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Volume 12
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Processes, Volume 12, Issue 8 (August 2024) – 139 articles

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17 pages, 2695 KiB  
Article
Studies of TLC-Chromatographic Quantification of Astaxanthin in Dietary Supplements and Its Antioxidant Activity
by Iwona Dymek, Joanna Żandarek, Małgorzata Starek and Monika Dąbrowska
Processes 2024, 12(8), 1680; https://doi.org/10.3390/pr12081680 (registering DOI) - 11 Aug 2024
Abstract
Astaxanthin is a red carotenoid pigment known for its strong antioxidant and immune-supporting properties, which are higher than other carotenoids. The aim of this study was the qualitative and quantitative evaluation of dietary supplements containing astaxanthin. First, optimal conditions for conducting analyses using [...] Read more.
Astaxanthin is a red carotenoid pigment known for its strong antioxidant and immune-supporting properties, which are higher than other carotenoids. The aim of this study was the qualitative and quantitative evaluation of dietary supplements containing astaxanthin. First, optimal conditions for conducting analyses using the TLC technique with densitometric detection were developed. The mobile phase consisting of methanol: ethyl acetate: 1,4-dioxane (1:3:6 v/v/v) was selected, while the stationary phase consisted of Silica gel 60 F254. Densitometric detection was performed at 460 nm. Next, the validation process of the developed method was carried out according to the guidelines of the International Conference on Harmonization (ICH). The range of linearity tested was 0.0026–0.0100 µg/spot, and the determined LOD and LOQ values were 0.85 and 2.57 ng/μL, respectively. The variation coefficient at the level of 4.75% proves good precision. The percentage of recovery was in the range of 95.25–104.94%. The obtained results confirmed the good accuracy of the method. Subsequently, quantitative analyses of the preparations were carried out. Analysis of dietary supplements showed significant deviations from the declared astaxanthin content. Astaxanthin solutions were stable in alkaline environments and when exposed to light and oxidizing substances; however, the substance degraded in acidic environments. The performed antioxidant capacity tests confirmed the high antioxidant activity of astaxanthin. Full article
(This article belongs to the Special Issue Applications of Chromatographic Separation Techniques in Food and Chemistry—Second Edition)
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<p>Densitometrically recorded absorption spectrum in the range from 200 to 800 nm for the astaxanthin standard solution (stationary phase used: TLC Silica gel 60F<sub>254</sub>, mobile phase: methanol: ethyl acetate: 1,4-dioxane (1:3:6 <span class="html-italic">v</span>/<span class="html-italic">v</span>/<span class="html-italic">v</span>).</p> Full article ">Figure 2
<p>Plot of surface area [mm<sup>2</sup>] vs. concentration of astaxanthin standard solution [µg/spot] (<b>a</b>) and plot of residues (<b>b</b>).</p> Full article ">Figure 3
<p>An example densitogram recorded for astaxanthin after UV–Vis irradiation for 24 h (analogous to that in alkaline and oxidizing conditions).</p> Full article ">Figure 4
<p>Densitogram recorded for an astaxanthin solution in 0.1 mol/L HCl (<b>a</b>) immediately after preparation, (<b>b</b>) after 2 h of incubation at 25 °C.</p> Full article ">Figure 5
<p>Absorbance vs. concentration graphs of antioxidant capacity for astaxanthin vs. reference ascorbic acid; DPPH method. Data presented as mean ± standard deviation (<span class="html-italic">n</span> = 3).</p> Full article ">Figure 6
<p>Absorbance vs. concentration graphs of antioxidant capacity for astaxanthin vs. reference ascorbic acid; reduction of iron(III) ions. Data presented as mean ± standard deviation (<span class="html-italic">n</span> = 3).</p> Full article ">Figure 7
<p>Absorbance vs. concentration graphs of antioxidant capacity for astaxanthin vs. reference ascorbic acid; phosphomolybdenum method. Data presented as mean ± standard deviation (<span class="html-italic">n</span> = 3).</p> Full article ">Figure 8
<p>Absorbance vs. concentration graphs of antioxidant capacity for astaxanthin vs. reference ascorbic acid; chelation of iron ions. Data presented as mean ± standard deviation (<span class="html-italic">n</span> = 3).</p> Full article ">Figure 9
<p>Chemical structure of astaxanthin.</p> Full article ">Figure 10
<p>Graph of ascorbic acid equivalents (AAE [%]) determined by (<b>a</b>) DPPH, (<b>b</b>) reduction of iron(III) ions, (<b>c</b>) phosphomolybdenum, and (<b>d</b>) chelation of iron ions methods. Data presented as mean ± standard deviation (<span class="html-italic">n</span> = 3).</p> Full article ">
24 pages, 5184 KiB  
Article
Mathematical Model of the Migration of the CO2-Multicomponent Gases in the Inorganic Nanopores of Shale
by Xiangji Dou, Hong Li, Sujin Hong, Mingguo Peng, Yanfeng He, Kun Qian, Luyao Guo and Borui Ma
Processes 2024, 12(8), 1679; https://doi.org/10.3390/pr12081679 (registering DOI) - 11 Aug 2024
Abstract
Nanopores in shale reservoirs refer to extremely small pores within the shale rock, categorised into inorganic and organic nanopores. Due to the differences in the hydrophilicity of the pore walls, the gas migration mechanisms vary significantly between inorganic and organic nanopores. By considering [...] Read more.
Nanopores in shale reservoirs refer to extremely small pores within the shale rock, categorised into inorganic and organic nanopores. Due to the differences in the hydrophilicity of the pore walls, the gas migration mechanisms vary significantly between inorganic and organic nanopores. By considering the impact of irreducible water and the variations in effective migration pathways caused by pore pressure and by superimposing the weights of different migration mechanisms, a mathematical model for the migration of CO2-multicomponent gases in inorganic nanopores of shale reservoirs has been established. The aim is to accurately clarify the migration laws of multi-component gases in shale inorganic nanopores. Additionally, this paper analyses the contributions of different migration mechanisms and studies the effects of various factors, such as pore pressure, pore size, component ratios, stress deformation, and water film thickness, on the apparent permeability of the multi-component gases in shale inorganic nanopores. The research results show that at high pressure and large pore size (pore pressure greater than 10 MPa, pore size greater than 4 nm), slippage flow dominates, while at low pressure and small pore size (pore pressure less than 10 MPa, pore size less than 4 nm), Knudsen diffusion dominates. With the increase of the stress deformation coefficient, the apparent permeability of gas gradually decreases. When the stress deformation coefficient is less than 0.05 MPa−1, the component ratio significantly impacts bulk apparent permeability. However, when the coefficient exceeds 0.05 MPa−1, this influence becomes negligible. The research results provide a theoretical basis and technical support for accurately predicting shale gas productivity, enhancing shale gas recovery, and improving CO2 storage efficiency. Full article
(This article belongs to the Special Issue Artificial Intelligent Techniques in the Optimal Operation of Oil and Gas Production Systems)
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<p>Schematic diagram of the migration mechanism of multi-component gases in inorganic nanopores.</p> Full article ">Figure 2
<p>Schematic diagram of gas migration. (The rightward-pointing arrow indicates the direction of motion of the CO<sub>2</sub> gas molecules, and the leftward-pointing arrow indicates the direction of motion of the CH<sub>4</sub> gas molecules) (<b>a</b>) Stage I; (<b>b</b>) Stage II; (<b>c</b>) Stage III.</p> Full article ">Figure 3
<p>Slippage flow: (<b>a</b>) Variation of permeability of slippage flow with pore pressure; (<b>b</b>) Variation of the weight of the slippage flow permeability with pore pressure (CO<sub>2</sub>:CH<sub>4</sub>:C<sub>2</sub>H<sub>6</sub> = 2:1:1).</p> Full article ">Figure 4
<p>Knudsen diffusion: (<b>a</b>) Variation of Knudsen diffusion permeability with pore pressure; (<b>b</b>) Variation of the weight of Knudsen diffusion permeability with pore pressure (CO<sub>2</sub>:CH<sub>4</sub>:C<sub>2</sub>H<sub>6</sub> = 2:1:1).</p> Full article ">Figure 5
<p>The weight of different migration mechanisms varies with pore pressure in different pore sizes (CO<sub>2</sub>:CH<sub>4</sub>:C<sub>2</sub>H<sub>6</sub> = 2:1:1): (<b>a</b>) r = 2 nm; (<b>b</b>) r = 4 nm; (<b>c</b>) r = 6 nm; (<b>d</b>) r = 8 nm; (<b>e</b>) r = 10 nm; (<b>f</b>) r = 15 nm.</p> Full article ">Figure 6
<p>Effect of pore pressure on bulk phase apparent permeability in different pore sizes (CO<sub>2</sub>:CH<sub>4</sub>:C<sub>2</sub>H<sub>6</sub> = 2:1:1).</p> Full article ">Figure 7
<p>Variation of bulk phase apparent permeability with pore pressure under different component proportions: (<b>a</b>) r = 2 nm; (<b>b</b>) r = 4 nm; (<b>c</b>) r = 6 nm; (<b>d</b>) r = 8 nm; (<b>e</b>) r = 10 nm; (<b>f</b>) r = 15 nm.</p> Full article ">Figure 8
<p>Variation of bulk phase apparent permeability with pore pressure under different stress sensitivity coefficients (CO<sub>2</sub>:CH<sub>4</sub>:C<sub>2</sub>H<sub>6</sub> = 2:1:1): (<b>a</b>) r = 2 nm; (<b>b</b>) r = 4 nm; (<b>c</b>) r = 6 nm; (<b>d</b>) r = 8 nm; (<b>e</b>) r = 10 nm; (<b>f</b>) r = 15 nm.</p> Full article ">Figure 8 Cont.
<p>Variation of bulk phase apparent permeability with pore pressure under different stress sensitivity coefficients (CO<sub>2</sub>:CH<sub>4</sub>:C<sub>2</sub>H<sub>6</sub> = 2:1:1): (<b>a</b>) r = 2 nm; (<b>b</b>) r = 4 nm; (<b>c</b>) r = 6 nm; (<b>d</b>) r = 8 nm; (<b>e</b>) r = 10 nm; (<b>f</b>) r = 15 nm.</p> Full article ">Figure 9
<p>Impact of stress sensitivity coefficient and component ratio on bulk phase permeability (r = 6 nm).</p> Full article ">Figure 10
<p>Effect of water film on bulk phase permeability (CO<sub>2</sub>:CH<sub>4</sub>:C<sub>2</sub>H<sub>6</sub> = 2:1:1): (<b>a</b>) r = 2 nm; (<b>b</b>) r = 4 nm; (<b>c</b>) r = 6 nm; (<b>d</b>) r = 8 nm; (<b>e</b>) r = 10 nm; (<b>f</b>) r = 15 nm.</p> Full article ">Figure A1
<p>Comparison of Knudsen Diffusion Permeability, Slippage Permeability, and Total Permeability with Previous Models (<b>a</b>–<b>c</b>).</p> Full article ">
14 pages, 2893 KiB  
Article
Effects of Biochar-Amended Composts on Selected Enzyme Activities in Soils
by Faraj Zaid, Nasruddeen Al-Awwal, John Yang, Stephen H. Anderson and Bouzeriba T. B. Alsunuse
Processes 2024, 12(8), 1678; https://doi.org/10.3390/pr12081678 (registering DOI) - 11 Aug 2024
Viewed by 106
Abstract
This study examines the effect of biochar as an agricultural soil supplement on soil quality indicators, specifically enzyme activity in Missouri regions. While the benefits of biochar on soil bulk density, soil organic carbon, and infiltration have been established, its effect on soil [...] Read more.
This study examines the effect of biochar as an agricultural soil supplement on soil quality indicators, specifically enzyme activity in Missouri regions. While the benefits of biochar on soil bulk density, soil organic carbon, and infiltration have been established, its effect on soil enzyme activity has remained underexplored in this region. A three-year field investigation was conducted with six treatments (compost, biochar, compost + biochar, biochar + compost tea, fescue, and control) to evaluate the effects on enzymes such as β-glucosidase (BG), acid and alkaline phosphatases (ACP-ALP), arylsulfatase (ARS), dehydrogenases (DG), arylamidase (AMD), cellulase (CLS), and urease (URS). Furthermore, soil pH, organic matter (OM), and cation exchange capacity (CEC) were determined. The results showed that compost and biochar treatments considerably increased soil enzyme activity compared to other treatments, with nitrogen application further increasing enzyme activity. Soil pH, OM, and CEC were all important determinants in determining enzyme activity, with BG demonstrating strong positive associations with ACP and AMD (99.5%). This study shows that compost and biochar amendments significantly improve soil physicochemical and biological properties, thereby enhancing soil health and assisting farmers’ sustainable soil management practices. Full article
(This article belongs to the Special Issue Application of Biochar in Environmental Research)
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<p>Pearson’s correlation chart between selected soil enzymes: β-glucosidase (BG), urease (URS), acid phosphatase (ACP), arylamidase (AMD), and arylsulfatase (ARS). *** <span class="html-italic">p</span> &lt; 0.0001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 2
<p>Effect of different treatments on β-glucosidase (BG) activities. Treatments include the control for April, June, and August. The different lower-case letters show significant differences (<span class="html-italic">p</span> &lt; 0.05) between the treatments.</p> Full article ">Figure 3
<p>Effect of different treatments on acid phosphatase (ACP) activities. Treatments include the control for April, June, and August. The different lower-case letters show significant differences (<span class="html-italic">p</span> &lt; 0.05) between the treatments.</p> Full article ">Figure 4
<p>Effect of different treatments on alkaline phosphatase (ALP) activities. Treatments include the control for April, June, and August. The different lower-case letters show significant differences (<span class="html-italic">p</span> &lt; 0.05) between the treatments.</p> Full article ">Figure 5
<p>Effect of different treatments on aylsulfatase (ARS) activity. Treatments include the control for April, June, and August. The different lower-case letters show significant differences (<span class="html-italic">p</span> &lt; 0.05) between the treatments.</p> Full article ">Figure 6
<p>Effect of different treatments on dehydrogenase (DG) activity. Treatments include the control for April, June, and August. The different lower-case letters show significant differences (<span class="html-italic">p</span> &lt; 0.05) between the treatments.</p> Full article ">Figure 7
<p>Effect of different treatments on arylamidase (AMD) activity. Treatments include the control for April, June, and August. The different lower-case letters show significant differences (<span class="html-italic">p</span> &lt; 0.05) between the treatments.</p> Full article ">Figure 8
<p>Effect of different treatments on cellulase (CLS) activity. Treatments include the control for April, June, and August. The different lower-case letters show significant differences (<span class="html-italic">p</span> &lt; 0.05) between the treatments.</p> Full article ">Figure 9
<p>Effect of different treatments on urease (URS) activity. Treatments include the control for April, June, and August. The different lower-case letters show significant differences (<span class="html-italic">p</span> &lt; 0.05) between the treatments.</p> Full article ">
11 pages, 1001 KiB  
Article
Thermal Safety Study of Emulsion Explosive Matrix under the Coupled Effects of Environmental Pressure and Bubble Content with Internal Heat Source
by Yi-Bo Zhang, Qian Liu and Xiao-Cen Shi
Processes 2024, 12(8), 1677; https://doi.org/10.3390/pr12081677 (registering DOI) - 10 Aug 2024
Viewed by 220
Abstract
Emulsion explosives have become a hot topic in various studies due to their explosive combustion characteristics and detonation performance under different environmental pressures. The thermal safety of an emulsified matrix was studied with ignition energy as the characterization. A minimum ignition energy test [...] Read more.
Emulsion explosives have become a hot topic in various studies due to their explosive combustion characteristics and detonation performance under different environmental pressures. The thermal safety of an emulsified matrix was studied with ignition energy as the characterization. A minimum ignition energy test experimental system for emulsion matrices was established in this research. The system simulated the occurrence of hot spots inside emulsion matrices using an electric heating wire. The effect of bubbles on the thermal safety of the emulsified matrix was studied by adding expanded perlite additive to the emulsified matrix. This study investigated the variation trend in the minimum ignition energy of the emulsion matrix under the coupled effect of bubbles and ambient pressure using the orthogonal experimental method. The impacts of two factors on the thermal safety of the emulsion matrix were studied at different hot-spot temperatures. Coupled analysis experiments were conducted on emulsion matrices containing 0%, 1.5%, and 3% expanded perlite under pressure environments of 1 atm, 2 atm, and 3 atm. The critical hot-spot temperature of the emulsion matrix significantly decreases with increasing bubble content at 1 atm and 2 atm pressures, as revealed by intuitive analysis and analysis of variance. However, at 3 atm of pressure, the bubble content in the emulsion matrix has no significant effect on its critical hot-spot temperature. The results show that the thermal safety of the emulsified matrix decreases with the increase in the content of expanded perlite and environmental pressure, and the influence of environmental pressure is more significant than that of the bubble content. This paper’s research content serves as a reference for a safe emulsified matrix and as an experimental basis for establishing a production line for developing new equipment. Full article
(This article belongs to the Section Energy Systems)
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<p>Schematic diagram of critical ignition energy measurement system for emulsion matrix.</p> Full article ">
12 pages, 4010 KiB  
Article
Improving Shale Stability through the Utilization of Graphene Nanopowder and Modified Polymer-Based Silica Nanocomposite in Water-Based Drilling Fluids
by Yerlan Kanatovich Ospanov, Gulzhan Abdullaevna Kudaikulova, Murat Smanovich Moldabekov and Moldir Zhumabaevna Zhaksylykova
Processes 2024, 12(8), 1676; https://doi.org/10.3390/pr12081676 (registering DOI) - 10 Aug 2024
Viewed by 198
Abstract
Shale formations present significant challenges to traditional drilling fluids due to fluid infiltration, cuttings dispersion, and shale swelling, which can destabilize the wellbore. While oil-based drilling fluids (OBM) effectively address these concerns about their environmental impact, their cost limits their widespread use. Recently, [...] Read more.
Shale formations present significant challenges to traditional drilling fluids due to fluid infiltration, cuttings dispersion, and shale swelling, which can destabilize the wellbore. While oil-based drilling fluids (OBM) effectively address these concerns about their environmental impact, their cost limits their widespread use. Recently, nanomaterials (NPs) have emerged as a promising approach in drilling fluid technology, offering an innovative solution to improve the efficiency of water-based drilling fluids (WBDFs) in shale operations. This study evaluates the potential of utilizing modified silica nanocomposite and graphene nanopowder to formulate a nanoparticle-enhanced water-based drilling fluid (NP-WBDF). The main objective is to investigate the impact of these nanoparticle additives on the flow characteristics, filtration efficiency, and inhibition properties of the NP-WBDF. In this research, a silica nanocomposite was successfully synthesized using emulsion polymerization and analyzed using FTIR, PSD, and TEM techniques. Results showed that the silica nanocomposite exhibited a unimodal particle size distribution ranging from 38 nm to 164 nm, with an average particle size of approximately 72 nm. Shale samples before and after interaction with the graphene nanopowder WBDF and the silica nanocomposite WBDF were analyzed using scanning electron microscopy (SEM). The NP-WBM underwent evaluation through API filtration tests (LTLP), high-temperature/high-pressure (HTHP) filtration tests, and rheological measurements conducted with a conventional viscometer. Experimental results showed that the silica nanocomposite and the graphene nanopowder effectively bridged and sealed shale pore throats, demonstrating superior inhibition performance compared to conventional WBDF. Post adsorption, the shale surface exhibited increased hydrophobicity, contributing to enhanced stability. Overall, the silica nanocomposite and the graphene nanopowder positively impacted rheological performance and provided favorable filtration control in water-based drilling fluids. Full article
(This article belongs to the Special Issue Phase Change, Interphase Coupling, and Multiphase Transport in Porous Structures)
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<p>Schematic illustration of modified polymer-based silica nanocomposite [<a href="#B16-processes-12-01676" class="html-bibr">16</a>].</p> Full article ">Figure 2
<p>SEM picture of SiO<sub>2</sub>-NPs (<b>a</b>) and graphene nanopowder (<b>b</b>).</p> Full article ">Figure 3
<p>OFITE 800 rotational viscosimeter.</p> Full article ">Figure 4
<p>OFITE HTHP filter press.</p> Full article ">Figure 5
<p>OFITE dynamic linear swellmeter.</p> Full article ">Figure 6
<p>FT-IR spectra of the silica nanocomposite.</p> Full article ">Figure 7
<p>PSD analysis of the diluted silica nanocomposite.</p> Full article ">Figure 8
<p>TEM image of the diluted silica nanocomposite.</p> Full article ">Figure 9
<p>FESEM micrograph of WBDF: (<b>a</b>) the base WBDF; (<b>b</b>) the silica nanocomposite WBDF; (<b>c</b>) graphene nanopowder WBDF.</p> Full article ">
20 pages, 8984 KiB  
Article
Numerical Study on the Heat Dissipation Performance of Diamond Microchannels under High Heat Flux Density
by Jiwen Zhao, Kunlong Zhao, Xiaobin Hao, Yicun Li, Sen Zhang, Benjian Liu, Bing Dai, Wenxin Cao and Jiaqi Zhu
Processes 2024, 12(8), 1675; https://doi.org/10.3390/pr12081675 (registering DOI) - 9 Aug 2024
Viewed by 283
Abstract
Heat dissipation significantly limits semiconductor component performance improvement. Thermal management devices are pivotal for electronic chip heat dissipation, with the enhanced thermal conductivity of materials being crucial for their effectiveness. This study focuses on single-crystal diamond, renowned for its exceptional natural thermal conductivity, [...] Read more.
Heat dissipation significantly limits semiconductor component performance improvement. Thermal management devices are pivotal for electronic chip heat dissipation, with the enhanced thermal conductivity of materials being crucial for their effectiveness. This study focuses on single-crystal diamond, renowned for its exceptional natural thermal conductivity, investigating diamond microchannels using finite element simulations. Initially, a validated mathematical model for microchannel flow heat transfer was established. Subsequently, the heat dissipation performance of typical microchannel materials was analyzed, highlighting the diamond’s impact. This study also explores diamond microchannel topologies under high-power conditions, revealing unmatched advantages in ultra-high heat flux density dissipation. At 800 W/cm2 and inlet flow rates of 0.4–1 m/s, diamond microchannels exhibit lower maximum temperatures compared to pure copper microchannels by 7.0, 7.2, 7.4, and 7.5 °C, respectively. Rectangular cross-section microchannels demonstrate superior heat dissipation, considering diamond processing costs. The exploration of angular structures with varying parameters shows significant temperature reductions with increasing complexity, such as a 2.4 °C drop at i = 4. The analysis of shape parameter ki indicates optimal heat dissipation performance at ki = 1.1. This research offers crucial insights for developing and optimizing diamond microchannel devices under ultra-high-heat-flux-density conditions, guiding future advancements in thermal management technology. Full article
(This article belongs to the Special Issue Advances of Multiphase Computational Fluid Dynamics in Energy Engineering)
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<p>Boundary conditions set in the model.</p> Full article ">Figure 2
<p>Grid division: (<b>a</b>) overall grid division and (<b>b</b>) microchannel region grid division.</p> Full article ">Figure 3
<p>Grid division with different densities: (<b>a</b>) Plan A, (<b>b</b>) Plan B, (<b>c</b>) Plan C, and (<b>d</b>) Plan D.</p> Full article ">Figure 4
<p>Calculation model validation: (<b>a</b>) geometric structure of microchannels; (<b>b</b>) comparison of experimental and simulated average wall temperatures along the channel direction (normal direction from the inlet to the outlet) [<a href="#B38-processes-12-01675" class="html-bibr">38</a>].</p> Full article ">Figure 5
<p>Relationship between maximum temperature and heat flux density of microchannels under different flow rate conditions: (<b>a</b>) silicon, (<b>b</b>) copper, (<b>c</b>) LTCC, and (<b>d</b>) aluminum.</p> Full article ">Figure 6
<p>Comparison of maximum temperatures of different substrate materials at various heat flux densities.</p> Full article ">Figure 7
<p>Comparison of heat dissipation performance of diamonds with different thermal conductivities.</p> Full article ">Figure 8
<p>Heat dissipation performance of diamond microchannels with varying thermal conductivity.</p> Full article ">Figure 9
<p>Influence of cross-sectional shape on heat dissipation performance: (<b>a</b>) schematic of microchannel model and (<b>b</b>) impact of cross-sectional variation on heat dissipation performance.</p> Full article ">Figure 10
<p>The effect of microchannel expansion on heat dissipation performance: (<b>a</b>) microchannel model and (<b>b</b>) influence of <span class="html-italic">k</span><sub>1</sub> on the heat dissipation performance of microchannels.</p> Full article ">Figure 11
<p>Temperature distribution of microchannels under different <span class="html-italic">k</span><sub>1</sub> values.</p> Full article ">Figure 12
<p>Flow velocity distributions of microchannels under different <span class="html-italic">k</span><sub>1</sub> values.</p> Full article ">Figure 13
<p>Pressure distribution of microchannels under different <span class="html-italic">k</span><sub>1</sub> values.</p> Full article ">Figure 14
<p>The influence of different numbers of diamond-shaped (hourglass-shaped) microchannels on heat dissipation performance: (<b>a1</b>–<b>c1</b>) models of diamond-shaped (hourglass-shaped) microchannels with different numbers and (<b>a2</b>–<b>c2</b>) heat dissipation performance of diamond-shaped (hourglass-shaped) microchannels with different numbers.</p> Full article ">Figure 15
<p>Effects of different numbers of diamond-shaped (hourglass-shaped) microchannels on maximum flow velocity and pressure: (<b>a1</b>–<b>c1</b>) effects on flow velocity; (<b>a2</b>–<b>c2</b>) effects on maximum pressure.</p> Full article ">
17 pages, 3574 KiB  
Article
Effect of Far-Infrared Drying on Broccoli Rhizomes: Drying Kinetics and Quality Evaluation
by Gen Zhang, Yun Yu, Chunyu Yao, Lang Zhou, Guangyao Zhu, Yushu Lai and Po Niu
Processes 2024, 12(8), 1674; https://doi.org/10.3390/pr12081674 (registering DOI) - 9 Aug 2024
Viewed by 267
Abstract
To clarify the effects of far-infrared drying technology on the drying kinetics and quality of broccoli rhizomes, broccoli rhizomes were dehydrated at 60, 65, 70, 75 and 80 °C, respectively. Eight thin-layer drying mathematical models were used to explore the drying kinetic characteristics [...] Read more.
To clarify the effects of far-infrared drying technology on the drying kinetics and quality of broccoli rhizomes, broccoli rhizomes were dehydrated at 60, 65, 70, 75 and 80 °C, respectively. Eight thin-layer drying mathematical models were used to explore the drying kinetic characteristics and comprehensively evaluate their quality. The results showed that the higher the drying temperature, the shorter the time required to dry the broccoli rhizomes to the endpoint, and the higher the drying rate. The drying temperature was 80 °C, the shortest drying time was 360 min, and the average drying rate was 4.72 g·g−1·min−1. The longest drying time at 60 °C was 660 min, and the minimum average drying rate was 1.99 g·g−1·min−1. The effective diffusion coefficients of moisture at different drying temperatures were 1.22 × 10−6, 1.25 × 10−6, 1.34 × 10−6, 1.46 × 10−6 and 1.55 × 10−6 m2/min, respectively. The activation energy was calculated to be 12.26 kJ/mol by the linear relationship between the effective moisture diffusion coefficient and time. From the thermodynamic parameters, the drying of broccoli rhizomes is a non-spontaneous process, and it is necessary to absorb heat from the medium to achieve dehydration. With an increase in the drying temperature, the drying effect is better. The fitting results of eight mathematical models showed that Modified Page, Page, and Wang and Singh were the best mathematical models for the far-infrared drying kinetics of the broccoli rhizomes. The membership function method comprehensively evaluated the quality of the broccoli rhizome dry products. The comprehensive order was 60 °C > 65 °C > 75 °C > 70 °C > 80 °C. When the temperature was 60 °C, the physicochemical properties and nutritional quality of broccoli rhizome were well preserved, and the quality was the best. Therefore, 60 °C is the best temperature for broccoli rhizome drying. The results provide a theoretical reference for further improving the far-infrared drying quality of broccoli rhizomes. Full article
(This article belongs to the Section Food Process Engineering)
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<p>Far-infrared dryer schematic diagram: 1, far-infrared original; 2, temperature control instrument; 3. over-temperature protection instrument; 4. timer; 5, power switch; 6. heating switch; 7. timing switch; 8, alarm switch; 9, buzzer; 10, standby switch; 11, stainless steel tray; 12. box door.</p> Full article ">Figure 2
<p>Effects of different temperatures on appearance (<b>A</b>), moisture ratio (<b>B</b>), and drying rate (<b>C</b>) of broccoli rhizomes.</p> Full article ">Figure 3
<p>Color difference and rehydration ratio of broccoli rhizomes under far-infrared drying at different temperatures.</p> Full article ">Figure 4
<p>The effects of flavonoid standard curve (<b>a</b>), different temperatures on total flavonoid content (<b>b</b>), polyphenol standard curve (<b>c</b>), total phenol content (<b>d</b>), VC content (<b>e</b>), and soluble protein content (<b>f</b>) in broccoli. Note: Different letters a, b, c, d, and e indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 4 Cont.
<p>The effects of flavonoid standard curve (<b>a</b>), different temperatures on total flavonoid content (<b>b</b>), polyphenol standard curve (<b>c</b>), total phenol content (<b>d</b>), VC content (<b>e</b>), and soluble protein content (<b>f</b>) in broccoli. Note: Different letters a, b, c, d, and e indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 5
<p>Correlation analysis of the effect of drying temperature on rhizome quality of broccoli.</p> Full article ">
15 pages, 3912 KiB  
Article
Hydrothermal Carbonization of Residual Biomass from Agricultural and Agro-Industrial Sector
by Carmine De Francesco, Thomas Gasperini, Daniele Duca, Giuseppe Toscano and Alessio Ilari
Processes 2024, 12(8), 1673; https://doi.org/10.3390/pr12081673 (registering DOI) - 9 Aug 2024
Viewed by 228
Abstract
Hydrothermal carbonization (HTC) is a promising method for the conversion of agricultural and agro-industrial residues into valuable products. HTC processes biomass through chemical reactions that produce hydrochar, a carbon-rich solid similar to lignite. Unlike other thermochemical processes, HTC can handle high-moisture biomass without [...] Read more.
Hydrothermal carbonization (HTC) is a promising method for the conversion of agricultural and agro-industrial residues into valuable products. HTC processes biomass through chemical reactions that produce hydrochar, a carbon-rich solid similar to lignite. Unlike other thermochemical processes, HTC can handle high-moisture biomass without pre-drying. This article evaluates the efficiency of HTC on wood chips, wheat straw, and grape pomace, examining their chemical and structural characteristics and critical operational parameters such as the temperature, pressure, biomass/water ratio, and reaction time. The obtained results highlight that the two key process parameters are the temperature and the ratio between the solid biomass and liquid phase. Increasing the first parameter increases the energy content by 20% and increases the carbon concentration by up to 50%, while reducing the oxygen content by 30% in the hydrochar. Varying the second parameter leads to the alternating reduction of the ash content but simultaneously reduces the energy content. The reaction time seems to have a limited influence on the quality parameters of the biochar produced. Lastly, HTC appears to successfully enhance the overall quality of widely available agricultural wastes, such as grape pomace. Full article
(This article belongs to the Special Issue Integrated Process Design and Development of Biorefinery)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Overall workflow relative to raw biomass processing and analysis before HTC treatment and product (hydrochar and process water) characterization. The blue lines refer to material flows (biomass, biochar and water). The orange lines concern data flows.</p> Full article ">Figure 2
<p>Hydrothermal carbonization test workflow. From above: sample (<b>a</b>), process water (<b>b</b>), and washing water (<b>c</b>) weighing; empty vessel (<b>d</b>), vessel filled with dry sample (<b>e</b>), vessel with biomass sample and process water (<b>f</b>); vessel loaded on the reactor (<b>g</b>) and fixed with flange and heating jacket (<b>h</b>); setting maximum treatment temperature and other parameters (<b>i</b>); opening of the system (<b>j</b>), recovery of liquid and solid fractions through washing with RO water (<b>k</b>), and separation of fractions with filter paper, Buchner funnel, and vacuum flask (<b>l</b>); storage of post-treatment process water and washing water (<b>m</b>) and recovery of treated sample after drying in an oven at 105 ± 2 °C (<b>n</b>,<b>o</b>).</p> Full article ">Figure 3
<p>Comparison between the three “raw” biomass samples tested (three images above, from the left: grape pomace, wood chips, and straw) and examples of the respective torrefied products (three images below).</p> Full article ">Figure 4
<p>Wheat straw ash loss at different temperatures and different WTBs.</p> Full article ">Figure 5
<p>Ash loss at different temperatures and WTB in wood chips (<b>a</b>) and in grape pomace (<b>b</b>).</p> Full article ">Figure 6
<p>Variation in LHV at different temperatures and different WTB ratios in WS.</p> Full article ">Figure 7
<p>Variation in LHV at different temperatures, WTB ratios, and times in wood chips (<b>a</b>) and grape pomace (<b>b</b>).</p> Full article ">Figure 8
<p>CHN elemental composition at different temperatures and WTB ratios in wheat straw’s hydrochar.</p> Full article ">Figure 9
<p>Elemental composition at different temperatures, WTB ratios, and residence times in wood chips’ hydrochar.</p> Full article ">Figure 10
<p>Elemental composition at different temperatures, WTB ratios, and residence times in grape pomace’s hydrochar.</p> Full article ">
17 pages, 24979 KiB  
Article
Segmentation Differences of the Salt-Related Qiulitage Fold and Thrust Belt in the Kuqa Foreland Basin
by Yingzhong Zhu, Chuanxin Li, Yuhang Zhang, Yibo Zhao and Tulujun Gulifeire
Processes 2024, 12(8), 1672; https://doi.org/10.3390/pr12081672 - 9 Aug 2024
Viewed by 217
Abstract
The Qiulitage fold and thrust belt (QFTB) is situated in the Kuqa Depression, exhibiting spectacular salt structures with well-defined geometric and kinematic characteristics and thereby playing a significant role in advancing the study of salt structures worldwide. This research, based on regional geology, [...] Read more.
The Qiulitage fold and thrust belt (QFTB) is situated in the Kuqa Depression, exhibiting spectacular salt structures with well-defined geometric and kinematic characteristics and thereby playing a significant role in advancing the study of salt structures worldwide. This research, based on regional geology, well logging, and newly acquired three-dimensional seismic data, applies principles of salt-related fault structures to interpret seismic data and restore structural equilibrium in the Qiulitage fold and thrust belt within the Kuqa Depression by conducting quantitative studies on structural geometry and kinematics. Results indicate clear differences in salt structures between the eastern and western segments of it, vertically divided into upper salt, salt layer, and lower salt and horizontally into four parts. The Dina segment features a single-row basement-involved thrust fault, the East QFTB segment displays detachment thrust faults involving cover layers, the Central QFTB segment exhibits detachment thrust faults involving multiple rows of cover layers, the leading edge forms structural wedges, and the West QFTB segment develops blind-thrust faults. During the deposition of the Kangcun formation, the eastern profile experiences an 18% shortening rate, 14% in the central part, and 9% in the western part. For the Kuqa formation, the eastern profile experiences a 10% shortening rate, 9% in the central part, and 3% in the western part, indicating more significant deformation in the east than in the west. Quantitative statistical analysis reveals that different types of detachments, paleogeomorphology, and northeast-directed compressive stress exert control over the Qiulitage fold-thrust belt. Full article
(This article belongs to the Special Issue Exploration, Exploitation and Utilization of Coal and Gas Resources)
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<p>Geological cross-section location map of the Qiulitage fold and thrust belt.</p> Full article ">Figure 2
<p>Distribution map of Mesozoic–Cenozoic layer in the Kuqa Depression [<a href="#B6-processes-12-01672" class="html-bibr">6</a>].</p> Full article ">Figure 3
<p>Typical section A–A’ interpretation results of the western QFTB segment. Notes: N<sub>2</sub>k–Kuqa formation; N<sub>1</sub>k–Kangcun formation; N<sub>1</sub>j–Jideke formation; E<sub>3</sub>s–Suwei formation; E<sub>(1–2)</sub>km–Kumugelm Group; K<sub>1</sub>b–Bashkiqu formation; J–Jurassic; P–Triassic.</p> Full article ">Figure 4
<p>Typical section B–B’ interpretation results of the Central QFTB segment. Notes: N<sub>2</sub>k–Kuqa formation; N<sub>1</sub>k–Kangcun formation; N<sub>1</sub>j–Jideke formation; E<sub>3</sub>s–Suwei formation; E<sub>(1–2)</sub> km–Kumugelm Group; K<sub>1</sub>b–Bashkiqu formation; J–Jurassic; P–Triassic.</p> Full article ">Figure 5
<p>Typical section C–C’ interpretation results of the East QFTB segment. Notes: N<sub>2</sub>k–Kuqa formation; N<sub>1</sub>k–Kangcun formation; N<sub>1</sub>j–Jideke formation; E<sub>3</sub>s–Suwei formation; E<sub>(1–2)</sub>km–Kumugelm Group; K<sub>1</sub>b–Bashkiqu formation; J–Jurassic; P–Triassic.</p> Full article ">Figure 6
<p>Typical section D–D’ interpretation results of the Dina segment. Notes: N<sub>2</sub>k–Kuqa formation; N<sub>1</sub>k–Kangcun formation; N<sub>1</sub>j–Jideke formation; E<sub>3</sub>s–Suwei formation; E<sub>(1–2)</sub>km–Kumugelm Group; K<sub>1</sub>b–Bashkiqu formation; J–Jurassic; P–Triassic.</p> Full article ">Figure 7
<p>Paleogeographic map of the Cretaceous in the Kuqa Depression [<a href="#B13-processes-12-01672" class="html-bibr">13</a>].</p> Full article ">Figure 8
<p>Evolutionary results of the western profile.</p> Full article ">Figure 9
<p>Evolutionary results of the central profile.</p> Full article ">Figure 10
<p>Evolutionary results of the eastern profile.</p> Full article ">Figure 11
<p>Statistical table of profile shortening ratios.</p> Full article ">Figure 12
<p>Contour map of thickness distribution of Paleogene and Neogene salt layers [<a href="#B7-processes-12-01672" class="html-bibr">7</a>].</p> Full article ">Figure 13
<p>Contour map of thickness distribution of Jurassic and Triassic mudstones [<a href="#B16-processes-12-01672" class="html-bibr">16</a>].</p> Full article ">Figure 14
<p>Development model map of the Qiulitage fold and thrust belt.</p> Full article ">
13 pages, 10573 KiB  
Article
Phase-Field Modeling of Hydraulic Fracture in Porous Media with In Situ Stresses
by Tao You
Processes 2024, 12(8), 1671; https://doi.org/10.3390/pr12081671 - 9 Aug 2024
Viewed by 203
Abstract
While the variational phase-field model has been widely used in modeling fracturing in porous media, it poses a challenge when applying high confining pressures on a model because the relatively large deformation induced by the confining pressures might cause undesired crack nucleation when [...] Read more.
While the variational phase-field model has been widely used in modeling fracturing in porous media, it poses a challenge when applying high confining pressures on a model because the relatively large deformation induced by the confining pressures might cause undesired crack nucleation when the strain decomposition scheme are used, which is not consistent with engineering observations. This study proposes a two-step strategy to incorporate in situ stresses into phase-field modeling of hydraulic fractures, addressing the limitations of previous approaches in capturing realistic fracture initiation and propagation under high confinement. A micromechanics-based hydromechanical phase-field model is presented first, and the proposed two-step strategy is investigated with different strain decomposition schemes: isotropic, volumetric–deviatoric, and no-tension models. Two numerical examples show that the two-step strategy effectively achieves a desired initial state with geostatic stresses and zero strain, allowing for accurate simulations even in the presence of complex natural fractures. The efficiency of the proposed two-step strategy for incorporating in situ stresses is highlighted, and the challenges associated with capturing stiffness recovery and shear fracture nucleation under high confinement using strain-based models are discussed. Full article
(This article belongs to the Section Advanced Digital and Other Processes)
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<p>The geometry, mesh, and boundary conditions of the numerical models, where (<b>a</b>) is used for nucleation and propagation of the hydraulic fracture from a borehole, and (<b>b</b>) is used to investigate the interaction between the hydraulic fracture and natural fractures.</p> Full article ">Figure 2
<p>The strain (<b>a</b>) and stress (<b>b</b>) field after the geostatic step for the homogeneous model with the confining pressures being <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi mathvariant="normal">v</mi> </msub> <mo>=</mo> <mn>9.7</mn> </mrow> </semantics></math> MPa and <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi mathvariant="normal">h</mi> </msub> <mo>=</mo> <mn>17.2</mn> </mrow> </semantics></math> MPa, where the ‘epsilon_0’ and ‘sigma_0’ denote the initial strain and stress states from which the simulation (fluid injection) starts.</p> Full article ">Figure 3
<p>The initial state of the naturally fractured model after the geostatic step for the isotropic model (<b>a)</b>, volumetric-deviatoric model (<b>b</b>) and no-tension model (<b>c</b>), with the confining pressures being <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi mathvariant="normal">v</mi> </msub> <mo>=</mo> <mn>9.7</mn> </mrow> </semantics></math> MPa and <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi mathvariant="normal">h</mi> </msub> <mo>=</mo> <mn>17.2</mn> </mrow> </semantics></math> MPa.</p> Full article ">Figure 4
<p>Hydraulic fracture propagation along the horizontal direction (<b>a</b>) and the correponding pressure field (<b>b</b>), with the confining pressures being <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi mathvariant="normal">v</mi> </msub> <mo>=</mo> <mn>9.7</mn> </mrow> </semantics></math> MPa and <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi mathvariant="normal">h</mi> </msub> <mo>=</mo> <mn>17.2</mn> </mrow> </semantics></math> MPa.</p> Full article ">Figure 5
<p>Hydraulic fracture propagation along the vertical direction (<b>a</b>) and the corresponding pressure field (<b>b</b>), with the confining pressures being <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi mathvariant="normal">v</mi> </msub> <mo>=</mo> <mn>17.2</mn> </mrow> </semantics></math> MPa and <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi mathvariant="normal">h</mi> </msub> <mo>=</mo> <mn>9.7</mn> </mrow> </semantics></math> MPa.</p> Full article ">Figure 6
<p>Hydraulic fracture propagation using different strain decomposition models, with the confining pressures being <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi mathvariant="normal">v</mi> </msub> <mo>=</mo> <mn>9.7</mn> </mrow> </semantics></math> MPa and <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi mathvariant="normal">h</mi> </msub> <mo>=</mo> <mn>17.2</mn> </mrow> </semantics></math> MPa.</p> Full article ">Figure 7
<p>The average pressure on the borehole with and without the geostatic step under the confining pressures <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi mathvariant="normal">v</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> MPa and <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi mathvariant="normal">h</mi> </msub> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> MPa.</p> Full article ">Figure 8
<p>Hydraulic fracture propagation interacted with the natural fractures (<b>a</b>) and the corresponding pressure field (<b>b</b>) with the confining pressures being <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi mathvariant="normal">v</mi> </msub> <mo>=</mo> <mn>9.7</mn> </mrow> </semantics></math> MPa and <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi mathvariant="normal">h</mi> </msub> <mo>=</mo> <mn>17.2</mn> </mrow> </semantics></math> MPa.</p> Full article ">
17 pages, 11530 KiB  
Article
Pore-Scale Modeling of Gas–Oil Two-Phase Flow Based on the Phase-Field Method—A Case Study of Glutenite Reservoirs in China
by Ya Tian, Li Yang, Yi Chen, Zhongkai Bai, Youxing Yang, Jianwei Wu and Suling Wang
Processes 2024, 12(8), 1670; https://doi.org/10.3390/pr12081670 - 8 Aug 2024
Viewed by 358
Abstract
This work employs the phase field method combined with a realistic microscopic heterogeneous pore structure model to conduct numerical simulations of CO2–oil two-phase flow. This study investigates the diffusion behavior of CO2 during the displacement process and analyzes the impact [...] Read more.
This work employs the phase field method combined with a realistic microscopic heterogeneous pore structure model to conduct numerical simulations of CO2–oil two-phase flow. This study investigates the diffusion behavior of CO2 during the displacement process and analyzes the impact of various parameters such as the flow rate, the contact angle, and interfacial tension on the displacement effect. The results indicate that, over time, saturated oil is gradually replaced by CO2, which primarily flows along channels with larger throat widths and lower resistance. The preferential flow paths of CO2 correspond to high flow rates and high pore pressures occupied by CO2. As the injection rate increases, the CO2 filtration rate increases, CO2 movement becomes more pronounced, and CO2 saturation rises. Beyond the optimal flow rate, however, the displacement effect worsens. The wettability of the porous medium predominantly determines the CO2 migration path during the displacement process. As the contact angle increases, CO2 wettability towards the rock improves, significantly enhancing the displacement effect. Under different interfacial tension conditions, the recovery rate increases with the amount of CO2 entering the porous medium, but no clear correlation is observed between interfacial tension and the recovery rate. Therefore, it is challenging to further improve the recovery rate by altering interfacial tension. The viscosity ratio affects wettability and thereby influences the displacement effect. Lower viscosity ratios result in reduced wettability effects, making CO2 diffusion more difficult. This study provides theoretical guidance and technical support for CO2-EOR (Enhanced Oil Recovery) in highly heterogeneous reservoirs on a field scale. Full article
(This article belongs to the Topic Carbon Capture, Storage and Utilisation Technologies (CCS/CCU) - 2nd Volume)
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<p>Computational model of a porous medium.</p> Full article ">Figure 2
<p>Computational domain meshing. (<b>a</b>) Overall grid division; (<b>b</b>) Local mesh division; (<b>c</b>) Grid independence.</p> Full article ">Figure 3
<p>Spatial distribution of CO<sub>2</sub> in the porous media channel; (<b>a</b>) 0.02 s from left to right; (<b>b</b>) 0.06 s; (<b>c</b>) 0.1 s; and (<b>d</b>) 0.5 s (white represents solid particles, blue represents oil, and red represents CO<sub>2</sub>).</p> Full article ">Figure 3 Cont.
<p>Spatial distribution of CO<sub>2</sub> in the porous media channel; (<b>a</b>) 0.02 s from left to right; (<b>b</b>) 0.06 s; (<b>c</b>) 0.1 s; and (<b>d</b>) 0.5 s (white represents solid particles, blue represents oil, and red represents CO<sub>2</sub>).</p> Full article ">Figure 4
<p>(<b>a</b>) Velocity and (<b>b</b>) pressure distributions in the flow field.</p> Full article ">Figure 5
<p>CO<sub>2</sub> saturation in porous media after CO<sub>2</sub> injection at different injection rates of (<b>a</b>) 5 mm/s; (<b>b</b>) 10 mm/s; (<b>c</b>) 15 mm/s; and (<b>d</b>) 20 mm/s.</p> Full article ">Figure 6
<p>Volume fraction of CO<sub>2</sub> at different injection rates.</p> Full article ">Figure 7
<p>Microscopic enlargement of the region of continuous momentary displacement. (<b>a</b>) 0.1 s; (<b>b</b>) 0.3 s.</p> Full article ">Figure 8
<p>Results of CO<sub>2</sub> transportation at different contact angles of (<b>a</b>) 30°, (<b>b</b>) 90°, (<b>c</b>) 60°, and (<b>d</b>) 120°.</p> Full article ">Figure 9
<p>Volume fraction of CO<sub>2</sub> at different contact angles.</p> Full article ">Figure 10
<p>Recovery rates at different contact angles.</p> Full article ">Figure 11
<p>Spatial distribution of CO<sub>2</sub> saturation in porous media at T = 0.1 s (<b>a</b>,<b>b</b>) and 1.0 s (<b>c</b>,<b>d</b>) after CO<sub>2</sub> injection under high interfacial tension (27 mN/m) and low interfacial tension (20 mN/m).</p> Full article ">Figure 12
<p>Volume fraction of CO<sub>2</sub> at different interfacial tensions.</p> Full article ">Figure 13
<p>The recovery curves for different interfacial tensions.</p> Full article ">Figure 14
<p>Comparison of CO<sub>2</sub> migration results under different viscosity ratios, where (<b>a</b>) logM = −3.2; (<b>b</b>) logM = −3.5; (<b>c</b>) logM = −3.8; and (<b>d</b>) logM = −4.1.</p> Full article ">Figure 15
<p>Effect of the CO<sub>2</sub> volume fraction on cracking at different viscosity ratios.</p> Full article ">
14 pages, 3500 KiB  
Article
Parameter Optimization of an Absorption Heat Exchanger with Large Temperature Difference
by Jiangtao Chen, Jinxing Wang, Huawei Jiang, Xin Yang, Xiangli Zuo and Miao Yuan
Processes 2024, 12(8), 1669; https://doi.org/10.3390/pr12081669 - 8 Aug 2024
Viewed by 312
Abstract
The absorption heat exchanger with a large temperature difference has a higher heat transfer superiority than the other heat exchangers (including plate heat exchanger), which is more suitable for long-distance heating. To improve its system performance, parameter collaborative optimization (including building accurate predictive [...] Read more.
The absorption heat exchanger with a large temperature difference has a higher heat transfer superiority than the other heat exchangers (including plate heat exchanger), which is more suitable for long-distance heating. To improve its system performance, parameter collaborative optimization (including building accurate predictive models) has become an effective method because it does not require too much investment. In this study, a heat exchange station was chosen as a case study, and a model of a long short-term memory (LSTM) neural network was used to predict the temperatures of primary return water and secondary return water. Accordingly, the reliability of the fitting result based on the model was confirmed through a contrastive analysis with the prediction results of a support vector machine (SVM) model, a random forest (RF) model, and an extreme gradient boosting (XGBoost) model. In addition, the algorithm of particle swarm optimization was used to optimize the flow rate of primary supply water. The results showed that the temperature of primary-side return water decreased from 29.6 °C to 28.2 °C, the temperature of secondary-side return water decreased from 39.8 °C to 38.6 °C, and the flow rate of primary-side supply water decreased from 39 t/h to 35.2 t/h after the optimization of the flow rate of primary supply water. The sensibility assessment emerged that the secondary-side flow rate to the secondary-side supply water temperature was about 7 times more sensitive than the primary-side supply water temperature, and concretely, the lower the temperature, the higher the sensibility. In summary, the accuracy of the proposed prediction model was validated and the optimization direction was pointed out, which can be used to provide guidance for designing and planning absorption heat exchange stations with large temperature differences. Full article
(This article belongs to the Special Issue Recent Advances in CO2 Capture, CO2 Utilization, and Green Chemistry for Sustainability)
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<p>Diagram of heat exchange process of absorption heat exchange station.</p> Full article ">Figure 2
<p>Variations in key parameters for an absorption heat exchange unit with large temperature difference in a single heating season. (<b>a</b>) Variations in water flow rate and (<b>b</b>) variations of supply water temperature and return water temperature on the primary side and the secondary side.</p> Full article ">Figure 3
<p>LSTM cell structure diagram.</p> Full article ">Figure 4
<p>Expansion plot of the LSTM network model.</p> Full article ">Figure 5
<p>Actual thermal index of secondary side of heat exchange station under different outdoor temperatures.</p> Full article ">Figure 6
<p>LSTM predicted value of primary (<b>a</b>) and secondary (<b>b</b>) return water temperature and actual temperature change.</p> Full article ">Figure 7
<p>(<b>a</b>) Variation in water flow rate of secondary side with supply water temperature of primary side. (<b>b</b>) Variation in water flow rate of secondary side with supply water temperature of secondary side.</p> Full article ">
14 pages, 6688 KiB  
Article
Analysis of Dielectric Attached on Sweep Frequency Microwave Heating Uniformity
by Can Liang, Yuehao Ma, Fengming Yang, Chengzhuo Wang, Huacheng Zhu, Yang Yang, Long Gao and Jia Liu
Processes 2024, 12(8), 1668; https://doi.org/10.3390/pr12081668 - 8 Aug 2024
Viewed by 354
Abstract
Traditional microwave heating faces challenges such as low efficiency and uneven heating, hindering its industrial application. Sweep frequency microwave heating is an effective way to improve uniformity. Larger cavity sizes result in better heating uniformity due to the generation of more resonant modes. [...] Read more.
Traditional microwave heating faces challenges such as low efficiency and uneven heating, hindering its industrial application. Sweep frequency microwave heating is an effective way to improve uniformity. Larger cavity sizes result in better heating uniformity due to the generation of more resonant modes. However, in industrial applications, large cavities occupy significant space, making them less flexible and limiting their usability. This paper introduces a method to enhance sweep frequency microwave heating uniformity by adding a dielectric substance to cavity walls. First, the impact of increasing cavity size on the uniformity of sweep frequency microwave heating was studied, with the theoretical analysis showing that filling the cavity with dielectric materials can be equivalent to enlarging the cavity size. Subsequently, a multiphysics simulation model for sweep frequency microwave heating was established to analyze the effects of dielectric substance thickness and permittivity on heating uniformity. A high-efficiency, high-uniformity microwave multimode cavity was designed, and the accuracy of the simulation model was validated through experiments. Finally, the effects of sweep frequency range and load variations on the heating performance were analyzed. This method effectively addresses the uniformity issues in industrial microwave heating and aids in promoting the application of microwave energy in industry. Full article
(This article belongs to the Section Chemical Processes and Systems)
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<p>Geometry of the 3D simulation model.</p> Full article ">Figure 2
<p>Model meshing: (<b>a</b>) the mesh division of the whole multimode cavity; (<b>b</b>) the mesh division of the load.</p> Full article ">Figure 3
<p>Sweep frequency microwave heating experimental system.</p> Full article ">Figure 4
<p>The result comparison of potato surface temperature distribution between simulation and experiment under frequency sweep (unit: K): (<b>a</b>) the simulated heating results without a dielectric substance; (<b>b</b>) the experimental heating results without a dielectric substance; (<b>c</b>) the simulated heating results with a ceramic dielectric substance; and (<b>d</b>) the experimental heating results with a ceramic dielectric substance.</p> Full article ">Figure 5
<p>The result comparison of S11 between simulation and experiment under different frequencies: (<b>a</b>) the simulated heating results without a dielectric substance; (<b>b</b>) the experimental heating results without a dielectric substance; (<b>c</b>) the simulated heating results with a ceramic dielectric substance; and (<b>d</b>) the experimental heating results with a ceramic dielectric substance.</p> Full article ">Figure 6
<p>The electric field distribution with or without a dielectric substance in different frequencies.</p> Full article ">Figure 7
<p>The electric field distribution of potato with or without a dielectric substance in different frequencies.</p> Full article ">Figure 8
<p>The influence of heating with different thicknesses of different dielectric substances: (<b>a</b>) the influence of average temperature and (<b>b</b>) the influence of the COV. The horizontal dash line is the average value among all of the dielectric substance.</p> Full article ">Figure 9
<p>The S11 curve of the multimode cavity with (<b>a</b>) or without (<b>b</b>) a dielectric substance.</p> Full article ">Figure 10
<p>The heating temperature distribution inside loads with or without a dielectric substance under different sweep frequency bandwidths.</p> Full article ">Figure 11
<p>The influence of the load property on the average temperature.</p> Full article ">Figure 12
<p>The influence of the load property on the COV.</p> Full article ">
39 pages, 5021 KiB  
Article
Novel Landfill-Gas-to-Biomethane Route Using a Gas–Liquid Membrane Contactor for Decarbonation/Desulfurization and Selexol Absorption for Siloxane Removal
by Guilherme Pereira da Cunha, José Luiz de Medeiros and Ofélia de Queiroz F. Araújo
Processes 2024, 12(8), 1667; https://doi.org/10.3390/pr12081667 - 8 Aug 2024
Viewed by 344
Abstract
A new landfill-gas-to-biomethane process prescribing decarbonation/desulfurization via gas–liquid membrane contactors and siloxane absorption using Selexol are presented in this study. Firstly, an extension for an HYSYS simulator was developed as a steady-state gas–liquid contactor model featuring: (a) a hollow-fiber membrane contactor for countercurrent/parallel [...] Read more.
A new landfill-gas-to-biomethane process prescribing decarbonation/desulfurization via gas–liquid membrane contactors and siloxane absorption using Selexol are presented in this study. Firstly, an extension for an HYSYS simulator was developed as a steady-state gas–liquid contactor model featuring: (a) a hollow-fiber membrane contactor for countercurrent/parallel contacts; (b) liquid/vapor mass/energy/momentum balances; (c) CO2/H2S/CH4/water fugacity-driven bidirectional transmembrane transfers; (d) temperature changes from transmembrane heat/mass transfers, phase change, and compressibility effects; and (e) external heat transfer. Secondly, contactor batteries using a countercurrent contact and parallel contact were simulated for selective landfill-gas decarbonation/desulfurization with water. Several separation methods were applied in the new process: (a) a water solvent gas–liquid contactor battery for adiabatic landfill-gas decarbonation/desulfurization; (b) water regeneration via high-pressure strippers, reducing the compression power for CO2 exportation; and (c) siloxane absorption with Selexol. The results show that the usual isothermal/isobaric contactor simplification is unrealistic at industrial scales. The process converts water-saturated landfill-gas (CH4 = 55.7%mol, CO2 = 40%mol, H2S = 150 ppm-mol, and Siloxanes = 2.14 ppm-mol) to biomethane with specifications of CH4MIN = 85%mol, CO2MAX = 3%mol, H2SMAX = 10 mg/Nm3, and SiloxanesMAX = 0.03 mg/Nm3. This work demonstrates that the new model can be validated with bench-scale literature data and used in industrial-scale batteries with the same hydrodynamics. Once calibrated, the model becomes economically valuable since it can: (i) predict industrial contactor battery performance under scale-up/scale-down conditions; (ii) detect process faults, membrane leakages, and wetting; and (iii) be used for process troubleshooting. Full article
(This article belongs to the Special Issue Sustainability Use of Wood/Wood Residues and Other Bioenergy Sources)
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<p>Representations of countercurrent and parallel GLMC modules as cascades of <span class="html-italic">M</span> elements (streams are numbered by the origin element). GLMC battery feed data: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <munder accentunder="true"> <mi>L</mi> <mo stretchy="true">¯</mo> </munder> </mrow> <mn style="font-style:italic">0</mn> </msub> <mo>,</mo> <msub> <mrow> <munder accentunder="true"> <mi>V</mi> <mo stretchy="true">¯</mo> </munder> </mrow> <mrow> <mi>M</mi> <mo>+</mo> <mn style="font-style:italic">1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>T</mi> <mrow> <msub> <mi>L</mi> <mn style="font-style:italic">0</mn> </msub> </mrow> </msub> <mo>,</mo> <msub> <mi>T</mi> <mrow> <msub> <mi>V</mi> <mrow> <mi>M</mi> <mo>+</mo> <mn style="font-style:italic">1</mn> </mrow> </msub> </mrow> </msub> <mo>,</mo> <msub> <mi>P</mi> <mrow> <msub> <mi>L</mi> <mn style="font-style:italic">0</mn> </msub> </mrow> </msub> <mo>,</mo> <msub> <mi>P</mi> <mrow> <msub> <mi>V</mi> <mrow> <mi>M</mi> <mo>+</mo> <mn style="font-style:italic">1</mn> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math>(GLMC-CCC-D) and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <munder accentunder="true"> <mi>L</mi> <mo stretchy="true">¯</mo> </munder> </mrow> <mn style="font-style:italic">0</mn> </msub> <mo>,</mo> <msub> <mrow> <munder accentunder="true"> <mi>V</mi> <mo stretchy="true">¯</mo> </munder> </mrow> <mn style="font-style:italic">0</mn> </msub> <mo>,</mo> <msub> <mi>T</mi> <mrow> <msub> <mi>L</mi> <mn style="font-style:italic">0</mn> </msub> </mrow> </msub> <mo>,</mo> <msub> <mi>T</mi> <mrow> <msub> <mi>V</mi> <mn style="font-style:italic">0</mn> </msub> </mrow> </msub> <mo>,</mo> <msub> <mi>P</mi> <mrow> <msub> <mi>L</mi> <mn style="font-style:italic">0</mn> </msub> </mrow> </msub> <mo>,</mo> <msub> <mi>P</mi> <mrow> <msub> <mi>V</mi> <mn style="font-style:italic">0</mn> </msub> </mrow> </msub> </mrow> </semantics></math>(GLMC-PC-D).</p> Full article ">Figure 2
<p>HFM bundle as equilateral triangular lattice (edge <span class="html-italic">p<sub>HF</sub></span>): triangle-free area, <span class="html-italic">S<sub>FREE</sub></span>, for shell-side liquid flow.</p> Full article ">Figure 3
<p>Algorithm flowcharts for solving: (<b>a</b>) a countercurrent-contact GLMC (GLMC-CCC-D) and (<b>b</b>) parallel-contact GLMC (GLMC-PC-D).</p> Full article ">Figure 4
<p>Block diagram of GLMC-based industrial-scale landfill-gas-to-biomethane process.</p> Full article ">Figure 5
<p>Landfill-gas compression.</p> Full article ">Figure 6
<p>Landfill-gas CO<sub>2</sub>/H<sub>2</sub>S removal via countercurrent pressurized-water GLMC.</p> Full article ">Figure 7
<p>CO<sub>2</sub>-to-EOR compression.</p> Full article ">Figure 8
<p>Landfill-gas siloxane removal via DEPG absorption.</p> Full article ">Figure 9
<p>GLMC-PC-D axial profiles: (<b>a</b>) <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mover> <mi mathvariant="bold-italic">f</mi> <mo mathvariant="bold">^</mo> </mover> </mrow> <mrow> <msub> <mi mathvariant="italic">CO</mi> <mn style="font-style:italic">2</mn> </msub> </mrow> <mi>V</mi> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mover> <mi mathvariant="bold-italic">f</mi> <mo mathvariant="bold">^</mo> </mover> </mrow> <mrow> <msub> <mi mathvariant="italic">CO</mi> <mn style="font-style:italic">2</mn> </msub> </mrow> <mi>L</mi> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mover> <mi mathvariant="bold-italic">f</mi> <mo mathvariant="bold">^</mo> </mover> </mrow> <mrow> <msub> <mi mathvariant="italic">CH</mi> <mn style="font-style:italic">4</mn> </msub> </mrow> <mi>V</mi> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mover> <mi mathvariant="bold-italic">f</mi> <mo mathvariant="bold">^</mo> </mover> </mrow> <mrow> <msub> <mi mathvariant="italic">CH</mi> <mn style="font-style:italic">4</mn> </msub> </mrow> <mi>L</mi> </msubsup> </mrow> </semantics></math>; (<b>b</b>) <span class="html-italic">V</span> (%mol) <span class="html-italic">CO<sub>2</sub></span>/<span class="html-italic">H<sub>2</sub>S</span>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mover> <mi mathvariant="bold-italic">f</mi> <mo mathvariant="bold">^</mo> </mover> </mrow> <mrow> <msub> <mi>H</mi> <mn style="font-style:italic">2</mn> </msub> <mi>S</mi> </mrow> <mi>V</mi> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mover> <mi mathvariant="bold-italic">f</mi> <mo mathvariant="bold">^</mo> </mover> </mrow> <mrow> <msub> <mi>H</mi> <mn style="font-style:italic">2</mn> </msub> <mi>S</mi> </mrow> <mi>L</mi> </msubsup> </mrow> </semantics></math>; (<b>d</b>) <span class="html-italic">V ppm-mol H<sub>2</sub>S</span>; (<b>e</b>) <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mover> <mi mathvariant="bold-italic">f</mi> <mo mathvariant="bold">^</mo> </mover> </mrow> <mrow> <msub> <mi>H</mi> <mn style="font-style:italic">2</mn> </msub> <mi>O</mi> </mrow> <mi>V</mi> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mover> <mi mathvariant="bold-italic">f</mi> <mo mathvariant="bold">^</mo> </mover> </mrow> <mrow> <msub> <mi>H</mi> <mn style="font-style:italic">2</mn> </msub> <mi>O</mi> </mrow> <mi>L</mi> </msubsup> </mrow> </semantics></math>; (<b>f</b>) <span class="html-italic">V</span> (%mol) <span class="html-italic">H<sub>2</sub>O</span>; (<b>g</b>) %<span class="html-italic">Recovery</span>, <span class="html-italic">%CH<sub>4</sub> Loss</span>; (<b>h</b>) <span class="html-italic">CO<sub>2</sub></span>/<span class="html-italic">CH<sub>4</sub> Selectivity</span>.</p> Full article ">Figure 10
<p>GLMC-PC-D axial profiles: (<b>a</b>) temperatures; (<b>b</b>) pressures.</p> Full article ">Figure A1
<p>GLMC-PC-D model validation by de Medeiros et al. [<a href="#B54-processes-12-01667" class="html-bibr">54</a>]: <span class="html-italic">profiles versus z</span> (m): (<b>a</b>) <span class="html-italic">V</span> fugacities (bar); (<b>b</b>) <span class="html-italic">L</span> fugacities (bar); (<b>c</b>) <span class="html-italic">V</span> flowrates (mol/s); (<b>d</b>) <span class="html-italic">L</span> flowrates (mol/s); (<b>e</b>) <span class="html-italic">V</span> composition (%mol); (<b>f</b>) %recovery; (<b>g</b>) CO<sub>2</sub>/CH<sub>4</sub> selectivity.</p> Full article ">Figure A2
<p>GLMC-PC-D <span class="html-italic">V</span>/<span class="html-italic">L</span> fugacity profiles: (<b>a</b>) CO<sub>2</sub>; (<b>b</b>) CH<sub>4</sub>; (<b>c</b>) H<sub>2</sub>O.</p> Full article ">Figure A3
<p>GLMC-PC-D asymptotic validation with HYSYS P-H flash—<span class="html-italic">profiles versus z</span> (m). <span class="html-italic">V</span> (%mol); (<b>a</b>) CO<sub>2</sub>/CH<sub>4</sub> (<b>b</b>) H<sub>2</sub>O; (<b>c</b>) CO<sub>2</sub>/CH<sub>4</sub> <span class="html-italic">V (mol/s)</span> (<b>d</b>) H<sub>2</sub>O; <span class="html-italic">V</span> (mol/s); (<b>e</b>) CO<sub>2</sub> <span class="html-italic">L (mol/s)</span>; (<b>f</b>) H<sub>2</sub>O <span class="html-italic">L (mol/s)</span>; (<b>g</b>) CH<sub>4</sub> <span class="html-italic">L (mol/s)</span> (<b>h</b>) <span class="html-italic">Temperatures</span> (°C): <span class="html-italic">V</span> and <span class="html-italic">L</span>.</p> Full article ">Figure A4
<p>GLMC biogas purification [<a href="#B47-processes-12-01667" class="html-bibr">47</a>].</p> Full article ">
11 pages, 4048 KiB  
Article
Effects of Room-Temperature Center Gas Distributor Injection on the H2 Shaft Furnace Process: A Numerical Study
by Lei Shao, Hongfu Yu and Chenxi Zhao
Processes 2024, 12(8), 1666; https://doi.org/10.3390/pr12081666 - 8 Aug 2024
Viewed by 306
Abstract
In the current work, a computational fluid dynamics-based model was utilized to investigate the performance of the H2 shaft furnace under a scenario where room-temperature H2 is injected through a center gas distributor (CGD) installed at the unit bottom. Modelling was [...] Read more.
In the current work, a computational fluid dynamics-based model was utilized to investigate the performance of the H2 shaft furnace under a scenario where room-temperature H2 is injected through a center gas distributor (CGD) installed at the unit bottom. Modelling was conducted to simulate scenarios where the CGD operation is applied with different feed gas rates (ranging from 0 to 250 Nm3/t-pellet). The results showed that a high temperature level and thus a better internal thermochemical state can be maintained with a proper CGD gas feed rate. However, an overly high CGD feed rate (being 150 Nm3/t-pellet or a higher value) induces a detrimental scenario where the thermal energy recycled by the room-temperature CGD gas is insufficient to compensate for the decrease of sensible heat of the preheated feed gas from the bustle-pipe. This eventually results in a noteworthy chemical reserve zone of high H2 content and little solid reduction in the furnace center. A large quantity of H2 consequently remains unutilized and leaves the furnace from the top. Under the investigated conditions, the final solid reduction degree rises to maximal value when the CGD gas feed rate is 100 Nm3/t-pellet. The findings of this work revealed that the room-temperature CGD gas injection operation holds significant promise for practical applications. Full article
(This article belongs to the Special Issue Recent Trends in Extractive Metallurgy)
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<p>Schematic of the hydrogen shaft furnace equipped with a center gas distributor and the discretized grids for numerical modeling.</p> Full article ">Figure 2
<p>Distributions of gas temperature under the conditions of Cases 1 and 2.</p> Full article ">Figure 3
<p>Distributions of solid temperature along the furnace bottom under the conditions of Cases 1 and 2.</p> Full article ">Figure 4
<p>Comparisons of 1073 K isotherm lines for Cases 1–6.</p> Full article ">Figure 5
<p>Distributions of H<sub>2</sub> mole fraction under the conditions of different CGD feed rates.</p> Full article ">Figure 6
<p>Reduction rate distributions of solid phase under the conditions of different CGD feed rates.</p> Full article ">Figure 7
<p>Values of recycled thermal energy by CGD gas, sensible heat carried by preheated feed gas as well as input of heat (orange circles) under the conditions of different CGD feed rates.</p> Full article ">Figure 8
<p>Reduction degree distributions of solid phase under the conditions of different CGD feed rates.</p> Full article ">Figure 9
<p>Final reduction degrees (both light green columns and orange circles) under the conditions of different CGD feed rates.</p> Full article ">
12 pages, 4048 KiB  
Article
Research on Micropore Development Characteristics and Influencing Factors during CO2 Huff-n-Puff
by Jilun Kang, Shenglai Yang, Wei Zhang, Hong Zhang, Changsong He, Xuechun Wang, Shuangbao Wei, Kun Yang and Lilong Wang
Processes 2024, 12(8), 1665; https://doi.org/10.3390/pr12081665 - 8 Aug 2024
Viewed by 269
Abstract
CO2 huff-n-puff is an important method for the development of shale oil reservoirs. In this study, HPMI and NMR technology was used to characterize the pore distribution of the cores. The CO2 huff-n-puff experiment experiments were conducted to study the effects [...] Read more.
CO2 huff-n-puff is an important method for the development of shale oil reservoirs. In this study, HPMI and NMR technology was used to characterize the pore distribution of the cores. The CO2 huff-n-puff experiment experiments were conducted to study the effects of injection pressure, soaking time, and heterogeneity on the CO2 huff-n-puff. The results showed that the Jimsar core pores are predominantly nanopores. Mesopores with a pore radius between 2 nm and 50 nm accounted for more than 70%. CO2 huff-n-puff has been shown to effectively enhance shale oil recovery. When the injection pressure was greater than the miscible pressure, higher injection pressures were able to improve the recovery of macropores, particularly in cores with higher permeability. Appropriately extending the soaking time enhanced the diffusion of CO2 in the mesopores, and the recovery increased to above 10%. Determining the optimal soaking time is crucial to achieve maximum CO2 huff-n-puff recovery. Artificial fractures can enhance the recovery of mesopores around them, resulting in core recovery of up to 60%. However, artificial fractures exacerbate reservoir heterogeneity and reduce the CO2 huff-n-puff recovery of matrix. Increasing the cycles of CO2 huff-n-puff can effectively reduce the impact of heterogeneity on the recovery of matrix. In conclusion, expanding the area of the fracturing transformation zone and selecting the appropriate injection pressure and soaking time for the multiple cycles of CO2 huff-n-puff can effectively improve the recovery of shale oil reservoirs. Full article
(This article belongs to the Special Issue Advances in Improving Oil Recovery in Low-Permeability Hydrocarbon Resources)
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<p>Casting thin sheet and contact angles of Jimsar Lucaogou Formation cores: (<b>a</b>,<b>b</b>) are casting thin sheet; (<b>c</b>) is the contact angles between water and core slices.</p> Full article ">Figure 2
<p>Schematic diagram of a CO<sub>2</sub> huff-n-puff device.</p> Full article ">Figure 3
<p>Capillary force curve and core pore distribution: (<b>a</b>) capillary force curve; (<b>b</b>) core pore distribution.</p> Full article ">Figure 4
<p>Core NMR T<sub>2</sub> curve and mercury intrusion curve: (<b>a</b>) curves of Core YJ-24; (<b>b</b>) curves of Core YJ-42.</p> Full article ">Figure 5
<p>Conversion of pore radius and NMR relaxation time: (<b>a</b>) fitting curve of Core YJ-24; (<b>b</b>) fitting curve of Core YJ-42.</p> Full article ">Figure 6
<p>Recovery and minimum operating radius of core under different injection pressures: (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) NMR T<sub>2</sub> spectra of saturated state and various CO<sub>2</sub> huff-n-puff cycles of YJ-7, YJ-40, YJ-6, and YJ-2, respectively. (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) pore recovery and total recovery of each cycle of YJ-7, YJ-40, YJ-6, and YJ-2, respectively.</p> Full article ">Figure 7
<p>Recovery and minimum operating radius of cores CO<sub>2</sub> huff-n-puff at different soaking times: (<b>a</b>) recovery of each CO<sub>2</sub> huff-n-puff cycle of three cores; (<b>b</b>) NMR T<sub>2</sub> spectra and minimum operating radius of core YJ-10 during soaking for 5 h; (<b>c</b>) NMR T<sub>2</sub> spectra and minimum operating radius of core YJ-7 during soaking for 10 h; (<b>d</b>) NMR T2 spectra and minimum operating radius of core YJ-3 during soaking for 15 h.</p> Full article ">Figure 8
<p>Pore recovery of parallel cores CO<sub>2</sub> huff-n-puff: (<b>a</b>) pore recovery and total recovery of each cycle of parallel core YJ-20; (<b>b</b>) pore recovery and total recovery of each cycle of parallel core YJ-13; (<b>c</b>) pore recovery and total recovery of each cycle of parallel core YJ-12.</p> Full article ">Figure 9
<p>Comparison of pore recovery between parallel core CO<sub>2</sub> huff-n-puff and single core CO<sub>2</sub> huff-n-puff.</p> Full article ">
13 pages, 1867 KiB  
Article
Comparison of Light Intensity Effect on Microalgal Growth in Cactus-like and Cylindrical Photo Bioreactors
by Rayane Mustafa Hijazi, Jihane Rahbani Mounsef and Hadi Youssef Kanaan
Processes 2024, 12(8), 1664; https://doi.org/10.3390/pr12081664 - 8 Aug 2024
Viewed by 442
Abstract
Improving photobioreactor performance for microalgae cultivation has been the aim of many researchers over the past few years. One of the primary challenges associated with existing photobioreactors is light penetration. An effective photobioreactor design should maximize light penetration, ensuring uniform illumination throughout the [...] Read more.
Improving photobioreactor performance for microalgae cultivation has been the aim of many researchers over the past few years. One of the primary challenges associated with existing photobioreactors is light penetration. An effective photobioreactor design should maximize light penetration, ensuring uniform illumination throughout the reactor. This study aims to assess the impact of light intensity on microalgal growth from the perspective of energy efficiency and productivity in two photobioreactors. A novel cactus-like and a cylindrical photobioreactor were designed and fabricated using three-dimensional printing technology. These two photobioreactors were used to cultivate two strains of microalgae. The novel photobioreactor achieved a maximum biomass productivity of 1 g/L/d and a maximum energy efficiency of 0.31 g/d/kWh. The cylindrical photobioreactor reached a maximum biomass productivity of 0.74 g/L/d and energy efficiency of 0.22 g/d/kWh. The increase in biomass productivity can be linked to enhancements in the photobioreactor’s surface-to-volume ratio and better light utilization. Full article
(This article belongs to the Section Catalysis Enhanced Processes)
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<p>(<b>a</b>) Cactus-like PBR; (<b>b</b>) Schematic of the circular sparger; (<b>c</b>) Elevation view of the cactus-like PBR.</p> Full article ">Figure 2
<p>Elevation view of the cylindrical PBR.</p> Full article ">Figure 3
<p>The setup of the cactus-like and the cylindrical PBRs.</p> Full article ">Figure 4
<p>Lux level inside the two PBRs in five light conditions.</p> Full article ">Figure 5
<p>The specific growth rate of two microalgae strains inside the two PBRs with different light conditions.</p> Full article ">
15 pages, 6518 KiB  
Article
Experimental and Numerical Simulation Study on Enhancing Gas Recovery with Impure CO2 in Gas Reservoirs
by Zihan Zhao, Shaomu Wen, Mengyu Wang, Lianjin Zhang, Cheng Cao, Changcheng Yang and Longxin Li
Processes 2024, 12(8), 1663; https://doi.org/10.3390/pr12081663 - 8 Aug 2024
Viewed by 278
Abstract
To achieve carbon peaking and carbon neutrality goals, using CO2 to enhance natural gas recovery has broad application prospects. However, the potential for CO2 to increase recovery rates remains unclear, the mechanisms are not fully understood, and the cost of purifying [...] Read more.
To achieve carbon peaking and carbon neutrality goals, using CO2 to enhance natural gas recovery has broad application prospects. However, the potential for CO2 to increase recovery rates remains unclear, the mechanisms are not fully understood, and the cost of purifying CO2 is high. Therefore, studying the effects of impure CO2 on natural gas extraction is of significant importance. This study investigated the effects of injection timing and gas composition on natural gas recovery through high-temperature, high-pressure, long-core displacement experiments. Based on the experimental results, numerical simulations of CO2-enhanced gas recovery and sequestration were conducted, examining the impact of impurity gas concentration, injection timing, injection speed, and water saturation on recovery efficiency. The results indicate that higher impurity levels in CO2 increase gas diffusion, reducing the effectiveness of natural gas recovery and decreasing CO2 sequestration. Earlier injection timing improves recovery efficiency but results in a lower ultimate recovery rate. Higher injection speeds and water saturation levels both effectively enhance recovery rates. Full article
(This article belongs to the Special Issue Advances in Enhancing Unconventional Oil/Gas Recovery, 2nd Edition)
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<p>Schematic diagram of the EGR core displacement experimental system.</p> Full article ">Figure 2
<p>Curves of gas recovery factor and the differential pressure between the inlet and outlet under various inlet pressures.</p> Full article ">Figure 3
<p>CH<sub>4</sub> recovery curves at different injection volumes.</p> Full article ">Figure 4
<p>Proportion curves of produced gas components after CO<sub>2</sub> injection at 2, 6, and 8 MPa. (<b>A</b>) Produced gas components of CO<sub>2</sub> injection at 6 MPa. (<b>B</b>) Produced gas components of CO<sub>2</sub> injection at 8 MPa. (<b>C</b>) Produced gas components of oxidation-absorption tail gas injection at 2 MPa. (<b>D</b>) Produced gas components of oxidation-absorption tail gas injection at 8 MPa.</p> Full article ">Figure 4 Cont.
<p>Proportion curves of produced gas components after CO<sub>2</sub> injection at 2, 6, and 8 MPa. (<b>A</b>) Produced gas components of CO<sub>2</sub> injection at 6 MPa. (<b>B</b>) Produced gas components of CO<sub>2</sub> injection at 8 MPa. (<b>C</b>) Produced gas components of oxidation-absorption tail gas injection at 2 MPa. (<b>D</b>) Produced gas components of oxidation-absorption tail gas injection at 8 MPa.</p> Full article ">Figure 5
<p>Pressure curves at different CO<sub>2</sub> injection volumes.</p> Full article ">Figure 6
<p>Core model parameter distribution diagram.</p> Full article ">Figure 7
<p>Injection gas content curves in produced gas with injection volume and the distribution of CH<sub>4</sub> content (0.8 HCPV).</p> Full article ">Figure 8
<p>Residual gas recovery, cumulative recovery, and CO<sub>2</sub> storage rate curves at different injection volumes.</p> Full article ">Figure 9
<p>CO<sub>2</sub> content curves of produced gas at different injection volumes, CO<sub>2</sub>/CH<sub>4</sub> density ratio and viscosity ratio curves at different pressures, and distributions of CO<sub>2</sub> content (0.8 HCPV).</p> Full article ">Figure 10
<p>Residual gas recovery, cumulative recovery, and CO<sub>2</sub> storage rate curves at different injection volumes.</p> Full article ">Figure 11
<p>CO<sub>2</sub> content curves of produced gas at different injection volumes and distributions of CO<sub>2</sub> content (0.8 HCPV).</p> Full article ">Figure 11 Cont.
<p>CO<sub>2</sub> content curves of produced gas at different injection volumes and distributions of CO<sub>2</sub> content (0.8 HCPV).</p> Full article ">Figure 12
<p>Residual gas recovery, cumulative recovery, and CO<sub>2</sub> storage rate curves at different injection volumes.</p> Full article ">Figure 13
<p>CO<sub>2</sub> content curves of produced gas with injection volumes and distributions of CO<sub>2</sub> content (0.8 HCPV).</p> Full article ">Figure 14
<p>Residual gas recovery, cumulative recovery, and CO<sub>2</sub> storage rate curves at different injection volumes.</p> Full article ">
13 pages, 3784 KiB  
Article
Removal of Lead and Nitrate from Simulated Lead- and Nitrate-Containing Wastewater via Hydroxide Precipitation
by Glyzel Ann C. Madlangbayan, Khyle Glainmer N. Quiton and Ming-Chun Lu
Processes 2024, 12(8), 1662; https://doi.org/10.3390/pr12081662 - 8 Aug 2024
Viewed by 551
Abstract
Lead and nitrate are pollutants that are commonly found in wastewater, and these pollutants pose significant risks to humans, animals, plants, and the environment. Therefore, it is essential to treat the wastewater to remove these toxic substances. This study utilized hydroxide precipitation for [...] Read more.
Lead and nitrate are pollutants that are commonly found in wastewater, and these pollutants pose significant risks to humans, animals, plants, and the environment. Therefore, it is essential to treat the wastewater to remove these toxic substances. This study utilized hydroxide precipitation for the removal of lead and nitrate from simulated lead- and nitrate-containing wastewater through jar testing. The effects of pH, lead nitrate (Pb(NO3)2) concentration, and precipitant-to-metal ([P]/[M]) ratio were examined. The hydroxide precipitation effectively removed lead and nitrate by forming basic lead nitrate precipitates, such as lead hydroxide nitrates and lead oxide hydroxide nitrates, and operated efficiently at a pH of around 8.0. Lead and nitrate removal was highly effective and primarily influenced by the [P]/[M] ratio, with [P]/[M] of 1.0 as the optimum condition. Varying the lead nitrate concentrations resulted in a higher sludge volume compared to other parameters; however, it was only significant in nitrate removal with an optimum concentration of 0.07 M. Full article
(This article belongs to the Section Environmental and Green Processes)
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<p>(<b>a</b>) Schematic and (<b>b</b>) actual experimental setup for the co-contaminated system of lead and nitrate.</p> Full article ">Figure 2
<p>Solubilities of different lead compounds at various pH levels [<a href="#B14-processes-12-01662" class="html-bibr">14</a>].</p> Full article ">Figure 3
<p>(<b>a</b>) The zeta potential of lead nitrate systems at varying pH levels and (<b>b</b>) the amount of precipitates formed with different parameters.</p> Full article ">Figure 4
<p>The effect of pH on (<b>a</b>) the removal of lead and nitrate and (<b>b</b>) the sludge volume and sludge settling rate.</p> Full article ">Figure 5
<p>The effect of lead nitrate concentration on (<b>a</b>) the removal of lead and nitrate and (<b>b</b>) the sludge volume and sludge settling rate.</p> Full article ">Figure 6
<p>The effect of precipitant-to-metal ratio on (<b>a</b>) the removal of lead and nitrate and (<b>b</b>) the sludge volume and sludge settling rate.</p> Full article ">Figure 7
<p>XRD patterns of basic lead nitrates formed at varying pH levels.</p> Full article ">Figure 8
<p>FTIR spectra of basic lead nitrates formed at varying pH levels.</p> Full article ">Figure 9
<p>Scanning electron micrographs of precipitated basic lead nitrates at pH (<b>a</b>) 6.0, (<b>b</b>) 7.0, (<b>c</b>) 8.0, (<b>d</b>) 9.0, and (<b>e</b>) 10.0.</p> Full article ">
20 pages, 10455 KiB  
Article
Experimental Study on the Effect of Unloading Paths on Coal Damage and Permeability Evolution
by Congmeng Hao, Youpai Wang and Guangyi Liu
Processes 2024, 12(8), 1661; https://doi.org/10.3390/pr12081661 - 7 Aug 2024
Viewed by 337
Abstract
Coal seam cavitation is one of the most effective techniques for gas disaster control in low-permeability coal. Due to the difference in cavitation method and process, the damage degree and fracture development range of the coal body around the cavern are greatly different, [...] Read more.
Coal seam cavitation is one of the most effective techniques for gas disaster control in low-permeability coal. Due to the difference in cavitation method and process, the damage degree and fracture development range of the coal body around the cavern are greatly different, and the effect of increasing the permeability of the coal body is further changed. In order to further understand the permeability enhancement mechanism of cavitation technology on low-permeability coal and effectively guide engineering applications, this paper conducted experimental research on the unloading damage and permeability evolution characteristics of coal under different cavitation paths using a coal-rock “adsorption-percolation-mechanics” coupling test system. Through the analysis of coal strength and deformation characteristics, coal damage characteristics, and the evolution law of coal permeability combined with the macroscopic damage characteristics of coal, the strength degradation mechanism of unloaded coal and the mechanism of increased permeability and flow were revealed. The results show that unloading can significantly reduce the strength of coal, and the greater the unloading rate, the more obvious the reduction. The essence of this is that unloading reduces the cohesion and internal friction angle of coal—damage and breakage are the most effective ways to improve the permeability of the coal body. Unloading damaged coal bodies not only significantly improves the permeability of the coal body but also improves the diffusion ability of gas, and finally, shows a remarkable strengthening effect of gas extraction. Full article
(This article belongs to the Special Issue Monitoring, Process Control, Simulation, and Optimization in Coal Mining)
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<p>Schematic diagram of stress evolution of coal mass around the cavern with different hole expansion paths: (<b>a</b>) Hydraulic cavitation process-repeated scouring into holes; (<b>b</b>) Hydraulic cavitation process-one-time scouring into holes; (<b>c</b>) Mechanical cavitation process.</p> Full article ">Figure 2
<p>Experimental coal sample and test system: (<b>a</b>) Coal sample preparation process; (<b>b</b>) The test system.</p> Full article ">Figure 3
<p>Stress–strain characteristics of coal under different confining pressures at the same unloading rate: (<b>a</b>) conventional triaxial loading; (<b>b</b>) confining pressure unloading at 25 N/s and loading; (<b>c</b>) confining pressure unloading at 50 N/s and loading.</p> Full article ">Figure 4
<p>Stress–strain characteristics of coal under the same confining pressure and different unloading rates: (<b>a</b>) initial confining pressure of 5 MPa; (<b>b</b>) initial confining pressure of 10 MPa; (<b>c</b>) initial confining pressure of 15 MPa.</p> Full article ">Figure 5
<p>Changes of peak stress of coal samples under different initial confining pressures.</p> Full article ">Figure 6
<p>Characteristics of acoustic emission signals (AE counts) of coal samples in different unloading paths.</p> Full article ">Figure 7
<p>Characteristics of acoustic emission signals (AE energy) of coal samples in different unloading paths.</p> Full article ">Figure 8
<p>Comparison of coal acoustic emission data in different unloading paths.</p> Full article ">Figure 9
<p>Permeability evolution of coal mass during full stress–strain process under different unloading behaviors.</p> Full article ">Figure 10
<p>Macroscopic damage and destruction characteristics of coal under different unloading behaviors.</p> Full article ">Figure 11
<p>Coal damage and destruction mechanism under different unloading conditions.</p> Full article ">Figure 12
<p>Permeability evolution model of coal mass during full stress–strain process under different unloading behaviors.</p> Full article ">Figure 13
<p>The path of permeability increase caused by pressure relief and damage of coal-mass.</p> Full article ">
19 pages, 6078 KiB  
Article
Prediction of Oil–Water Two-Phase Flow Patterns Based on Bayesian Optimisation of the XGBoost Algorithm
by Dudu Wang, Haimin Guo, Yongtuo Sun, Haoxun Liang, Ao Li and Yuqing Guo
Processes 2024, 12(8), 1660; https://doi.org/10.3390/pr12081660 - 7 Aug 2024
Viewed by 301
Abstract
With the continuous advancement of petroleum extraction technologies, the importance of horizontal and inclined wells in reservoir exploitation has been increasing. However, accurately predicting oil–water two-phase flow regimes is challenging due to the complexity of subsurface fluid flow patterns. This paper introduces a [...] Read more.
With the continuous advancement of petroleum extraction technologies, the importance of horizontal and inclined wells in reservoir exploitation has been increasing. However, accurately predicting oil–water two-phase flow regimes is challenging due to the complexity of subsurface fluid flow patterns. This paper introduces a novel approach to address this challenge by employing extreme gradient boosting (XGBoost, version 2.1.0) optimised through Bayesian techniques (using the Bayesian-optimization library, version 1.4.3) to predict oil–water two-phase flow regimes. The integration of Bayesian optimisation aims to enhance the efficiency of parameter tuning and the precision of predictive models. The methodology commenced with experimental studies utilising a multiphase flow simulation apparatus to gather data across a spectrum of water cut rate, well inclination angles, and flow rates. Flow patterns were meticulously recorded via direct visual inspection, and these empirical datasets were subsequently used to train and validate both the conventional XGBoost model and its Bayesian-optimised counterpart. A total of 64 datasets were collected, with 48 sets used for training and 16 sets for testing, divided in a 3:1 ratio. The findings highlight a marked improvement in predictive accuracy for the Bayesian-optimised XGBoost model, achieving a testing accuracy of 93.8%, compared to 75% for the traditional XGBoost model. Precision, recall, and F1-score metrics also showed significant improvements: precision increased from 0.806 to 0.938, recall from 0.875 to 0.938, and F1-score from 0.873 to 0.938. The training accuracy further supported these results, with the Bayesian-optimised XGBoost (BO-XGBoost) model achieving an accuracy of 0.948 compared to 0.806 for the traditional XGBoost model. Comparative analyses demonstrate that Bayesian optimisation enhanced the predictive capabilities of the algorithm. Shapley additive explanations (SHAP) analysis revealed that well inclination angles, water cut rates, and daily flow rates were the most significant features contributing to the predictions. This study confirms the efficacy and superiority of the Bayesian-optimised XGBoost (BO-XGBoost) algorithm in predicting oil–water two-phase flow regimes, offering a robust and effective methodology for investigating complex subsurface fluid dynamics. The research outcomes are crucial in improving the accuracy of oil–water two-phase flow predictions and introducing innovative technical approaches within the domain of petroleum engineering. This work lays a foundational stone for the advancement and application of multiphase flow studies. Full article
(This article belongs to the Section Automation Control Systems)
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<p>Bayesian optimisation algorithm optimisation XGBoost model process.</p> Full article ">Figure 2
<p>XGBoost training flow chart.</p> Full article ">Figure 3
<p>Schematic of oil–water flow patterns (<b>left</b>) and the photographed diagram (<b>right</b>).</p> Full article ">Figure 4
<p>Schematic of the experimental setup, including: 1. simulation well; 2. well inclination regulator; 3. oil–water mixer; 4, 5. position control valves; 6, 7. flow meters; 8. water pump; 9. oil pump; 10. water tank; 11. oil tank; 12. oil–water separation tank.</p> Full article ">Figure 5
<p>Confusion matrix of prediction results of the XGBoost algorithm training set. (<b>a</b>) Non-normalized data; (<b>b</b>) Normalized data.</p> Full article ">Figure 6
<p>Confusion matrix of prediction results of the XGBoost algorithm test set. (<b>a</b>) Non-normalized data; (<b>b</b>) Normalized data.</p> Full article ">Figure 7
<p>Scatter plot of the XGBoost training set and test set flow prediction results.</p> Full article ">Figure 8
<p>Confusion matrix of the prediction results of the BO-XGBoost algorithm training set. (<b>a</b>) Non-normalized data; (<b>b</b>) Normalized data.</p> Full article ">Figure 9
<p>Confusion matrix of the prediction results of the BO-XGBoost algorithm test set. (<b>a</b>) Non-normalized data; (<b>b</b>) Normalized data. I have added the explanations to the figure titles.</p> Full article ">Figure 10
<p>Scatter plot of the BO-XGBoost training set and test set flow prediction results.</p> Full article ">Figure 11
<p>XGBoost ROC curve.</p> Full article ">Figure 12
<p>BO-XGBoost ROC curve.</p> Full article ">Figure 13
<p>Flow pattern prediction accuracy statistics.</p> Full article ">Figure 14
<p>Feature importance image.</p> Full article ">Figure 15
<p>Feature global explanation image.</p> Full article ">
16 pages, 3431 KiB  
Article
Radioiodinated Anastrozole and Epirubicin for HER2-Targeted Cancer Therapy: Molecular Docking and Dynamics Insights with Implications for Nuclear Imaging
by Mazen Abdulrahman Binmujlli
Processes 2024, 12(8), 1659; https://doi.org/10.3390/pr12081659 - 7 Aug 2024
Viewed by 315
Abstract
This study evaluates radioiodinated anastrozole ([125I]anastrozole) and epirubicin ([125I]epirubicin) for HER2-targeted cancer therapy, utilizing radiopharmaceutical therapy (RPT) for personalized treatment of HER2-positive cancers. Through molecular docking and dynamics simulations (200 ns), it investigates these compounds’ binding affinities and mechanisms [...] Read more.
This study evaluates radioiodinated anastrozole ([125I]anastrozole) and epirubicin ([125I]epirubicin) for HER2-targeted cancer therapy, utilizing radiopharmaceutical therapy (RPT) for personalized treatment of HER2-positive cancers. Through molecular docking and dynamics simulations (200 ns), it investigates these compounds’ binding affinities and mechanisms to the HER2 receptor compared to lapatinib, a known HER2 inhibitor. Molecular docking studies identified [125I]epirubicin with the highest ΔGbind (−10.92 kcal/mol) compared to lapatinib (−10.65 kcal/mol) and [125I]anastrozole (−9.65 kcal/mol). However, these differences were not statistically significant. Further molecular dynamics (MD) simulations are required to better understand the implications of these findings on the therapeutic potential of the compounds. MD simulations affirmed a stable interaction with the HER2 receptor, indicated by an average RMSD of 4.51 Å for [125I]epirubicin. RMSF analysis pointed to significant flexibility at key receptor regions, enhancing the inhibitory action against HER2. The [125I]epirubicin complex maintained an average of four H-bonds, indicating strong and stable interactions. The average Rg values for [125I]anastrozole and [125I]epirubicin complexes suggest a modest increase in structural flexibility without compromising protein compactness, reflecting their potential to induce necessary conformational changes in the HER2 receptor function. These analyses reveal enhanced flexibility and specific receptor region interactions, suggesting adaptability in binding, which could augment the inhibitory action against HER2. MM-PBSA calculations indicate the potential of these radioiodinated compounds as HER2 inhibitors. Notably, [125I]epirubicin exhibited a free binding energy of −65.81 ± 0.12 kJ/mol, which is comparable to lapatinib at −64.05 ± 0.11 kJ/mol and more favorable than [125I]anastrozole at −57.18 ± 0.12 kJ/mol. The results suggest electrostatic interactions as a major contributor to the binding affinity. The computational analysis underscores that [125I]anastrozole and [125I]epirubicin may have a promising role as HER2 inhibitors, especially [125I]epirubicin due to its high binding affinity and dynamic receptor interactions. These findings, supported by molecular docking scores and MM-PBSA binding energies, advocate for their potential superior inhibitory capability against the HER2 receptor. To validate these computational predictions and evaluate the therapeutic potential of these compounds for HER2-targeted cancer therapy, it is essential to conduct empirical validation through both in vitro and in vivo studies. Full article
(This article belongs to the Special Issue Application of Computational Techniques in Analytical Chemistry and Molecular Systems)
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<p>Depiction of (<b>a</b>) radioiodinated anastrozole ([<sup>125</sup>I]anastrozole), (<b>b</b>) radioiodinated epirubicin ([<sup>125</sup>I]epirubicin), and (<b>c</b>) lapatinib.</p> Full article ">Figure 2
<p>Three-dimensional and two-dimensional interactions of lapatinib (<b>a</b>,<b>b</b>), [<sup>125</sup>I]anastrozole (<b>c</b>,<b>d</b>), and [<sup>125</sup>I]epirubicin (<b>e</b>,<b>f</b>) within the active binding site of the human HER2 receptor (PDB ID: 3RCD). Distances are given in angstroms (Å). Discovery Studio visualizer was used to generate these models.</p> Full article ">Figure 3
<p>Root-mean-square deviation (RMSD) analysis for molecular dynamics (MD) simulation trajectories over a span of 200 ns. (<b>a</b>) RMSD plots of the HER2 protein backbone, reflecting molecular variations after interaction with [<sup>125</sup>I]anastrozole, [<sup>125</sup>I]epirubicin, lapatinib, and the co-crystallized ligand TAK-285. (<b>b</b>) The RMSD plots also reveal structural modifications of [<sup>125</sup>I]anastrozole, [<sup>125</sup>I]epirubicin, lapatinib, and the co-crystallized ligand TAK-285 into the active binding site HER2.</p> Full article ">Figure 4
<p>RMSF (root-mean-square fluctuation) plots illustrating the behavior of backbone atoms in HER2 over a 200 ns MD simulation across all systems. The RMSF data show the fluctuations of individual protein residues as they interact with the ligands throughout the simulation.</p> Full article ">Figure 5
<p>Radius of gyration (Rg) plots for HER2 backbone atoms across all systems during a 200 ns MD simulation.</p> Full article ">Figure 6
<p>Hydrogen bond profiles for interactions of (<b>a</b>) HER2–TAK-285, (<b>b</b>) HER2–lapatinib, (<b>c</b>) HER2–[<sup>125</sup>I]anastrozole, and (<b>d</b>) HER2–[<sup>125</sup>I]epirubicin were derived from MD simulations spanning 0–200 ns.</p> Full article ">
16 pages, 6153 KiB  
Article
Applicability of a Fractal Model for Sandstone Pore-Fracture Structure Heterogeneity by Using High-Pressure Mercury Intrusion Tests
by Shuangying Zou, Mingyuan Sun, Yongmei Chen, Qinglin Li, Xiangchun Chang, Junjian Zhang and Guangying Ren
Processes 2024, 12(8), 1658; https://doi.org/10.3390/pr12081658 - 7 Aug 2024
Viewed by 363
Abstract
Pore structure heterogeneity affects the porosity and permeability variation of tight sandstone, thereby restricting sandstone gas production. In total, 11 sandstone samples were taken as a target in the northwest margin of the Junggar Basin. Then, scanning electron microscope and high-pressure mercury injection [...] Read more.
Pore structure heterogeneity affects the porosity and permeability variation of tight sandstone, thereby restricting sandstone gas production. In total, 11 sandstone samples were taken as a target in the northwest margin of the Junggar Basin. Then, scanning electron microscope and high-pressure mercury injection tests are used to study the distribution of a pore and fracture system in the target sandstone. On this basis, single and multifractal models are used to quantitatively characterize the heterogeneity of pore structure, and the applicability of the classification model in characterizing the heterogeneity of the pore-fracture structure is explored. The results are as follows. (1) The target samples are divided into two types, with the mercury removal efficiency of type A samples ranging from 44.6 to 51.8%, pore size mainly distributed between 100 and 1000 nm, and pore volume percentage ranging from 43 to 69%. The mercury removal efficiency of type B samples ranges from 14 to 28%, and pore diameter distribution is relatively uniform. (2) Different fractal models represent different physical meanings. The calculation results of sponge and thermodynamic fractal models indicate that the heterogeneity of pore structure distribution in the type B sample is significantly stronger than that in type A, which is inconsistent with the conclusions of the Sierpinski model. This is because the aforementioned two models characterize the complexity of pore surface area, while the Sierpinski model characterizes the roughness of pore volume. The comparison shows that there is a significant correlation between the thermal dimensionality value DT and the volume percentage of macropores and mesopores. Therefore, the thermodynamic model can better quantitatively characterize the heterogeneity of macropore and mesoporous pore distribution. (3) The results indicate that higher pore volume range is mainly influenced by mesopores and macropores. From the relationship curve between mercury removal efficiency and single fractal dimension, it can be seen that mercury removal efficiency is greatly affected by distribution heterogeneity of the lower value area of pore volume, and it has no obvious relationship with distribution heterogeneity in the lower value area of the pore volume. Full article
(This article belongs to the Section Chemical Processes and Systems)
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<p>Stratigraphic column of the study area.</p> Full article ">Figure 2
<p>Parametric sub-sample results of high-pressure mercury testing.</p> Full article ">Figure 3
<p>SEM Image analysis of thin sections.</p> Full article ">Figure 4
<p>Different types of high-pressure mercury pressure curves and pore distribution. (<b>a</b>) High pressure mercury injection curve of type A; (<b>b</b>) Pore size distribution of type A sample; (<b>c</b>) High pressure mercury injection curve of type B; (<b>d</b>) Pore size distribution of type B sample.</p> Full article ">Figure 4 Cont.
<p>Different types of high-pressure mercury pressure curves and pore distribution. (<b>a</b>) High pressure mercury injection curve of type A; (<b>b</b>) Pore size distribution of type A sample; (<b>c</b>) High pressure mercury injection curve of type B; (<b>d</b>) Pore size distribution of type B sample.</p> Full article ">Figure 5
<p>Comparison of pore structure parameters of different types of samples. (<b>a</b>) The percentage of pore volume between 1000~10,000 in type A and type B samples; (<b>b</b>) The percentage of pore volume between 100~1000 in type A and type B samples; (<b>c</b>) The percentage of pore size less than 100 pore volume in type A and type B samples; (<b>d</b>) The pore volume percentage of total pore volume in type A and type B samples.</p> Full article ">Figure 6
<p>Fractal dimension of the <span class="html-italic">M</span> model. (<b>a</b>) Single fractal dimension of type A sample M model; (<b>b</b>) Single fractal dimension of type B sample M model; (<b>c</b>) Single fractal dimension.</p> Full article ">Figure 7
<p>Fractal dimension of the <span class="html-italic">S</span> model (log (dv/dp)) is the logarithm of mercury-injected volume per unit pressure; D is fractal dimension, dimensionless. (<b>a</b>) Single fractal dimension of type A sample S model; (<b>b</b>) Single fractal dimension of type B sample S model; (<b>c</b>) Single fractal dimension.</p> Full article ">Figure 8
<p>Fractal dimension of the <span class="html-italic">T</span> model. (<b>a</b>) Single fractal dimension of type A sample T model; (<b>b</b>) Single fractal dimension of type B sample T model; (<b>c</b>) Single fractal dimension.</p> Full article ">Figure 9
<p>Multifractal dimension of the pores of different types of samples. (<b>a</b>) Multifractal characterization of lg(ɛ) and lg[μ<sub>1</sub>(q, ɛ)]; (<b>b</b>) Multifractal characterization of pores in type A samples; (<b>c</b>) Multifractal characterization of pores in type B samples.</p> Full article ">Figure 10
<p>Comparison of multifractal variations of different lithofacies. (<b>a</b>) Comparison of pore low value distinguishing shape dimension; (<b>b</b>) Comparison of pore high value distinguishing shape dimension; (<b>c</b>) Multiple fractal dimension.</p> Full article ">Figure 11
<p>Relationship between fractal dimension values calculated using different fractal models. (<b>a</b>) S model diversion values <span class="html-italic">D<sub>S</sub></span> ~ Sponge model dimension values; (<b>b</b>) S model diversion values <span class="html-italic">D<sub>S</sub></span> ~ Fractal dimension value of thermodynamic model <span class="html-italic">D<sub>T</sub></span>; (<b>c</b>) Fractal dimension of sponge model <span class="html-italic">D<sub>M</sub></span>; (<b>d</b>) Relationship between <span class="html-italic">D</span><sub>0</sub><span class="html-italic">–D</span><sub>10</sub> and <span class="html-italic">D</span><sub>–10</sub><span class="html-italic">–D</span><sub>0</sub>; (<b>e</b>) Relationship between <span class="html-italic">D</span><sub>–10</sub><span class="html-italic">–D</span><sub>10</sub> and <span class="html-italic">D</span><sub>–10</sub><span class="html-italic">–D</span><sub>0</sub>; (<b>f</b>) Relationship between <span class="html-italic">D</span><sub>–10</sub><span class="html-italic">–D</span><sub>10</sub> and <span class="html-italic">D</span><sub>0</sub><span class="html-italic">–D</span><sub>–10</sub>.</p> Full article ">Figure 12
<p>Relationship between pore volume and single fractal dimension at different stages of the process. (<b>a</b>) Total pore volume percentage; (<b>b</b>) The pore volume percentage is &lt;1000 nm; (<b>c</b>) The pore volume percentage is between 100~1000 nm; (<b>d</b>) The pore volume percentage is &gt;1000 nm.</p> Full article ">Figure 13
<p>Relationship between pore volume and multifractal dimension at different stages of the process. (<b>a</b>) Total pore volume percentage; (<b>b</b>) The pore volume percentage is &lt;1000 nm; (<b>c</b>) The pore volume percentage is between 100~1000 nm; (<b>d</b>) The pore volume percentage is &gt;1000 nm.</p> Full article ">Figure 13 Cont.
<p>Relationship between pore volume and multifractal dimension at different stages of the process. (<b>a</b>) Total pore volume percentage; (<b>b</b>) The pore volume percentage is &lt;1000 nm; (<b>c</b>) The pore volume percentage is between 100~1000 nm; (<b>d</b>) The pore volume percentage is &gt;1000 nm.</p> Full article ">Figure 14
<p>Mercury removal efficiency as a function of single and multiple fractal dimension. (<b>a</b>) Relationship between Mercury removal efficiency and Fractal dimension <span class="html-italic">D</span>; (<b>b</b>) Relationship between Mercury removal efficiency and Multifractal dimension <span class="html-italic">D</span>.</p> Full article ">
18 pages, 2918 KiB  
Article
Assessing the Physiochemical and Sensorial Quality of Pea Sauce Canned in Plastic Trays vs. Metal Cans
by Hedi Abdelaali, Wafa Hajji, Rachid Selmi, Hana Mallek, Imen Ben Khalifa, Sihem Bellagha, Mounir Jebali and Iness Essid
Processes 2024, 12(8), 1657; https://doi.org/10.3390/pr12081657 - 7 Aug 2024
Viewed by 314
Abstract
Metal cans, while boasting excellent barrier properties, raise concerns about leaching and environmental impacts. This study explored plastic trays, a potential alternative for canned food packaging. First we delved into the plastic tray’s characteristics, including its composition and permeability to oxygen and water [...] Read more.
Metal cans, while boasting excellent barrier properties, raise concerns about leaching and environmental impacts. This study explored plastic trays, a potential alternative for canned food packaging. First we delved into the plastic tray’s characteristics, including its composition and permeability to oxygen and water vapor. Secondly, we conducted a comparison between the newly introduced plastic packaging and traditional metal cans, focusing on their interactions with food during the sterilization process and their effects on the quality of Tunisian pea sauce. The composition analysis revealed that the plastic tray was composed of polypropylene (PP) (with a single endothermic peak at 168 °C), while the film was found to have a mixture of PP internally and polyethylene terephthalate (PET) externally (with two endothermic peaks at 161.96 °C and 243.81 °C). Plastic trays showed good results in water vapor permeability (0.832 g/m2.d) but exhibited higher oxygen permeability (190 g/m2.d), raising oxidation concerns. Migration testing confirmed plastic packaging safety (<10 mg/dm2), while some simulants exceeded limits in metal cans. pH levels remained consistent between both packaging types, but varied significantly over a 28-day storage. Total Volatile Basic Nitrogen (TVBN) levels differed significantly between plastic and metal packaging, with notable variations observed over time with maximums of 0.3 mg/100 g for plastic trays and 0.17 mg/100 g for metal cans. Sensory evaluation revealed that tasters were adept at differentiating between canned pea sauce in plastic trays and metal cans (83%, 10/12), with taste and color exhibiting significant differences (p < 0.05). This underlines the impact of packaging material on canned food quality and consumer preference, with minimal influence on other sensory aspects. This data empowers manufacturers to make informed packaging decisions for a diverse range of canned foods. Full article
(This article belongs to the Section Food Process Engineering)
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<p>Plastic trays (<b>a</b>,<b>b</b>) and metal cans (<b>c</b>,<b>d</b>) used for canned pea sauce.</p> Full article ">Figure 2
<p>Diagram of the canned pea sauce in metal cans and plastic trays adopted in this study.</p> Full article ">Figure 3
<p>FTIR spectra of the used trays (<b>a</b>), the internal face of the film (<b>b</b>), and the external face of the film (<b>c</b>).</p> Full article ">Figure 4
<p>Thermogram (DSC) of the used plastic tray (<b>a</b>) and plastic film (<b>b</b>).</p> Full article ">Figure 5
<p>Evolution of the biological destruction value Lv = f(t) for scale B1 (110 °C, 60 min) (<b>a</b>) and scale B2 (115 °C, 45 min) (<b>b</b>).</p> Full article ">Figure 6
<p>Aspects of the intact (<b>a</b>,<b>b</b>) and deformed (<b>c</b>,<b>d</b>) packaging of the used plastic trays.</p> Full article ">Figure 7
<p>Aspects of meat appearance (<b>a</b>,<b>b</b>) and sauce color (<b>c</b>,<b>d</b>) after 28 days of storage in cans and plastic trays, respectively.</p> Full article ">
22 pages, 7864 KiB  
Article
Simulation and Analysis of Hydrodynamic Behavior in Different Nozzles and Its Corresponding Fluidized Beds
by Minghang Tian, Junqiang Li, Wenlong Mo, Kunpeng Jiao, Wei Peng, Xiaoqin Yang and Shupei Zhang
Processes 2024, 12(8), 1656; https://doi.org/10.3390/pr12081656 - 7 Aug 2024
Viewed by 250
Abstract
Uniform air distribution is the basic condition for the stable operation of circulating fluidized beds and closely related to the hole layout of nozzles and the air outlet conditions. In this paper, CAD modeling software is used to establish different opening types for [...] Read more.
Uniform air distribution is the basic condition for the stable operation of circulating fluidized beds and closely related to the hole layout of nozzles and the air outlet conditions. In this paper, CAD modeling software is used to establish different opening types for nozzles and the corresponding gasifier models, and Fluent simulation software for numerical simulations (k-ε model) is introduced to the hydrodynamic behavior of the upper opening, the side opening and the combined opening types of nozzles, as well as the corresponding single-nozzle fluidized bed gasifiers. The flow field distribution under the above opening modes is obtained, including the velocity distribution, static pressure distribution, and total pressure distribution, and the influence of the boundary conditions, including the inlet gas velocity and outlet pressure, on the flow field distribution inside the nozzle and in the single-nozzle fluidized bed gasifier is also investigated. The simulation results show that the suitable optimal operating conditions for the coal gasifier can be achieved with an inlet velocity of 30 m/s and an outlet pressure of 25 kPaG. Under the above conditions, the local fluidization dead zone at the elbow and top of the nozzle is narrower, the uniformity of the wind velocity can be improved, the pressure drop of the inner core tube of the nozzle is gentle, and the pressure distribution tends to be stable. Theoretically, the anti-slag performance of the nozzle is improved, which will enhance the stability and reliability of the operation of the gasification unit. Full article
(This article belongs to the Section Chemical Processes and Systems)
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<p>A conceptual drawing of the modeling process.</p> Full article ">Figure 2
<p>Circulating fluidized bed coal gasifier—gasification process (arrows represent the direction of the used materials) [<a href="#B21-processes-12-01656" class="html-bibr">21</a>].</p> Full article ">Figure 3
<p>Wear and blockage of a nozzle. (<b>a</b>) The damaged nozzle of a company’s gasifier. (<b>b</b>) Worn-out nozzle duct. (<b>c</b>) Block back nozzle—the exit channel.</p> Full article ">Figure 4
<p>A bell-shaped nozzle installed by a company. (<b>a</b>) Nozzle arrangement; (<b>b</b>) actual layout.</p> Full article ">Figure 5
<p>Structure diagram of bell-shaped nozzle (arrows represent the direction of the gasification agent) [<a href="#B21-processes-12-01656" class="html-bibr">21</a>].</p> Full article ">Figure 6
<p>Bell nozzle model and contours. (<b>a</b>) Three-dimensional model; (<b>b</b>) cross-section static pressure contour; (<b>c</b>) cross-section velocity contour.</p> Full article ">Figure 7
<p>Physical simplification model of nozzle and its meshing. (<b>a</b>) Three-dimensional model diagram; (<b>b</b>) imported ANSYS workbench; (<b>c</b>) wall setting; (<b>d</b>) meshing.</p> Full article ">Figure 8
<p>Physical simplification model of gasifier and its meshing. (<b>a</b>) Three-dimensional model diagram; (<b>b</b>) meshing; (<b>c</b>) meshing of cross-section.</p> Full article ">Figure 9
<p>Model simplification and meshing of circulating fluidized bed gasifier. (<b>a</b>) Three-dimensional model diagram; (<b>b</b>) meshing.</p> Full article ">Figure 10
<p>Contours of static pressure and total pressure. (<b>a</b>) Top opening only, (<b>b</b>) side opening only, and (<b>c</b>) combined opening for static pressure. (<b>d</b>) Top opening only, (<b>e</b>) side opening only, and (<b>f</b>) combined opening for total pressure.</p> Full article ">Figure 11
<p>Contours of velocity. (<b>a</b>) Top opening only; (<b>b</b>) side opening only; (<b>c</b>) combined opening.</p> Full article ">Figure 12
<p>Contours of static pressure and total pressure: (<b>a</b>–<b>i</b>) 10, 15, 20, 25, 30, 35, 40, 45, and 50 m/s for static pressure; (<b>j</b>–<b>r</b>) 10, 15, 20, 25, 30, 35, 40, 45, and 50 m/s for total pressure.</p> Full article ">Figure 12 Cont.
<p>Contours of static pressure and total pressure: (<b>a</b>–<b>i</b>) 10, 15, 20, 25, 30, 35, 40, 45, and 50 m/s for static pressure; (<b>j</b>–<b>r</b>) 10, 15, 20, 25, 30, 35, 40, 45, and 50 m/s for total pressure.</p> Full article ">Figure 13
<p>Contours of velocity: (<b>a</b>–<b>i</b>) 10, 15, 20, 25, 30, 35, 40, 45, and 50 m/s.</p> Full article ">Figure 14
<p>Contours of static pressure and total pressure: (<b>a</b>–<b>f</b>) 0, 5, 15, 25, 35, and 50 kPaG for static pressure; (<b>g</b>–<b>l</b>) 0, 5, 15, 25, 35, and 50 kPaG for total pressure.</p> Full article ">Figure 15
<p>Contours of velocity: (<b>a</b>–<b>f</b>) 0, 5, 15, 25, 35, and 50 kPaG.</p> Full article ">Figure 16
<p>Contours of static pressure and total pressure. (<b>a</b>) Top opening only, (<b>b</b>) side opening only, and (<b>c</b>) combined opening for static pressure. (<b>d</b>) Top opening only, (<b>e</b>) side opening only, and (<b>f</b>) combined opening for total pressure.</p> Full article ">Figure 17
<p>Contours of velocity. (<b>a</b>) Top opening only; (<b>b</b>) side opening only; (<b>c</b>) combined opening.</p> Full article ">Figure 18
<p>Contours of static pressure and total pressure: (<b>a</b>–<b>i</b>) 10, 15, 20, 25, 30, 35, 40, 45, and 50 m/s for static pressure; (<b>j</b>–<b>r</b>) 10, 15, 20, 25, 30, 35, 40, 45, and 50 m/s for total pressure.</p> Full article ">Figure 18 Cont.
<p>Contours of static pressure and total pressure: (<b>a</b>–<b>i</b>) 10, 15, 20, 25, 30, 35, 40, 45, and 50 m/s for static pressure; (<b>j</b>–<b>r</b>) 10, 15, 20, 25, 30, 35, 40, 45, and 50 m/s for total pressure.</p> Full article ">Figure 19
<p>Contours of velocity: (<b>a</b>–<b>i</b>) 10, 15, 20, 25, 30, 35, 40, 45, and 50 m/s.</p> Full article ">Figure 20
<p>Contours of static pressure and total pressure: (<b>a</b>–<b>f</b>) 0, 5, 15, 25, 35, and 50 kPaG for static pressure; (<b>g</b>–<b>l</b>) 0, 5, 15, 25, 35, and 50 kPaG for total pressure.</p> Full article ">Figure 21
<p>Contours of velocity: (<b>a</b>–<b>f</b>) 0, 5, 15, 25, 35, and 50 kPaG.</p> Full article ">Figure 22
<p>Contour of velocity.</p> Full article ">
18 pages, 5647 KiB  
Article
Multiapproach Design Methodology of a Downscaled Wet Scrubber to Study the Collection of Submicronic Particles from Waste Incineration Flue Gas
by Angela Hoyos, Aurélie Joubert, Ala Bouhanguel, Marc Henry, Sylvain Durécu and Laurence Le Coq
Processes 2024, 12(8), 1655; https://doi.org/10.3390/pr12081655 - 7 Aug 2024
Viewed by 257
Abstract
Wet scrubbers are traditionally used as dedusting systems in waste incineration plants for wet flue gas treatment. Although these devices are not particularly performant at capturing submicron particles, which are associated with health and environmental hazards, their collection efficiency can be improved by [...] Read more.
Wet scrubbers are traditionally used as dedusting systems in waste incineration plants for wet flue gas treatment. Although these devices are not particularly performant at capturing submicron particles, which are associated with health and environmental hazards, their collection efficiency can be improved by optimizing operating conditions. This study presents the design methodology of a downscaled wet scrubber, constructed and implemented at a municipal waste incineration plant to be fed with real fumes, and to study its efficiency towards the removal of submicronic particles. The downscaled scrubber was designed to operate with flue gas at 200 °C, high humidity (1% RH), and an average total particle concentration of 200 mg/Nm3. A criterion of geometric, aerodynamic, and residence time similarities to an existing industrial scrubber was targeted. The height of the device was selected by matching the theoretical fractional particle collection efficiencies of the industrial and downscaled scrubbers. Featuring a cylindrical shape, the downscaled scrubber has a diameter of 0.3 m and a height of 2.5 m. It operates in co-current with water injected through four spray levels. Computational fluid dynamics simulations were conducted to analyze the gas flow structure within the device, and the results were validated by hot wire anemometer velocity measurements. Full article
(This article belongs to the Section Separation Processes)
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<p>Process diagram of the Alcea municipal waste incineration plant (Nantes, France).</p> Full article ">Figure 2
<p>Picture of the water jet generated by the industrial nozzle technology at 1/8th scale. Taken with a NIKON D7200 camera (Nikon, Tokyo, Japan) coupled with a Sigma 150–600 mm f/5–6.3 lens, set at a focal length of 150 mm.</p> Full article ">Figure 3
<p>Overview of the methodology and the criteria considered for the design of the downscaled wet scrubber.</p> Full article ">Figure 4
<p>SEM image of FG particulate matter at the downscaled WS installation point (after the boiler and the cooling tower).</p> Full article ">Figure 5
<p>Comparative particle collection efficiency of industrial-scale and downscaled scrubbers.</p> Full article ">Figure 6
<p>Diagram of the setup of the downscaled wet scrubber installed on the WIP of Nantes.</p> Full article ">Figure 7
<p>(<b>a</b>) Downscaled wet scrubber installed at Alcea WIP. (<b>b</b>) Liquid circuit of the downscaled wet scrubber installed at Alcea WIP.</p> Full article ">Figure 8
<p>CFD Y velocity contour of (<b>a</b>) the original geometry, (<b>b</b>) the high-cone geometry, and (<b>c</b>) the geometry with the double disruptor device. (<b>d</b>) Geometry of the flow disruption system for the gas homogenization system. (<b>e</b>) Velocity vectors at plane z = 0.</p> Full article ">Figure 9
<p>Y velocity along the radial axis at different heights of the downscaled wet scrubber (CFD vs. measurement).</p> Full article ">
15 pages, 4523 KiB  
Article
Effect of Partial Elimination of Mitochondrial DNA on Genome-Wide Identified AOX Gene Family in Chlamydomonas reinhardtii
by Asadullah Khan, Zuo Jihong, Haolin Luo, Ali Raza, Quaid Hussain and Zhangli Hu
Processes 2024, 12(8), 1654; https://doi.org/10.3390/pr12081654 - 7 Aug 2024
Viewed by 289
Abstract
Using Chlamydomonas as a model organism, we attempted to eliminate mitochondrial DNA (mtDNA) similar to rho0 or rho− cells (completely or partially mtDNA-eliminated cells) in yeast. We successfully generated partially mtDNA-eliminated cells named as crm- cells, causing the inactivation of mitochondrial [...] Read more.
Using Chlamydomonas as a model organism, we attempted to eliminate mitochondrial DNA (mtDNA) similar to rho0 or rho− cells (completely or partially mtDNA-eliminated cells) in yeast. We successfully generated partially mtDNA-eliminated cells named as crm- cells, causing the inactivation of mitochondrial activity. We used three different chemicals to eliminate mtDNA including acriflavine (AF), ethidium bromide (EB) and dideoxycytidine (ddC) which prevents replication, inhibits POLG (DNA polymerase gamma) and terminates the mtDNA chain, respectively. The qPCR method was used to detect the mtDNA copy number and the selected rrnL6 gene for the detection of mitochondria, as well as the selected Chlamydomonas CC-124 strain. A reduction in the mitochondrial copy number led to a higher expression of AOX1, UCP1, PGRL1 and ICL1, which indicates the disturbance of the mitochondria–chloroplast ATP and NADPH balance. We selected AOX genes to further study this family and carried out a genome-wide search to identify AOX genes in green algae (C. reinhardtii). Our results revealed that C. reinhardtii contains four AOX genes, i.e., CrAOX1, CrAOX2, CrAOX3 and CrAOX4, which are distributed on Chr 3, Chr7 and Chr9. All CrAOX genes were predicted to localize in mitochondria using bioinformatics tools. Phylogenetic analysis suggests that these CrAOXs are subdivided into four groups and genes existing in the same group could perform identical functions. Collinearity analysis describes the strong evolutionary relationships of AOXs between the unicellular green algae Chlamydomonas reinhardtii and the multicellular green algae Volvox carteri. GO (gene ontology) annotation analysis predicted that CrAOXs played an integral part in carrying out alternate oxidative and respirative activities. Three putative miRNAs, cre-miR1162-3p, cre-miR1171 and cre-miR914, targeting the CrAOX2 gene were identified. Our studies have laid a foundation for the further use of partially mtDNA-eliminated cells and elucidating the functional characteristics of the AOX gene family. Full article
(This article belongs to the Special Issue Advances in Chemical Characterization, Pharmacological Applications and Synthetic Biology of Natural Products)
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<p>The mtDNA copy number levels of the treatment and control. (<b>A</b>) The <span class="html-italic">AF</span>, <span class="html-italic">EB</span> and <span class="html-italic">ddC</span> chemicals with different concentrations and combinations such as 5-AF (5 µg/mL), 7-AF (7 µg/mL), 2-EB (2 µg/mL), 6-EB (6 µg/mL), 5-AF+4-EB (mixture of AF (5 µg/mL) and EB (4 µg/mL)), 150-DDC+1.5 EB (mixture of ddC (150 mM) and EB (1.5 µg/mL)) and 200-DDC+2 EB (mixture of ddC (200 mM) and EB (2 µg/mL)) compared with the control (CK) were screened to obtain one which gave the minimum copy number so that it could be used in further experiments. (<b>B</b>) The results 5, 7 and 9 days after treatment (DAT) were checked as to which day gives the minimum mitochondrial copy number. A <span class="html-italic">t</span>-test was used to calculate significance (** <span class="html-italic">p</span> &lt; 0.01). The mean and SD values were derived from three biological repetitions.</p> Full article ">Figure 2
<p>The mtDNA copy number levels of the AF treatment and control group. (<b>A</b>) The <span class="html-italic">rrnL6</span>, <span class="html-italic">nad5</span> and <span class="html-italic">cox1</span> genes from mitochondria were screened to obtain genes which gave the minimum copy number and could be used in further experiments. (<b>B</b>) Two strains of CR were screened to select the minimum mtDNA copy numbers. A <span class="html-italic">t</span>-test was used to calculate significance (** <span class="html-italic">p</span> &lt; 0.01). The mean and SD values were derived from three biological and three technical repetitions.</p> Full article ">Figure 3
<p>RT-qPCR results of seven designated genes studied to inhibit mETC in different studies. Asterisks indicate that the corresponding genes were distinctly up- or downregulated following different treatments by <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01). The mean and SD values were derived from three biological and three technical repetitions.</p> Full article ">Figure 4
<p><span class="html-italic">Chlamydomonas reinhardtii</span> (Cr) <span class="html-italic">AOX</span> family gene structure and motif analysis: (<b>A</b>) Phylogenetic tree. (<b>B</b>) Gene structure for <span class="html-italic">AOX</span>. The gray horizontal line denotes intron regions, while the yellow horizontal line denotes exon regions. (<b>C</b>) <span class="html-italic">AOX</span> gene distribution on three <span class="html-italic">Chlamydomonas reinhardtii</span> chromosomes is shown schematically, along with the gene names in red on the left side. The scale on the left side indicates the location of the <span class="html-italic">AOX</span> genes on chromosomes. The top of each chromosome (Chr) is where you may find the chromosomal numbers.</p> Full article ">Figure 5
<p>A phylogenetic and collinearity analysis of <span class="html-italic">AOX</span> proteins. (<b>A</b>) A phylogenetic analysis of <span class="html-italic">AOX</span> proteins from <span class="html-italic">Chlamydomonas reinhardtii</span> (Cr), <span class="html-italic">Marchantia polymorpha</span> (Mp), <span class="html-italic">Coccomyxa subellipsoidea</span> (<span class="html-italic">Cs</span>), <span class="html-italic">Volvox carteri</span> (Vc), <span class="html-italic">Physcomitrella patens</span> (Pp) and <span class="html-italic">Arabidopsis thaliana</span> (At) was carried out using the maximum likelihood method. There are four groups of <span class="html-italic">AOX</span> proteins, each of which is represented by a red, gray, purple and blue color. Genes from <span class="html-italic">Chlamydomonas reinhardtii</span> (Cr) are highlighted in blue. (<b>B</b>) Collinearity analysis of AOX proteins between <span class="html-italic">Chlamydomonas reinhardtii</span> (Cr), <span class="html-italic">Marchantia polymorpha</span> (Mp), <span class="html-italic">Volvox carteri</span> (Vc), <span class="html-italic">Physcomitrella patens</span> (Pp) and <span class="html-italic">Arabidopsis thaliana</span> (At). The blue, green and orange colors represent ≤40%, ≤60% and ≤80% identity, respectively.</p> Full article ">Figure 6
<p>Gene ontology enrichment analysis and target cleavage sites of <span class="html-italic">CrmiRNAs</span> in <span class="html-italic">CrAOX</span> genes of <span class="html-italic">C. reinhardtii</span>. (<b>A</b>) Predicted target cleavage sites of <span class="html-italic">CrmiRNAs</span> in <span class="html-italic">CrAOX</span> in <span class="html-italic">C. reinhardtii</span>. (<b>B</b>) Enriched GO molecular function, cellular component and biological process terms visualized as a network. (<b>C</b>) Enriched GO molecular function, cellular component and biological process terms visualized as a chart. GO enrichment analysis of <span class="html-italic">CrAOX</span> genes was performed and visualized using the online tool ShinyGO 0.80.</p> Full article ">Figure 7
<p>The mtDNA copy number of AF treatments and their effect on the relative expression level of <span class="html-italic">CrAOX</span> genes. (<b>A</b>) mtDNA copy number after 7 days of AF treatment and after 45 days without treatment (DWT) after being recovered from AF treatment. (<b>B</b>) Relative expression level of <span class="html-italic">CrAOX</span> genes after 7 days of AF treatment and after 45 days without AF treatment. A <span class="html-italic">t</span>-test was used to calculate significance (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01). The mean and SD values were derived from three biological and three technical repetitions.</p> Full article ">
17 pages, 5535 KiB  
Article
Permeability Upscaling Conversion Based on Reservoir Classification
by Jiali Li, Chuqiao Gao, Bin Zhao and Xincai Cheng
Processes 2024, 12(8), 1653; https://doi.org/10.3390/pr12081653 - 7 Aug 2024
Viewed by 242
Abstract
Deep and ultra-deep reservoirs are characterized by low porosity and permeability, pronounced heterogeneity, and complex pore structures, complicating permeability evaluations. Permeability, directly influencing the fluid production capacity of reservoirs, is a key parameter in comprehensive reservoir assessments. In the X Depression, low-porosity and [...] Read more.
Deep and ultra-deep reservoirs are characterized by low porosity and permeability, pronounced heterogeneity, and complex pore structures, complicating permeability evaluations. Permeability, directly influencing the fluid production capacity of reservoirs, is a key parameter in comprehensive reservoir assessments. In the X Depression, low-porosity and low-permeability formations present highly discrete and variable core data points for porosity and permeability, rendering single-variable regression models ineffective. Consequently, accurately representing permeability in heterogeneous reservoirs proves challenging. In the following study, lithological and physical property data are integrated with mercury injection data to analyze pore structure types. The formation flow zone index (FZI) is utilized to differentiate reservoir types, and permeability is calculated based on core porosity–permeability relationships from logging data for each flow unit. Subsequently, the average permeability for each flow unit is computed according to reservoir classification, followed by a weighted average according to effective thickness. This approach transforms logging permeability into drill stem test permeability. Unlike traditional point-by-point averaging methods, this approach incorporates reservoir thickness and heterogeneity, making it more suitable for complex reservoir environments and resulting in more reasonable conversion outcomes. Full article
(This article belongs to the Section Energy Systems)
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<p>The relationship between core porosity and core permeability in the study area.</p> Full article ">Figure 2
<p>Sand casting body slices of the X Depression observed using a polarizing microscope: (<b>a</b>) Well XX7-1, 3843.7 m; (<b>b</b>) Well XY7-5, 4297 m; (<b>c</b>) Well XY7-1, 3126 m; (<b>d</b>) Well XY2-1, 3853.4 m.</p> Full article ">Figure 2 Cont.
<p>Sand casting body slices of the X Depression observed using a polarizing microscope: (<b>a</b>) Well XX7-1, 3843.7 m; (<b>b</b>) Well XY7-5, 4297 m; (<b>c</b>) Well XY7-1, 3126 m; (<b>d</b>) Well XY2-1, 3853.4 m.</p> Full article ">Figure 3
<p>Histogram of particle size distribution.</p> Full article ">Figure 4
<p>Layered mercury curves of different types of reservoirs: (<b>a</b>) Type I reservoir’s layered mercury curve; (<b>b</b>) Type II reservoir’s layered mercury curve; (<b>c</b>) Type III reservoir’s layered mercury curve; (<b>d</b>) Type IV reservoir’s layered mercury curve.</p> Full article ">Figure 5
<p>The cumulative frequency distribution of the FZI in the X Depression.</p> Full article ">Figure 6
<p>Distribution of different flow units.</p> Full article ">Figure 7
<p>The relationship between porosity and permeability after reservoir classification based on the FZI.</p> Full article ">Figure 8
<p>Conventional logging response diagram of well XY2-1.</p> Full article ">Figure 9
<p>The correlation between single logging curves and flow unit index.</p> Full article ">Figure 10
<p>Upscale conversion model diagram, where <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <msub> <mi>K</mi> <mn>1</mn> </msub> <mo>+</mo> <msub> <mi>K</mi> <mn>2</mn> </msub> <mo>+</mo> <msub> <mi>K</mi> <mn>3</mn> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <msub> <mi>h</mi> <mn>1</mn> </msub> <mo>+</mo> <msub> <mi>h</mi> <mn>2</mn> </msub> <mo>+</mo> <msub> <mi>h</mi> <mn>3</mn> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <msub> <mi>Q</mi> <mn>1</mn> </msub> <mo>+</mo> <msub> <mi>Q</mi> <mn>2</mn> </msub> <mo>+</mo> <msub> <mi>Q</mi> <mn>3</mn> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 11
<p>The comparison diagram of permeability calculated by two methods with the DST permeability of different wells in the X Depression.</p> Full article ">
17 pages, 8434 KiB  
Article
Dynamic Evolution Law of Production Stress Field in Fractured Tight Sandstone Horizontal Wells Considering Stress Sensitivity of Multiple Media
by Maotang Yao, Qiangqiang Zhao, Jun Qi, Jianping Zhou, Gaojie Fan and Yuxuan Liu
Processes 2024, 12(8), 1652; https://doi.org/10.3390/pr12081652 - 6 Aug 2024
Viewed by 385
Abstract
Inter-well frac-hit has become an important challenge in the development of unconventional oil and gas resources such as fractured tight sandstone. Due to the presence of hydraulic fracturing fractures, secondary induced fractures, natural fractures, and other seepage media in real formations, the acquisition [...] Read more.
Inter-well frac-hit has become an important challenge in the development of unconventional oil and gas resources such as fractured tight sandstone. Due to the presence of hydraulic fracturing fractures, secondary induced fractures, natural fractures, and other seepage media in real formations, the acquisition of stress fields requires the coupling effect of seepage and stress. In this process, there is also stress sensitivity, which leads to unclear dynamic evolution laws of stress fields and increases the difficulty of the staged multi-cluster fracturing of horizontal wells. The use of a multi-stage stress-sensitive horizontal well production stress field prediction model is an effective means of analyzing the influence of natural fracture parameters, main fracture parameters, and multi-stage stress sensitivity coefficients on the stress field. This article considers multi-stage stress sensitivity and, based on fractured sandstone reservoir parameters, establishes a numerical model for the dynamic evolution of the production stress field in horizontal wells with matrix self-supporting fracture-supported fracture–seepage–stress coupling. The influence of various factors on the production stress field is analyzed. The results show that under constant pressure production, for low-permeability reservoirs, multi-stage stress sensitivity has a relatively low impact on reservoir stress, and the amplitude of principal stress change in the entire fracture length direction is only within the range of 0.27%, with no significant change in stress distribution; The parameters of the main fracture have a significant impact on the stress field, with a variation amplitude of within 2.85%. The ability of stress to diffuse from the fracture tip to the surrounding areas is stronger, and the stress concentration area spreads from an elliptical distribution to a semi-circular distribution. The random natural fracture parameters have a significant impact on pore pressure. As the density and angle of the fractures increase, the pore pressure changes within the range of 3.32%, and the diffusion area of pore pressure significantly increases, making it easy to communicate with the reservoir on both sides of the fractures. Full article
(This article belongs to the Section Energy Systems)
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Figure 1

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<p>Schematic diagram of dynamic evolution of stress field.</p> Full article ">Figure 2
<p>Geological Model Diagram.</p> Full article ">Figure 3
<p>Comparison of formation pore pressure. (<b>a</b>) Validation model. (<b>b</b>) This article’s model.</p> Full article ">Figure 4
<p>Comparison of stress in the direction of maximum horizontal principal stress. (<b>a</b>)Validation model. (<b>b</b>)This article’s model.</p> Full article ">Figure 5
<p>Comparison of stress in the direction of minimum horizontal principal stress. (<b>a</b>)Validation model. (<b>b</b>)This article’s model.</p> Full article ">Figure 6
<p>Changes in the influence of stress sensitivity coefficients of different matrix porosity on horizontal principal stress in A–A′ direction and monitoring point stress. (<b>a</b>) Minimum horizontal principal stress change. (<b>b</b>) Maximum horizontal principal stress change. (<b>c</b>) Monitoring point minimum horizontal principal stress over time curve. (<b>d</b>) Monitoring point maximum horizontal principal stress over time curve. (<b>e</b>) Monitoring point pore pressure over time curve. Where Cp is the stress sensitivity coefficient of matrix porosity.</p> Full article ">Figure 7
<p>Cloud map of minimum horizontal principal stress variation under stress sensitivity coefficient conditions of different matrix porosities.</p> Full article ">Figure 8
<p>Changes in the influence of stress sensitivity coefficients of different matrix permeability on horizontal principal stress in the A–A′ direction and monitoring point stress. (<b>a</b>) Minimum horizontal principal stress change. (<b>b</b>) Maximum horizontal principal stress change. (<b>c</b>) Monitoring point minimum horizontal principal stress over time curve. (<b>d</b>) Monitoring point maximum horizontal principal stress over time curve. (<b>e</b>) Monitoring point pore pressure over time curve. Where Ca is the matrix permeability stress sensitivity coefficient.</p> Full article ">Figure 9
<p>Changes in the influence of different main fracture compression coefficients on the horizontal principal stress in the A–A′ direction and the stress at monitoring points. (<b>a</b>) Minimum horizontal principal stress change. (<b>b</b>) Maximum horizontal principal stress change; (<b>c</b>) Monitoring point minimum horizontal principal stress over time curve. (<b>d</b>) Monitoring point maximum horizontal principal stress over time curve. (<b>e</b>) Monitoring point pore pressure over time curve. Where Cf is the compression coefficient of main fracture.</p> Full article ">Figure 10
<p>Changes in the influence of different secondary fracture compression coefficients on the horizontal principal stress in the A–A′ direction and the stress at monitoring points. (<b>a</b>) Minimum horizontal principal stress change. (<b>b</b>)Maximum horizontal principal stress change. (<b>c</b>) Monitoring point minimum horizontal principal stress over time curve. (<b>d</b>) Monitoring point maximum horizontal principal stress over time curve. (<b>e</b>) Monitoring point pore pressure over time curve. Where C’f is the compression coefficient of secondary fracture.</p> Full article ">Figure 11
<p>Changes in the influence of different numbers of main fractures on the horizontal principal stress in the A–A′ direction and the stress at monitoring points. (<b>a</b>) Minimum horizontal principal stress change. (<b>b</b>) Maximum horizontal principal stress change. (<b>c</b>) Monitoring point minimum horizontal principal stress over time curve. (<b>d</b>) Monitoring point maximum horizontal principal stress over time curve. (<b>e</b>) Monitoring point pore pressure over time curve. Where N is the number of main fractures.</p> Full article ">Figure 12
<p>Cloud map of minimum horizontal principal stress variation under different numbers of main fractures.</p> Full article ">Figure 13
<p>The variation curve of the influence of different main fracture spacing on the horizontal principal stress in the A–A′ direction and the stress at the monitoring point. (<b>a</b>) Minimum horizontal principal stress change. (<b>b</b>) Maximum horizontal principal stress change. (<b>c</b>) Monitoring point minimum horizontal principal stress over time curve. (<b>d</b>) Monitoring point maximum horizontal principal stress over time curve. (<b>e</b>) Monitoring point pore pressure over time curve. Where S is the main fracture spacing.</p> Full article ">Figure 14
<p>Cloud map of minimum horizontal principal stress variation under different main fracture spacing.</p> Full article ">Figure 15
<p>Changes in the influence of different main fracture lengths on the horizontal principal stress in the A–A′ direction and the stress at monitoring points. (<b>a</b>) Minimum horizontal principal stress change. (<b>b</b>) Maximum horizontal principal stress change. (<b>c</b>) Monitoring point minimum horizontal principal stress over time curve. (<b>d</b>) Monitoring point maximum horizontal principal stress over time curve. (<b>e</b>) Monitoring point pore pressure over time curve. Where L is the main fracture length.</p> Full article ">Figure 16
<p>Cloud map of minimum horizontal principal stress variation under different main fracture lengths.</p> Full article ">Figure 17
<p>Cloud map of pore pressure changes under different natural fracture densities.</p> Full article ">Figure 18
<p>Time-varying curves of pore pressure at different natural fracture density monitoring points. Where D is the natural fracture density.</p> Full article ">Figure 19
<p>Cloud map of pore pressure changes under different natural fracture angles.</p> Full article ">Figure 20
<p>Time-varying curves of pore pressure at monitoring points with different natural fracture angles. Where A is the natural fracture angle.</p> Full article ">
13 pages, 2644 KiB  
Article
Green Hydrogen, a Solution for Replacing Fossil Fuels to Reduce CO2 Emissions
by Stoica Dorel, Mihăescu Lucian, Lăzăroiu Gheorghe and Lăzăroiu George Cristian
Processes 2024, 12(8), 1651; https://doi.org/10.3390/pr12081651 - 6 Aug 2024
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Abstract
The article examines the role of green hydrogen in reducing CO2 emissions in the transition to climate neutrality, highlighting both its benefits and challenges. It starts by discussing the production of green hydrogen from renewable sources and provides a brief analysis of [...] Read more.
The article examines the role of green hydrogen in reducing CO2 emissions in the transition to climate neutrality, highlighting both its benefits and challenges. It starts by discussing the production of green hydrogen from renewable sources and provides a brief analysis of primary resource structures for energy production in European countries, including Romania. Despite progress, there remains a significant reliance on fossil fuels in some countries. Economic technologies for green hydrogen production are explored, with a note that its production alone does not solve all issues due to complex and costly compression and storage operations. The concept of impure green hydrogen, derived from biomass gasification, pyrolysis, fermentation, and wastewater purification, is also discussed. Economic efficiency and future trends in green hydrogen production are outlined. The article concludes with an analysis of hydrogen-methane mixture combustion technologies, offering a conceptual framework for economically utilizing green hydrogen in the transition to a green hydrogen economy. Full article
(This article belongs to the Section Energy Systems)
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<p>Areas of use for H<sub>2</sub>.</p> Full article ">Figure 2
<p>Green hydrogen production and use chain (1—electrifier; 2—compressor; 3—storage; 4—fue cell).</p> Full article ">Figure 3
<p>Operating parameters of the most economical electrolysis plants (PEM—with polymer electrolyte, SOE—with solid cxides).</p> Full article ">Figure 4
<p>Economic technologies for the production of green hydrogen.</p> Full article ">Figure 5
<p>Scheme of producti on and use of green hydrogen.</p> Full article ">Figure 6
<p>Energy consumption for green hydrogen production.</p> Full article ">Figure 7
<p>The efficiency (yield) of the production and use of green hydrogen (1—electrelyzer, 2—compression system; 3—storage, 4—fuel cell).</p> Full article ">Figure 8
<p>The need to increase the pressure of the gas network CH<sub>4</sub>/H<sub>2</sub> to maintain the Wobbe criterion.</p> Full article ">Figure 9
<p>The variation in the color power of the gaseous mixture CH<sub>4</sub> with H<sub>2</sub>.</p> Full article ">Figure 10
<p>The variation in the density of the gaz mixture CH<sub>4</sub> with H<sub>2</sub>.</p> Full article ">
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