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
Adsorption and Desorption Behavior of Partially Hydrolyzed Polyacrylamide on Longmaxi Shale
Processes 2024, 12(3), 606; https://doi.org/10.3390/pr12030606 (registering DOI) - 18 Mar 2024
Abstract
Large-scale volumetric fracturing is generally used during shale gas development. The return rate of fracturing fluid is low, and a large amount of slickwater is retained in the reservoir. The adsorption and desorption of partially hydrolyzed polyacrylamide (HPAM), an additive commonly used in
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Large-scale volumetric fracturing is generally used during shale gas development. The return rate of fracturing fluid is low, and a large amount of slickwater is retained in the reservoir. The adsorption and desorption of partially hydrolyzed polyacrylamide (HPAM), an additive commonly used in slickwater, on the surface of shale was studied using Longmaxi shale from the Sichuan Basin. The experimental results showed that the mass ratio of the HPAM solution to shale reached saturation adsorption at 20:1 when the concentration of HPAM solution was 1000 mg/L and 25:1 when the concentration of HPAM solution was 500 mg/L. The mass ratio of the HPAM solution to shale was fixed at 25:1, and the adsorption equilibrium was reached at a HPAM concentration of 1000 mg/L when the aqueous solution temperature was 30 °C and 800 mg/L when the aqueous solution temperature was 60 °C. The Langmuir adsorption model yielded a better fit than the Freundlich adsorption model. The adsorption equilibrium time at 30 °C was at 60 min for a HPAM concentration of 500 mg/L, while for a concentration of 1000 mg/L, it was at 90 min. The adsorption equilibrium time at 60 °C was 40 min for a HPAM concentration of 500 mg/L, whereas it was 60 min for a HPAM concentration at 1000 mg/L. The pseudo-second order (PSO) kinetics model yielded better fits than the pseudo-first order (PFO) kinetics model. The adsorption of HPAM on shale was strong, and the adsorbed HPAM resembled cobwebs adhering to the shale surface. HPAM on the surface of shale after adsorption was able to resist the desorption capacity of water. However, when the amount of adsorbed HPAM on shale increased significantly, the amount of residual HPAM on the surface of the shale decreased rapidly during desorption in deionized water. The desorption of HPAM on the shale surface followed a modified desorption model. The higher the concentration of HPAM adsorbed on the shale surface was, the easier it was to desorb and the easier it was to be removed from the shale.
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(This article belongs to the Special Issue Advances in Technology for Enhancing Oil and Gas Recovery in Shale Reservoirs)
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Research on the Performance Characteristics of a Waste Heat Recovery Compound System for Series Hybrid Electric Vehicles
by
Huifang Dang and Yongqiang Han
Processes 2024, 12(3), 605; https://doi.org/10.3390/pr12030605 (registering DOI) - 18 Mar 2024
Abstract
In this paper, a waste heat recovery compound system for series hybrid electric vehicles is established. The existing components of vehicle air conditioning are used in the organic Rankine cycle (ORC) to realize miniaturization. The waste heat recovery compound system is constructed using
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In this paper, a waste heat recovery compound system for series hybrid electric vehicles is established. The existing components of vehicle air conditioning are used in the organic Rankine cycle (ORC) to realize miniaturization. The waste heat recovery compound system is constructed using GT-SUITE, and the objective of the analysis is to increase the power output and engine thermal efficiency increase ratio (ETEIR). The effects of the expander speed, pump speed, working fluid mass flow rate, and working fluid type on the waste heat recovery compound system are analyzed. The simulation results show that the optimal schemes for the ORC system and compound system corresponding to the expander speed and pump speed are 1000 pm, 2500 rpm, 1200 rpm, and 2500 rpm, respectively. Compared with the ORC system, the maximum power output of the compound system with the same working fluid in three states (1500 rpm, 2500 rpm, and 3500 rpm) of the engine is increased by 21.67%, 24.05%, and 28.23%, respectively. Working fluid supplies of 0.4 kg/s, 0.4 kg/s, and 0.6 kg/s in the three engine states are also considered the best solutions. The working fluid R1234yf and R1234ze are the preferred choices for a waste heat recovery compound system, which have a high system power output and ETEIR and are environmentally friendly.
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(This article belongs to the Special Issue Advanced Thermodynamic Analysis of Chemical Systems)
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Study on Heat Transfer Synergy and Optimization of Capsule-Type Plate Heat Exchangers
by
Chao Yu, Mingzhen Shao, Wenbao Zhang, Guangyi Wang and Mian Huang
Processes 2024, 12(3), 604; https://doi.org/10.3390/pr12030604 (registering DOI) - 18 Mar 2024
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An efficient and accurate method for optimizing capsule-type plate heat exchangers is proposed in this paper. This method combines computational fluid dynamics simulation, a backpropagation algorithm and multi-objective optimization to obtain better heat transfer performance of heat exchanger structures. For plate heat exchangers,
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An efficient and accurate method for optimizing capsule-type plate heat exchangers is proposed in this paper. This method combines computational fluid dynamics simulation, a backpropagation algorithm and multi-objective optimization to obtain better heat transfer performance of heat exchanger structures. For plate heat exchangers, the heat transfer coefficient j and friction coefficient f are a pair of contradictory objectives. The optimization of capsule-type plate heat exchangers is a multi-objective optimization problem. In this paper, a backpropagation neural network was used to construct an approximate model. The plate shape was optimized by a multi-objective genetic algorithm. The optimized capsule-type plate heat exchanger has lower flow resistance and higher heat exchange efficiency. After optimization, the heat transfer coefficient is increased by 8.3% and the friction coefficient is decreased by 14.3%, and the heat transfer effect is obviously improved. Further, analysis of flow field characteristics through field co-ordination theory provides guidance for the further optimization of plates.
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Open AccessArticle
Research and Optimization of Operating Parameters of a Rotor Classifier for Calcined Petroleum Coke
by
Jiaxiang Peng, Chenxi Hui, Ziwei Zhao and Ying Fang
Processes 2024, 12(3), 603; https://doi.org/10.3390/pr12030603 (registering DOI) - 18 Mar 2024
Abstract
This article explores the impact of operating parameters on the classification efficiency of a rotor classifier. Based on the experimental data of calcined petroleum coke classification, a single-factor experimental analysis is conducted to find the relationship between operating parameters and classification performance. The
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This article explores the impact of operating parameters on the classification efficiency of a rotor classifier. Based on the experimental data of calcined petroleum coke classification, a single-factor experimental analysis is conducted to find the relationship between operating parameters and classification performance. The cut size becomes progressively smaller as the rotor speed and feeding speed increase, and progressively larger as the inlet air volume increases. Newton’s classification efficiency and classification accuracy decreased with the increase in feeding speed. The range analysis of the orthogonal experiment shows that the rotor speed and inlet air volume have significant effects on the classification performance, but the effect of feed speed is relatively weak. In addition, the optimal combination of operating parameters is obtained by optimizing the operating parameters. Newton’s classification efficiency under this combination is estimated, and the estimated value is 82%. The verification experiment reveals that the Newton’s classification efficiency is 83.5%, which is close to the estimated value. Meanwhile, the classification accuracy is 0.626. This study provides theoretical guidance for the industrial production of calcined petroleum coke and accumulates basic experimental data for the development of air classifiers.
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(This article belongs to the Section Particle Processes)
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Open AccessFeature PaperArticle
An Evaluation of the Coalbed Methane Mining Potential of Shoushan I Mine Based on the Subject–Object Combination Weighting Method
by
Shunxi Liu, Jie Yang, Yi Jin, Huibo Song, Baoyu Wang, Jiabin Dong and Junling Zheng
Processes 2024, 12(3), 602; https://doi.org/10.3390/pr12030602 - 18 Mar 2024
Abstract
The parameters of coalbed methane reservoirs have large differences, and the precise values cannot represent the resource and production characteristics of the whole block. In order to address these problems, an index system for evaluating the production potential of coalbed methane blocks was
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The parameters of coalbed methane reservoirs have large differences, and the precise values cannot represent the resource and production characteristics of the whole block. In order to address these problems, an index system for evaluating the production potential of coalbed methane blocks was constructed, the weights of evaluation parameters were determined, and a model for the preferential selection of coalbed methane blocks based on the subjective–objective combination of weights method was established. The main coal seams (No. 2-1 and No. 4-2) of the Pingdingshan-Shoushan I Mine Block were taken as the research objects to rank the development potential of CBM blocks in a preferential way. The results show that the six resource and production parameters of No. 2-1 coal are gas content, top and bottom rock properties, coal seam thickness, coal seam depth, coal body structure, and tectonic conditions, in descending order of importance, and the parameters of No. 4-2 coal are gas content, coal body structure, coal seam thickness, top and bottom rock properties, coal seam depth, and tectonic conditions, in descending order of importance. It is predicted that the favorable CBM gas development sweet spot areas of the No. 2-1 coal seam and No. 4-2 coal seam will be located along the exploration wells W15–W29 and W31, respectively. This paper aims to make a multi-dimensional and more comprehensive evaluation of coalbed methane mining potential in the Shoushan I mine, and provide a technical basis for the next step of coalbed methane mining in the study area.
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(This article belongs to the Special Issue Exploration, Exploitation and Utilization of Coal and Gas Resources)
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Open AccessArticle
Defect Detection Algorithm for Battery Cell Casings Based on Dual-Coordinate Attention and Small Object Loss Feedback
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Tianjian Li, Jiale Ren, Qingping Yang, Long Chen and Xizhi Sun
Processes 2024, 12(3), 601; https://doi.org/10.3390/pr12030601 - 18 Mar 2024
Abstract
To address the issue of low accuracy in detecting defects of battery cell casings with low space ratio and small object characteristics, the low space ratio feature and small object feature are studied, and an object detection algorithm based on dual-coordinate attention and
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To address the issue of low accuracy in detecting defects of battery cell casings with low space ratio and small object characteristics, the low space ratio feature and small object feature are studied, and an object detection algorithm based on dual-coordinate attention and small object loss feedback is proposed. Firstly, the EfficientNet-B1 backbone network is employed for feature extraction. Secondly, a dual-coordinate attention module is introduced to preserve more positional information through dual branches and embed the positional information into channel attention for precise localization of the low space ratio features. Finally, a small object loss feedback module is incorporated after the bidirectional feature pyramid network (BiFPN) for feature fusion, balancing the contribution of small object loss to the overall loss. Experimental comparisons on a battery cell casing dataset demonstrate that the proposed algorithm outperforms the EfficientDet-D1 object detection algorithm, with an average precision improvement of 4.23%. Specifically, for scratches with low space ratio features, the improvement is 13.21%; for wrinkles with low space ratio features, the improvement is 9.35%; and for holes with small object features, the improvement is 3.81%. Moreover, the detection time of 47.6 ms meets the requirements of practical production.
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(This article belongs to the Special Issue Manufacturing Processes: Enhancements through Smart and Sustainable Approaches)
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Open AccessArticle
Comparing Quality and Functional Properties of Protein Isolates from Soybean Cakes: Effect of De-Oiling Technologies
by
Giulia Cestonaro, Rodrigo Gonzalez-Ortega, Antonella L. Grosso, Ksenia Morozova, Giovanna Ferrentino, Matteo Scampicchio and Enrico Costanzo
Processes 2024, 12(3), 600; https://doi.org/10.3390/pr12030600 - 17 Mar 2024
Abstract
Driven by growing concerns about food supply and the environment, research on alternative protein sources has become increasingly important. In this context, de-oiled seed cakes, particularly soybean cakes, have emerged as a promising option. However, the conventional methods, such as organic solvent extraction,
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Driven by growing concerns about food supply and the environment, research on alternative protein sources has become increasingly important. In this context, de-oiled seed cakes, particularly soybean cakes, have emerged as a promising option. However, the conventional methods, such as organic solvent extraction, from which these cakes are obtained present several limitations. This study aims to evaluate the efficiency of supercritical fluid extraction (SFE) as an alternative method for de-oiling soybean seeds and obtaining related protein isolates. By using SFE for de-oiling, it was possible to achieve 19% more protein isolates from soybean cakes than the conventional de-oiling method using hexane. Moreover, protein isolates from the SFE de-oiled cake reported significantly improved (p < 0.05) emulsifying abilities and water absorption capacity. Gel electrophoresis and differential scanning calorimetry indicated the presence of a higher concentration of proteins in their native state in the SFE de-oiled flour. Finally, results from the sulfhydryl group content, surface hydrophobicity, and protein dispersibility index also supported these conclusions. The SFE process produced de-oiled soybean cakes with superior functional characteristics and lower environmental impact. Thus, this study provided important information for the food industry to develop more sustainable and healthier production methods.
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(This article belongs to the Special Issue Recent Advances in Processing Technologies for Substance Extraction, Separation, and Enrichment)
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Open AccessFeature PaperArticle
Research on an Optimal Maintenance and Inventory Model Based on Carbon Tax Policy
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Wei-Jen Chen, Chi-Jie Lu, Pei-Ti Hsu and Chih-Te Yang
Processes 2024, 12(3), 599; https://doi.org/10.3390/pr12030599 - 17 Mar 2024
Abstract
The equipment in a factory will gradually deteriorate during production, leading to the production of defective products. Without appropriate maintenance, the defect rate will increase over time. Consequently, the production cost will rise, the inventory quality will be affected, the profit will decrease,
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The equipment in a factory will gradually deteriorate during production, leading to the production of defective products. Without appropriate maintenance, the defect rate will increase over time. Consequently, the production cost will rise, the inventory quality will be affected, the profit will decrease, and the risk of carbon emissions will increase, leading to more customer complaints and damaging the corporate image. In addition to focusing on preventive maintenance to ensure the quality of products, companies should also take carbon emissions into consideration. Furthermore, the frequency of maintenance must be carefully considered, as both carbon emissions and maintenance costs will increase if the frequency is too high; conversely, if the maintenance frequency is too low or non-existent, the defect rate may increase cumulatively, or production may be suspended due to equipment failure. Therefore, this research explores preventive maintenance and inventory management issues within an imperfect production system and develops an extended economic production quantity model that incorporates defective products as well as taking carbon tax and preventive maintenance into consideration. The main purpose is to determine the optimal maintenance frequency, production, and replenishment cycle length, so as to maximize the total profit under the carbon tax policy. This study demonstrates a computing process with relatively impractical product data based on the actual business situation of a disposable diaper manufacturer. Furthermore, a sensitivity analysis is implemented to the model parameters in the proposed model. The managemental insights are illustrated based on the results of theoretical analysis to provide a reference to policy makers during decision making, hence, to secure the sustainability and green transitions of corporates. The results of this study not only help to reduce environmental impact but can also improve the competitiveness and sustainable development of enterprises.
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(This article belongs to the Collection Tools, Approaches and Modeling in Sustainable Supply Chain Management)
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Open AccessArticle
Performance Evaluation of a Double-Helical-Type-Channel Reinforced Heat Sink Based on Energy and Entropy-Generation Analysis
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Liyi He, Xue Hu, Lixin Zhang, Feng Chen and Xinwang Zhang
Processes 2024, 12(3), 598; https://doi.org/10.3390/pr12030598 - 17 Mar 2024
Abstract
Heat-transfer enhancement and entropy generation were investigated for a double-helical-type-channel heat sink with different rib structures set on the upper wall. Based on available experimental data, a series of simulations with various turbulence models were conducted to find the best numerical model. Five
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Heat-transfer enhancement and entropy generation were investigated for a double-helical-type-channel heat sink with different rib structures set on the upper wall. Based on available experimental data, a series of simulations with various turbulence models were conducted to find the best numerical model. Five different rib structures were considered, which were diamond (FC-DR), rectangular (FC-RR), drop-shaped (FC-DSR), elliptic (FC-ER) and frustum (FC-FR). The research was carried out under turbulent flow circumstances with a Reynolds number range of 10,000–60,000 and a constant heat-flow density. The numerical results show that the thermal performance of the flow channel set with a rib structure is better than that of the smooth channel. FC-ER offers the lowest average temperature and the highest temperature uniformity, with a Nusselt number improvement percentage ranging from 15.80% to 30.77%. Overall, FC-ER shows the most excellent performance evaluation criteria and lowest augmentation entropy-generation number compared with the other reinforced flow channels.
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(This article belongs to the Special Issue Flow, Heat and Mass Transfer in Energy Utilization)
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Fluent Integration of Laboratory Data into Biocatalytic Process Simulation Using EnzymeML, DWSIM, and Ontologies
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Alexander S. Behr, Julia Surkamp, Elnaz Abbaspour, Max Häußler, Stephan Lütz, Jürgen Pleiss, Norbert Kockmann and Katrin Rosenthal
Processes 2024, 12(3), 597; https://doi.org/10.3390/pr12030597 - 16 Mar 2024
Abstract
The importance of biocatalysis for ecologically sustainable syntheses in the chemical industry and for applications in everyday life is increasing. To design efficient applications, it is important to know the related enzyme kinetics; however, the measurement is laborious and error-prone. Flow reactors are
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The importance of biocatalysis for ecologically sustainable syntheses in the chemical industry and for applications in everyday life is increasing. To design efficient applications, it is important to know the related enzyme kinetics; however, the measurement is laborious and error-prone. Flow reactors are suitable for rapid reaction parameter screening; here, a novel workflow is proposed including digital image processing (DIP) for the quantification of product concentrations, and the use of structured data acquisition with EnzymeML spreadsheets combined with ontology-based semantic information, leading to rapid and smooth data integration into a simulation tool for kinetics evaluation. One of the major findings is that a flexibly adaptive ontology is essential for FAIR (findability, accessibility, interoperability, reusability) data handling. Further, Python interfaces enable consistent data transfer.
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(This article belongs to the Special Issue Development, Modelling and Simulation of Biocatalytic Processes)
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Open AccessArticle
Point Source Capture of Methane Using Ionic Liquids in Packed Bed Absorbers/Strippers: Experimental and Modelling
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Hamid Reza Rahimpour, Jafar Zanganeh and Behdad Moghtaderi
Processes 2024, 12(3), 596; https://doi.org/10.3390/pr12030596 - 16 Mar 2024
Abstract
Fugitive methane emissions from the mining industry, particularly so-called ventilation air methane (VAM) emissions, are considered among the largest sources of greenhouse gas (GHG) emissions. VAM emissions not only contribute to the global warming but also pose a significant hazard to mining safety
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Fugitive methane emissions from the mining industry, particularly so-called ventilation air methane (VAM) emissions, are considered among the largest sources of greenhouse gas (GHG) emissions. VAM emissions not only contribute to the global warming but also pose a significant hazard to mining safety due to the risk of accidental fires and explosions. This research presents a novel approach that investigates the capture of CH4 in a controlled environment using 1-butyl-3-methylimidazolium bis (trifluoromethylsulfonyl) imide [BMIM][TF2N] ionic liquid (IL), which is an environmentally friendly solvent. The experimental and modelling results confirm that CH4 absorption in [BMIM][TF2N], in a packed column, can be a promising technique for capturing CH4 from point sources, particularly the outlet streams of ventilation shafts in underground coal mines, which typically accounts for <1% v/v of the flow. This study assessed the effectiveness of CH4 removal in a packed bed column by testing various factors such as absorption temperature, liquid and gas flow rates, flow pattern, packing size, desorption temperature, and desorption pressure. According to the optimisation results, the following parameters can be used to achieve a CH4 removal efficiency of 23.8%: a gas flow rate of 0.1 L/min, a liquid flow rate of 0.5 L/min, a packing diameter of 6 mm, and absorption and desorption temperatures of 303 K and 403.15 K, respectively. Additionally, the experimental results indicated that ILs could concentrate CH4 in the simulated VAM stream by approximately 4 fold. It is important to note that the efficiency of CH4 removal was determined to be 3.5-fold higher compared to that of N2. Consequently, even though the VAM stream primarily contains N2, the IL used in the same stream shows a notably superior capacity for removing CH4 compared to N2. Furthermore, CH4 absorption with [BMIM][TF2N] is based on physical interactions, leading to reduced energy requirements for regeneration. These findings validate the method’s effectiveness in mitigating CH4 emissions within the mining sector and enabling the concentration of VAM through a secure and energy-efficient procedure.
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(This article belongs to the Special Issue Applications of Ionic Liquids and Deep Eutectic Solvents in Separation Processes for the Circular Economy)
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Open AccessReview
Post-Production Finishing Processes Utilized in 3D Printing Technologies
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Antreas Kantaros, Theodore Ganetsos, Florian Ion Tiberiu Petrescu, Liviu Marian Ungureanu and Iulian Sorin Munteanu
Processes 2024, 12(3), 595; https://doi.org/10.3390/pr12030595 - 15 Mar 2024
Abstract
Additive manufacturing (AM) has revolutionized production across industries, yet challenges persist in achieving optimal part quality. This paper studies the enhancement of post-processing techniques to elevate the overall quality of AM-produced components. This study focuses on optimizing various post-processing methodologies to address prevalent
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Additive manufacturing (AM) has revolutionized production across industries, yet challenges persist in achieving optimal part quality. This paper studies the enhancement of post-processing techniques to elevate the overall quality of AM-produced components. This study focuses on optimizing various post-processing methodologies to address prevalent issues such as surface roughness, dimensional accuracy, and material properties. Through an extensive review, this article identifies and evaluates a spectrum of post-processing methods, encompassing thermal, chemical, and mechanical treatments. Special attention is given to their effects on different types of additive manufacturing technologies, including selective laser sintering (SLS), fused deposition modeling (FDM), and stereolithography (SLA) and their dedicated raw materials. The findings highlight the significance of tailored post-processing approaches in mitigating inherent defects, optimizing surface finish, and enhancing mechanical properties. Additionally, this study proposes novel post-processing procedures to achieve superior quality while minimizing fabrication time and infrastructure and material costs. The integration of post-processing techniques such as cleaning, surface finishing, heat treatment, support structure removal, surface coating, electropolishing, ultrasonic finishing, and hot isostatic pressing (HIP), as steps directly within the additive manufacturing workflow can immensely contribute toward this direction. The outcomes displayed in this article not only make a valuable contribution to the progression of knowledge regarding post-processing methods but also offer practical implications for manufacturers and researchers who are interested in improving the quality standards of additive manufacturing processes.
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(This article belongs to the Section Sustainable Processes)
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Study on the Simplified Chemical Kinetic Combustion Mechanism of Mixed Methanol/PODE Fuel for Marine Diesel Engines
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Changxiong Li, Yihuai Hu and Hao Guo
Processes 2024, 12(3), 594; https://doi.org/10.3390/pr12030594 - 15 Mar 2024
Abstract
As a clean alternative fuel oil for marine engines, methanol has received increasing attention, but its low cetane number requires diesel ignition, which increases the difficulty of retrofitting existing engine fuel injection systems. Polymethoxy dimethyl ether (PODEn) is an ether fuel
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As a clean alternative fuel oil for marine engines, methanol has received increasing attention, but its low cetane number requires diesel ignition, which increases the difficulty of retrofitting existing engine fuel injection systems. Polymethoxy dimethyl ether (PODEn) is an ether fuel mixture whose chemical structural formula can be expressed as CH3O(CH2O)nCH3 ( ). PODE3 is the predominant component in the blend, and its properties are representative of the blend. PODE is a low-carbon fuel with a high cetane number and is easy to compression ignite, and, as such, can be used to ignite methanol in a marine diesel engine. This article explores the combustion mechanism of mixed methanol–PODE fuel using the characteristics of PODE that can be easily mixed with methanol for combustion. Taking methanol and PODE3 as representative fuels, the detailed combustion mechanism of PODE3 and the detailed combustion mechanism of methanol are simplified using a DRGEPSA (direct relationship graph with error propagation (DRGEP) and sensitivity analysis (SA)) method. Based on the target engine cylinder combustion environment, a simplified mechanism for mixed methanol–PODE fuel is obtained, and the new mechanism is validated in terms of the ignition delay period and laminar flame speed. The results indicate that the newly constructed simplified mechanism is basically consistent with the ignition delay data and flame propagation speed data measured by a rapid compression machine (RCM), laying the foundation for the application of alternative methanol fuels in marine engines.
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(This article belongs to the Section Chemical Processes and Systems)
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Automated Symbolic Processes for Dynamic Modeling of Redundant Manipulator Robots
by
Claudio Urrea, Daniel Saa and John Kern
Processes 2024, 12(3), 593; https://doi.org/10.3390/pr12030593 - 15 Mar 2024
Abstract
In this study, groundbreaking software has been developed to automate the generation of equations of motion for manipulator robots with varying configurations and degrees of freedom (DoF). The implementation of three algorithms rooted in the Lagrange–Euler (L-E) formulation is achieved through the utilization
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In this study, groundbreaking software has been developed to automate the generation of equations of motion for manipulator robots with varying configurations and degrees of freedom (DoF). The implementation of three algorithms rooted in the Lagrange–Euler (L-E) formulation is achieved through the utilization of .m files in MATLAB R2020a software.This results in the derivation of a symbolic dynamic model for industrial manipulator robots. To comprehend the unique features and advantages of the developed software, dynamic simulations are conducted for two 6- and 9-DoF redundant manipulator robots as well as for a 3-DoF non-redundant manipulator robot equipped with prismatic and rotational joints, which is used to simplify the dynamic equations of the redundant prototypes. Notably, for the 6-DoF manipulator robot, model predictive control (MPC) is employed using insights gained from the dynamic model. This enables optimal control by predicting the future evolution of state variables: specifically, the values of the robot’s joint variables. The software is executed to model the dynamics of different types of robots, and the CPU time for a MacBook Pro with a 3 GHz Dual-Core Intel Core i7 processor is less than a minute. Ultimately, the theoretical findings are validated through response graphs and performance indicators of the MPC, affirming the accurate functionality of the developed software. The significance of this work lies in the automation of motion equation generation for manipulator robots, paving the way for enhanced control strategies and facilitating advancements in the field of robotics.
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(This article belongs to the Section Automation Control Systems)
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Open AccessArticle
Simulation of Ni2+ Chelating Peptides Separation in IMAC: Prediction of Langmuir Isotherm Parameters from SPR Affinity Data
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Rachel Irankunda, Pauline Jambon, Alexandra Marc, Jairo Andrés Camaño Echavarría, Laurence Muhr and Laetitia Canabady-Rochelle
Processes 2024, 12(3), 592; https://doi.org/10.3390/pr12030592 - 15 Mar 2024
Abstract
Chromatography modeling for simulation is a tool that can help to predict the separation of molecules inside the column. Knowledge of sorption isotherms in chromatography modeling is a crucial step and methods such as frontal analysis or batch are used to obtain sorption
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Chromatography modeling for simulation is a tool that can help to predict the separation of molecules inside the column. Knowledge of sorption isotherms in chromatography modeling is a crucial step and methods such as frontal analysis or batch are used to obtain sorption isotherm parameters, but they require a significant quantity of samples. This study aims to predict Langmuir isotherm parameters from Surface Plasmon Resonance (SPR) affinity data (requiring less quantity of sample) to simulate metal chelating peptides (MCPs) separation in Immobilized Metal ion Affinity Chromatography (IMAC), thanks to the analogy between both techniques. The validity of simulation was evaluated by comparing the peptide’s simulated retention time with its experimental retention time obtained by IMAC. Results showed that the peptide affinity constant (KA) can be conserved between SPR and IMAC. However, the maximal capacity (qmax) must be adjusted by a correction factor to overcome the geometry differences between IMAC (spherical particles) and SPR (plane sensor ship). Therefore, three approaches were studied; the best one was to use qmax,IMAC imidazole determined experimentally while a correction factor was applied on qmax,SPR to obtain the qmax,IMAC of the peptide, thus minimizing the discrepancy between the experimental and simulated retention times of a peptide.
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(This article belongs to the Special Issue New Frontiers in Chromatographic Separation Technology)
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Open AccessArticle
Development of Thin-Layer Chromatography–Densitometric Procedure for Qualitative and Quantitative Analyses and Stability Studies of Cefazolin
by
Joanna Żandarek, Małgorzata Starek and Monika Dąbrowska
Processes 2024, 12(3), 591; https://doi.org/10.3390/pr12030591 - 15 Mar 2024
Abstract
Cefazolin is a first-generation cephalosporin used to treat severe infections of the respiratory tract, urinary tract, skin, and soft tissues. This study presents the optimal conditions for the determination of cefazolin by thin-layer chromatography with densitometric detection. A chloroform–methanol–glacial acetic acid mixture (6:4:0.5,
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Cefazolin is a first-generation cephalosporin used to treat severe infections of the respiratory tract, urinary tract, skin, and soft tissues. This study presents the optimal conditions for the determination of cefazolin by thin-layer chromatography with densitometric detection. A chloroform–methanol–glacial acetic acid mixture (6:4:0.5, v/v/v) was selected as the mobile phase, while TLC silica gel 60F254 plates were used as the stationary phase. Next, the developed procedure was validated in accordance with ICH guidelines. The obtained results showed that the method is selective, precise, and accurate in a linearity range of 0.04–1.00 µg/spot (r > 0.99). Subsequently, qualitative and quantitative analyses of formulations containing cefazolin were performed. It was found that the amount of antibiotic is highly consistent with the content declared by manufacturers. The suitability of the developed method for stability testing under varying environmental conditions was also verified. It was found that under the tested conditions, the degradation process follows first-order kinetics. The lowest stability was registered in an alkaline environment and in the presence of an oxidizing agent, and the highest stability was recorded in water, and these results were confirmed by the calculated kinetic parameters. The developed method can be used in qualitative and quantitative analyses and stability studies of the analyzed antibiotic.
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(This article belongs to the Section Biological Processes and Systems)
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Open AccessArticle
Generation and Transmission Expansion Planning: Nexus of Resilience, Sustainability, and Equity
by
Dahlia Byles, Patrick Kuretich and Salman Mohagheghi
Processes 2024, 12(3), 590; https://doi.org/10.3390/pr12030590 - 15 Mar 2024
Abstract
The problem of power grid capacity expansion focuses on adding or modernizing generation and transmission resources to respond to the rise in demand over a long-term planning period. Traditionally, the problem has been mainly viewed from technical and financial perspectives. However, with the
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The problem of power grid capacity expansion focuses on adding or modernizing generation and transmission resources to respond to the rise in demand over a long-term planning period. Traditionally, the problem has been mainly viewed from technical and financial perspectives. However, with the rise in the frequency and severity of natural disasters and their dire impacts on society, it is paramount to consider the problem from a nexus of resilience, sustainability, and equity. This paper presents a novel multi-objective optimization framework to perform power grid capacity planning, while balancing the cost of operation and expansion with the life cycle impacts of various technologies. Further, to ensure equity in grid resilience, a social vulnerability metric is used to weigh the energy not served based on the capabilities (or lack thereof) of communities affected by long-duration power outages. A case study is developed for part of the bulk power system in the state of Colorado. The findings of the study show that, by considering life cycle impacts alongside cost, grid expansion solutions move towards greener alternatives because the benefits of decommissioning fossil-fuel-based generation outweigh the costs associated with deploying new generation resources. Furthermore, an equity-based approach ensures that socially vulnerable populations are less impacted by disaster-induced, long-duration power outages.
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(This article belongs to the Special Issue Optimal Design for Renewable Power Systems)
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Open AccessReview
Challenges and Perspectives of the Conversion of Lignin Waste to High-Value Chemicals by Pyrolysis
by
Zhouqing Tan, Yuanyuan Li, Feifei Chen, Jiashu Liu, Jianxiong Zhong, Li Guo, Ran Zhang and Rong Chen
Processes 2024, 12(3), 589; https://doi.org/10.3390/pr12030589 - 14 Mar 2024
Abstract
The pyrolysis process is a thermochemical conversion reaction that encompasses an intricate array of simultaneous and competitive reactions occurring in oxygen-depleted conditions. The final products of biomass pyrolysis are bio-oil, biochar, and some gases, with their proportions determined by the pyrolysis reaction conditions
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The pyrolysis process is a thermochemical conversion reaction that encompasses an intricate array of simultaneous and competitive reactions occurring in oxygen-depleted conditions. The final products of biomass pyrolysis are bio-oil, biochar, and some gases, with their proportions determined by the pyrolysis reaction conditions and technological pathways. Typically, low-temperature slow pyrolysis (reaction temperature below 500 °C) primarily yields biochar, while high-temperature fast pyrolysis (reaction temperature 700–1100 °C) mainly produces combustible gases. In the case of medium-temperature rapid pyrolysis (reaction temperature around 500–650 °C), conducted at very high heating rates and short vapor residence times (usually less than 1 s), the maximum liquid yield can reach up to 85 wt% (on a wet basis) or achieve 70 wt% (on a dry basis), with bio-oil being the predominant product. By employing the pyrolysis technique, valuable utilization of tobacco stem waste enriched with lignin can be achieved, resulting in the production of desired pyrolysis products such as transportation fuels, bio-oil, and ethanol. The present review focuses on catalytic pyrolysis, encompassing catalytic hydropyrolysis and catalytic co-pyrolysis, and meticulously compares the impact of catalyst structure on product distribution. Initially, we provide a comprehensive overview of the recent pyrolysis mechanism of lignin and tobacco waste. Subsequently, an in-depth analysis is presented, elucidating how to effectively design the catalyst structure to facilitate the efficient conversion of lignin through pyrolysis. Lastly, we delve into other innovative pyrolysis methods, including microwave-assisted and solar-assisted pyrolysis.
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(This article belongs to the Special Issue Catalysis for Production of Sustainable Fuels and Chemicals)
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Open AccessArticle
Design, Construction, and Characterization of a Solar Photovoltaic Hybrid Heat Exchanger Prototype
by
Sandro Guadalupe Perez Grajales, Angel Horacio Hernández, David Juárez-Romero, Guadalupe Lopez Lopez and Gustavo Urquiza-Beltran
Processes 2024, 12(3), 588; https://doi.org/10.3390/pr12030588 - 14 Mar 2024
Abstract
In this experimental work, a prototype of a hybrid solar–thermal–photovoltaic (HE-PV/T) heat exchanger has been designed, built, and characterized, with rectangular geometry and 12 fins inside, to obtain better heat flow and higher performance in order to achieve a better heat transfer coefficient,
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In this experimental work, a prototype of a hybrid solar–thermal–photovoltaic (HE-PV/T) heat exchanger has been designed, built, and characterized, with rectangular geometry and 12 fins inside, to obtain better heat flow and higher performance in order to achieve a better heat transfer coefficient, reducing and optimizing the working area. The heat exchanger contains 12 photovoltaic cells connected in series, with an angle of inclination of approximately 18° towards the south and a surface area of 0.22 m2, smaller than those available on the market, which individually capture 147.05 W/m2 as a photovoltaic panel and 240 W/m2 as a solar collector. Mathematical models found in the literature from previous work were used for the electrical and thermal evaluations. The temperature of the PV cells was reduced to 13.2 °C and the thermal level of the water was raised to a temperature above 70 °C, with a photovoltaic–thermal coupling power of 307.11 W and a heat transfer coefficient of 5790 W/m2 °C. The efficiencies obtained were as follows: thermal up to 0.78 and electrical up to 0.095. The novelty of these results was achieved in a reduced space of 40% less than those reported and available on the market.
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(This article belongs to the Special Issue Research and Development in Heat and Mass Transfer and Refrigeration Systems)
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Open AccessArticle
A Novel Ensemble Machine Learning Model for Oil Production Prediction with Two-Stage Data Preprocessing
by
Zhe Fan, Xiusen Liu, Zuoqian Wang, Pengcheng Liu and Yanwei Wang
Processes 2024, 12(3), 587; https://doi.org/10.3390/pr12030587 - 14 Mar 2024
Abstract
Petroleum production forecasting involves the anticipation of fluid production from wells based on historical data. Compared to traditional empirical, statistical, or reservoir simulation-based models, machine learning techniques leverage inherent relationships among historical dynamic data to predict future production. These methods are characterized by
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Petroleum production forecasting involves the anticipation of fluid production from wells based on historical data. Compared to traditional empirical, statistical, or reservoir simulation-based models, machine learning techniques leverage inherent relationships among historical dynamic data to predict future production. These methods are characterized by readily available parameters, fast computational speeds, high precision, and time–cost advantages, making them widely applicable in oilfield production. In this study, time series forecast models utilizing robust and efficient machine learning techniques are formulated for the prediction of production. We have fused the two-stage data preprocessing methods and the attention mechanism into the temporal convolutional network-gated recurrent unit (TCN-GRU) model. Firstly, the random forest (RF) algorithm is employed to extract key dynamic production features that influence output, serving to reduce data dimensionality and mitigate overfitting. Next, the mode decomposition algorithm, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), is introduced. It employs a decomposition–reconstruction approach to segment production data into high-frequency noise components, low-frequency regular components and trend components. These segments are then individually subjected to prediction tasks, facilitating the model’s ability to capture more accurate intrinsic relationships among the data. Finally, the TCN-GRU-MA model, which integrates a multi-head attention (MA) mechanism, is utilized for production forecasting. In this model, the TCN module is employed to capture temporal data features, while the attention mechanism assigns varying weights to highlight the most critical influencing factors. The experimental results indicate that the proposed model achieves outstanding predictive performance. Compared to the best-performing comparative model, it exhibits a reduction in RMSE by 3%, MAE by 1.6%, MAPE by 12.7%, and an increase in R2 by 2.6% in Case 1. Similarly, in Case 2, there is a 7.7% decrease in RMSE, 7.7% in MAE, 11.6% in MAPE, and a 4.7% improvement in R2.
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(This article belongs to the Special Issue Artificial Intelligent Techniques in the Optimal Operation of Oil and Gas Production Systems)
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