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Volume 12
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Processes, Volume 12, Issue 12 (December 2024) – 334 articles

Cover Story (view full-size image): This study presents the degradation competition and pathways of the electrochemical co-degradation of two emerging environmental contaminants, polar acetaminophen (AP) and non-polar bisphenol A (BPA), on a boron-doped diamond (BDD) electrode in aqueous solutions. Both compounds mainly rely on hydroxyl radicals to trigger the indirect oxidation for their electrochemical degradation, although AP also undergoes direct oxidation during electrolysis. BPA exhibits a better performance in mono-degradation than AP, while the opposite tendency is observed for their co-degradation. Their degradation pathways appear to be partially similar. A model is proposed to simulate the formation and degradation rate constants of benzoquinone (an intermediate). View this paper
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13 pages, 2133 KiB  
Article
A Series Arc Fault Diagnosis Method Based on an Extreme Learning Machine Model
by Lichun Qi, Takahiro Kawaguchi and Seiji Hashimoto
Processes 2024, 12(12), 2947; https://doi.org/10.3390/pr12122947 - 23 Dec 2024
Viewed by 469
Abstract
In this study, we address the critical issue of accurately detecting series AC arc faults, which are often challenging to identify due to their small fault currents and can lead to devastating electrical fires. We propose an intelligent diagnosis method based on the [...] Read more.
In this study, we address the critical issue of accurately detecting series AC arc faults, which are often challenging to identify due to their small fault currents and can lead to devastating electrical fires. We propose an intelligent diagnosis method based on the extreme learning machine (ELM) model to enhance detection accuracy and real-time monitoring capabilities. Our approach involves collecting high-frequency current signals from 23 types of loads using a self-developed AC series arc fault data acquisition device. We then extract 14 features from both the time and frequency domains as candidates for arc fault diagnosis, employing a random forest to select the most significantly changed features. Finally, we design an ELM classifier for series arc fault diagnosis, achieving an identification accuracy of 99.00% ± 0.26%. Compared to existing series arc fault diagnosis methods, our ELM-based method demonstrates superior recognition performance. This study contributes to the field by providing a more accurate and efficient diagnostic tool for series AC arc faults, with broad implications for electrical safety and fire prevention. Full article
(This article belongs to the Special Issue Research on Intelligent Fault Diagnosis Based on Neural Network)
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18 pages, 3603 KiB  
Article
Prediction of a Hydrogen Vapor Cloud Explosion with a Barrier Wall Using Various Machine Learning Methods
by Hyunseok Min and Hyungseok Kang
Processes 2024, 12(12), 2946; https://doi.org/10.3390/pr12122946 - 23 Dec 2024
Viewed by 499
Abstract
Hydrogen is considered the next energy to replace fossil fuels, but it must be handled with care given that it is a flammable gas. A barrier wall is an effective way to mitigate the effect of an explosion, and to build a safe [...] Read more.
Hydrogen is considered the next energy to replace fossil fuels, but it must be handled with care given that it is a flammable gas. A barrier wall is an effective way to mitigate the effect of an explosion, and to build a safe barrier wall, research on hydrogen explosions is necessary. Experiments and CFD (computational fluid dynamics) are two commonly used methods, but both are costly to use under any condition. Machine learning can be used to enhance the data from experiments and CFD as the trained model can predict explosion pressure levels very rapidly under various conditions. We propose the prediction of a hydrogen VCE (vapor cloud explosion) with a barrier wall using various machine learning methods. This research uses CFD simulation data from KAERI (Korea Atomic Energy Research Institute) as training data. MLP (multi-layer perceptron), LSTM (long short-term memory), and the Transformer architectures are used to train the hydrogen VCE and are compared. In our research, MLP produces the best score among all learning processes, with an R2 value exceeding 0.97, outperforming both LSTM and Transformer in terms of accuracy and speed. The trained machine learning model can be used to build safe barrier walls in hydrogen refueling stations. Evaluating the safe distance from the barrier wall and evaluating the optimal position of the barrier wall are possible usages. Full article
(This article belongs to the Section Energy Systems)
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24 pages, 8748 KiB  
Review
Organic Electronics: Basic Fundamentals and Recent Applications Involving Carbazole-Based Compounds
by Matheus Costa Ximenes, Jorge Luiz Martins Ferreira, Ana Paula Nazar de Souza, Luiz Phelipe de Souza Tomaso, Gabriel Francisco Souza da Silva, Adriano dos Santos Marques, José Brant de Campos, Luiz Fernando Brum Malta and Jaqueline Dias Senra
Processes 2024, 12(12), 2945; https://doi.org/10.3390/pr12122945 - 23 Dec 2024
Viewed by 507
Abstract
Carbazoles and their derivatives are ubiquitous in organic electronics since these compounds combine relatively low cost, chemical and thermal stability, and good hole transport properties, along with a tunable electronic structure. Thus, the application of carbazole molecules in the development of optoelectronic and [...] Read more.
Carbazoles and their derivatives are ubiquitous in organic electronics since these compounds combine relatively low cost, chemical and thermal stability, and good hole transport properties, along with a tunable electronic structure. Thus, the application of carbazole molecules in the development of optoelectronic and photovoltaic devices, such as OLEDs and solar cells, has been explored with different patterns of functionalization (N-substitution, di- and polyfunctionalization) in the quest for increased efficiencies. In this review, we provide a brief overview of the basic aspects related to solar cells and OLEDs with a focus on the applications involving these versatile and promising building blocks. Full article
(This article belongs to the Special Issue Transport and Energy Conversion at the Nanoscale and Molecular Scale)
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12 pages, 560 KiB  
Article
Identification for Antioxidant Peptides in Porcine Liver and Heart Hydrolysates Using SWATH-MS Analysis
by Ignė Juknienė, Gintarė Zaborskienė and Jūratė Stankevičienė
Processes 2024, 12(12), 2944; https://doi.org/10.3390/pr12122944 - 23 Dec 2024
Viewed by 565
Abstract
The use of animal by-products to produce bioactive peptides is a promising and sustainable approach in the food and nutrition industry. Meat by-products can be used as a key raw material for the production of high-value-added components, such as bioactive peptides, to ensure [...] Read more.
The use of animal by-products to produce bioactive peptides is a promising and sustainable approach in the food and nutrition industry. Meat by-products can be used as a key raw material for the production of high-value-added components, such as bioactive peptides, to ensure sustainability. Porcine livers and hearts classified as category three by-products were selected for the study, together with those intended for human consumption, in which no changes were observed after veterinary post-mortem examination. Hydrolysis was performed at three different times 3 h, 6 h, and 24 h, using pepsin and papain. The influence of different hydrolysis times and enzymes on the degree of hydrolysis (DH) value was determined. The highest value of DH was found in porcine hearts after 24 h following hydrolysis with pepsin enzymes (33.56 ± 0.31). The antiradical activity was assessed by measuring the absorbance of DPPH• and ABTS•+ in hydrolysates obtained from porcine meat by-products. Porcine livers hydrolysates treated with papain for 24 h showed the highest radical scavenging abilities ABTS•+ (97.2 ± 1.79%) and DPPH• (92.07 ± 2.23%). The identification and quantification of peptides from porcine livers and hearts were conducted using SWATH-MS technology. The most abundant peptides that showed a relationship with antioxidant capacity were WGKVNVDEVGGEALGRL, WGKVNVDEVGGEAL, and GLWGKVNVDEVGGEALGRL from beta hemoglobin. Full article
(This article belongs to the Topic Advances in Sustainable Materials and Products)
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21 pages, 14267 KiB  
Article
Optimisation of Heat Exchanger Performance Using Modified Gyroid-Based TPMS Structures
by Martin Beer and Radim Rybár
Processes 2024, 12(12), 2943; https://doi.org/10.3390/pr12122943 - 23 Dec 2024
Viewed by 648
Abstract
Triply periodic minimal surfaces (TPMS) represent an innovative approach to the design of heat exchangers, enabling the optimisation of thermal and hydraulic performance. This study presents a comparative analysis of three geometric TPMS configurations: sheet gyroid, skeletal gyroid, and the newly proposed combined [...] Read more.
Triply periodic minimal surfaces (TPMS) represent an innovative approach to the design of heat exchangers, enabling the optimisation of thermal and hydraulic performance. This study presents a comparative analysis of three geometric TPMS configurations: sheet gyroid, skeletal gyroid, and the newly proposed combined gyroid geometry. Using numerical analysis based on simulations of fluid flow and heat transfer, key parameters such as the heat transfer coefficient, Nusselt number, friction factor, Chilton–Colburn j-factor, and pressure drop were evaluated. The results demonstrated that the combined gyroid geometry achieves the highest heat transfer efficiency, exhibiting significant improvements in the Nusselt number and heat transfer coefficient across the entire flow range. Simultaneously, it maintains low pressure losses, making it well suited for applications demanding high thermal performance with minimal energy losses. This study highlights the potential of TPMS geometries for optimising heat exchanger design and opens new paths for their implementation in industrial systems. Full article
(This article belongs to the Special Issue Fluid Dynamics and Processes of Heat Transfer Enhancement)
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15 pages, 8029 KiB  
Article
Study on Length–Diameter Ratio of Axial–Radial Flux Hybrid Excitation Machine
by Mingyu Guo, Jiakuan Xia, Qimin Wu, Wenhao Gao and Hongbo Qiu
Processes 2024, 12(12), 2942; https://doi.org/10.3390/pr12122942 - 23 Dec 2024
Viewed by 339
Abstract
To improve the flux regulation range of the Axial–Radial Flux Hybrid Excitation Machine (ARFHEM) and the utilization rate of permanent magnets (PMs), the effects of different length–diameter ratios (LDRs) on the ARFHEM performance are studied. Firstly, the principle of the flux regulation of [...] Read more.
To improve the flux regulation range of the Axial–Radial Flux Hybrid Excitation Machine (ARFHEM) and the utilization rate of permanent magnets (PMs), the effects of different length–diameter ratios (LDRs) on the ARFHEM performance are studied. Firstly, the principle of the flux regulation of the ARFHEM is introduced by means of the structure and equivalent magnetic circuit method. Then, based on the principle of the bypass effect, the analytical formulas of LDRs, the number of pole-pairs, and the flux regulation ability are derived, and then the restrictive relationship between the air-gap magnetic field, LDR, and the number of pole-pairs is revealed. On this basis, the influence of an electric LDR on motor performance is studied. By comparing and analyzing the air-gap magnetic density and no-load back electromotive force (EMF) of motors with different LDRs, the variation in the magnetic flux regulation ability of motors with different LDRs is obtained and its influence mechanism is revealed. In addition, the torque regulation ability and loss of motors with different LDRs are compared and analyzed, and the influence mechanism of the LDR on torque and loss is determined. Finally, the above analysis is verified by experiments. Full article
(This article belongs to the Section Manufacturing Processes and Systems)
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20 pages, 1839 KiB  
Article
Towards Wine Waste Reduction: Up-Cycling Wine Pomace into Functional Fruit Bars
by Maja Benković, Filip Cigić, Davor Valinger, Tea Sokač Cvetnić, Ana Jurinjak Tušek, Tamara Jurina, Jasenka Gajdoš Kljusurić and Ivana Radojčić Redovniković
Processes 2024, 12(12), 2941; https://doi.org/10.3390/pr12122941 - 23 Dec 2024
Viewed by 442
Abstract
Due to the beneficial composition of wine pomace, it has found several applications in the food industry, mostly in the form of flour or extracts. This study suggests the use of grape skin separated from the pomace as a functional ingredient for fruit [...] Read more.
Due to the beneficial composition of wine pomace, it has found several applications in the food industry, mostly in the form of flour or extracts. This study suggests the use of grape skin separated from the pomace as a functional ingredient for fruit bars based on the hypothesis that grape skin can contribute to fruit bar antioxidant potential. Fruit bars were produced with dried figs/dates, grape skin, and cocoa/hazelnut mix in different proportions (48–70%, 30–50%, and 0–2%, respectively). The addition of grape skin proved beneficial for the total polyphenolic content (TPC) and antioxidant capacity. Furthermore, consumers appeared to like the newly developed functional product, and the addition of up to 30% grape skin did not have an adverse effect of sensory properties. The bars were graded A based on the NutriScore value and were microbiologically compliant to food safety regulations. These results demonstrate the possibility of grape skin use in the development of a functional fruit bar product, which can be beneficial not only from chemical and sensory point of view, but also economically feasible and environmentally friendly. Full article
(This article belongs to the Special Issue Feature Papers in the "Food Process Engineering" Section)
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19 pages, 6643 KiB  
Article
High-Precision Recognition Algorithm for Equipment Defects Based on Mask R-CNN Algorithm Framework in Power System
by Mingyong Xin, Changbao Xu, Jipu Gao, Yu Wang and Bo Wang
Processes 2024, 12(12), 2940; https://doi.org/10.3390/pr12122940 - 23 Dec 2024
Viewed by 412
Abstract
In current engineering applications, target detection based on power vision neural networks has problems with low accuracy and difficult defect recognition. Thus, this paper proposes a high-precision substation equipment defect recognition algorithm based on the Mask R-CNN algorithm framework to achieve high-precision substation [...] Read more.
In current engineering applications, target detection based on power vision neural networks has problems with low accuracy and difficult defect recognition. Thus, this paper proposes a high-precision substation equipment defect recognition algorithm based on the Mask R-CNN algorithm framework to achieve high-precision substation equipment defect monitoring. The effectiveness of the Mask R-CNN algorithm is compared and analyzed in substation equipment defect recognition and the applicability of the Mask R-CNN algorithm in edge computing. According to different types of substation equipment defect characteristics, substation equipment defect recognition guidelines were developed. The guideline helps to calibrate the existing training set and build defect recognition models for substation equipment based on different algorithms. In the end, the system based on a power edge vision neural network was built. The feasibility and accuracy of the algorithm was verified by model training and actual target detection results. Full article
(This article belongs to the Section Process Control and Monitoring)
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17 pages, 2618 KiB  
Article
Performance Evaluation of Modified Biochar as a Polycyclic Aromatic Hydrocarbon Adsorbent and Microbial-Immobilized Carrier
by Shuying Geng, Shushuai Mao, Guangming Xu, Aizhong Ding, Feiyong Chen, Junfeng Dou and Fuqiang Fan
Processes 2024, 12(12), 2939; https://doi.org/10.3390/pr12122939 - 23 Dec 2024
Viewed by 483
Abstract
Herein, biochars derived from corn stalks, rice husks, and bamboo powder were modified by nitric acid oxidation and sodium hydroxide alkali activation to identify efficient and cost-effective polycyclic aromatic hydrocarbon-adsorbent and microbial-immobilized carriers. The surface characterization and adsorption investigation results suggested that acid/alkali [...] Read more.
Herein, biochars derived from corn stalks, rice husks, and bamboo powder were modified by nitric acid oxidation and sodium hydroxide alkali activation to identify efficient and cost-effective polycyclic aromatic hydrocarbon-adsorbent and microbial-immobilized carriers. The surface characterization and adsorption investigation results suggested that acid/alkali modification promoted the phenanthrene removal ability in an aqueous solution of biochars via facilitating π–π/n–π electron donor–acceptor interactions, electrostatic interactions, hydrogen bonds, and hydrophobic interactions. Subsequently, the degrading bacteria Rhodococcus sp. DG1 was successfully immobilized on the rice husk-derived biochar with nitric acid oxidation (RBO), which exhibited the maximum phenanthrene adsorption efficiency (3818.99 µg·g−1), abundant surface functional groups, and a larger specific surface area (182.6 m2·g−1) and pore volume (0.141 m3·g−1). Degradation studies revealed that the microorganisms immobilized on RBO by the adsorption method yielded a significant phenanthrene removal rate of 80.15% after 30 days, which was 38.78% higher than that of the control. Conversely, the polymer gel network-based microenvironment in the microorganism-immobilized RBO by the combined adsorption–embedding method restricted the migration and diffusion of nutrients and pollutants in the reaction system. This study thus introduces an innovative modified biochar-based microbial immobilization technology characterized by a simple design, convenient operation, and high adsorption efficiency, offering valuable insights into material selection for PAH contamination bioremediation. Full article
(This article belongs to the Special Issue State-of-the-Art Wastewater Treatment Techniques)
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23 pages, 4588 KiB  
Review
Plant-Derived Extracellular Vesicles: Natural Nanocarriers for Biotechnological Drugs
by Eleonora Calzoni, Agnese Bertoldi, Gaia Cusumano, Sandra Buratta, Lorena Urbanelli and Carla Emiliani
Processes 2024, 12(12), 2938; https://doi.org/10.3390/pr12122938 - 23 Dec 2024
Viewed by 722
Abstract
Plant-derived extracellular vesicles (PDEVs) are lipid bilayer nanoparticles, naturally produced by plant cells, with sizes ranging from 50 to 500 nm. Recent studies have highlighted their great potential in the biotechnological and medical fields, due to their natural origin, high biocompatibility and intrinsic [...] Read more.
Plant-derived extracellular vesicles (PDEVs) are lipid bilayer nanoparticles, naturally produced by plant cells, with sizes ranging from 50 to 500 nm. Recent studies have highlighted their great potential in the biotechnological and medical fields, due to their natural origin, high biocompatibility and intrinsic therapeutic properties. PDEVs contain a complex biological cargo of proteins, lipids, nucleic acids and secondary metabolites, including antioxidants and anti-inflammatory molecules, making them ideal for biomedical applications such as drug delivery. These vesicles play a key role in intercellular communication and gene regulation, proving to be particularly promising in personalized medicine. Recent studies have highlighted their ability to improve drug stability and bioavailability, optimizing targeted release and minimizing side effects. Despite some challenges, such as compositional variability and the need for standardized protocols, PDEVs are at the gunsight of innovative research aimed at improving their loading capacity and therapeutic specificity. This review aims to provide a comprehensive overview of PDEVs, exploring their structure, isolation methods, functional characteristics, and applications, highlighting their advantages over synthetic nanoparticles and animal-derived extracellular vesicles, leading to an innovative and sustainable solution for the development of new therapeutic approaches. Full article
(This article belongs to the Special Issue Synthesis and Extraction Processes of Biotechnological Drugs of Plant Origin)
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72 pages, 7015 KiB  
Article
Modeling and Predicting Self-Organization in Dynamic Systems out of Thermodynamic Equilibrium: Part 1: Attractor, Mechanism and Power Law Scaling
by Matthew Brouillet and Georgi Yordanov Georgiev
Processes 2024, 12(12), 2937; https://doi.org/10.3390/pr12122937 - 23 Dec 2024
Viewed by 919
Abstract
Self-organization in complex systems is a process associated with reduced internal entropy and the emergence of structures that may enable the system to function more effectively and robustly in its environment and in a more competitive way with other states of the system [...] Read more.
Self-organization in complex systems is a process associated with reduced internal entropy and the emergence of structures that may enable the system to function more effectively and robustly in its environment and in a more competitive way with other states of the system or with other systems. This phenomenon typically occurs in the presence of energy gradients, facilitating energy transfer and entropy production. As a dynamic process, self-organization is best studied using dynamic measures and principles. The principles of minimizing unit action, entropy, and information while maximizing their total values are proposed as some of the dynamic variational principles guiding self-organization. The least action principle (LAP) is the proposed driver for self-organization; however, it cannot operate in isolation; it requires the mechanism of feedback loops with the rest of the system’s characteristics to drive the process. Average action efficiency (AAE) is introduced as a potential quantitative measure of self-organization, reflecting the system’s efficiency as the ratio of events to total action per unit of time. Positive feedback loops link AAE to other system characteristics, potentially explaining power–law relationships, quantity–AAE transitions, and exponential growth patterns observed in complex systems. To explore this framework, we apply it to agent-based simulations of ants navigating between two locations on a 2D grid. The principles align with observed self-organization dynamics, and the results and comparisons with real-world data appear to support the model. By analyzing AAE, this study seeks to address fundamental questions about the nature of self-organization and system organization, such as “Why and how do complex systems self-organize? What is organization and how organized is a system?”. We present AAE for the discussed simulation and whenever no external forces act on the system. Given so many specific cases in nature, the method will need to be adapted to reflect their specific interactions. These findings suggest that the proposed models offer a useful perspective for understanding and potentially improving the design of complex systems. Full article
(This article belongs to the Special Issue Non-equilibrium Processes and Structure Formation)
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17 pages, 2714 KiB  
Article
From Microalgae to Biofuels: Investigating Valorization Pathways Towards Biorefinery Integration
by Panagiotis Fotios Chatzimaliakas, Ermis Koutsaftis-Fragkos, Sofia Mai, Dimitris Malamis and Elli Maria Barampouti
Processes 2024, 12(12), 2936; https://doi.org/10.3390/pr12122936 - 22 Dec 2024
Viewed by 1025
Abstract
The rapid growth of the world population led to an exponential growth in industrial activity all around the world. Consequently, CO2 emissions have risen almost 400% since 1950 due to human activities. In this context, microalgae biomass has emerged as a renewable [...] Read more.
The rapid growth of the world population led to an exponential growth in industrial activity all around the world. Consequently, CO2 emissions have risen almost 400% since 1950 due to human activities. In this context, microalgae biomass has emerged as a renewable and sustainable feedstock for producing third-generation biofuels. This study explores the laboratory-scale production of bioethanol and biomethane from dried algal biomass. The first step was to evaluate and optimize the production of glucose from the biomass. Thus, three different techniques with three different solvents were tested to identify the most effective and efficient in terms of saccharification yield. With the assistance of an autoclave or a high-temperature water bath and 0.2 M NaOH as a solvent, yields of 79.16 ± 3.03% and 85.73 ± 3.23% were achieved which correspond to 9.24 and 9.80 g/L of glucose, respectively. Furthermore, the most efficient method from the pretreatment step was chosen to carry out a factorial design to produce bioethanol. The experiments showed that the loading of cellulase was of crucial importance to the optimization of the process. Optimized ethanolic fermentation yielded ethanol concentrations up to 4.40 ± 0.28 g/L (76.12 ± 4.90%) (0.3 Μ NaOH, 750 μL/gcellulose and 65 μL/gstarch), demonstrating the critical role of cellulase loading. Biomethane potential (BMP) assays on fermentation residues showed increased yields compared to untreated feedstock, with a maximum methane yield of 217.88 ± 10.40 mL/gVS. Combined energy production from bioethanol and biomethane was calculated at up to 1044.48 kWh/tn of algae feedstock, with biomethane contributing 75.26% to the total output. These findings highlight the potential of integrated algae-based biorefineries to provide scalable and sustainable biofuel solutions, aligning with circular economy principles. Full article
(This article belongs to the Special Issue Progress on Biomass Processing and Conversion)
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20 pages, 3764 KiB  
Article
Corrosion State Monitoring Based on Multi-Granularity Synergistic Learning of Acoustic Emission and Electrochemical Noise Signals
by Rui Wang, Guangbin Shan, Feng Qiu, Linqi Zhu, Kang Wang, Xianglong Meng, Ruiqin Li, Kai Song and Xu Chen
Processes 2024, 12(12), 2935; https://doi.org/10.3390/pr12122935 - 22 Dec 2024
Viewed by 443
Abstract
Corrosion monitoring is crucial for ensuring the structural integrity of equipment. Acoustic emission (AE) and electrochemical noise (EN) have been proven to be highly effective for the detection of corrosion. Due to the complementary nature of these two techniques, previous studies have demonstrated [...] Read more.
Corrosion monitoring is crucial for ensuring the structural integrity of equipment. Acoustic emission (AE) and electrochemical noise (EN) have been proven to be highly effective for the detection of corrosion. Due to the complementary nature of these two techniques, previous studies have demonstrated that combining both signals can facilitate research on corrosion monitoring. However, current machine learning models have not yet been able to effectively integrate these two different modal types of signals. Therefore, a new deep learning framework, CorroNet, is designed to synergistically integrate AE and EN signals at the algorithmic level for the first time. The CorroNet leverages multimodal learning, enhances accuracy, and automates the monitoring process. During training, paired AE-EN data and unpaired EN data are used, with AE signals serving as anchors to help the model better align EN signals with the same corrosion stage. A new feature alignment loss function and a probability distribution consistency loss function are designed to facilitate more effective feature learning to improve classification performance. Experimental results demonstrate that CorroNet achieves superior accuracy in corrosion stage classification compared to other state-of-the-art models, with an overall accuracy of 97.01%. Importantly, CorroNet requires only EN signals during the testing phase, making it suitable for stable and continuous monitoring applications. This framework offers a promising solution for real-time corrosion detection and structural health monitoring. Full article
(This article belongs to the Special Issue Advances in Process Systems Engineering—Selected Papers From China PSE Annual Meeting)
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14 pages, 7871 KiB  
Article
Failure and Permeability Characteristics of Coal Pillar in Closely Coal Seams Gob Under Multiple Mining
by Hui Qiao, Song Liu, Lei Dong, Pinkun Guo and Ruifeng Gao
Processes 2024, 12(12), 2934; https://doi.org/10.3390/pr12122934 - 22 Dec 2024
Viewed by 440
Abstract
Coal pillars are loaded and unloaded repeatedly when mining, which lead to fractures in the coal close, open, generate and expand. As a result, the permeability of coal is changed. The high permeability fractures in coal and rock between the upper gobs and [...] Read more.
Coal pillars are loaded and unloaded repeatedly when mining, which lead to fractures in the coal close, open, generate and expand. As a result, the permeability of coal is changed. The high permeability fractures in coal and rock between the upper gobs and the lower working faces are the main channels for fresh air entering the upper gob, which could induce spontaneous combustion of coal in gob. To identifying the air leakage channels, multiple mining of closely coal seams was numerically conducted with three working face layouts. The failure and permeability characteristic of coal pillar in closely coal seams gob under multiple mining were obtained and analyzed. When the working faces are mined, the vertical stress and horizontal stress of the upper coal pillar in gob load and unload synchronously in all three working face layouts. The laterally directed horizontal stress could unload to zero due to no confine on the lateral side of coal pillar. The stress in the middle of upper coal pillar loads continuously until the lower working face is mined. When the lower coal seam working face is mined, the coal and rock between the upper and lower coal seams damage in shear and tension. When the lower coal seam working face is staggered from the upper coal seam working face, the permeability of the coal and rock pillar increases more than 22000 times due to tension damage of the coal and rock pillar. As a result, the coal and rock pillar is the main channel for fresh air flowing into the upper gob. The high permeability coal pillar provides favorable conditions for spontaneous combustion of coal in gob. Full article
(This article belongs to the Special Issue Exploration, Exploitation and Utilization of Coal and Gas Resources, 2nd Edition)
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23 pages, 11798 KiB  
Article
Study on the Influencing Factors of CO2 Storage in Low Porosity-Low Permeability Heterogeneous Saline Aquifer
by Hongchang Hu, Dongdong Wang, Yujie Diao, Chunyuan Zhang and Ting Wang
Processes 2024, 12(12), 2933; https://doi.org/10.3390/pr12122933 - 22 Dec 2024
Viewed by 494
Abstract
The safety and long-term storage capacity of CO2 geological storage are necessary factors for project design and engineering development. Evaluating the influencing factors of CO2 storage and quantitatively analyzing the sensitivity of each parameter have an important guiding role in the [...] Read more.
The safety and long-term storage capacity of CO2 geological storage are necessary factors for project design and engineering development. Evaluating the influencing factors of CO2 storage and quantitatively analyzing the sensitivity of each parameter have an important guiding role in the design and development of storage projects. In this paper, the Liujiagou Formation in the northeast of the Ordos Basin is taken as an example. Based on the TOUGH/Petrasim simulation tool, the RZ2D geological storage model is established. Seven influencing factors, namely salinity, temperature, horizontal and vertical permeability ratio, pore geometry factor, residual gas saturation, liquid saturation and pore compression coefficient, were compared and analyzed to control the plume migration behavior, interlayer pressure accumulation and storage capacity of low porosity and low permeability heterogeneous reservoirs, and the sensitivity of each parameter to interlayer pressure and storage capacity was quantitatively analyzed. The simulation results show that the uncertain factors affect the safety of CO2 geological storage to a certain extent by affecting the speed of the residual storage and dissolution storage mechanism. High residual gas saturation and salinity will make CO2 mostly exist in the form of free state, which will adversely affect the safety and storage capacity of CO2 saline aquifer storage. High temperature and high vertical permeability ratio will lead to higher interlayer pressure accumulation, which is not conducive to the safety of the storage project but is beneficial to the storage capacity. Temperature, transverse and longitudinal permeability ratio and pore geometry factor control the propagation velocity of plume. The larger these factors are, the faster the plume velocity is. Higher liquid phase saturation is not better; higher liquid phase saturation leads to a large build-up of pressure in the reservoir and can have an adverse effect on the storage volume. The sensitivity analysis of all factors shows that the liquid saturation and temperature have the greatest influence on CO2 geological storage, and the pore compression coefficient has the least influence. The conclusions of this paper can provide a theoretical reference for the design and development of a CO2 saline aquifer storage project in a low porosity and low permeability reservoir area. Full article
(This article belongs to the Special Issue Comprehensive Evaluation and Utilization of Coal Measure Mineral Resources)
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14 pages, 6071 KiB  
Article
Assessment of the Productivity of Hydrogen and Nano-Carbon Through Liquid-Plasma Cracking of Waste Organic Solvent Using PrxNiyFeO3 Perovskite Catalysts
by Sang-Chul Jung, Chan-Seo You and Kyong-Hwan Chung
Processes 2024, 12(12), 2932; https://doi.org/10.3390/pr12122932 - 21 Dec 2024
Viewed by 523
Abstract
In this study, a process for the simultaneous production of hydrogen and carbon from waste organic solvents using liquid plasma was investigated. Ferrite-based perovskites were introduced as catalysts to evaluate the productivity of hydrogen and carbon. A novel ferrite-based perovskite composite, Prx [...] Read more.
In this study, a process for the simultaneous production of hydrogen and carbon from waste organic solvents using liquid plasma was investigated. Ferrite-based perovskites were introduced as catalysts to evaluate the productivity of hydrogen and carbon. A novel ferrite-based perovskite composite, PrxNiyFeO3, was synthesized. The waste organic solvent was converted into liquid hydrocarbons, primarily composed of toluene, through a simple distillation process. Hydrogen (>98%) and nanocarbon were produced through the liquid plasma reaction of the purified organic solvent. The ferrite-based perovskites demonstrated excellent absorption capacities for visible light. Among them, PrxNiyFeO3 exhibited the highest absorption capacities for both UV and visible light and had the smallest band gap energy (approximately 1.72 eV). In the liquid plasma decomposition of organic solvents, the ferrite-based perovskites enhanced the hydrogen production rate and carbon yield. The highest hydrogen production rate and carbon yield were achieved with the newly synthesized PrxNiyFeO3 perovskite composite. PrxNiyFeO3, which has the narrowest band gap compared to other catalysts, is highly sensitive to the strong visible light emitted from plasma and exhibits excellent catalytic activity. This catalyst also demonstrated remarkable reaction activity sustainability and the potential for recycling through regeneration. Full article
(This article belongs to the Special Issue Metal Oxides and Their Composites for Photocatalytic Degradation)
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13 pages, 5875 KiB  
Article
Propagation Law of Hydraulic Fractures in Continental Shale Reservoirs with Sandstone–Shale Interaction
by Yuan Gao, Qiuping Qin, Xiaobing Bian, Xiaoyang Wang, Wenjun Xu and Yanxin Zhao
Processes 2024, 12(12), 2931; https://doi.org/10.3390/pr12122931 - 21 Dec 2024
Viewed by 503
Abstract
There are significant lithological and stress differences between continental shale layers, posing challenges for hydraulic fractures (HFs) to propagate through the formations, leading to weak fracture effects. To address this, this article adopts the finite element and cohesive force element methods to formulate [...] Read more.
There are significant lithological and stress differences between continental shale layers, posing challenges for hydraulic fractures (HFs) to propagate through the formations, leading to weak fracture effects. To address this, this article adopts the finite element and cohesive force element methods to formulate a three-dimensional numerical model for hydraulic fracture (HF) propagation through layers, considering interlayer lithology and stress variations. The accuracy of the model was verified by physical experiments, and the one-factor analysis method was used to creatively reveal the complex mechanism of the effect of geological and engineering variables on the diffusion of HFs in continental shale reservoirs. The results show that high interlayer stress difference, high interlayer tensile strength difference, low interlayer Young’s modulus difference and large interlayer thickness are not conducive to the penetration of HFs, but increasing the injection rate and the viscosity of fracturing fluid can effectively improve the penetration of HFs. The influence ranking of each factor was determined using the grey relational degree analysis method: interlayer stress difference > interlayer Young’s modulus difference > interlayer tensile strength difference > interlayer thickness > injection rate > fracturing fluid viscosity. Full article
(This article belongs to the Special Issue Advanced Reservoir Simulation and Modelling, Thermal and Enhanced Oil Recovery Processes)
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15 pages, 7864 KiB  
Article
An Improved Prediction Method for Failure Probability of Natural Gas Pipeline Based on Multi-Layer Bayesian Network
by Yueyue Weng, Xu Sun, Yufeng Yang, Mengmeng Tao, Xiaoben Liu, Hong Zhang and Qiang Zhang
Processes 2024, 12(12), 2930; https://doi.org/10.3390/pr12122930 - 21 Dec 2024
Viewed by 655
Abstract
The failure probability of a pipeline is a quantification of the likelihood of an accident occurring in the pipeline, which is an indispensable part of the pipeline risk assessment process. To solve the problems of strong subjectivity, low feasibility, and low accuracy in [...] Read more.
The failure probability of a pipeline is a quantification of the likelihood of an accident occurring in the pipeline, which is an indispensable part of the pipeline risk assessment process. To solve the problems of strong subjectivity, low feasibility, and low accuracy in the existing pipeline failure probability calculation methods, a three-layer Bayesian network topology model of “pipeline failure–failure cause–influencing factor” is proposed, with the pipeline failure as the subnode, the type of pipeline failure as the intermediate node, and the factors affecting the pipeline failure as the parent node of the network. Based on data fitting and fuzzy theory analysis methods, the functional relationship between the impact factor and the failure frequency of various pipelines is quantified. Using the mean value theorems for definite integrals and the analytic hierarchy process, the conditional probability of the directed edge in the network is calculated. The proposed function relationship provides a method to calculate the prior probability according to the parameters of the pipeline and its surroundings and a new idea to train the network model even without sufficient data. Full article
(This article belongs to the Section Energy Systems)
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15 pages, 4298 KiB  
Article
Slurry Transportation Characteristics of Potash Mine Cemented Paste Backfills via Loop Test Processing
by Rongzhen Jin, Xue Wang, Siqi Zhang, Huimin Huo, Jiajie Li and Wen Ni
Processes 2024, 12(12), 2929; https://doi.org/10.3390/pr12122929 - 21 Dec 2024
Viewed by 566
Abstract
This study evaluated the properties and processing of cemented paste backfills (CPBs) for potash mining through loop tests. The CPBs were made with steel slags as the binder, granulated potash tailings as the aggregate, and waste brine water as the liquid phase. The [...] Read more.
This study evaluated the properties and processing of cemented paste backfills (CPBs) for potash mining through loop tests. The CPBs were made with steel slags as the binder, granulated potash tailings as the aggregate, and waste brine water as the liquid phase. The effects of solid concentration and steel slag dosage on the transport and mechanical properties of CPBs were assessed. The loop test demonstrated that all CPB slurries performed well, exhibiting strong long-distance pipeline transport capabilities. The 28-day compressive strength of the backfills exceeded 1 MPa, meeting the design requirements for backfill strength. The key rheological parameters, including yield stress (τ0) and viscosity coefficient (η), were comprehensively and theoretically analyzed based on the variations in pressure loss per unit distance of the filling slurry measured during the loop test. The empirical formulas for CPB pressure loss, accounting for varying flow rates and pipeline diameters, were derived with an error margin under 2%. The response surface analysis showed that the affecting extents of factors on pressure loss in CPB slurry were ranked as follows: solid concentration > cementing agent content > flow rate. This study offered valuable guidance for the processing of potash mine backfill operations. Full article
(This article belongs to the Special Issue Advanced Materials for Sustainable and Green Sample Preparation)
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22 pages, 12725 KiB  
Article
Application of the Hydrocarbon Generation Potential Method in Resource Potential Evaluation: A Case Study of the Qiongzhusi Formation in the Sichuan Basin, China
by Hanxuan Yang, Chao Geng, Majia Zheng, Zhiwei Zheng, Hui Long, Zijing Chang, Jieke Li, Hong Pang and Jian Yang
Processes 2024, 12(12), 2928; https://doi.org/10.3390/pr12122928 - 21 Dec 2024
Viewed by 595
Abstract
Global recoverable shale gas reserves are estimated to be 214.5 × 1012 m3. Estimation methods for shale gas resources, such as volumetric, analog, and genetic approaches, have been widely used in previous studies. However, these approaches have notable limitations, including [...] Read more.
Global recoverable shale gas reserves are estimated to be 214.5 × 1012 m3. Estimation methods for shale gas resources, such as volumetric, analog, and genetic approaches, have been widely used in previous studies. However, these approaches have notable limitations, including the substantial effect of rock heterogeneity, difficulties in determining the similarity of analog accumulations, and unsuitability for evaluating high-mature–overmature source rocks. In the Qiongzhusi Formation (Є1q) of the Sichuan Basin, China, extensive development of high-mature–overmature shales has led to significant advancements in conventional and unconventional shale gas exploration. This progress highlights the need for the development of an integrated evaluation system for conventional and unconventional resources. Hence, this study uses the whole petroleum system theory and an improved hydrocarbon generation potential method to analyze the distribution patterns of hydrocarbon generation, retention, and expulsion during various stages of oil and gas accumulation in the Є1q. In addition, it assesses the resource potential of conventional and shale oil and gas. Hydrocarbon generation and expulsion centers are favorable exploration targets for conventional oil and gas, primarily located in the central and northern regions of the Mianyang—Changning rift trough, with an estimated resource potential of 6560 × 1012 m3. Hydrocarbon retention centers represent promising targets for shale oil and gas exploration, concentrated in the central Mianyang—Changning rift trough, with a resource potential of 287 × 1012 m3. This study provides strategic guidance for future oil and gas exploration in the Є1q and offers a methodological reference for integrated resource assessments of conventional and unconventional oil and gas systems of high-mature–overmature source rocks in similar basins worldwide. Full article
(This article belongs to the Special Issue Model of Unconventional Oil and Gas Exploration)
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33 pages, 9678 KiB  
Article
A Novel High Vacuum MSF/MED Hybrid Desalination System for Simultaneous Production of Water, Cooling and Electrical Power, Using Two Barometric Ejector Condensers
by Francisco J. Caballero-Talamantes, Nicolás Velázquez-Limón, Jesús Armando Aguilar-Jiménez, Cristian A. Casares-De la Torre, Ricardo López-Zavala, Juan Ríos-Arriola and Saúl Islas-Pereda
Processes 2024, 12(12), 2927; https://doi.org/10.3390/pr12122927 - 20 Dec 2024
Viewed by 769
Abstract
This work presents a novel trigeneration system for the simultaneous production of desalinated water, electrical energy, and cooling, addressing the challenges of water scarcity and climate change through an integrated and efficient approach. The proposed system combines an 8-stage Multi Stage Flash Distillation [...] Read more.
This work presents a novel trigeneration system for the simultaneous production of desalinated water, electrical energy, and cooling, addressing the challenges of water scarcity and climate change through an integrated and efficient approach. The proposed system combines an 8-stage Multi Stage Flash Distillation (MSF) process with a 6-effect Multiple Effect Distillation (MED) process, complemented by an expander-generator to optimize steam utilization. Cooling production is achieved through a dual ejectocondensation mechanism, which enhances energy recovery and expands operational flexibility. The system’s performance was analyzed using Aspen Plus simulations, demonstrating technical feasibility across a broad operating range: 28.3 to 0.8 kPa and 68 to 4 °C. In cogeneration mode, the system achieves a Performance Ratio (PR) of 12.06 and a Recovery Ratio (RR) of 54%, producing 67,219.2 L/day of desalinated water and reducing electrical consumption by 12.03%. In trigeneration mode, it achieves a PR of 17.81 and an RR of 80%, with a cooling capacity of 1225 kW, generating 99,273.6 L/day of desalinated water while reducing electrical consumption by 3.69%. These results underscore the system’s capability to significantly enhance the efficiency and capacity of thermal desalination technologies, offering a sustainable and high-performing solution for coastal communities worldwide. Full article
(This article belongs to the Special Issue Advanced Heat Transfer Technologies for the Design, Operation and Optimization of Steam Power Systems)
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19 pages, 6030 KiB  
Article
Modeling and Experimental Verification of In-House Built Portable Ultrafiltration (PUF) System to Maintain Water Quality
by Azman Ariffin, Ahmad Khairi Abdul Wahab and Mohd Azlan Hussain
Processes 2024, 12(12), 2926; https://doi.org/10.3390/pr12122926 - 20 Dec 2024
Viewed by 518
Abstract
At present, over 2.6 billion people live without access to a continuous water supply, and nearly 900 million people do not obtain drinking water from reliable sources. To solve these problems, one of this study’s goals is to come up with a water-supply [...] Read more.
At present, over 2.6 billion people live without access to a continuous water supply, and nearly 900 million people do not obtain drinking water from reliable sources. To solve these problems, one of this study’s goals is to come up with a water-supply system that uses a simple, inexpensive, portable ultrafiltration (PUF) unit. To determine the effectiveness of the portable system, water-quality analysis has been carried out to determine if the system produces filtered water from various sources of water, reaching drinking-water standards. A simple model of the system using Darcy’s Law was also obtained to predict permeate flux and transmembrane pressure (TMP). Initially, simulation was performed using nominal values taken from the literature for four (4) parameters, i.e., membrane hydraulic resistance, initial rapid fouling constant, mass transfer coefficient, and foulant bulk concentration. By minimizing the error between the model with these nominal values and experimental values, an improved model with updated parameters was obtained using the Evolutionary Programming (EP) approach. With the updated model, the average error between the model and the experiment was reduced from 32% to 9%. This was further validated with new data taken from the experiment. This improved model with the updated parameter was then used to predict the TMP and compared with the experimental value. Contrasting the optimized model with the existing model indicates that the optimized model predicts membrane performance better, leading to a competent and reliable model for the purification of water using a PUF system built in-house. Full article
(This article belongs to the Special Issue Industrial Applications of Modeling Tools)
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18 pages, 6838 KiB  
Article
A Parallel Prognostic Method Integrating Uncertainty Quantification for Probabilistic Remaining Useful Life Prediction of Aero-Engine
by Rongqiu Wang, Ya Zhang, Chen Hu, Zhengquan Yang, Huchang Li, Fuqi Liu, Linling Li and Junyu Guo
Processes 2024, 12(12), 2925; https://doi.org/10.3390/pr12122925 - 20 Dec 2024
Viewed by 517
Abstract
Remaining useful life (RUL) prediction plays a fundamental role in the prognostics and health management of mechanical equipment. Consequently, extensive research has been devoted to estimating the RUL of mechanical equipment. Owing to the development of modern advanced sensor technologies, a significant amount [...] Read more.
Remaining useful life (RUL) prediction plays a fundamental role in the prognostics and health management of mechanical equipment. Consequently, extensive research has been devoted to estimating the RUL of mechanical equipment. Owing to the development of modern advanced sensor technologies, a significant amount of monitoring data is recorded. Traditional methods, such as machine-learning-based methods and statistical-data-driven methods, are ineffective in matching when faced with big data thus leading to poor predictions. As a result, deep-learning-based methods are extensively utilized due to their efficient capability to excavate deep features and realize accurate predictions. However, most deep-learning-based methods only provide point estimations and ignore the prediction uncertainty. To address this limitation, this paper proposes a parallel prognostic network to sufficiently excavate the degradation features from multiple dimensions for more accurate RUL prediction. In addition, accurate calculation of model evidence is extremely difficult when dealing with big data so the Monte Carlo dropout is employed to infer the model weights under low computational cost and high scalability to obtain a probabilistic RUL prediction. Finally, the C-MAPSS aero-engine dataset is employed to validate the proposed dual-channel framework. The experimental results illustrate its superior prediction performance compared to other deep learning methods and the ability to quantify prediction uncertainty. 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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19 pages, 5705 KiB  
Article
Study on Dense Phase Separation of Associated Gas with High Carbon Dioxide Content
by Yuxiao Jing, Ming Zhang, Qihang Wang, Jianlu Zhu, Naiya Xie and Yuxing Li
Processes 2024, 12(12), 2924; https://doi.org/10.3390/pr12122924 - 20 Dec 2024
Viewed by 494
Abstract
With the continuous exploitation of offshore natural gas, the content of CO2 produced gradually increases. It is not economical to separate more CO2 from natural gas after transportation, and more CO2 will aggravate the corrosion of pipelines. The commonly used [...] Read more.
With the continuous exploitation of offshore natural gas, the content of CO2 produced gradually increases. It is not economical to separate more CO2 from natural gas after transportation, and more CO2 will aggravate the corrosion of pipelines. The commonly used decarburization process is not suitable for offshore platforms, and there are problems of high energy consumption and large space occupation. Therefore, dense phase separation of associated gas with high carbon dioxide content is a better separation method. In this paper, the equation of state is optimized by comparing the experimental and CO2 system phase characteristics simulation. Based on the selected equation of state (EOS), a three-level separation model of phase equilibrium characteristics is established. The separation efficiency is simulated to complete the separation of CO2 and methane. The separation process is optimized by a genetic algorithm, and the temperature and pressure under the best separation efficiency are determined. The PR-EOS was selected as the equation with the highest calculation accuracy. Through process simulation and algorithm optimization, the best separation efficiency was 72.23%. Full article
(This article belongs to the Topic Carbon Capture Science and Technology (CCST), 2nd Edition)
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17 pages, 5112 KiB  
Article
Impact of Temperature on Cement Displacement Efficiency: Analysis of Velocity, Centralization, and Density Differences
by Xiaowei Tang, Jian Zhang, Dewei Liu, Guoshuai Ju and Xiaofeng Sun
Processes 2024, 12(12), 2923; https://doi.org/10.3390/pr12122923 - 20 Dec 2024
Viewed by 503
Abstract
This paper investigates the impact of temperature on the rheological behavior of cement slurry and drilling fluid and examines how various factors, such as displacement speed, casing centralization, and density difference, influence displacement efficiency during cementing operations. Using numerical simulations validated against experimental [...] Read more.
This paper investigates the impact of temperature on the rheological behavior of cement slurry and drilling fluid and examines how various factors, such as displacement speed, casing centralization, and density difference, influence displacement efficiency during cementing operations. Using numerical simulations validated against experimental data, we explore how these factors interact under different temperature conditions. Results indicate that temperature changes significantly affect the flow characteristics, displacement interface stability, and overall displacement efficiency. Findings demonstrate that optimizing these parameters according to temperature conditions can significantly enhance cementing performance and reduce the risk of fluid channeling and instability. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Drilling Techniques)
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17 pages, 12025 KiB  
Article
Spatiotemporal Analysis and Risk Prediction of Water Quality Using Copula Bayesian Networks: A Case in Qilu Lake, China
by Xiang Cheng, Shengrui Wang, Yue Dong, Zhaokui Ni and Yan Hong
Processes 2024, 12(12), 2922; https://doi.org/10.3390/pr12122922 - 20 Dec 2024
Viewed by 682
Abstract
Lake water pollution under anthropogenic influences exhibits characteristics of high uncertainty, rapid evolution, and complex control challenges, presenting substantial threats to ecological systems and human health. Quantitative risk prediction provides crucial support for water quality deterioration prevention and management. This study employed the [...] Read more.
Lake water pollution under anthropogenic influences exhibits characteristics of high uncertainty, rapid evolution, and complex control challenges, presenting substantial threats to ecological systems and human health. Quantitative risk prediction provides crucial support for water quality deterioration prevention and management. This study employed the Copula Bayesian Network model to conduct a comprehensive risk assessment of water quality in Qilu Lake, China (2010–2020), incorporating inter-indicator correlations and multiple uncertainty sources. Analysis revealed generally “worse” water quality conditions (5.10 ± 1.35) according to established index classifications, with predicted probabilities of reaching “deteriorated” status ranging from 11.80% to 47.90%. Significant spatial and temporal variations in water quality and pollution risk were observed, primarily attributed to intensive agricultural non-point source loading and water resource deficiency. The study established early warning thresholds through key indicator concentration predictions, particularly for the southern region where “deteriorated” risk levels corresponded to specific ranges: TN (3.42–8.43 mg/L), TP (0.07–1.29 mg/L), and CODCr (27.75–67.19 mg/L). This methodology effectively characterizes lake water quality evolution while enabling risk prediction through key indicator monitoring. The findings provide substantial support for water pollution control strategies, risk management protocols, and regulatory decision-making for lake ecosystem administrators. Full article
(This article belongs to the Special Issue State-of-the-Art Wastewater Treatment Techniques)
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15 pages, 3322 KiB  
Article
Development of a Fleet Management System for Multiple Robots’ Task Allocation Using Deep Reinforcement Learning
by Yanyan Dai, Deokgyu Kim and Kidong Lee
Processes 2024, 12(12), 2921; https://doi.org/10.3390/pr12122921 - 20 Dec 2024
Viewed by 537
Abstract
This paper presents a fleet management system (FMS) for multiple robots, utilizing deep reinforcement learning (DRL) for dynamic task allocation and path planning. The proposed approach enables robots to autonomously optimize task execution, selecting the shortest and safest paths to target points. A [...] Read more.
This paper presents a fleet management system (FMS) for multiple robots, utilizing deep reinforcement learning (DRL) for dynamic task allocation and path planning. The proposed approach enables robots to autonomously optimize task execution, selecting the shortest and safest paths to target points. A deep Q-network (DQN)-based algorithm evaluates path efficiency and safety in complex environments, dynamically selecting the optimal robot to complete each task. Simulation results in a Gazebo environment demonstrate that Robot 2 achieved a path 20% shorter than other robots while successfully completing its task. Training results reveal that Robot 1 reduced its cost by 50% within the first 50 steps and stabilized near-optimal performance after 1000 steps, Robot 2 converged after 4000 steps with minor fluctuations, and Robot 3 exhibited steep cost reduction, converging after 10,000 steps. The FMS architecture includes a browser-based interface, Node.js server, rosbridge server, and ROS for robot control, providing intuitive monitoring and task assignment capabilities. This research demonstrates the system’s effectiveness in multi-robot coordination, task allocation, and adaptability to dynamic environments, contributing significantly to the field of robotics. Full article
(This article belongs to the Special Issue New Trends in Fuzzy Control System Applications in Complex Industrial Processes and Energy Systems)
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18 pages, 11101 KiB  
Article
Steam-Alternating CO2/Viscosity Reducer Huff and Puff for Improving Heavy Oil Recovery: A Case of Multi-Stage Series Sandpack Model with Expanded Sizes
by Lei Tao, Guangzhi Yin, Wenyang Shi, Jiajia Bai, Zhengxiao Xu, Na Zhang, Qingjie Zhu, Chunhao Wang, Yong Song and Lili Cao
Processes 2024, 12(12), 2920; https://doi.org/10.3390/pr12122920 - 20 Dec 2024
Viewed by 557
Abstract
Aiming at the challenges of rapid heat dissipation, limited swept efficiency, and a rapid water cut increase in steam huff and puff development in heavy oil reservoirs, an alternating steam and CO2/viscosity reducer huff and puff method for IOR was proposed. [...] Read more.
Aiming at the challenges of rapid heat dissipation, limited swept efficiency, and a rapid water cut increase in steam huff and puff development in heavy oil reservoirs, an alternating steam and CO2/viscosity reducer huff and puff method for IOR was proposed. In this work, the effect of CO2 on the physical properties of heavy oil was evaluated, and the optimal concentration of viscosity reducer for synergistic interaction between CO2 and the viscosity reducer was determined. Next, novel huff and puff simulation experiments by three sandpack models of different sizes in series were analyzed. Then, the IOR difference between the pure steam huff and puff experiments and the steam-alternating CO2/viscosity reducer huff and puff were compared. Finally, the CO2 storage rate was obtained based on the principle of the conservation of matter. The results show that the optimal viscosity reducer concentration, 0.8 wt%, can achieve a 98.5% reduction after combining CO2. The steam-alternating CO2/viscosity reducer huff and puff reached about 45 cm at 80 °C in the fifth cycle due to the CO2/viscosity reducer effects. CO2/viscosity reducer huff and puff significantly reduces water cut during cold production, with an ultimate IOR 15.89% higher than pure steam huff and puff. The viscosity reducer alleviates heavy oil blockages, and CO2 decreases oil viscosity and enhances elastic repulsion energy. The highest CO2 storage rate of 76.8% occurs in the initial stage, declining to 15.2% by the sixth cycle, indicating carbon sequestration potential. These findings suggest that steam-alternating CO2/viscosity reducer huff and puff improves heavy oil reservoir development and provides theoretical guidance for optimizing steam huff and puff processes. Full article
(This article belongs to the Section Energy Systems)
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13 pages, 2157 KiB  
Article
Energy Recovery Decision of Electric Vehicles Based on Improved Fuzzy Control
by En-Hou Zu, Ming-Hung Shu, Jui-Chan Huang and Hsiang-Tsen Lin
Processes 2024, 12(12), 2919; https://doi.org/10.3390/pr12122919 - 20 Dec 2024
Viewed by 573
Abstract
With the advancement of electric vehicles, their low energy recovery efficiency has become the main obstacle to development. This study focuses on the problem of braking energy loss in electric vehicles during urban road driving and proposes an improved fuzzy control strategy to [...] Read more.
With the advancement of electric vehicles, their low energy recovery efficiency has become the main obstacle to development. This study focuses on the problem of braking energy loss in electric vehicles during urban road driving and proposes an improved fuzzy control strategy to optimize the energy management of electric vehicles. The exploration first introduces fuzzy control logic to adjust and optimize the energy recovery system of electric vehicles and then introduces a sparrow search algorithm to optimize the adjustment parameters. Finally, using MATLAB R2022a simulation software environment, a comparative analysis is conducted on two driving cycles: urban dynamometer driving schedule and New York City conditions. Simulation results show that the improved fuzzy control strategy can recover 906.41 kJ of energy under urban driving cycle conditions, and the energy recovery rate reaches 49.00%, while the ADVISOR strategy is 507.47 kJ and 27.13%, respectively. The energy recovery rate of the research method is 21.87% higher than that of the comparison method. Improved energy recovery rate of 80.68%. In the driving cycle with New York City, the improved strategy recovered 294.45 kJ of energy, and the energy recovery rate was 48.54%. Compared with the ADVISOR strategy, the energy recovery rate increased by 100.20%, and the energy recovery rate increased by about 110.77%. The research results indicate that the improved fuzzy control strategy is significantly superior to the ADVISOR control strategy, effectively improving energy recovery efficiency and battery charge state maintenance ability under an urban dynamometer driving schedule, achieving more efficient energy management. Full article
(This article belongs to the Section Process Control and Monitoring)
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18 pages, 42509 KiB  
Article
Effect of Ultrafine Water Mist with K2CO3 Additives on the Combustion and Explosion Characteristics of Methane/Hydrogen/Air Premixed Flames
by Haoliang Zhang, Hongfu Mi, Peng Shao, Nan Luo, Kaixuan Liao, Wenhe Wang, Yulong Duan and Yihui Niu
Processes 2024, 12(12), 2918; https://doi.org/10.3390/pr12122918 - 20 Dec 2024
Viewed by 667
Abstract
To ensure the safe utilization of hydrogen-enriched natural gas (HENG), it is essential to explore effective explosion suppressants to prevent and mitigate potential explosions. This study experimentally investigates the impact of ultrafine water mist containing K2CO3 additives on the explosion [...] Read more.
To ensure the safe utilization of hydrogen-enriched natural gas (HENG), it is essential to explore effective explosion suppressants to prevent and mitigate potential explosions. This study experimentally investigates the impact of ultrafine water mist containing K2CO3 additives on the explosion characteristics of methane/hydrogen/air premixed combustion. The influence of varying K2CO3 concentrations on pressure rise rates and flame propagation was analyzed across different hydrogen blending ratios. The results demonstrate that the addition of K2CO3 to ultrafine water mist significantly enhances its suppression effects. The peak overpressure decreased by 41.60%, 56.15%, 64.94%, and 72.98%, the flame speed decreased by 30.66%, 70.56%, 46.72%, and 65.65%, and the flame propagation time was prolonged by 25%, 20.83%, 22.92%, and 18.75%, respectively, for different hydrogen blending ratios, showing a similar trend. However, the suppression effectiveness diminishes under high hydrogen blending ratios and low K2CO3 concentrations. Further analysis using thermogravimetric infrared spectroscopy and chemical kinetics simulations revealed that the heat release rate and the generation rate of active free radicals significantly decrease after the addition of K2CO3 to the ultrafine water mist. The recombination cycle of KOH → K → KOH, formed by reactions (R211: K + OH + M = KOH + M) and (R259: H + KOH = K + H2O), continuously combines active free radicals (·O, ·OH) into stable product molecules, such as H2O. However, at low K2CO3 concentrations, reaction R211, which suppresses laminar combustion sensitivity and consumes a larger quantity of active free radicals, does not dominate, leading to a reduced suppression effect of K2CO3 ultrafine water mist. Several factors during the reaction process also adversely affect the performance of K2CO3-containing ultrafine water mist. These factors include the premature onset of laminar flame instability at low K2CO3 concentrations, the increased flame-front propagation speed due to the addition of hydrogen to methane, which shortens the residence time of K2CO3 in the reaction zone, and the turbulence caused by unvaporized droplets. Full article
(This article belongs to the Section Chemical Processes and Systems)
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