Journal Description
Processes
Processes
is an international, peer-reviewed, open access journal on processes/systems in chemistry, biology, material, energy, environment, food, pharmaceutical, manufacturing, automation control, catalysis, separation, particle and allied engineering fields published monthly online by MDPI. The Systems and Control Division of the Canadian Society for Chemical Engineering (CSChE S&C Division) and the Brazilian Association of Chemical Engineering (ABEQ) are affiliated with Processes and their members receive discounts on the article processing charges. Please visit Society Collaborations for more details.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, Inspec, AGRIS, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Chemical) / CiteScore - Q2 (Chemical Engineering (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 13.7 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.5 (2022);
5-Year Impact Factor:
3.4 (2022)
Latest Articles
Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations
Processes 2024, 12(6), 1140; https://doi.org/10.3390/pr12061140 (registering DOI) - 31 May 2024
Abstract
For mitigating global warming, polymer electrolyte membrane fuel cells have become promising, clean, and sustainable alternatives to existing energy sources. To increase the energy density and efficiency of polymer electrolyte membrane fuel cells (PEMFC), a comprehensive numerical modeling approach that can adequately predict
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For mitigating global warming, polymer electrolyte membrane fuel cells have become promising, clean, and sustainable alternatives to existing energy sources. To increase the energy density and efficiency of polymer electrolyte membrane fuel cells (PEMFC), a comprehensive numerical modeling approach that can adequately predict the multiphysics and performance relative to the actual test such as an acceptable depiction of the electrochemistry, mass/species transfer, thermal management, and water generation/transportation is required. However, existing models suffer from reliability issues due to their dependency on several assumptions made for the sake of modeling simplification, as well as poor choices and approximations in material characterization and electrochemical parameters. In this regard, data-driven machine learning models could provide the missing and more appropriate parameters in conventional computational fluid dynamics models. The purpose of the present overview is to explore the state of the art in computational fluid dynamics of individual components of the modeling of PEMFC, their issues and limitations, and how they can be significantly improved by hybrid modeling techniques integrating with machine learning approaches. Furthermore, a detailed future direction of the proposed solution related to PEMFC and its impact on the transportation sector is discussed.
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(This article belongs to the Special Issue Modeling, Simulation and Control in Energy Systems)
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Comparison of Tetraselmis suecica Cell Disruption Techniques: Kinetic Study and Extraction of Hydrosoluble Compounds
by
Hussein Rida, Jérôme Peydecastaing, Hosni Takache, Ali Ismail and Pierre-Yves Pontalier
Processes 2024, 12(6), 1139; https://doi.org/10.3390/pr12061139 (registering DOI) - 31 May 2024
Abstract
The optimization of cell disruption is a critical step in microalgal biorefineries. We used the same batch of Tetraselmis suecica culture to compare two mechanical cell disruption techniques, focusing on the extraction yield of water-soluble molecules. The conditions for high-pressure homogenization (HPH) studied were
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The optimization of cell disruption is a critical step in microalgal biorefineries. We used the same batch of Tetraselmis suecica culture to compare two mechanical cell disruption techniques, focusing on the extraction yield of water-soluble molecules. The conditions for high-pressure homogenization (HPH) studied were two passes at a moderate pressure of 300 bars. For ultrasound (US) treatment, we used an amplitude of 20% (equivalent to 100 W) for 25 min. These conditions were chosen on the basis of a preliminary screen of extraction conditions. HPH extracted proteins and pigments more efficiently than US, whereas US was superior for uronic acid extraction. Interestingly, the two methods had similar extraction yields for carbohydrates under the studied conditions. We also analyzed the kinetics of molecule release by considering the centrifugation time lag for HPH and applying a first-order kinetic model for US. HPH outperformed US in terms of the immediate extraction and release of molecules.
Full article
(This article belongs to the Special Issue Recent Advances in Processing Technologies for Substance Extraction, Separation, and Enrichment)
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Open AccessArticle
Study on the Deactivation Mechanism of Ru/C Catalysts
by
Zhi Cao, Tianchi Li, Baole Li, Xiwen Chen, Chen Zuo and Weifang Zheng
Processes 2024, 12(6), 1138; https://doi.org/10.3390/pr12061138 - 31 May 2024
Abstract
Employing catalytic decomposition to break down reducing agents in intermediate-level radioactive waste during nuclear fuel reprocessing offers significant advantages. This study focuses on investigating the deactivation behavior of 5% Ru/C catalysts by two different synthesis processes used for reducing agent destruction. Deactivation experiments
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Employing catalytic decomposition to break down reducing agents in intermediate-level radioactive waste during nuclear fuel reprocessing offers significant advantages. This study focuses on investigating the deactivation behavior of 5% Ru/C catalysts by two different synthesis processes used for reducing agent destruction. Deactivation experiments were conducted by subjecting the 5% Ru/C catalysts to 100 and 150 reaction cycles. Changes in the concentration of free radicals on the carbon-based carrier were measured to analyze the loading position and loss of Ru ions. Additionally, sorption–desorption curves and pore size distributions of the four catalysts were obtained. Analysis results reveal that Ru ions on the catalyst adsorb onto active free radical sites on the carbon-based carrier. Under ultrasonic conditions, some Ru ions partially desorb from the free radical sites on the carbon-based carrier, and desorbed Ru ions may adsorb onto weak free radical sites, while undesorbed Ru ions may adsorb onto strong free radical sites. After hundreds of hours of reaction, SM1 and SM2 exhibited approximately a 30% decrease in specific surface area and pore volume compared to SM0. However, the catalyst activity remained unchanged, and the catalyst pore size remained essentially unchanged, which primarily means that the micropores on the catalyst’s surface have undergone corrosion and damage.
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(This article belongs to the Section Catalysis Enhanced Processes)
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Production Feature Analysis of Global Onshore Carbonate Oil Reservoirs Based on XGBoost Classier
by
Guilin Qi and Baolei Liu
Processes 2024, 12(6), 1137; https://doi.org/10.3390/pr12061137 - 31 May 2024
Abstract
Carbonate reservoirs account for 60% of global reserves for oil, making them one of the most important types of sedimentary rock reservoirs for petroleum production. This study aimed to identify key production features that significantly impact oil production rates, enhancing reservoir management and
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Carbonate reservoirs account for 60% of global reserves for oil, making them one of the most important types of sedimentary rock reservoirs for petroleum production. This study aimed to identify key production features that significantly impact oil production rates, enhancing reservoir management and optimizing production strategies. A comprehensive dataset is built from reserves and production history data of 377 onshore carbonate oilfields globally, encompassing features such as production, recovery rate, and recovery degree of the whole lifecycle of an oilfield. XGBoost classifier is trained by K-fold cross-validation and its hyperparameters are optimized by Optuna optimization framework. The results show that XGBoost has the best performance evaluated with metrics including accuracy, precision, recall, and F1 score comparing with decision tree, random forest, and support vector machine. Key production features are identified by analyzing the classification feature importance of XGBoost classifier, including build-up stage cumulative production, plateau stage cumulative production, plateau stage recovery rate, plateau stage recovery degrees, and peak production. In conclusion, oilfield reserve size, build-up stage cumulative production, plateau stage cumulative production, and peak production increase, while plateau stage recovery rate decreases, and the plateau stage recovery degree of small-sized oilfields is slightly greater than that of moderate and large oilfields. The research methodology of this study can serve as a reference for studying production features of other types of oil and gas reservoirs. By applying the methodology to low-permeability oilfields, this paper concludes the key production features that are as follows: low-permeability oilfields generally have lower peak recovery rate, lower plateau stage recovery rate, lower decline stage recovery degree, and lower decline stage recovery rate, along with a wide but generally lower range of decline stage cumulative production compared to conventional oilfields.
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(This article belongs to the Special Issue Advances in Improving Oil Recovery in Low-Permeability Hydrocarbon Resources)
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A Lightweight Safety Helmet Detection Algorithm Based on Receptive Field Enhancement
by
Changpeng Ji, Zhibo Hou and Wei Dai
Processes 2024, 12(6), 1136; https://doi.org/10.3390/pr12061136 - 31 May 2024
Abstract
Wearing safety helmets is an important way to ensure the safety of workers’ lives. To address the challenges associated with low accuracy, large parameter values, and slow detection speed of existing safety helmet detection algorithms, we propose a receptive field-enhanced lightweight safety helmet
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Wearing safety helmets is an important way to ensure the safety of workers’ lives. To address the challenges associated with low accuracy, large parameter values, and slow detection speed of existing safety helmet detection algorithms, we propose a receptive field-enhanced lightweight safety helmet detection algorithm called YOLOv5s-CR. First, we use a lightweight backbone, a high-resolution feature fusion network, and a small object detection layer to improve the detection accuracy of small objects while substantially decreasing the model parameters. Next, we embed a coordinate attention mechanism into the feature extraction network to improve the localization accuracy of the detected object. Finally, we propose a new receptive field enhancement module (RFEM) to substitute the SPPF module in the original network, enabling the model to acquire features under multiple receptive fields, thereby enhancing the detection precision of multi-scale objects. Using the Safety Helmet Detection dataset for validation, in contrast to the initial YOLOv5s, the parameters of the improved algorithm were reduced by 62.8% to 2.61 M, and P, R, and mAP0.5 were increased by 1.5%, 1.2%, and 2.0%, respectively. The detection speed can reach 149FPS on the RTX3070 GPU, which satisfies the accuracy and real-time requirements for detecting safety helmets.
Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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Open AccessFeature PaperArticle
Classification of Microseismic Signals Using Machine Learning
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Ziyang Chen, Yi Cui, Yuanyuan Pu, Yichao Rui, Jie Chen, Deren Mengli and Bin Yu
Processes 2024, 12(6), 1135; https://doi.org/10.3390/pr12061135 - 31 May 2024
Abstract
The classification of microseismic signals represents a fundamental preprocessing step in microseismic monitoring and early warning. A microseismic signal source rock classification method based on a convolutional neural network is proposed. First, the characteristic parameters of the microseismic signals are extracted, and a
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The classification of microseismic signals represents a fundamental preprocessing step in microseismic monitoring and early warning. A microseismic signal source rock classification method based on a convolutional neural network is proposed. First, the characteristic parameters of the microseismic signals are extracted, and a convolutional neural network is constructed for the analysis of these parameters; then, the mapping relationship model between the characteristic parameters of the microseismic signals and the rock class is established. The feasibility of the proposed method in differentiating acoustic emission signals under different load conditions is verified by using acoustic emission data from laboratory uniaxial compression tests, Brazilian splitting tests, and shear tests. In the three distinct laboratory experiments, the proposed method achieved a source rock classification accuracy of greater than 90% for acoustic emission signals. The proposed and verified method provides a new basis for the preprocessing of microseismic signals.
Full article
(This article belongs to the Special Issue Security Intelligent Monitoring and Big Data Utilization in Coal Mining Process)
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The Effect of Microbial Compound Fertilizer on the Heavy Metal Binding Forms and Enzyme Activity in Soil
by
Zheng Zhao, Changyin Huang, Baohui Liang, Siyu Wang, Huiwen Sun, Simeng Bian and Xiaoran Sun
Processes 2024, 12(6), 1134; https://doi.org/10.3390/pr12061134 - 31 May 2024
Abstract
Nowadays, heavy metal pollution in soil caused by human production activities is increasingly serious. The heavy metal ions in soil inhibit plant growth and endanger human health as they can disrupt the physicochemical properties of soil. However, the elimination of heavy metals in
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Nowadays, heavy metal pollution in soil caused by human production activities is increasingly serious. The heavy metal ions in soil inhibit plant growth and endanger human health as they can disrupt the physicochemical properties of soil. However, the elimination of heavy metals in soil is so difficult that more and more researchers are studying effective soil conditioners. The negatively charged groups in microbial communities can bind with heavy metal ions in the soil to remove them. In this paper, Cr- and Cd-polluted soils were used to simulate heavy-metal-polluted soil, and microbial compound fertilizer (MOF) was used as a soil conditioner for removing Cr and Cd in soil. The effects of different additive amounts of MOF on the physicochemical properties, the concentration of metal binding forms in soil and the enzyme activity of soil were investigated. The results showed that when the addition amount of fertilizer was 10%, the improvement effect on Cr- and Cd-polluted soils was the best. Compared with polluted soils without MOF addition, the physicochemical properties of MOF-treated polluted soils improved significantly, the concentration of effective forms of heavy metals decreased significantly, and the concentration of organic and residual forms as well as soil enzyme activity increased significantly. This indicates that the addition of MOF can increase the activity of soil microbial communities and soil fertility, and has the ability to remediate heavy-metal-polluted soil. MOF is expected to become an efficient soil conditioner for heavy metals.
Full article
(This article belongs to the Special Issue Sustainable Technologies for Removing Heavy Metals from Contaminated Soils and Wastewater)
Open AccessArticle
Synthesis of Silver-Decorated Magnetite Nanoparticles Using Self-Assembly Methods
by
Gye Seok An
Processes 2024, 12(6), 1133; https://doi.org/10.3390/pr12061133 - 31 May 2024
Abstract
This study investigated the synthesis and functional characteristics of Fe3O4@Ag core–shell nanoparticles, focusing on the impact of amino functionalization on their structural and chemical properties. Utilizing self-assembly methods driven by electrostatic interactions, we achieved the effective adsorption of Ag
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This study investigated the synthesis and functional characteristics of Fe3O4@Ag core–shell nanoparticles, focusing on the impact of amino functionalization on their structural and chemical properties. Utilizing self-assembly methods driven by electrostatic interactions, we achieved the effective adsorption of Ag nanoparticles into Fe3O4 cores previously modified with silane (APTES) or polymer (PEI) precursors. Our results elucidate how the type of amino precursor affects the surface charge and subsequent adsorption dynamics, revealing that PEI-modified Fe3O4 nanoparticles exhibit more substantial Ag nanoparticle adsorption than those modified with APTES. This enhanced adsorption was attributed to the higher density of the amine groups introduced by PEI, which also affected the electrostatic properties of the nanoparticles, as evidenced by their zeta-potential values. Moreover, this study highlighted the role of electrostatic attraction in the self-assembly process, facilitating a controlled synthesis environment that enhances the stability and functionality of nanoparticles for potential biomedical and catalytic applications. This research not only advances our understanding of nanoparticle behavior under different surface chemistries but also demonstrates the importance of surface engineering in optimizing nanoparticle performance for targeted applications.
Full article
(This article belongs to the Special Issue Recent Advances in Ceramic Materials: Processing, Characterization and Applications)
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Open AccessFeature PaperArticle
Recovery of High-Value Compounds from Yarrowia lipolytica IMUFRJ 50682 Using Autolysis and Acid Hydrolysis
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Rhonyele Maciel da Silva, Bernardo Dias Ribeiro, Ailton Cesar Lemes and Maria Alice Zarur Coelho
Processes 2024, 12(6), 1132; https://doi.org/10.3390/pr12061132 - 30 May 2024
Abstract
This study aimed to evaluate the sequential hydrolysis of the biomass from unconventional and versatile Y. lipolytica to recover mannoproteins, carbohydrates, and other compounds as well as to determine the antioxidant activity of ultrafiltered fractions. The crude biomass underwent autolysis, and the resulting
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This study aimed to evaluate the sequential hydrolysis of the biomass from unconventional and versatile Y. lipolytica to recover mannoproteins, carbohydrates, and other compounds as well as to determine the antioxidant activity of ultrafiltered fractions. The crude biomass underwent autolysis, and the resulting supernatant fraction was used for mannoprotein recovery via precipitation with ethanol. The precipitate obtained after autolysis underwent acid hydrolysis, and the resulting supernatant was ultrafiltered, precipitated, and characterized. The process yields were 55.5% and 46.14% for the crude biomass grown in glucose and glycerol, respectively. The mannoprotein with higher carbohydrate content (from crude biomass grown in glycerol) exhibited a higher emulsification index of 47.35% and thermal stability (60% weight loss). In contrast, the mannoprotein with higher protein content (from crude biomass grown in glucose) showed a better surface tension reduction of 44.50 mN/m. The technological properties showed that the crude biomass and the food ingredients are feasible to apply in food processing. The fractionation of the acid hydrolysis portion allowed the evaluation of the antioxidant power synergism among the components present in the hydrolysate, mostly the protein peptide chain. The sequential hydrolysis method is viable for extracting valuable products from Y. lipolytica.
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(This article belongs to the Special Issue Advances in Lipid Chemistry: Extraction, Process and Analysis)
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Adsorption and Diffusion Characteristics of CO2 and CH4 in Anthracite Pores: Molecular Dynamics Simulation
by
Yufei Gao, Yaqing Wang and Xiaolong Chen
Processes 2024, 12(6), 1131; https://doi.org/10.3390/pr12061131 - 30 May 2024
Abstract
CO2-enhanced coalbed methane recovery (CO2-ECBM) has been demonstrated as an effective enhanced oil recovery (EOR) technique that enhances the production of coalbed methane (CBM) while achieving the goal of CO2 sequestration. In this paper, the grand canonical Monte
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CO2-enhanced coalbed methane recovery (CO2-ECBM) has been demonstrated as an effective enhanced oil recovery (EOR) technique that enhances the production of coalbed methane (CBM) while achieving the goal of CO2 sequestration. In this paper, the grand canonical Monte Carlo simulation is used to investigate the dynamic mechanism of CO2-ECBM in anthracite pores. First, an anthracite pore containing both organic and inorganic matter was constructed, and the adsorption and diffusion characteristics of CO2 and CH4 in the coal pores under different temperature and pressure conditions were studied by molecular dynamics (MD) simulations. The results indicate that the interaction energy of coal molecules with CO2 and CH4 is positively associated with pressure but negatively associated with temperature. At 307.15 K and 101.35 kPa, the interaction energies of coal adsorption of single-component CO2 and CH4 are −1273.92 kJ·mol−1 and −761.53 kJ·mol−1, respectively. The interaction energy between anthracite molecules and CO2 is significantly higher compared to CH4, indicating that coal has a greater adsorption capacity for CO2 than for CH4. Furthermore, the distribution characteristics of gas in the pores before and after injection indicate that CO2 mainly adsorbs and displaces CH4 by occupying adsorption sites. Under identical conditions, the diffusion coefficient of CH4 surpasses that of CO2. Additionally, the growth rate of the CH4 diffusion coefficient as the temperature increases is higher than that of CO2, which indicates that CO2-ECBM is applicable to high-temperature coal seams. The presence of oxygen functional groups in anthracite molecules greatly influences the distribution of gas molecules within the pores of coal. The hydroxyl group significantly influences the adsorption of both CH4 and CO2, while the ether group has a propensity to impact CH4 adsorption, and the carbonyl group is inclined to influence CO2 adsorption. The research findings are expected to provide technical support for the effective promotion of CO2-ECBM technology.
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(This article belongs to the Special Issue Shale Gas and Coalbed Methane Exploration and Practice)
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Simulation of Bubble Behavior Characteristics in a Rolling Fluidized Bed with the Addition of Longitudinal Internal Members
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Rongsheng Xu, Ruojin Wang, Banghua Wu, Xiaopei Yuan, Dewu Wang, Yan Liu and Shaofeng Zhang
Processes 2024, 12(6), 1130; https://doi.org/10.3390/pr12061130 - 30 May 2024
Abstract
To address the effect of a ship’s rolling on the fluidization quality of fluidized beds, in this study, a simulation of a rolling fluidized bed with longitudinal internal members added (R-FBLIM) was carried out and compared with that of a rolling fluidized bed
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To address the effect of a ship’s rolling on the fluidization quality of fluidized beds, in this study, a simulation of a rolling fluidized bed with longitudinal internal members added (R-FBLIM) was carried out and compared with that of a rolling fluidized bed without internal members added (R-FBWIM). The transient motion, as well as the behavioral characteristics of the bubbles within the R-FBLIM, was analyzed; the variation patterns of the number of bubbles, as well as the equivalent diameter of the bubbles, were compared for different apparent gas velocities, oscillation periods, and amplitudes; and the mechanism of the action of the longitudinal internal members was investigated. The results show that the structural design of the longitudinal internal members can effectively improve the gas–solid fluidization quality of the rolling fluidized bed. The horizontal support plate and the cap hole structure can effectively break the air bubbles, the cap hole structure promotes the radial mixing of the gas–solid fluid, and the internal and outer rings of the curved surface plate roll in rows, which inhibit the aggregation behavior of the gas–solid fluid to the two sides of the oscillating planes, respectively, by cooperating with the cap hole structure. Compared with R-FBWIM, the gas–solid phase within R-FBLIM is more spatially distributed, with the number of bubbles increasing by about 2–4 times and the mean diameter decreasing by about 50–60%. The number of bubbles increases with the gas velocity but decreases with the rolling amplitude; the mean diameter decreases with the gas velocity but responds less to the rolling amplitude change.
Full article
(This article belongs to the Special Issue Multiphase Mass Transfer and Phase Equilibrium in Chemical Processes)
Open AccessArticle
Mus4mCPred: Accurate Identification of DNA N4-Methylcytosine Sites in Mouse Genome Using Multi-View Feature Learning and Deep Hybrid Network
by
Xiao Wang, Qian Du and Rong Wang
Processes 2024, 12(6), 1129; https://doi.org/10.3390/pr12061129 - 30 May 2024
Abstract
N4-methylcytosine (4mC) is a critical epigenetic modification that plays a pivotal role in the regulation of a multitude of biological processes, including gene expression, DNA replication, and cellular differentiation. Traditional experimental methods for detecting DNA N4-methylcytosine sites are time-consuming, labor-intensive, and costly, making
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N4-methylcytosine (4mC) is a critical epigenetic modification that plays a pivotal role in the regulation of a multitude of biological processes, including gene expression, DNA replication, and cellular differentiation. Traditional experimental methods for detecting DNA N4-methylcytosine sites are time-consuming, labor-intensive, and costly, making them unsuitable for large-scale or high-throughput research. Computational methods for identifying DNA N4-methylcytosine sites enable the rapid and cost-effective analysis of DNA 4mC sites across entire genomes. In this study, we focus on the identification of DNA 4mC sites in the mouse genome. Although there are already some computational methods that can predict DNA 4mC sites in the mouse genome, there is still significant room for improvement in accurately predicting them due to their inability to fully capture the multifaceted characteristics of DNA sequences. To address this issue, we propose a new deep learning predictor called Mus4mCPred, which utilizes multi-view feature learning and deep hybrid networks for accurately predicting DNA 4mC sites in the mouse genome. The predictor Mus4mCPred firstly employed different encoding methods to extract the feature vectors of DNA sequences, then input these features generated by different encoding methods into various hybrid deep learning models for the learning and extraction of more sophisticated representations of these features, and finally fused the extracted multi-view features to serve as the final features for DNA 4mC site prediction in the mouse genome. Multi-view features enabled the more comprehensive capture of data characteristics, enhancing the feature representation of DNA sequences. The independent test results showed that the sensitivity (Sn), specificity (Sp), accuracy (Acc), and Matthews’ correlation coefficient (MCC) were 0.7688, 0.9375, 0.8531, and 0.7165, respectively. The predictor Mus4mCPred outperformed other state-of-the-art methods, achieving the accurate identification of 4mC sites in the mouse genome.
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(This article belongs to the Special Issue Application of Machine Learning Algorithms for Biological Data and Biological Systems)
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Study of Draft Tube Optimization Using a Neural Network Surrogate Model for Micro-Francis Turbines Utilized in the Water Supply System of High-Rise Buildings
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Qilong Xin, Jianmin Wu, Jiyun Du, Zhan Ge, Jinkuang Huang, Wei Yu, Fangyang Yuan, Dongxiang Wang and Xinjun Yang
Processes 2024, 12(6), 1128; https://doi.org/10.3390/pr12061128 - 30 May 2024
Abstract
With the increasing popularity of clean energy, the use of micro turbines to recover surplus energy in the water supply pipelines of high-rise buildings has attracted more attention. This study adopts a predictor model based on Radial Basis Function Neural Network (RBFNN) to
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With the increasing popularity of clean energy, the use of micro turbines to recover surplus energy in the water supply pipelines of high-rise buildings has attracted more attention. This study adopts a predictor model based on Radial Basis Function Neural Network (RBFNN) to optimize the draft tube shape for micro-Francis turbines. The predictor model is formed on a dataset provided by numerical simulations, which are validated by lab tests. Specifically, numerical investigations are carried out in the shape of a draft tube to determine an optimal model. Additionally, the superiority of the RBFNN model in nonlinear optimization is verified by comparing it with other models under the same date sets. After that, the design parameters are optimized using RBFNN and sequential quadratic programming algorithm (SQPA). Finally, the turbine prototype is fabricated and tested on a lab test rig. The experimental results indicate that the numerical method adopted in this research is accurate enough for such a micro-Francis turbine performance prediction. Under the design conditions, the proposed micro-Francis turbine produces a power of 147 W with an efficiency of over 29%, which shows a considerable improvement compared to the initial prototype.
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(This article belongs to the Section Energy Systems)
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Green Supply Chain Optimization Based on Two-Stage Heuristic Algorithm
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Chunrui Lei, Heng Zhang, Xingyou Yan and Qiang Miao
Processes 2024, 12(6), 1127; https://doi.org/10.3390/pr12061127 - 30 May 2024
Abstract
Green supply chain management is critical for driving sustainable development and addressing escalating environmental challenges faced by companies. However, due to the multidimensionality of cost–benefit analysis and the intricacies of supply chain operations, strategic decision-making regarding green supply chains is inherently complex. This
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Green supply chain management is critical for driving sustainable development and addressing escalating environmental challenges faced by companies. However, due to the multidimensionality of cost–benefit analysis and the intricacies of supply chain operations, strategic decision-making regarding green supply chains is inherently complex. This paper proposes a green supply chain optimization framework based on a two-stage heuristic algorithm. First, anchored in the interests of intermediary core enterprises, this work integrates upstream procurement and transportation of products with downstream logistics and distribution. In this aspect, a three-tier green complex supply chain model incorporating economic and environmental factors is developed to consider carbon emissions, product non-conformance rates, delay rates, and transportation costs. The overarching goal is to comprehensively optimize the trade-off between supply chain costs and carbon emissions. Subsequently, a two-stage heuristic algorithm is devised to solve the model by combining the cuckoo search algorithm with the brainstorming optimization algorithm. Specifically, an adaptive crossover–mutation operator is introduced to enhance the search performance of the brainstorming optimization algorithm, which caters to both global and local search perspectives. Experimental results and comparison studies demonstrate that the proposed method performs well within the modeling and optimization of the green supply chain. The proposed method facilitates the efficient determination of ordering strategies and transportation plans within tight deadlines, thereby offering valuable support to decision-makers in central enterprises for supply chain management, ultimately maximizing their benefits.
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(This article belongs to the Section Advanced Digital and Other Processes)
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Assessment of Wearable Cooling and Dehumidifying System Used under Personal Protective Clothing through Human Subject Testing
by
Yiying Zhou, Lun Lou and Jintu Fan
Processes 2024, 12(6), 1126; https://doi.org/10.3390/pr12061126 - 30 May 2024
Abstract
Healthcare professionals wearing personal protective equipment (PPE) during outbreaks often experience heat strain and discomfort, which can negatively impact their work performance and well-being. This study aimed to evaluate the physiological and psychological effects of a newly designed wearable cooling and dehumidifying system
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Healthcare professionals wearing personal protective equipment (PPE) during outbreaks often experience heat strain and discomfort, which can negatively impact their work performance and well-being. This study aimed to evaluate the physiological and psychological effects of a newly designed wearable cooling and dehumidifying system (WCDS) on healthcare workers wearing PPE via a 60 min treadmill walking test. Core temperature, mean skin temperature, heart rate, and subjective assessments of thermal sensation, wetness sensation, and thermal comfort were measured throughout the test. Additionally, ratings of wearing comfort and movement comfort were recorded during a wearing trial. The results showed that the WCDS significantly reduced core temperature, improved thermal sensation, and reduced wetness sensation compared to the non-cooling condition. The microclimatic temperature within the PPE was significantly lower in the cooling condition, indicating the WCDS’s ability to reduce heat buildup. The wearing trial results demonstrated general satisfaction with the wearability and comfort of the WCDS across various postures. These findings contribute to the development of enhanced PPE designs and the improvement in working conditions for healthcare professionals on the frontlines during outbreaks.
Full article
(This article belongs to the Special Issue Smart Wearable Technology: Thermal Management and Energy Applications)
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Open AccessArticle
Experimental Analysis of the Mechanical Properties and Failure Behavior of Deep Coalbed Methane Reservoir Rocks
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Haiyang Wang, Shugang Yang, Linpeng Zhang, Yunfeng Xiao, Xu Su, Wenqiang Yu and Desheng Zhou
Processes 2024, 12(6), 1125; https://doi.org/10.3390/pr12061125 - 30 May 2024
Abstract
A comprehensive understanding of the mechanical characteristics of deep coalbed methane reservoir rocks (DCMRR) is crucial for the safe and efficient development of deep coalbed gas resources. In this study, the microstructural and mechanical features of the coal seam roof, floor, and the
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A comprehensive understanding of the mechanical characteristics of deep coalbed methane reservoir rocks (DCMRR) is crucial for the safe and efficient development of deep coalbed gas resources. In this study, the microstructural and mechanical features of the coal seam roof, floor, and the coal seam itself were analyzed through laboratory experiments. The impact mechanisms of drilling fluid and fracturing fluid hydration on the mechanical properties and failure behavior of coal seam rocks were investigated. The experimental results indicate that the main minerals in coal seams are clay and amorphous substances, with kaolinite being the predominant clay mineral component in coal seam rocks. The rock of the coal seam roof and floor exhibits strong elasticity and high compressive strength, while the rock in the coal seam section shows a lower compressive capacity with pronounced plastic deformation characteristics. The content of kaolinite shows a good correlation with the mechanical properties of DCMRR. As the kaolinite content increases, the strength of DCMRR gradually decreases, and deformability enhances. After immersion in drilling fluid and slickwater, the strength of coal seam rocks significantly decreases, leading to shear fracture zones and localized strong damage features after rock compression failure. The analysis of the mechanical properties of DCMRR suggests that the horizontal well trajectory should be close to the coal seam roof, and strong sealing agents should be added to drilling fluid to reduce the risk of wellbore collapse. Enhancing the hydration of slickwater is beneficial for the formation of a more complex fracture network in deep coalbed methane reservoir.
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(This article belongs to the Special Issue Coal Mining and Unconventional Oil Exploration)
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Three-Dimensional Heterogeneous Salt Cavern Underground Gas Storage Water Solution Cavity Model Study
by
Xueqi Cen, Xinggang Meng, Zongxiao Ren and Jiajun Cao
Processes 2024, 12(6), 1124; https://doi.org/10.3390/pr12061124 - 29 May 2024
Abstract
In recent years, with the rapid development of salt cavern gas storage reservoir construction in China, the characteristics of salt rock reservoirs with strong non-homogeneity and many interlayers have brought challenges to the dynamic prediction of water solution cavity construction. Aiming to solve
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In recent years, with the rapid development of salt cavern gas storage reservoir construction in China, the characteristics of salt rock reservoirs with strong non-homogeneity and many interlayers have brought challenges to the dynamic prediction of water solution cavity construction. Aiming to solve this problem, this paper constructs a three-dimensional non-homogeneous salt cavern reservoir water-soluble cavity building prediction model, which takes into full consideration the non-homogeneity of salt rock reservoirs, interlayers, reservoir temperatures, and water injection process parameters, among other factors. By comparing the calculation results of the software compiled by the model with those of other numerical simulation software, we show that the model can accurately reflect the influence of geological parameters on the cavity morphology under the condition of non-uniform geological parameters, with higher simulation accuracy, and ultimately analyze individual examples. It can provide important theoretical support and practical guidance for the construction of a salt cavern gas storage reservoir.
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(This article belongs to the Section Energy Systems)
Open AccessFeature PaperArticle
Development of Macro-Encapsulated Phase-Change Material Using Composite of NaCl-Al2O3 with Characteristics of Self-Standing
by
Shenghao Liao, Xin Zhou, Xiaoyu Chen, Zhuoyu Li, Seiji Yamashita, Chaoyang Zhang and Hideki Kita
Processes 2024, 12(6), 1123; https://doi.org/10.3390/pr12061123 - 29 May 2024
Abstract
Developing thermal storage materials is crucial for the efficient recovery of thermal energy. Salt-based phase-change materials have been widely studied. Despite their high thermal storage density and low cost, they still face issues such as low thermal conductivity and easy leaks. Therefore, a
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Developing thermal storage materials is crucial for the efficient recovery of thermal energy. Salt-based phase-change materials have been widely studied. Despite their high thermal storage density and low cost, they still face issues such as low thermal conductivity and easy leaks. Therefore, a new type of NaCl-Al2O3@SiC@Al2O3 macrocapsule was developed to address these drawbacks, and it exhibited excellent rapid heat storage and release capabilities and was extremely stable, significantly reducing the risk of leakage at high temperatures for industrial waste heat recovery and in concentrated solar power systems above 800 °C. Thermal storage macrocapsules consisted of a double-layer encapsulation of silicon carbide and alumina and a self-standing core of NaCl-Al2O3. After enduring over 1000 h at a high temperature of 850 °C, the encapsulated phase-change material exhibited an extremely low weight loss rate of less than 5% compared with NaCl@Al2O3 and NaCl-Al2O3@Al2O3 macrocapsules, for which the weight loss rate was reduced by 25% and 10%, respectively, proving their excellent leakage prevention. The SiC powder layer, serving as an intermediate coating, further prevented leakage, while the use of Al2O3 ceramics for encapsulation enhanced the overall mechanical strength. It was innovatively discovered that the Al2O3 particles formed a network structure around the molten NaCl, playing an important role in maintaining the shape and preventing leakage of the composite thermal storage phase-change material. Furthermore, the addition of Al2O3 significantly enhanced the rapid heat storage and release rate of NaCl-Al2O3 compared to pure NaCl. This encapsulated phase-change material demonstrated outstanding durability and rapid heat storage and release performance, offering an innovative approach to the application of salt phase-change materials in the field of high temperature rapid heat storage and release and encapsulating NaCl as a high-temperature thermal storage material in a packed bed system. Compared with conventional salt-based phase-change materials, the developed product is expected to significantly improve the reliability and thermal efficiency of thermal storage systems.
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(This article belongs to the Special Issue Innovations in Phase-Change Materials for High-Temperature Heat Storage)
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Open AccessFeature PaperArticle
Bioprocess Design and Evaluation of Hydrothermal Hydrolysates from Sargassum sp. for Enhancing Arthrospira platensis Growth and Protein Content
by
Alejandra Cabello-Galindo, Rosa M. Rodríguez-Jasso, Gabriela Cid-Ibarra, K. D. González-Gloria, Ruth Belmares, Mayela Govea-Salas, Luciane Maria Colla and Héctor A. Ruiz
Processes 2024, 12(6), 1122; https://doi.org/10.3390/pr12061122 - 29 May 2024
Abstract
The proliferation of Sargassum biomass in various coastal areas has led to environmental and socio-economic problems. However, due to their unique composition, these biomasses offer versatile applications, prompting research into their potential in third-generation biorefineries. In this study, the hydrothermal processing of Sargassum
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The proliferation of Sargassum biomass in various coastal areas has led to environmental and socio-economic problems. However, due to their unique composition, these biomasses offer versatile applications, prompting research into their potential in third-generation biorefineries. In this study, the hydrothermal processing of Sargassum sp. was evaluated under specific conditions at 190 °C/50 min and 150 °C/30 min. The resulting hydrolysates (liquid phase) were used as alternative culture media for cultivation. Nine treatments for the cultivation of Arthrospira platensis were assessed, varying the concentration of hydrothermal hydrolysates (HH) at 190 °C/50 min: T1 (5% v/v), T2 (10% v/v), and T3 (15% v/v). T4 (5% v/v), T5 (10% v/v), and T6 (15% v/v), maintaining the same HH conditions, and with the addition of 0.7 g/L NaNO3; and treatments T7, T8, and T9 had concentrations of 5%, 10%, and 15% of HH, respectively, at 150 °C/30 min with the addition of 0.7 g/L NaNO3, respectively. Each treatment was inoculated with 15% (v/v) of A. platensis. Growth kinetics were performed by sampling every three days for 24 days. Quantification of soluble proteins was performed for the best conditions of biomass production. The microalgae demonstrated the ability to grow under mixotrophic medium conditions and to utilize the available carbon sources in the culture medium. Treatment 4 has the highest biomass, with an Xmax (g/L) of 1.94 ± 0.06 and a protein production of 24.17 ± 0.86% (w/w). Therefore, this microalgal biomass can be used in the food matrix according to the biorefinery concept.
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(This article belongs to the Special Issue Extraction, Exploitation and Application of Algae Biomass)
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Open AccessArticle
A Fractional Creep Model for Deep Coal Based on Conformable Derivative Considering Thermo-Mechanical Damage
by
Lei Zhang, Chunwang Zhang, Ke Hu, Senlin Xie, Wenhao Jia and Lei Song
Processes 2024, 12(6), 1121; https://doi.org/10.3390/pr12061121 - 29 May 2024
Abstract
In deep high-geostress and high-temperature environments, understanding the creep deformation of deep coal is of great significance for effectively controlling coal deformation and improving gas control efficiency. In this paper, the Abel dashpot is defined based on the conformable derivative, and a damage
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In deep high-geostress and high-temperature environments, understanding the creep deformation of deep coal is of great significance for effectively controlling coal deformation and improving gas control efficiency. In this paper, the Abel dashpot is defined based on the conformable derivative, and a damage variable is introduced into the conformable derivative order, thereby constructing a damaged Abel dashpot. Combining the Weibull distribution and the Drucker–Prager yield criterion, the thermo-mechanical coupling damage variable is defined, and the coupling damage variable is introduced into the damaged Abel dashpot to establish a thermo-mechanical coupling damaged Abel dashpot. Based on the traditional framework of the Burgers creep model, a three-dimensional fractional creep model of deep coal considering the influence of thermo-mechanical coupling damage is proposed. Experimental data on coal creep under different temperatures and stress conditions are utilized to validate the effectiveness and applicability of the proposed three-dimensional fractional creep model and to determine the model parameters. A comparison between experimental data and model results reveals that the creep model effectively characterizes the time-dependent deformation of coal samples under varying temperature and stress influences. Additionally, an in-depth analysis is carried out on the influence mechanism of key parameters in the creep model, particularly focusing on the effects of stress levels and temperature on creep deformation.
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(This article belongs to the Section Chemical Processes and Systems)
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