2
<p>Cold model of novel reactor concept consisting of two interconnected bubble columns (BC1 and BC2) and two gas separators (GS1 and GS2).</p> Full article ">Figure 3
<p>Measuring principle of the ultrasonic flow meter for measuring the liquid circulation rate.</p> Full article ">Figure 4
<p>General overview of the modelling process.</p> Full article ">Figure 5
<p>Schematic diagram of the general MLP structure.</p> Full article ">Figure 6
<p>Schematic diagram of the bagging method applied in EXT.</p> Full article ">Figure 7
<p>Gas holdup in BC1 (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>G</mi> <mo>,</mo> <mi>B</mi> <mi>C</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>) and BC2 (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>G</mi> <mo>,</mo> <mi>B</mi> <mi>C</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>) and the liquid circulation rate (LCR) (<math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>Q</mi> </mrow> <mo>˙</mo> </mover> </mrow> <mrow> <mi>L</mi> </mrow> </msub> </mrow> </semantics></math>) for different superficial gas velocities (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>u</mi> </mrow> <mrow> <mi>G</mi> </mrow> </msub> </mrow> </semantics></math>).</p> Full article ">Figure 8
<p>Influence of superficial gas velocity (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>u</mi> </mrow> <mrow> <mi>G</mi> </mrow> </msub> </mrow> </semantics></math>) on the liquid circulation rate (<math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>Q</mi> </mrow> <mo>˙</mo> </mover> </mrow> <mrow> <mi>L</mi> </mrow> </msub> </mrow> </semantics></math>): (<b>a</b>) effect of different liquid levels (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>h</mi> </mrow> <mrow> <mi>L</mi> </mrow> </msub> </mrow> </semantics></math>); (<b>b</b>) effect of water quality expressed in different electrical conductivities (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>e</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math>).</p> Full article ">Figure 9
<p>Influence of water quality expressed in different electrical conductivities (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>e</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math>) on gas holdup (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>G</mi> </mrow> </msub> </mrow> </semantics></math>) at different superficial gas velocities (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>u</mi> </mrow> <mrow> <mi>G</mi> </mrow> </msub> </mrow> </semantics></math>): (<b>a</b>) in BC1 and (<b>b</b>) in BC2.</p> Full article ">Figure 10
<p>Influence of superficial gas velocity (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>u</mi> </mrow> <mrow> <mi>G</mi> </mrow> </msub> </mrow> </semantics></math>) on the liquid circulation rate (<math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>Q</mi> </mrow> <mo>˙</mo> </mover> </mrow> <mrow> <mi>L</mi> </mrow> </msub> </mrow> </semantics></math>): (<b>a</b>) effect of different liquid exit heights (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>h</mi> </mrow> <mrow> <mi>L</mi> <mo>,</mo> <mi>e</mi> <mi>x</mi> <mi>i</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math>); (<b>b</b>) effect of different tube inner diameters (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>d</mi> </mrow> <mrow> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math>).</p> Full article ">Figure 11
<p>Dependency of the liquid circulation rate (<math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>Q</mi> </mrow> <mo>˙</mo> </mover> </mrow> <mrow> <mi>L</mi> </mrow> </msub> </mrow> </semantics></math>) on total gas holdup (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>G</mi> <mo>,</mo> <mi>t</mi> <mi>o</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math>) and total gas volume flow (<math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>V</mi> </mrow> <mo>˙</mo> </mover> </mrow> <mrow> <mi>G</mi> <mo>,</mo> <mi>t</mi> <mi>o</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math>) for different reactor configurations: Tube inner diameter (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>d</mi> </mrow> <mrow> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math>), electrical conductivity (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>e</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math>) and liquid exit height (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>h</mi> </mrow> <mrow> <mi>L</mi> <mo>,</mo> <mi>e</mi> <mi>x</mi> <mi>i</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math>).</p> Full article ">Figure 12
<p>The predicted (<math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>Q</mi> </mrow> <mo>˙</mo> </mover> </mrow> <mrow> <mi>L</mi> <mo>,</mo> <mi>p</mi> <mi>r</mi> <mi>e</mi> <mi>d</mi> </mrow> </msub> </mrow> </semantics></math>) and experimental (<math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>Q</mi> </mrow> <mo>˙</mo> </mover> </mrow> <mrow> <mi>L</mi> <mo>,</mo> <mi>e</mi> <mi>x</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math>) liquid circulation rate for different reactor configurations: tube inner diameter (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>d</mi> </mrow> <mrow> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math>), electrical conductivity (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>e</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math>) and liquid exit height (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>h</mi> </mrow> <mrow> <mi>L</mi> <mo>,</mo> <mi>e</mi> <mi>x</mi> <mi>i</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math>); parity plot of training and testing data for both models: (<b>a</b>) extra trees and (<b>b</b>) multilayer perceptron.</p> Full article ">Figure A1
<p>Influence of liquid level (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>h</mi> </mrow> <mrow> <mi>L</mi> </mrow> </msub> </mrow> </semantics></math>) on gas holdup (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>G</mi> </mrow> </msub> </mrow> </semantics></math>) at different superficial gas velocities (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>u</mi> </mrow> <mrow> <mi>G</mi> </mrow> </msub> </mrow> </semantics></math>): (<b>a</b>) in BC 1 and (<b>b</b>) in BC2.</p> Full article ">Figure A2
<p>Influence of available training data on (<b>a</b>) MAPE and (<b>b</b>) R<sup>2</sup> for EXT and MLP model. The testing data remain constant.</p> Full article ">
22 pages, 5563 KiB  
Article
Study of Microstructure, Texture, and Cooking Qualities of Reformulated Whole Wheat Flour Pasta by Substituting Water with Stearic Acid–Candelilla Wax–Groundnut Oil Oleogel
by Diksha Chaturvedi, Somali Dhal, Deblu Sahu, Maciej Jarzębski, Arfat Anis, Doman Kim and Kunal Pal
ChemEngineering 2024, 8(3), 51; https://doi.org/10.3390/chemengineering8030051 - 4 May 2024
Viewed by 1506
Abstract
Oleogels, which are traditionally utilized to reduce saturated and trans fats in bakery foods, have recently shown promising applications in non-bakery foods, particularly in the enhancement of their food texture and cooking qualities. This study investigates the impact of incorporating stearic acid-containing candelilla [...] Read more.
Oleogels, which are traditionally utilized to reduce saturated and trans fats in bakery foods, have recently shown promising applications in non-bakery foods, particularly in the enhancement of their food texture and cooking qualities. This study investigates the impact of incorporating stearic acid-containing candelilla wax–groundnut oil oleogel in various proportions on the production of whole wheat pasta. Five different pasta samples were prepared by replacing water with oleogels in varying concentrations (2.5%, 5%, 10%, and 15%), and their physicochemical attributes were evaluated using a range of analytical methods for both cooked and uncooked pasta (like microscopy, colorimetry, dimensional analysis, texture, cooking qualities, moisture content, and FTIR). Significant differences in width, thickness, and color properties were observed between the control sample (0% oleogel) and those containing oleogel, with notable variations in surface texture and color intensities, particularly with the higher oleogel content (p < 0.05). Cooked pasta exhibited lower L* values and higher a* values than uncooked pasta. Stereo zoom microscope and field emission scanning electron microscope (FESEM) micrographs demonstrated a change in the pasta surface topology and microstructures. Dark spots on the pasta with greater oleogel concentrations (samples with 10% and 15% oleogel replacement) suggest the formation of starch–lipid complexes. Cooking induced pore formation, which was more pronounced when the oleogel content was increased, impacted the water absorption capacity, swelling index, and moisture content. The cooked samples exhibited higher moisture content and improved polymer network stability compared to the uncooked ones, indicating the potential of oleogel incorporation to modulate pasta properties in a concentration-dependent manner. These findings underscore the versatility of oleogels when their applications are diversified in non-bakery foods to enhance food texture and quality. Full article
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<p>Photographs depicting various samples of pasta.</p> Full article ">Figure 2
<p>Physical measurements of the pasta samples: (<b>a</b>) thickness and (<b>b</b>) width. The symbol ‘*’ on the bars shows the significant difference between the two samples.</p> Full article ">Figure 3
<p>Color parameters of the pasta samples: (<b>a</b>) L*, (<b>b</b>) a*, (<b>c</b>) b*, (<b>d</b>) WI, (<b>e</b>) YI, and (<b>f</b>) ∆E. (The symbol ‘*’ on the bars shows the significant difference between the two samples at <span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 4
<p>Surface topography of the uncooked and cooked (for 12 min and 15 min) pasta samples. (Red and yellow arrows indicate cracks and grooves; black arrows indicate the darker spots; green arrows indicate the bright spot; and violet arrows indicate bulged surfaces).</p> Full article ">Figure 5
<p>FESEM micrographs of all the pasta samples (yellow arrows indicate defects on the surface of the pasta, whereas pink arrows indicate pores). (Magnification: 250×).</p> Full article ">Figure 6
<p>FESEM images of uncooked pasta samples at higher magnification show the starch granules and gluten protein (red arrows indicate starch granules, whereas blue arrows indicate gluten protein granules). (Magnification: 1000×).</p> Full article ">Figure 7
<p>Cooking quality of pasta: (<b>a</b>) water absorption capacity (%); (<b>b</b>) swelling index; and (<b>c</b>) % dry matter content of the pasta samples. (The symbol ‘*’ on the bars shows the significant difference between the two samples at <span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 8
<p>Moisture content (% m) of the pasta samples. (The symbol ‘*’ on the bars shows the significant difference between the two samples at <span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 9
<p>Stress relaxation profiles of (<b>a</b>) uncooked, (<b>b</b>) 12 min cooked, and (<b>c</b>) 15 min cooked pasta samples; test analysis parameters: (<b>d</b>) F<sub>0</sub>, (<b>e</b>) F<sub>60</sub>, and (<b>f</b>) % SR. (The symbol ‘*’ on the bars shows the significant difference between the two samples at <span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 10
<p>Puncture test profile of (<b>a</b>) uncooked, (<b>b</b>) 12 min cooked, and (<b>c</b>) 15 min cooked pasta samples; test analysis parameters: (<b>d</b>) F<sub>max</sub> and (<b>e</b>) work of penetration. (The symbol ‘*’ on the bars shows the significant difference between the two samples at <span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 11
<p>FTIR spectra of uncooked (<b>a</b>), 12 min (<b>b</b>), and 15 min (<b>c</b>) cooked pasta samples.</p> Full article ">
22 pages, 6052 KiB  
Article
Photocatalytic Degradation of Tartrazine and Naphthol Blue Black Binary Mixture with the TiO2 Nanosphere under Visible Light: Box-Behnken Experimental Design Optimization and Salt Effect
by Fadimatou Hassan, Bouba Talami, Amira Almansba, Pierre Bonnet, Christophe Caperaa, Sadou Dalhatou, Abdoulaye Kane and Hicham Zeghioud
ChemEngineering 2024, 8(3), 50; https://doi.org/10.3390/chemengineering8030050 - 3 May 2024
Cited by 2 | Viewed by 1285
Abstract
In this study, TiO2 nanospheres (TiO2-NS) were synthesized by the solvothermal method. Firstly, the synthesized nanomaterial was characterized by X-ray diffraction (XRD), Fourier Transformed Infrared (FTIR), scanning electron microscopy (SEM) and UV-Vis Diffuse Reflectance Spectroscopy (DRS). To study the photocatalytic [...] Read more.
In this study, TiO2 nanospheres (TiO2-NS) were synthesized by the solvothermal method. Firstly, the synthesized nanomaterial was characterized by X-ray diffraction (XRD), Fourier Transformed Infrared (FTIR), scanning electron microscopy (SEM) and UV-Vis Diffuse Reflectance Spectroscopy (DRS). To study the photocatalytic degradation of Tartrazine (TTZ) and Naphthol Blue Black (NBB) in a binary mixture, the influence of some key parameters such as pH, pollutant concentration and catalyst dose was taken into account under visible and UV light. The results show a 100% degradation efficiency for TTZ after 150 min of UV irradiation and 57% under visible irradiation at 180 min. The kinetic study showed a good pseudo-first-order fit to the Langmuir–Hinshelwood model. Furthermore, in order to get closer to the real conditions of textile wastewater, the influence of the presence of salt on TiO2-NS’s photocatalytic performance was explored by employing NaCl as an inorganic ion. The optimum conditions provided by the Response Surface Methodology (RSM) were low concentrations of TTZ (2 ppm) and NBB (2.33 ppm) and negligible salt (NaCl) interference. The percentage of photodegradation was high at low pollutant and NaCl concentrations. However, this yield became very low as NaCl concentrations increased. The photocatalytic treatment leads to 31% and 53% of mineralization yield after 1 and 3 h of visible light irradiation. The synthesis of TiO2-NS provides new insights that will help to develop an efficient photocatalysts for the remediation of contaminated water. Full article
(This article belongs to the Special Issue The Synthesis, Characterization, and Application of Novel Photocatalytic Materials)
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<p>(<b>a</b>) XRD diffractogram, (<b>b</b>) Raman spectra, (<b>c</b>) FTIR spectra and (<b>d</b>) Elemental Composition of TiO<sub>2</sub>−NS.</p> Full article ">Figure 2
<p>SEM images of TiO<sub>2</sub>-NS at different magnifications: (<b>a</b>) ×33,000 (0.5 μm), (<b>b</b>) ×18,000 (1 μm), (<b>c</b>) ×12,000 (1 μm) and (<b>d</b>) ×6000 (2 μm).</p> Full article ">Figure 3
<p>(<b>a</b>) Diffuse reflectance spectra and (<b>b</b>) Plot of transferred Kubelka-Munk Versus energy of TiO<sub>2</sub>−NS.</p> Full article ">Figure 4
<p>Adsorption equilibrium and photocatalytic degradation of tartrazine with TiO<sub>2</sub>−NS catalyst under UV and visible light (C<sub>0</sub>: 5 ppm. C<sub>TiO<sub>2</sub>−NS</sub>: 0.2 g/L. V: 200 mL. natural pH: 6).</p> Full article ">Figure 5
<p>Effect of catalyst dose on tartrazine degradation under visible light (C<sub>0</sub>: 5 ppm. V<sub>solution</sub>: 200 mL. Natural pH: 6).</p> Full article ">Figure 6
<p>(<b>a</b>) Effect of initial Tartrazine concentration with 200 mL of solution and 40 mg of catalyst at natural pH, under visible light irradiation; (<b>b</b>) PFO kinetics for tartrazine degradation under visible light ([TTZ]<sub>0</sub> = 2–12 ppm. V<sub>solution</sub>: 200 mL. Natural pH: 6. C<sub>TiO<sub>2</sub>-NS</sub>: 0.2 g/L. Reaction time = 180 min), (<b>c</b>) Langmuir–Hinshelwood plot for photodegradation of tartrazine under visible light ([TTZ]<sub>0</sub> = 2–12 ppm. V<sub>solution</sub>: 200 mL.Natural pH: 6. C<sub>TiO<sub>2</sub>-NS</sub>: 0.2 g/L. Reaction time = 180 min).</p> Full article ">Figure 7
<p>Photocatalytic degradation of binary solution of TTZ and NBB with TiO<sub>2</sub> nanosphere catalyst under visible light at different pH values (C<sub>TTZ</sub>: 2 ppm. C<sub>NBB</sub>: 2.33 ppm. C<sub>TiO<sub>2</sub>-NS</sub>: 0.2 g/L. V<sub>solution</sub>: 200 mL. Treatment duration: 120 min, 10 ppm of NaCl presence).</p> Full article ">Figure 8
<p>(<b>a</b>) Photocatalytic degradation of Tartrazine (TTZ) (C<sub>NBB</sub>: 2.33 ppm. m<sub>TiO<sub>2</sub>-NS</sub>: 40 mg. V<sub>solution</sub>: 200 mL. Natural pH: 6, 10 ppm of NaCl presence) and of (<b>b</b>) Naphthol Blue Black (NBB) (C<sub>TTZ</sub>: 2 ppm. m<sub>TiO<sub>2</sub>-NS</sub>: 40 mg. V<sub>solution</sub>: 200 mL. natural pH: 6, 10 ppm of NaCl presence) in binary solution under visible light.</p> Full article ">Figure 9
<p>Predicted vs. experimental results of degradation efficiency in binary system: (<b>a</b>) Tartrazine; (<b>b</b>) Naphthol Blue Black.</p> Full article ">Figure 10
<p>RSM surfaces plots and 2D contour plots of the interaction effects between: (<b>a</b>) TTZ and NBB concentrations; (<b>b</b>) TTZ and NaCl concentrations and (<b>c</b>) NaCl and NBB concentrations.</p> Full article ">Figure 10 Cont.
<p>RSM surfaces plots and 2D contour plots of the interaction effects between: (<b>a</b>) TTZ and NBB concentrations; (<b>b</b>) TTZ and NaCl concentrations and (<b>c</b>) NaCl and NBB concentrations.</p> Full article ">Figure 11
<p>RSM surfaces plots and 2D contour plots of the interaction effects between (<b>a</b>) NBB and TTZ concentrations; (<b>b</b>) NaCl and Tartrazine concentrations; and (<b>c</b>) NBB and NaCl concentrations.</p> Full article ">Figure 11 Cont.
<p>RSM surfaces plots and 2D contour plots of the interaction effects between (<b>a</b>) NBB and TTZ concentrations; (<b>b</b>) NaCl and Tartrazine concentrations; and (<b>c</b>) NBB and NaCl concentrations.</p> Full article ">Figure 12
<p>Mineralization of tartrazine with TiO<sub>2</sub> nanosphere catalyst under visible light (C<sub>0</sub>: 6 ppm. C<sub>TiO<sub>2</sub>-NS</sub>: 0.2 g/L. V<sub>solution</sub>: 200 mL. Natural pH: 6).</p> Full article ">Figure 13
<p>Reusability cycles of TiO<sub>2</sub>-NS for photocatalytic degradation of tartrazine under visible light (C<sub>0</sub>: 6 ppm. m<sub>TiO<sub>2</sub>-NS</sub>: 100 mg. V<sub>solution</sub>: 200 mL. natural pH: 6).</p> Full article ">
16 pages, 5209 KiB  
Article
Mechanical Dewatering of Homogeneous and Segregated Filter Cakes by Vibration Compaction
by Tolga Yildiz, Una Stankovic, Julius Zolg, Marco Gleiß and Hermann Nirschl
ChemEngineering 2024, 8(3), 49; https://doi.org/10.3390/chemengineering8030049 - 3 May 2024
Viewed by 982
Abstract
The solid volume fraction of a slurry requiring solid–liquid separation often fluctuates in industrial cake filtration processes. For low solid volume fractions, particle segregation arises, resulting in an inhomogeneous filter cake structure. Particle segregation has significant impacts on cake formation such as a [...] Read more.
The solid volume fraction of a slurry requiring solid–liquid separation often fluctuates in industrial cake filtration processes. For low solid volume fractions, particle segregation arises, resulting in an inhomogeneous filter cake structure. Particle segregation has significant impacts on cake formation such as a longer cake formation time compared to homogeneous cakes. This work addresses the impact of this effect on vibration compaction, which is an alternative deliquoring method applying oscillatory shears to the filter cake. The dewatering results of homogeneous and segregated cakes made of the same material with a broad particle size distribution are compared. Although cake deliquoring is achievable despite particle segregation, vibration compaction is more effective for homogeneous cakes. The reason is that no particle size homogenization within segregated cakes occurs due to oscillatory shear, as particle size analyses indicate. The particle size measurements of cakes before and after vibration compaction reveal that the material’s particle size distribution is preserved despite vibration application. Vibration compaction achieves higher deliquoring than the common compaction method by squeezing, as elastic recovery effects after squeezing lead to the reabsorbing of liquid, already expressed and stored in the filter cloth. This demonstrates that vibration compaction is a real alternative for cake deliquoring. Full article
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<p>(<b>a</b>) Scanning electron microscope image of the model particles and (<b>b</b>) cumulative particle size distribution of the model material measured in deionized water with HELOS H0309 laser diffraction instrument (Sympatec GmbH, Germany).</p> Full article ">Figure 2
<p>Apparatus developed by Illies et al. [<a href="#B30-ChemEngineering-08-00049" class="html-bibr">30</a>] for filter cake compaction by oscillatory shear at a low compressive pressure at a laboratory scale. The figure is from Yildiz et al. [<a href="#B33-ChemEngineering-08-00049" class="html-bibr">33</a>], reprinted with the permission of the publisher (Taylor &amp; Francis Ltd., <a href="http://www.tandfonline.com" target="_blank">http://www.tandfonline.com</a>, accessed on 23 Januar 2024).</p> Full article ">Figure 3
<p>Setup of the compression–permeability cell developed by Alles [<a href="#B37-ChemEngineering-08-00049" class="html-bibr">37</a>]. The figure was taken from Yildiz et al. [<a href="#B33-ChemEngineering-08-00049" class="html-bibr">33</a>], reprinted with the permission of the publisher (Taylor &amp; Francis Ltd., <a href="http://www.tandfonline.com" target="_blank">http://www.tandfonline.com</a>, accessed on 23 January 2024).</p> Full article ">Figure 4
<p>(<b>a</b>) Sampling of filter cakes in the laboratory vibration apparatus and (<b>b</b>) device for cutting a filter cake sample into several layers.</p> Full article ">Figure 5
<p>Cumulative particle size distribution in the different layers of a filter cake formed on a filter plate of a vibration apparatus from a slurry with a solid volume fraction of (<b>a</b>) 40% and (<b>b</b>) 10%. As the coordinate describing the filter cake height starts from the filter medium, the 0–1 mm layer is the lowest cake layer directly on the filter medium.</p> Full article ">Figure 6
<p>Residual moisture of homogeneous and segregated formed filter cakes after cake formation and vibration compaction at a frequency of (<b>a</b>) 17 Hz and (<b>b</b>) 40 Hz. The dashed line indicates the approximation of the data using Equation (<a href="#FD6-ChemEngineering-08-00049" class="html-disp-formula">6</a>).</p> Full article ">Figure 7
<p>Cumulative particle size distributions of (<b>a</b>) homogeneous and (<b>b</b>) segregated filter cakes in the full cake height after cake formation and vibration compaction at 17 Hz (1000 oscillations) and 40 Hz (6000 oscillations). The reference samples in (<b>a</b>,<b>b</b>) represent the particle size distributions of the model material in the original state. Particle size distributions in the different layers of (<b>c</b>) homogeneous and (<b>d</b>) segregated filter cakes after cake formation and vibration compaction at 40 Hz (6000 oscillations). As the coordinate describing the filter cake height starts from the filter medium, the 0–1 mm layer is the lowest cake layer directly on the filter medium.</p> Full article ">Figure 8
<p>Minimum residual moisture of (<b>a</b>) homogeneous and (<b>b</b>) segregated filter cakes during and after squeezing at different pressures in the CP-cell and after vibration compaction. The values at a squeezing pressure of 0 kPa are the reference cake conditions after cake formation.</p> Full article ">
25 pages, 1225 KiB  
Article
Resolved Simulation for the Prediction of Classification in Decanter Centrifuges
by Helene Katharina Baust, Hermann Nirschl and Marco Gleiß
ChemEngineering 2024, 8(3), 48; https://doi.org/10.3390/chemengineering8030048 - 2 May 2024
Viewed by 1257
Abstract
Solid–liquid separation plays a decisive role in various industrial applications particularly in the treatment and purification of suspensions. Solid bowl centrifuges, such as the decanter centrifuge, are commonly employed in these processes as they operate continuously and enable high throughputs with short processing [...] Read more.
Solid–liquid separation plays a decisive role in various industrial applications particularly in the treatment and purification of suspensions. Solid bowl centrifuges, such as the decanter centrifuge, are commonly employed in these processes as they operate continuously and enable high throughputs with short processing times. However, predicting the separation performance of solid bowl centrifuges proves to be challenging due to dynamic phenomena within the apparatus, such as particle settling, sediment build-up, consolidation and sediment transport. In practice, design considerations and the dimensioning of the apparatus rely on analytical models and the manufacturer’s expertise. Computational Fluid Dynamics (CFD) offers a way to deepen our understanding of these devices by allowing detailed examination of flow phenomena and their influence on the separation processes. This study utilizes the open-source software OpenFOAM to simulate multiphase flow in a laboratory-scale decanter centrifuge, solving individual transport equations for each particle size class. The basis is the characterization of the material through targeted laboratory experiments to derive material functions that describe the hindered settling and the sediment consolidation. Furthermore, experiments on a laboratory decanter served as validation. The results demonstrate the solver’s capability to replicate clarification and classification within the apparatus. Furthermore, the solver supports the definition of geometries tailored to specific separation tasks. This research demonstrates the potential of CFD for a better understanding of complex centrifuge processes and for optimizing their design to improve performance. Full article
(This article belongs to the Special Issue Process Intensification for Chemical Engineering and Processing)
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<p>Schematic description of a decanter centrifuge.</p> Full article ">Figure 2
<p>Simplified illustration of the screw geometry variation.</p> Full article ">Figure 3
<p>Mesh of the decanter centrifuge generated with ANSYS DesignModeler. To obtain a structured mesh consisting of hexahedrons, the geometry was divided into 368 segments. As an example, two sections of the mesh are shown enlarged. The magnification factor is about six.</p> Full article ">Figure 4
<p>Discretization of the particle size distribution of limestone, by one (<b>a</b>), three (<b>b</b>), five (<b>c</b>), ten (<b>d</b>,<b>e</b>) and twenty (<b>f</b>) particle classes.</p> Full article ">Figure 5
<p>Influence of the solids volume fraction on the hindered settling velocity for different particle size classes of a limestone–water suspension.</p> Full article ">Figure 6
<p>Solids effective stress as a function of the solids volume fraction for the product limestone.</p> Full article ">Figure 7
<p>Influence of the number of particle size classes on the sediment structure in the beaker centrifuge: The solids volume fraction of the suspension was <math display="inline"><semantics> <mrow> <msub> <mi>ϕ</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.08</mn> </mrow> </semantics></math>, the rotational speed was <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>2000</mn> </mrow> </semantics></math> min<sup>−1</sup>. The asterisk (*) indicates a non-homogeneously discretized distribution according to <a href="#ChemEngineering-08-00048-f004" class="html-fig">Figure 4</a>e.</p> Full article ">Figure 8
<p>Particle size distribution in the individual sediment layers: The solids volume fraction of the suspension was <math display="inline"><semantics> <mrow> <msub> <mi>ϕ</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.08</mn> </mrow> </semantics></math>, the rotational speed was <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>2000</mn> </mrow> </semantics></math> min<sup>−1</sup>. Ten particle size classes were used for the discretization.</p> Full article ">Figure 9
<p>Influence of the number <span class="html-italic">n</span> of particle size classes on the particle size distribution in the centrate and the sediment: the solids volume fraction of the feed suspension was <math display="inline"><semantics> <mrow> <msub> <mi>ϕ</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics></math>, the volume flow rate was <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>V</mi> <mo>˙</mo> </mover> <mo>=</mo> <mn>36</mn> </mrow> </semantics></math> Lh<sup>−1</sup> and the rotational speed was <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>3000</mn> </mrow> </semantics></math> min<sup>−1</sup>. The asterisk (*) indicates a non-homogeneously discretized distribution according to <a href="#ChemEngineering-08-00048-f004" class="html-fig">Figure 4</a>e.</p> Full article ">Figure 10
<p>Required computation time for the simulation of the beaker centrifuge and the decanter centrifuge depending on the number of particle size classes. The asterisk (*) indicates a non-homogeneously discretized distribution according to <a href="#ChemEngineering-08-00048-f004" class="html-fig">Figure 4</a>e.</p> Full article ">Figure 11
<p>Solids volume fraction of the centrate (blue) and the sediment (orange) at a volumetric flow rate of 36 Lh<sup>−1</sup> with varying rotational speed: Comparison of simulation and experiment.</p> Full article ">Figure 12
<p>Influence of the rotational speed <span class="html-italic">n</span> on the particle size distribution of the centrate: The solids volume fraction of the feed suspension was <math display="inline"><semantics> <mrow> <msub> <mi>ϕ</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics></math>, the volume flow rate was <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>V</mi> <mo>˙</mo> </mover> <mo>=</mo> <mn>36</mn> </mrow> </semantics></math> Lh<sup>−1</sup> and the rotational speed varies between 1000 rpm and 5000 rpm.</p> Full article ">Figure 13
<p>Influence of the rotational speed <span class="html-italic">n</span> on the particle size distribution of the centrate: The solids volume fraction of the feed suspension was <math display="inline"><semantics> <mrow> <msub> <mi>ϕ</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics></math>, the rotational speed was <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>3000</mn> </mrow> </semantics></math> rpm and the volume flow rate varies between 24 Lh<sup>−1</sup> and 72 Lh<sup>−1</sup>.</p> Full article ">Figure 14
<p>Particle size distribution of the centrate in different segments in the decanter centrifuge: The solids volume fraction of the feed suspension was <math display="inline"><semantics> <mrow> <msub> <mi>ϕ</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics></math>, the volume flow rate was <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>V</mi> <mo>˙</mo> </mover> <mo>=</mo> <mn>36</mn> </mrow> </semantics></math> Lh<sup>−1</sup> and the rotational speed was <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>5000</mn> </mrow> </semantics></math> <math display="inline"><semantics> <mi>rpm</mi> </semantics></math>.</p> Full article ">Figure 15
<p>Solid distribution in the cylindrical section of the decanter centrifuge with various flight designs. The color scale indicates the solids volume fraction in the apparatus. The sediment (<math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>&gt;</mo> <msub> <mi>ϕ</mi> <mi>gel</mi> </msub> </mrow> </semantics></math>) is shown in gray.</p> Full article ">Figure 16
<p>Separation efficiency for different flight designs of the screw: The solids volume fraction of the feed suspension was <math display="inline"><semantics> <mrow> <msub> <mi>ϕ</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics></math>, the volume flow rate was <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>V</mi> <mo>˙</mo> </mover> <mo>=</mo> <mn>36</mn> </mrow> </semantics></math> Lh<sup>−1</sup>.</p> Full article ">Figure A1
<p>Mesh independence study: (<b>a</b>) the axial velocity component <math display="inline"><semantics> <msub> <mi>U</mi> <mi>ax</mi> </msub> </semantics></math> and (<b>b</b>) the solids volume fraction <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math> are plotted against the radial position <span class="html-italic">r</span> in the screw channel [<a href="#B36-ChemEngineering-08-00048" class="html-bibr">36</a>].</p> Full article ">
17 pages, 2815 KiB  
Article
Magnetic Three-Dimensional Graphene: A Superior Adsorbent for Selective and Sensitive Determination of Nitrite in Water Samples by Ion-Pair Based-Surfactant-Assisted Solid-Phase Extraction Combined with Spectrophotometry
by Mina Vasheghani Farahani, Sajad Karami, Hassan Sereshti, Shokouh Mahpishanian, Somayeh Koupaei Malek and Shahabaldin Rezania
ChemEngineering 2024, 8(3), 47; https://doi.org/10.3390/chemengineering8030047 - 1 May 2024
Viewed by 1436
Abstract
A straightforward, fast and efficient analytical method was developed which utilizes a magnetic composite called three-dimensional graphene (3D-G@Fe3O4) as an adsorbent to recover nitrite ions (NO2−) from environmental water samples. The investigation into the synthesized adsorbent [...] Read more.
A straightforward, fast and efficient analytical method was developed which utilizes a magnetic composite called three-dimensional graphene (3D-G@Fe3O4) as an adsorbent to recover nitrite ions (NO2−) from environmental water samples. The investigation into the synthesized adsorbent contained an examination of its morphology, chemical composition, structural attributes, and magnetic properties. This comprehensive analysis was conducted using various instrumental techniques, including Fourier transform-infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), Raman spectroscopy, X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET), Barrett-Joyner-Halenda (BJH), and vibrating sample magnetometry (VSM). The adsorbent surface was activated by adding cetyltrimethylammonium bromide (CTAB) to the sample solution. To improve the selectivity and sensitivity of the method, nitrite ions were reacted with sulfanilic acid and chromotropic acid sequentially. An orange-red azo-dye complex was formed in the presence of nitrite ions with a clear absorbance peak at 514 nm. The effect of the main experimental parameters such as the pH of the sample solution, adsorbent dosage, and CTAB dosage was explored, and the optimization process was performed using a central composite design (CCD). The linear dynamic range (20–100 ng mL−1) was determined under optimal experimental circumstances, yielding a reasonable determination coefficient (R2, 0.9993), a detection limit of 5.12 ng mL−1, an enrichment factor of 167, and precision values of 1.0% intraday and 2.9% inter-day. The methodology successfully identified minute nitrite ions in environmental water samples with relative recoveries that varied between 96.05 and 101.6 ng mL−1. Full article
(This article belongs to the Collection Green and Environmentally Sustainable Chemical Processes)
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<p>The characterization of the 3D-G-Fe<sub>3</sub>O<sub>4</sub> nanocomposite by: (<b>a</b>) SEM micrograph of 3D-G-Fe<sub>3</sub>O<sub>4</sub>; (<b>b</b>) FT-IR spectrum of 3D-G-Fe<sub>3</sub>O<sub>4</sub>; (<b>c</b>) VSM magnetization curve of 3D-G-Fe<sub>3</sub>O<sub>4</sub>; (<b>d</b>) XRD pattern of the GO, Fe<sub>3</sub>O<sub>4</sub> and 3D-G-Fe<sub>3</sub>O<sub>4</sub>; (<b>e</b>) the Raman spectra of graphene oxide (GO) and three-dimensional graphene-iron (3D-G-Fe<sub>3</sub>O<sub>4</sub>) are presented. (<b>f</b>) The nitrogen adsorption-desorption isotherms of three-dimensional graphene-iron (3D-G-Fe<sub>3</sub>O<sub>4</sub>) are shown in the inset, along with the BJH pore-size distributions.</p> Full article ">Figure 1 Cont.
<p>The characterization of the 3D-G-Fe<sub>3</sub>O<sub>4</sub> nanocomposite by: (<b>a</b>) SEM micrograph of 3D-G-Fe<sub>3</sub>O<sub>4</sub>; (<b>b</b>) FT-IR spectrum of 3D-G-Fe<sub>3</sub>O<sub>4</sub>; (<b>c</b>) VSM magnetization curve of 3D-G-Fe<sub>3</sub>O<sub>4</sub>; (<b>d</b>) XRD pattern of the GO, Fe<sub>3</sub>O<sub>4</sub> and 3D-G-Fe<sub>3</sub>O<sub>4</sub>; (<b>e</b>) the Raman spectra of graphene oxide (GO) and three-dimensional graphene-iron (3D-G-Fe<sub>3</sub>O<sub>4</sub>) are presented. (<b>f</b>) The nitrogen adsorption-desorption isotherms of three-dimensional graphene-iron (3D-G-Fe<sub>3</sub>O<sub>4</sub>) are shown in the inset, along with the BJH pore-size distributions.</p> Full article ">Figure 2
<p>Effect of types of solvent on recovery (Sample solution: 25 mL, 40 ng mL<sup>−1</sup> NO<sub>2</sub>).</p> Full article ">Figure 3
<p>The influence of desorption solvent (DMSO) volume on the recovery of analyte. Sample solution: 25 mL, 40 ng mL<sup>−1</sup> NO<sub>2</sub>.</p> Full article ">Figure 4
<p>(<b>a</b>) Three-dimensional (3D) response surface and counter plots the effect of pH-adsorbent dosage on the extraction recovery (the CTAB dosage was 5.5 mg); and (<b>b</b>) 3D response surface and counter plots of the effect of pH-CTAB dosage on the extract.</p> Full article ">Figure 5
<p>Proposed mechanism for selective adsorption of NO<sub>2</sub><sup>−</sup> ions over the magnetic graphene oxide.</p> Full article ">
20 pages, 3266 KiB  
Article
A Framework for Upscaling of Emerging Chemical Processes Based on Thermodynamic Process Modeling and Simulation
by Hafiz Farooq Imtiaz
ChemEngineering 2024, 8(3), 46; https://doi.org/10.3390/chemengineering8030046 - 1 May 2024
Viewed by 1369
Abstract
Prospective environmental and technological assessment of emerging chemical processes is necessary to identify, analyze and evaluate the technologies that are highly imperative in the transition towards climate neutrality. The investigation of the environmental impacts and material and energy requirements of the processes at [...] Read more.
Prospective environmental and technological assessment of emerging chemical processes is necessary to identify, analyze and evaluate the technologies that are highly imperative in the transition towards climate neutrality. The investigation of the environmental impacts and material and energy requirements of the processes at the low technology readiness level (TRL) is important in making early decisions about the feasibility of adapting and upscaling the process to the industrial level. However, the upscaling of new chemical processes has always been a major challenge; and in this context, there is no general methodological guidance available in the literature. Hence, a new comprehensive methodological framework for upscaling of novel chemical processes is designed and presented based on thermodynamic process modeling and simulation. The practical implementation of the proposed methodology is extensively discussed by developing a scaled-up novel carbon capture and utilization (CCU) process comprised of sequestration of carbon dioxide (CO2) from blast furnace gas with a capacity of 1000 liter per hour (L/h) using methanol and its utilization as a precursor to produce methane (CH4). It was found that thermodynamic process modeling and simulations based on the perturbed-chain statistical associating (PC-SAFT) equation of state (EOS) can precisely estimate the CO2 solubility in methanol and conversion to CH4 at various temperature and pressure conditions. The achieved thermophysical property and kinetics parameters can be employed in process simulations to estimate scaled-up environmental flows and material and energy requirements of the process. Full article
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<p>Schematic diagram of upscaling from the emerging process to the pilot/industrial scale.</p> Full article ">Figure 2
<p>Methodologies for upscaling of emerging chemical processes.</p> Full article ">Figure 3
<p>Methodological framework for upscaling of emerging chemical processes based on thermodynamic process modeling and simulation.</p> Full article ">Figure 4
<p>Model classification and their derived mathematical equations.</p> Full article ">Figure 5
<p>Novel process of carbon dioxide capture from blast furnace gas and its utilization in synthetic natural gas production.</p> Full article ">Figure 6
<p>Material balance diagram of the absorption column for the physical separation of CO<sub>2</sub> from BFG using methanol.</p> Full article ">Figure 7
<p>Material and energy balance diagram for the flash separation of CO<sub>2</sub> from methanol.</p> Full article ">Figure 8
<p>Material and energy balance diagram for CO<sub>2</sub> conversion to CH<sub>4</sub> in a fixed-bed reactor.</p> Full article ">Figure 9
<p>Comparison between experimental and PC-SAFT EOS model estimated equilibrium P-x and P-y plots of CO<sub>2</sub>-methanol.</p> Full article ">Figure 10
<p>Comparison of experimental CO<sub>2</sub> conversion to model predictions at various temperatures.</p> Full article ">Figure 11
<p>Comparison of experimental CO<sub>2</sub> conversion to model predictions at various gas hourly space velocities (GHSV).</p> Full article ">Figure 12
<p>Aspen Plus process simulations flow diagram.</p> Full article ">
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 1219
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)
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<p>Multiscale decomposition of a heavy-sine signal using Haar wavelet.</p> Full article ">Figure 2
<p>Multiscale PCA-KD based fault detection strategy.</p> Full article ">Figure 3
<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> Full article ">Figure 4
<p>Correlation matrix heatmap depicting the Pearson correlation among variables in the fault-free distillation column dataset.</p> Full article ">Figure 5
<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> Full article ">Figure 6
<p>Calculation of decomposition depth for (<b>a</b>) SNR = 15 and (<b>b</b>) SNR = 5.</p> Full article ">Figure 7
<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> Full article ">Figure 8
<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> Full article ">Figure 9
<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> Full article ">Figure 10
<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> Full article ">Figure 11
<p>A schematic of distillation column process.</p> Full article ">Figure 12
<p>Correlation matrix of the fault-free CSTR data.</p> Full article ">Figure 13
<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> Full article ">Figure 14
<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> Full article ">Figure 16
<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 ">
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