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Volume 13
Issue 11
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Processes, Volume 13, Issue 11 (November 2025) – 382 articles

Cover Story (view full-size image): Azo dyes in wastewater are persistent pollutants that are difficult to remove using conventional methods. This paper investigates a rapid aqueous microwave synthesis of monoclinic BiVO4 nanoparticles and their application in the visible-light-driven degradation of Acid Orange 7 in aqueous solution. The obtained BiVO4 shows an average crystallite size of 19 nm, a BET surface area of 7.5 m2 g−1 and a direct band gap of about 2.55 eV, which together support efficient photocatalytic performance. Under visible-light irradiation, up to 77% of Acid Orange 7 is degraded within 120 min, following pseudo-first-order kinetics. The material also exhibits a measurable antibacterial effect against Escherichia coli, indicating its potential for combined dye degradation and disinfection in wastewater treatment. View this paper
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33 pages, 7356 KB  
Article
Data-Driven Sidetrack Well Placement Optimization
by Xiang Wang, Ming Li, Cheng Rui, Qi Guo, Yuhao Zhuang, Wenjie Yu and Tingting Zhang
Processes 2025, 13(11), 3756; https://doi.org/10.3390/pr13113756 - 20 Nov 2025
Viewed by 431
Abstract
Sidetracking technology has become a relatively mature approach for redeveloping mature fields and restoring the productivity of old wells. However, the design of conventional sidetracking projects has largely relied on expert experience or numerical simulation, methods that are often time-consuming, labor-intensive, and subjective. [...] Read more.
Sidetracking technology has become a relatively mature approach for redeveloping mature fields and restoring the productivity of old wells. However, the design of conventional sidetracking projects has largely relied on expert experience or numerical simulation, methods that are often time-consuming, labor-intensive, and subjective. To overcome these limitations, this study proposes a data-driven optimization framework for sidetrack well placement. It utilizes machine learning techniques trained on a large-scale synthetic dataset generated from field-informed numerical simulations, to establish a robust machine-learning proxy model. Four predictive models—Linear Regression, Polynomial Regression, Random Forest, and a Backpropagation (BP) Neural Network—were systematically compared, among which the Random Forest model achieved the best predictive accuracy. After hyperparameter optimization, a robust prediction model for sidetracking performance was established, achieving a Mean Squared Error (MSE) of 0.0008 (Root Mean Squared Error, RMSE, of 0.0283) and an R2 of 0.8059 on the test set. To further optimize well placement, a mathematical model was formulated with the objective of maximizing the production enhancement rate. Three optimization algorithms—the Multi-Level Coordinate Search (MCS), Differential Evolution (DE), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES)—were evaluated, with the DE algorithm demonstrating superior performance. By integrating the optimized Random Forest predictor with the DE optimizer, a systematic methodology for sidetrack well placement optimization was developed. A field case study validated the approach, showing significant improvements, including a reduced water cut and an incremental cumulative oil production of 82.7 tons. This research demonstrates the simulation-based feasibility of intelligent sidetrack well placement optimization and provides practical guidance for future sidetracking development strategies. Full article
(This article belongs to the Special Issue Advances in Improving Efficiency, Decarbonization, Modeling and Intelligent Operations of Modern Oilfield Development)
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35 pages, 6556 KB  
Review
Artificial Intelligence-Guided Pulsed Synthesis of Zinc Oxide Nanostructures on Thin Metal Shells
by Serguei P. Murzin
Processes 2025, 13(11), 3755; https://doi.org/10.3390/pr13113755 - 20 Nov 2025
Viewed by 691
Abstract
Zinc oxide (ZnO) nanostructures have been intensively investigated for applications in sensing, photocatalysis, and optoelectronic devices, where functional performance is strongly governed by morphology, crystallinity, and defect structure. Conventional wet-chemical and vapor-phase growth methods often require long processing times or complex chemistries and [...] Read more.
Zinc oxide (ZnO) nanostructures have been intensively investigated for applications in sensing, photocatalysis, and optoelectronic devices, where functional performance is strongly governed by morphology, crystallinity, and defect structure. Conventional wet-chemical and vapor-phase growth methods often require long processing times or complex chemistries and face reproducibility and compatibility challenges when applied to thin, flexible, or curved metallic substrates. Pulsed high-energy techniques—such as pulsed laser deposition (PLD), high-power impulse magnetron sputtering (HiPIMS), and pulsed laser or plasma processing—offer a versatile alternative, enabling rapid and localized synthesis both from and on Zn-bearing thin shells. These methods create transient nonequilibrium conditions that accelerate oxidation and promote spatially controlled nanostructure formation. This review highlights the emerging integration of artificial intelligence (AI) with pulsed ZnO synthesis on thin metallic substrates, emphasizing standardized data reporting, Bayesian optimization and active learning for efficient parameter exploration, physics-informed and graph-based neural networks for predictive modeling, and reinforcement learning for adaptive process control. By connecting synthesis dynamics with data-driven modeling, the review outlines a path toward predictive and autonomous control of ZnO nanostructure formation. Future perspectives include autonomous experimental workflows, machine-vision-assisted diagnostics, and the extension of AI-guided pulsed synthesis strategies to other functional metal oxide systems. Full article
(This article belongs to the Special Issue Artificial Intelligence: An Innovative Solution to the Optimization and Discovery of Functional Materials)
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23 pages, 3849 KB  
Article
Multi-AGV Collaborative Task Scheduling and Deep Reinforcement Learning Optimization Under Multi-Feature Constraints
by Dongping Zhao, Hui Li, Ziyang Wang and Hang Li
Processes 2025, 13(11), 3754; https://doi.org/10.3390/pr13113754 - 20 Nov 2025
Viewed by 529
Abstract
To address the challenges of low efficiency, instability, and difficulties in meeting multiple constraints simultaneously in multi-AGV (Automated Guided Vehicle) task scheduling for intelligent manufacturing and logistics, this paper introduces a scheduling method based on multi-feature constraints and an improved deep reinforcement learning [...] Read more.
To address the challenges of low efficiency, instability, and difficulties in meeting multiple constraints simultaneously in multi-AGV (Automated Guided Vehicle) task scheduling for intelligent manufacturing and logistics, this paper introduces a scheduling method based on multi-feature constraints and an improved deep reinforcement learning (DRL) approach (Improved Proximal Policy Optimization, IPPO). The method integrates multiple constraints, including minimizing task completion time, reducing penalty levels, and minimizing scheduling time deviation, into the scheduling optimization process. Building on the conventional PPO algorithm, several enhancements are introduced: a dynamic penalty mechanism is implemented to adaptively adjust constraint weights, a structured reward function is designed to boost learning efficiency, and sampling bias correction is combined with global state awareness to improve training stability and global coordination. Simulation experiments demonstrate that, after 10,000 iterations, the minimum task completion time drops from 98.2 s to 30 s, the penalty level decreases from 130 to 82, and scheduling time deviation reduces from 12 s to 0.5 s, representing improvements of 69.4%, 37%, and 95.8% in the same scenario, respectively. Compared to genetic algorithms (GAs) and rule-based scheduling methods, the IPPO approach demonstrates significant advantages in average task completion time, total system makespan, and overall throughput, along with faster convergence and better stability. These findings demonstrate that the proposed methodology enables effective multi-objective collaborative optimization and efficient task scheduling within complex dynamic environments, holding significant value for intelligent manufacturing and logistics systems. Full article
(This article belongs to the Special Issue Revolutionizing Smart Manufacturing: Cutting-Edge Technologies in the Industry's Future)
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28 pages, 4965 KB  
Article
A Comparative Study Between a Lattice Boltzmann Method and a Finite Volume Method in Resolving Turbulent Heat Transfer in a Low Porosity Face-Centered Cubic Unit
by Mona Al-Mqbas, Tony Rosemann, Nico Jurtz, Harald Kruggel-Emden and Matthias Kraume
Processes 2025, 13(11), 3753; https://doi.org/10.3390/pr13113753 - 20 Nov 2025
Viewed by 321
Abstract
Direct Numerical Simulations (DNS) are widely employed to simulate thermo-fluid dynamics in packed bed reactors, offering high-fidelity insights into complex flow and heat transfer phenomena. However, recent studies have revealed notable differences in isothermal turbulent flow results across different DNS frameworks, leaving open [...] Read more.
Direct Numerical Simulations (DNS) are widely employed to simulate thermo-fluid dynamics in packed bed reactors, offering high-fidelity insights into complex flow and heat transfer phenomena. However, recent studies have revealed notable differences in isothermal turbulent flow results across different DNS frameworks, leaving open the question of how conjugate heat transfer is affected. This study presents a comparison between DNS based on a finite volume method (FVM) and a lattice Boltzmann method (LBM) for predicting turbulent heat transfer in a low porosity face-centered cubic (FCC) packed unit. First, the methods are compared with respect to the required resolution and computational cost. Subsequently, global parameters for drag, heat transfer, and spatial as well as temporal variances are evaluated. The flow topology is further analyzed by examining the mean and fluctuating components of hydrodynamic and thermal fields. While good agreement between the methods is shown regarding time-averaged velocity and temperature profiles, more pronounced differences are observed when comparing the respective temporal variances between the two methods. Additionally, the FVM, which relies on a surface-fitted mesh, requires more degrees of freedom to obtain a grid-converged solution but delivers results of higher certainty than the LBM. These findings highlight important methodological considerations when selecting DNS approaches for resolving turbulent heat transfer in complex porous geometries. Full article
(This article belongs to the Topic Heat and Mass Transfer in Engineering)
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22 pages, 11121 KB  
Article
Comprehensive Performance Evaluation of Conductive Asphalt Mixtures Using Multi-Phase Carbon Fillers
by Xiao Zhang, Yafeng Pang, Hongwei Lin and Xiaobo Du
Processes 2025, 13(11), 3752; https://doi.org/10.3390/pr13113752 - 20 Nov 2025
Viewed by 301
Abstract
This study explores the synergistic effects of recycled carbon fiber (RCF) and recycled carbon fiber powder (RCFP) on the performance of conductive asphalt mixtures (CAMs). Laboratory tests were conducted to evaluate optimal asphalt content (OAC), electrical and heating behavior, and key pavement properties, [...] Read more.
This study explores the synergistic effects of recycled carbon fiber (RCF) and recycled carbon fiber powder (RCFP) on the performance of conductive asphalt mixtures (CAMs). Laboratory tests were conducted to evaluate optimal asphalt content (OAC), electrical and heating behavior, and key pavement properties, including rutting, cracking, and freeze–thaw resistance. Results showed that OAC increased with RCF and RCFP dosage due to their high surface area and strong asphalt absorption. The composite achieved stable conductivity, where RCF formed a macro-scale skeleton and RCFP established a micro-bridging network, reducing resistivity to a minimum of 1.60 Ω·m. This dual conductive mechanism significantly enhanced heating efficiency, with a peak rate of 4.85 °C/min at 0.5% RCF + 3% RCFP. Mechanically, RCF provided three-dimensional reinforcement while RCFP improved cohesion, together enhancing high-temperature and freeze–thaw performance. However, low-temperature cracking resistance exhibited a parabolic trend due to the risk of material agglomeration at excessive dosages. Multi-indicator TOPSIS analysis identified 0.4% RCF + 3% RCFP as the optimal composition. Critically, this optimal mixture is also technically and economically feasible, demonstrating an excellent balance characterized by a low specific energy consumption of 2.38 W·h/°C and a competitive cost (≈CNY 528.4/t). This study provides a sustainable, energy-efficient, and multi-functional solution for pavement heating and de-icing in cold regions. Full article
(This article belongs to the Section Materials Processes)
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22 pages, 4602 KB  
Article
Variable Structure Learning-Based Spatio-Temporal Graph Convolutional Networks for Chemical Process Quality Prediction with SHAP-Enhanced Interpretability
by Siyuan Tang, Zheren Zhu, Yuanqiang Zhou, Bingbing Shen, Ziyan Shen, Zeyu Yang and Le Yao
Processes 2025, 13(11), 3751; https://doi.org/10.3390/pr13113751 - 20 Nov 2025
Viewed by 287
Abstract
Product quality control in chemical processes faces challenges from dynamic non-stationary data, underutilized variable spatial correlations, and overreliance on prior knowledge. This paper addresses these issues by proposing an enhanced Spatio-Temporal Graph Convolutional Networks (STGCN) for chemical process soft sensing. In this method, [...] Read more.
Product quality control in chemical processes faces challenges from dynamic non-stationary data, underutilized variable spatial correlations, and overreliance on prior knowledge. This paper addresses these issues by proposing an enhanced Spatio-Temporal Graph Convolutional Networks (STGCN) for chemical process soft sensing. In this method, the spatio-temporal graph attention mechanism is integrated into the Graph Convolutional Networks, enabling dynamic weighting of neighboring nodes to improve spatiotemporal feature mining and accelerate convergence. Unlike traditional STGCN models that rely on predefined graph structures and prior domain knowledge, this paper proposes the Variable Structure Learning-based Spatio-Temporal Graph Convolutional Networks (VSL-STGCN), which autonomously learns variable relational structures via end-to-end gradient descent and uses SHAP algorithm to select critical variables, reducing computational burden and overfitting risks. Finally, the proposed VSL-STGCN is validated on two real chemical processes, outperforming baseline models in prediction accuracy. Based on the experimental results, the proposed VSL-STGCN achieves about 15% lower RMSE and about 10% higher R2 compared to baseline STGCN models. The learned adjacency matrix aligns with actual process mechanisms, ensuring interpretability. Full article
(This article belongs to the Special Issue Innovative Approaches to Modeling, Optimization, Control, and Monitoring in Industrial Processes)
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34 pages, 7403 KB  
Article
Numerical Simulation of Aortic Valve Leaflets Calcification Influence on Hemodynamic Performance Using Fluid–Structure Interaction Approach
by Polina Fedotova, Nikita Pil, Alex G. Kuchumov, Ekaterina Barbashina, Vladimir Tsilibin, Fulufhelo Nemavhola, Thanyani Pandelani, Bakytbek Kadyraliev and Truong Sang Ha
Processes 2025, 13(11), 3750; https://doi.org/10.3390/pr13113750 - 20 Nov 2025
Viewed by 386
Abstract
Aortic valve calcification is the process of calcium buildup on the leaflets of the aortic valve, preceding functional insufficiency. Calcification underlies the development of aortic stenosis by stiffening the valve leaflets, leading to restricted aortic valve opening during systole and obstructed blood flow. [...] Read more.
Aortic valve calcification is the process of calcium buildup on the leaflets of the aortic valve, preceding functional insufficiency. Calcification underlies the development of aortic stenosis by stiffening the valve leaflets, leading to restricted aortic valve opening during systole and obstructed blood flow. However, a more comprehensive understanding of the hemodynamic effects of altered valve properties is required. Therefore, it is crucial to investigate the biomechanical properties of aortic valve leaflets susceptible to calcification. To examine fluid flow in an aorta segment with leaflets of different stiffness, a two-way fluid–structure interaction model was developed. The leaflet’s behavior was modeled using two constitutive laws—linear-elastic and isotropic hyperelastic—followed by numerical testing and comparative analysis. Using the material parameter values c01 and c10 within the ranges of 22–60 and 22–60 kPa, respectively, the hyperelastic model was examined. The valve leaflets’ Young’s modulus ranged from 1 to 22 MPa, while their Poisson’s ratio ranged from 0.35 to 0.45. A high correlation between Poisson’s ratio and wall shear stress was found. With an elastic modulus of 22 MPa and the highest Poisson’s ratio of 0.45, the maximum wall shear stress was 81.78 Pa during peak flow velocity and complete valve opening, while the lowest wall shear stress was 0.38 Pa. We can infer from the study’s results that, when considering the isotropic structure and nonlinear characteristics of valve leaflets, the Delfino hyperelastic model more accurately depicts their complex behavior. Full article
(This article belongs to the Special Issue Design, Fabrication, Modeling, and Control in Biomedical Systems)
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27 pages, 2699 KB  
Article
Carbon Economic Dispatching for Active Distribution Networks via a Cyber–Physical System: A Demand-Side Carbon Penalty
by Jingfeng Zhao, Qi You, Yongbin Wang, Hong Xu, Huiping Guo, Lan Bai, Kunhua Liu, Zhenyu Liu and Ziqi Fan
Processes 2025, 13(11), 3749; https://doi.org/10.3390/pr13113749 - 20 Nov 2025
Viewed by 379
Abstract
To address the challenges of climate change mitigation and operational flexibility in active distribution networks (ADNs) amid high renewable energy penetration, this paper proposes a low-carbon economic dispatch framework integrating demand-side carbon regulation and cyber–physical system (CPS)-enabled shared energy storage. First, a consumer-side [...] Read more.
To address the challenges of climate change mitigation and operational flexibility in active distribution networks (ADNs) amid high renewable energy penetration, this paper proposes a low-carbon economic dispatch framework integrating demand-side carbon regulation and cyber–physical system (CPS)-enabled shared energy storage. First, a consumer-side emission penalty mechanism is developed by fusing a carbon emission flow (CEF) model with price elasticity coefficients. This mechanism embeds carbon costs into end-user electricity pricing, guiding users to adjust consumption patterns (e.g., reducing usage during high-carbon-intensity periods) and shifting partial carbon responsibility to the demand side. Second, a CPS-based shared energy storage mechanism is constructed, featuring a three-layer architecture (physical layer, control decision layer, security layer) that aggregates distributed energy storage (DES) resources into a unified, schedulable pool. A cooperative, game-based profit-sharing strategy using Shapley values is adopted to allocate benefits based on each DES participant’s marginal contribution, ensuring fairness and motivating resource pooling. Finally, a unified mixed-integer linear programming (MILP) optimization model is formulated for ADNs, co-optimizing locational marginal prices, DES state-of-charge trajectories, and demand curtailment to minimize operational costs and carbon emissions simultaneously. Simulations on a modified IEEE 33-bus system demonstrate that the proposed framework reduces carbon emissions by 4.5–4.7% and renewable energy curtailment by 71.1–71.3% compared to traditional dispatch methods, while lowering system operational costs by 6.6–6.8%. The results confirm its effectiveness in enhancing ADN’s low-carbon performance, renewable energy integration, and economic efficiency. Full article
(This article belongs to the Special Issue Power System Optimization for Energy Storage: Methods and Applications)
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21 pages, 1511 KB  
Article
Research on Intelligent Early Warning and Emergency Response Mechanism for Tunneling Face Gas Concentration Based on an Improved KAN-iTransformer
by Lei An, Shaoqi Kong and Kunjie Li
Processes 2025, 13(11), 3748; https://doi.org/10.3390/pr13113748 - 20 Nov 2025
Viewed by 249
Abstract
The tunneling face poses a significant risk for gas disaster in coal mining due to the complex interplay of geological conditions, ventilation strategies, and construction techniques, resulting in nonlinear and spatiotemporal dynamics in gas concentration distribution. Accurate prediction of gas levels is crucial [...] Read more.
The tunneling face poses a significant risk for gas disaster in coal mining due to the complex interplay of geological conditions, ventilation strategies, and construction techniques, resulting in nonlinear and spatiotemporal dynamics in gas concentration distribution. Accurate prediction of gas levels is crucial for ensuring the safety of coal mining operations. This study introduces a novel approach for gas concentration forecasting at the tunneling face by integrating the Kolmogorov–Arnold Network (KAN) with an enhanced iTransformer model, leveraging multi-source sensor data for enhanced predictive capabilities. KAN improves the feature extraction ability due to flexible mapping kernel function that is capable of capturing complicated nonlinearities between gas emission volume and environmental variables. iTransformer, with concentrated attention mechanism and sparsity pattern, can further model very long-term sequence dependencies and learn to capture multi-scale features. As a whole, they address the problem of gradient vanishing and insufficient feature extraction in the temporal sequential prediction models based on deep learning methods with long sequences input, leading to significant improvements in prediction accuracy and model stability. Experiments on site monitoring datasets demonstrate that the proposed KAN + iTransformer model achieved better fitting and generalization capacity than two baseline models (iTransformer, Transformer) for gas concentration prediction. Full article
(This article belongs to the Topic Green Mining, 3rd Edition)
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12 pages, 3414 KB  
Article
Thermal Cycling Stability of NiO/YSZ Anode-Supported SOFC Button Cells: An Experimental Study
by Meng Zhu, Bowen Cai, Yangtian Yan and Keqing Zheng
Processes 2025, 13(11), 3747; https://doi.org/10.3390/pr13113747 - 20 Nov 2025
Viewed by 307
Abstract
Solid oxide fuel cell (SOFC) technology is an electrochemical power generation apparatus that enables the direct conversion of chemical fuel energy into electrical energy. To address the issue of thermal cycling stability, which is critical for commercialization, a thermal cycling stability test was [...] Read more.
Solid oxide fuel cell (SOFC) technology is an electrochemical power generation apparatus that enables the direct conversion of chemical fuel energy into electrical energy. To address the issue of thermal cycling stability, which is critical for commercialization, a thermal cycling stability test was performed on a NiO/YSZ anode-supported SOFC button cell. This study investigates the influence of key thermal cycling parameters (heating/cooling rate and number of thermal cycles) on the cell’s electrochemical performance and microstructure evolution. The main findings are as follows: thermal cycling adversely affects the electrochemical performance of the SOFC, with the degree of degradation directly correlated to both the number of cycles and the heating/cooling rate. After 20 thermal cycles at a rate of 5 °C/min, the peak power density decreased by 20.57%. Furthermore, thermal cycling leads to an increase in both ohmic and activation polarization, with the performance degradation predominantly governed by the rise in ohmic polarization. It was demonstrated that the number of thermal cycles has a more significant impact on ohmic losses than the heating/cooling rate. This work offers valuable insight into the degradation mechanisms induced by thermal cycling in SOFC button cells. Full article
(This article belongs to the Special Issue Engineering of Solid Oxide Fuel Cells: From Powder to Power)
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23 pages, 2706 KB  
Review
Sustainable Production of Alternative Proteins from Basidiomycetes: Valorization of Mycelial and Fruiting Body Biomass
by Amanda Rubia de Figueiredo Trindade, Isadora de Brito Hilario, Ederson Aparecido Gimenes da Rocha, Leonardo Antônio da Rosa Borges dos Santos, Cristina Giatti Marques de Souza, Marina Proença Dantas, Bruna Mayara Roldão Ferreira, Rúbia Carvalho Gomes Corrêa, Natália Ueda Yamaguchi, Adelar Bracht and Rosane Marina Peralta
Processes 2025, 13(11), 3746; https://doi.org/10.3390/pr13113746 - 20 Nov 2025
Viewed by 550
Abstract
Global population growth, climate change, and the environmental impact of livestock production have accelerated the search for sustainable and efficient protein sources. Fruiting bodies (mushrooms) and mycelial biomass have emerged as promising alternatives due to their high nutritional quality, low ecological footprint, and [...] Read more.
Global population growth, climate change, and the environmental impact of livestock production have accelerated the search for sustainable and efficient protein sources. Fruiting bodies (mushrooms) and mycelial biomass have emerged as promising alternatives due to their high nutritional quality, low ecological footprint, and compatibility with circular bioeconomy principles. This review highlights the nutritional, biotechnological, and environmental aspects of fungal proteins obtained from both fruiting bodies and mycelial biomass of Basidiomycetes. Emphasis is placed on amino acid composition, protein digestibility, and advances in cultivation and fermentation systems for large-scale production. Submerged and solid-state fermentation processes are analyzed in terms of scalability, resource efficiency, and integration with agro-industrial residues for sustainable bioprocessing. Comparative analyses reveal that mycelial biomass production achieves high protein yields with significantly reduced land, water, and energy requirements compared to conventional protein sources. Emerging fungal species such as Schizophyllum commune and Auricularia polytricha demonstrate strong potential for producing protein-rich mycelia applicable to functional and plant-based foods. Finally, the review discusses current technological innovations, regulatory frameworks, and market perspectives that position fungal biomass as a strategic component in the ongoing global protein transition. Full article
(This article belongs to the Special Issue Sustainable Bioprocesses for Valorization of Food Industry Waste: Microbial and Enzymatic Innovations and Reactor Design)
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8 pages, 185 KB  
Editorial
Special Issue on “Challenges and Advances of Process Control Systems”
by Olympia Roeva and Tsonyo Slavov
Processes 2025, 13(11), 3745; https://doi.org/10.3390/pr13113745 - 20 Nov 2025
Viewed by 471
Abstract
Control systems stand as the crucial nervous system of modern industrial and technological processes, essential for achieving high performance, safety, and efficiency across diverse applications, from complex microgrids to advanced robotics [...] Full article
(This article belongs to the Special Issue Challenges and Advances of Process Control Systems)
19 pages, 12731 KB  
Article
Influence of Tectonic Movements on Hydrocarbon Accumulation in the Dongying Formation, Western Bohai Bay Basin, China
by Jieqiong Zhu, Xiaodong Li, Longchuan Pu, Xiwei Li, Ketong Chen, Chengyun Wang, Jichao Zhang, Yawen Li, Yan Li, Yi Zhao, Zeqi Song, Zongbao Liu and Rongsheng Zhao
Processes 2025, 13(11), 3744; https://doi.org/10.3390/pr13113744 - 20 Nov 2025
Viewed by 306
Abstract
The hydrocarbon accumulation process in the Dongying Formation (a key Cenozoic reservoir in the western Bohai Bay Basin) is complex due to superimposed tectonic activities. To reconstruct its evolution, we integrated fluid inclusion analysis, burial–thermal history reconstruction, and fault–cap rock sealing evaluation. Results [...] Read more.
The hydrocarbon accumulation process in the Dongying Formation (a key Cenozoic reservoir in the western Bohai Bay Basin) is complex due to superimposed tectonic activities. To reconstruct its evolution, we integrated fluid inclusion analysis, burial–thermal history reconstruction, and fault–cap rock sealing evaluation. Results reveal that the Baoding Sag experienced at least two major hydrocarbon charging stages, further subdivided into four sub-periods (I–IV) based on the microthermometry and fluorescence characteristics (ranging from yellow, indicating marginal maturity, to blue, indicating maturity). The hydrocarbons exhibit a mixed composition, as reflected in the fluid inclusions and Ro evolution, indicating a complex charging history derived from two source rocks (Es1 and Es3). However, two key tectonic activities that significantly influenced the hydrocarbon accumulation process can still be distinguished: uplift erosion at 22.3 Ma and strike-slip faulting at 11.8 Ma. Specifically, the 11.8 Ma strike-slip faulting caused the leakage of early hydrocarbons (Periods I–II) from the Dongying Formation into the Guantao Formation, with a critical fault juxtaposition threshold of ~86 m. This fault activity also introduced water washing of the hydrocarbon reservoir. Later, around 0.36 Ma, gas washing occurred as the source rocks matured, further complicating the calculation of the mixing ratio between the two source rock contributions. Full article
(This article belongs to the Special Issue Advances in Unconventional Reservoir Development and CO2 Storage)
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16 pages, 2670 KB  
Article
Green and Controllable Crosslinked Gel Plugging Technology Based on Modified Natural Biofibers
by Zhe Ma and Junyi Liu
Processes 2025, 13(11), 3743; https://doi.org/10.3390/pr13113743 - 20 Nov 2025
Viewed by 282
Abstract
To overcome the limited mechanical strength and poor stability of conventional gels in high-temperature, high-salinity oilfield environments, a novel nanocellulose-reinforced hydrogel (AM/AA/PCNF) was developed through a multistep chemical modification strategy. Nanocellulose served as a rigid backbone and was successively modified via epoxide ring-opening, [...] Read more.
To overcome the limited mechanical strength and poor stability of conventional gels in high-temperature, high-salinity oilfield environments, a novel nanocellulose-reinforced hydrogel (AM/AA/PCNF) was developed through a multistep chemical modification strategy. Nanocellulose served as a rigid backbone and was successively modified via epoxide ring-opening, methacryloyl esterification, and polydopamine functionalization, forming a three-dimensional network with multiple dynamic crosslinking interactions. The resulting composite hydrogel exhibited outstanding comprehensive properties when the PCNF content was 3 wt%: a tensile strength of 2.6 MPa, fracture energy of 8.95 MJ/m3, and compressive strength of 360 kPa—all markedly superior to those of conventional hydrogel systems. Under simulated downhole conditions (120 °C, 6 MPa, and 5 wt% salinity), the hydrogel demonstrated excellent plugging performance across sand beds of varying particle sizes (60–80 mesh to 20–40 mesh), maintaining cumulative fluid loss within 28.4–42.5 mL. Mechanistic investigations indicate that the enhanced performance stems from the synergistic combination of a rigid nanocellulose scaffold and multiple dynamic interactions, which facilitate a self-adaptive plugging mechanism. The study delivers both theoretical and practical foundations for designing advanced plugging systems. Full article
(This article belongs to the Special Issue Advances in Enhanced Oil Recovery Processes)
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9 pages, 999 KB  
Article
Selection of Binder Materials for the Production of Extruded Briquettes
by Maral Almagambetov, Yerlan Zhumagaliyev, Yerbol Shabanov, Nursultan Ulmaganbetov, Nurzhan Kairakbaev and Albina Yersaiynova
Processes 2025, 13(11), 3742; https://doi.org/10.3390/pr13113742 - 20 Nov 2025
Viewed by 284
Abstract
This study presents the results of a comprehensive study on various agglomeration methods and binder types for producing briquettes from raw materials. Also, this research focuses on one of the major issues in the production of extrusion briquettes, namely their mechanical strength during [...] Read more.
This study presents the results of a comprehensive study on various agglomeration methods and binder types for producing briquettes from raw materials. Also, this research focuses on one of the major issues in the production of extrusion briquettes, namely their mechanical strength during handling, transportation, and water exposure. Laboratory experiments were conducted to identify the most suitable binding agents, followed by industrial-scale trials of several formulations. The paper also includes the results of pilot-scale tests. Four types of binders were examined: bentonite, TD 021.005.BS, TD 000.411.BS, and TD 000.414.BS. The strength characteristics of the briquettes were evaluated in accordance with the relevant GOST standards: GOST 21289-75 for hot strength, GOST 25471-82 for drop strength, and GOST 15137-77 for impact and abrasion resistance. The findings indicate that, from a technological perspective, the most efficient binder for briquette production is TD 021.005.BS, when applied within the range of 2.5–3%. Notably, briquettes produced with this binder demonstrate superior moisture resistance compared to other formulations. After 24 h of immersion in water, they retained their original shape and structural integrity, confirming the binder’s high effectiveness for industrial applications. Full article
(This article belongs to the Section Materials Processes)
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27 pages, 20330 KB  
Article
Data-Driven High-Temperature Superheater Wall Temperature Prediction Using Polar Lights Optimized Kolmogorov–Arnold Networks
by Zhiqian He, Yuhan Wang, Guangmin Yang, Chen Han, Jia Gao, Shiming Xu, Ge Yin, Xuefeng Tian, Zhi Wang and Xianyong Peng
Processes 2025, 13(11), 3741; https://doi.org/10.3390/pr13113741 - 20 Nov 2025
Viewed by 267
Abstract
The flexible operation of coal-fired boilers poses significant challenges to thermal safety, particularly due to delayed responses in wall temperature under variable load conditions, which may lead to overheating risks and reduced equipment lifespan. To address this issue, we propose a PLO-KAN framework [...] Read more.
The flexible operation of coal-fired boilers poses significant challenges to thermal safety, particularly due to delayed responses in wall temperature under variable load conditions, which may lead to overheating risks and reduced equipment lifespan. To address this issue, we propose a PLO-KAN framework for high-precision prediction of high-temperature superheater wall temperatures. The framework integrates a Kolmogorov–Arnold Network (KAN) with learnable B-spline activation functions to enhance interpretability, a sliding-window strategy to capture temporal dependencies, and Polar Lights Optimization (PLO) for automated hyperparameter tuning, balancing local exploitation and global exploration. The method is validated using 10,000 operational samples from a 1000 MW ultra-supercritical once-through boiler, with 68 key features selected from 106 candidates. Results show that the proposed model achieves high accuracy and robustness in both single-step and multi-step forecasting, maintaining reliable performance within a five-minute prediction horizon. The proposed method provides an efficient and interpretable solution for real-time wall temperature prediction, supporting proactive thermal management and enhancing operational safety in coal-fired power plants. Full article
(This article belongs to the Section Energy Systems)
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24 pages, 2693 KB  
Article
Multi-Objective Optimal Partitioning of Active Distribution Networks Integrating Consideration of Load Balancing and Solution Efficiency of Parallel Optimization
by Qing Ge, Yuezhou Xia, Qi Li, Ling Zeng, Zhangbin Huang and Chuanjie Lin
Processes 2025, 13(11), 3740; https://doi.org/10.3390/pr13113740 - 20 Nov 2025
Viewed by 420
Abstract
To address the optimization challenges arising from the large-scale integration of distributed energy resources into active distribution networks, this paper proposes a multi-objective optimization partitioning method that balances system security/stability with parallel computing efficiency. To address the limitations of existing partitioning approaches, particularly [...] Read more.
To address the optimization challenges arising from the large-scale integration of distributed energy resources into active distribution networks, this paper proposes a multi-objective optimization partitioning method that balances system security/stability with parallel computing efficiency. To address the limitations of existing partitioning approaches, particularly their neglect of parallel computing efficiency and poor adaptability to the radial topology of distribution networks, a three-objective optimization model is constructed. This model incorporates reactive power–voltage control, load balancing, and power balance constraints, while introducing partition scale constraints and connectivity constraints. The NSGA-III algorithm is employed to solve the Pareto front, and an optimal compromise solution is obtained using a fuzzy membership function. A partition adjustment strategy ensures topological connectivity. Validation on 10 kV distribution networks with 47-node, 124-node, and 300-node systems demonstrates that this method achieves reasonable reactive power–voltage partitioning, ensures intra-partition power balance and load balancing, and exhibits significant advantages over traditional methods. Full article
(This article belongs to the Special Issue Analysis and Optimization Control of Active Distribution Networks and Smart Grids)
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34 pages, 4941 KB  
Article
Improvement of Energy Performance of Glass Furnaces Using Modelling and Optimization Techniques
by Onur Kodak, Miraç Burak Kaya, Farshid Sadeghi-Khaneghah, Emre Dumankaya, Gizem Yumru Alanat, Levent Kılıç, Neşet Arzan and Alp Er S. Konukman
Processes 2025, 13(11), 3739; https://doi.org/10.3390/pr13113739 - 19 Nov 2025
Viewed by 433
Abstract
Glass furnaces are a key component of the energy-intensive glass industry. Therefore, optimization of their energy performance is crucial for both economic and environmental sustainability. This study focused on optimizing the performance of an electric-boosted natural gas glass furnace. For this purpose, firstly, [...] Read more.
Glass furnaces are a key component of the energy-intensive glass industry. Therefore, optimization of their energy performance is crucial for both economic and environmental sustainability. This study focused on optimizing the performance of an electric-boosted natural gas glass furnace. For this purpose, firstly, raw operational data were collected from a glass furnace. Next, reconciled data were obtained via a modelling process, data reconciliation, and gross error detection to establish a reliable dataset. Two linear regression models were developed and tested using both raw and reconciled data and compared with each other. The constrained optimization problem was constructed using a linear regression model and other process constraints and solved via the interior-point method to minimize specific energy consumption. The findings indicate that the reconciled data-based linear regression model yielded more reliable results. The specific energy consumption can be reduced to a minimum of 3660.088 kJ/kg-glass under an optimal setpoint for raw material, cullet, water, raw material temperature, electric boosting, and fuel. Furthermore, the analysis reveals that energy performance is enhanced with increased glass production and greater utilization of electric boosting. These results emphasize that the integrated statistical modelling approach provides valuable and actionable insights for energy performance improvements in the glass industry. Full article
(This article belongs to the Section Chemical Processes and Systems)
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23 pages, 3384 KB  
Article
An Enhanced Workflow for Quantitative Evaluation of Fluid and Proppant Distribution in Multistage Fracture Treatment with Distributed Acoustic Sensing
by Wenqiang Liu, Bobo Li, Zhengguang Zhao, Rou Wen, Yu Bai, Haoran Guo, Jizhou Tang and Chunlei Wang
Processes 2025, 13(11), 3738; https://doi.org/10.3390/pr13113738 - 19 Nov 2025
Cited by 1 | Viewed by 377
Abstract
Distributed Acoustic Sensing (DAS) technology has emerged as a valuable tool for monitoring fluid and proppant injection during hydraulic fracturing. One of its applications involves estimating cluster-level fluid and proppant allocations in real time. However, significant uncertainties remain in the quantitative calculation of [...] Read more.
Distributed Acoustic Sensing (DAS) technology has emerged as a valuable tool for monitoring fluid and proppant injection during hydraulic fracturing. One of its applications involves estimating cluster-level fluid and proppant allocations in real time. However, significant uncertainties remain in the quantitative calculation of injected volumes due to limitations in frequency band energy (FBE) data extraction, cluster depth determination, and volume estimation algorithms. This study presents an enhanced workflow for quantitatively estimating fluid and proppant allocations from DAS-derived FBE data while minimizing uncertainties. The workflow integrates multi-band and summed-energy analyses with the optimized selection of calculation algorithms to reduce interpretation uncertainties. The results show that FBE [50–200 Hz] exhibits the highest sensitivity to injection activities, local minima on summed FBE can accurately pinpoint top and bottom depths of each cluster, and a power-law model linking acoustic energy to flow rate allows for quantitative calculation. Field applications demonstrate consistent improvements in fluid and proppant volume estimation accuracy. Validation against post-frac numerical simulations shows that estimated fluid and proppant allocations agree within a 6% error, confirming the method’s quantitative reliability. By addressing key sources of uncertainty, this approach enhances DAS-based fracture diagnostics and provides actionable guidance for real-time decision making in unconventional completions. Full article
(This article belongs to the Special Issue Geological and Engineering Problems in the Development of Unconventional Oil and Gas Reservoirs (2nd Edition))
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18 pages, 1011 KB  
Article
Optimization of Green Extraction of Antioxidant Compounds from Blackthorn Pomace (Prunus spinosa L.) Using Natural Deep Eutectic Solvents (NADES)
by Sara Hourani, Jelena Vukosavljević, Nemanja Teslić, Ružica Ždero Pavlović, Boris M. Popović and Branimir Pavlić
Processes 2025, 13(11), 3737; https://doi.org/10.3390/pr13113737 - 19 Nov 2025
Viewed by 335
Abstract
Blackthorn (Prunus spinosa L.) is a wild, understudied plant rich in bioactive compounds such as polyphenols with designated antioxidant potential. The main objective of this research was to optimize ultrasound-assisted extraction of blackthorn pomace using natural deep eutectic solvents (NADES). To obtain [...] Read more.
Blackthorn (Prunus spinosa L.) is a wild, understudied plant rich in bioactive compounds such as polyphenols with designated antioxidant potential. The main objective of this research was to optimize ultrasound-assisted extraction of blackthorn pomace using natural deep eutectic solvents (NADES). To obtain the highest yield of polyphenols and improved in vitro antioxidant activity, response surface methodology (RSM) and central composite experimental design were used. The screening step of the study included ten different NADESs using a one-factor-at-a-time approach. Two NADES mixtures (N12, containing proline and lactic acid in a molar ratio of 1:2, and N14, containing choline chloride and glycerol in a molar ratio of 1:1) were chosen for the second step of the study, which aimed to select the most influential process parameters. A fractional factorial 25−1 design was used, varying five different parameters at two levels: extraction time (30 and 60 min), extraction temperature (40 and 50 °C), and liquid-to-solid ratio (10 and 20 mL/g), water content in NADES (15 and 20%), and NADES type (N12 and N14). After the second step, N12 containing 20% water was chosen as the most potent solvent for the optimization study. For the final step, the other three parameters were varied on three levels, and thus optimal conditions were obtained (extraction time 90 min, extraction temperature 65 °C, and liquid-to-solid ratio 22.65 mL/g). Blackthorn juice was also tested in the first step, as well as under optimal conditions established for pomace, in order to evaluate whether these conditions are suitable for juice and to determine the percentage of improvement in extraction efficiency. Full article
(This article belongs to the Special Issue Advances in Green Extraction and Separation Processes)
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18 pages, 3174 KB  
Article
Hydration Properties and Modeling of Ternary Systems of Mechanically Modified Municipal Solid Waste Incineration Fly Ash–Blast Furnace Slag–Cement
by Zedong Qiu, Ziling Peng, Zhen Hu, Sha Wan, Gang Li, Xintong Xiao, Kun Liu, Zhicheng Xiang and Xian Zhou
Processes 2025, 13(11), 3736; https://doi.org/10.3390/pr13113736 - 19 Nov 2025
Viewed by 383
Abstract
Municipal solid waste incineration fly ash (MSWIFA) can be reused as an admixture in cementitious materials, but its low activity limits its utilization as a resource. In this study, we systematically investigated the mineral and grinding characteristics of MSWIFA and then studied its [...] Read more.
Municipal solid waste incineration fly ash (MSWIFA) can be reused as an admixture in cementitious materials, but its low activity limits its utilization as a resource. In this study, we systematically investigated the mineral and grinding characteristics of MSWIFA and then studied its pretreatment and activation via mechanical force–surface modification. The results indicate that the fineness and angle of repose of MSWIFA during grinding are inversely proportional to grinding time, while specific surface area and powder fluidity increase. Agglomeration occurs in the later stage, and particle size fluctuates. Gray correlation analysis shows that MSWIFA powder with a particle size of 16–45 μm contributes most to compressive strength improvement. The composite surface modifier TEA-STPP benefits grinding, shortens ball-milling time, and increases active particle size content, thereby promoting hydration activity. The best process regarding the modifier was determined. MSWIFA and blast furnace slag (BFS) accelerate early hydration of ordinary Portland cement (OPC) and increase its reaction participation, promoting the generation of calcium chloroaluminate (Friedel’s salt) and monosulfate-aluminate phases (SO4-AFm) and significantly enhancing the hydration of tricalcium aluminate (C3A) in OPC. Full article
(This article belongs to the Section Chemical Processes and Systems)
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20 pages, 2348 KB  
Article
Experimental Study on Gas Particle Flow Characteristics of a Novel Stable Combustion Burner Under Different Primary Air Velocities
by Xiangjun Long, Leikai Deng, Nan Zhang, Weiyu Wang, Defu Xin, Zhen Chen and Zhengqi Li
Processes 2025, 13(11), 3735; https://doi.org/10.3390/pr13113735 - 19 Nov 2025
Viewed by 205
Abstract
Existing faulty coal-fired units generally achieve oil-free stable combustion only at loads over 30%, failing to meet low load regulation demands. To address the insufficient flexibility of boilers, a novel flame-stabilization theory was developed for retrofitting a 350 MW faulty coal-fired unit boiler. [...] Read more.
Existing faulty coal-fired units generally achieve oil-free stable combustion only at loads over 30%, failing to meet low load regulation demands. To address the insufficient flexibility of boilers, a novel flame-stabilization theory was developed for retrofitting a 350 MW faulty coal-fired unit boiler. Based on the actual burner dimensions of the 350 MW unit boiler, a geometric scaling ratio of 1:7 between model and actual burners was established. Phase Doppler Anemometry (PDA) was employed to conduct gas particle flow experiments on the model burner, revealing the impact of different primary air velocities on the gas particle flow characteristics of the novel stabilized flow burner. The analysis of experimental results suggests that, When the primary air velocity is 9 m/s, a central recirculation zone forms at the burner outlet. At a primary air velocity of 10 m/s, an annular recirculation zone develops with a relatively large coverage area. When the primary air velocity increases to 11 m/s, the extent of the annular recirculation zone diminishes. At a primary air velocity of 10 m/s, an extensive annular recirculation zone forms at the burner outlet, which appears to provide sufficient energy for the ignition of pulverized coal. Elevated pulverized coal concentration near the burner centerline facilitates the formation of a high-temperature oxygen-lean reducing atmosphere, suppressing fuel-based NOx generation. Therefore, it is recommended to set the actual operating parameters of the novel stabilized flow burner based on the 10 m/s primary air velocity condition in the gas particle flow experiments. Full article
(This article belongs to the Section Energy Systems)
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25 pages, 15402 KB  
Article
Voltage Balancing of a Bipolar DC Microgrid with Unbalanced Unipolar Loads and Sources
by Mateus Pinheiro Dias, Debora P. Damasceno, Eliabe Duarte Queiroz, Kristian P. dos Santos, Jose C. U. Penã and José A. Pomilio
Processes 2025, 13(11), 3734; https://doi.org/10.3390/pr13113734 - 19 Nov 2025
Viewed by 257
Abstract
This paper presents the validation of a voltage balancing converter for a bipolar DC microgrid designed to ensure reliable operation in both grid-connected and islanded modes. This microgrid includes unipolar constant power loads (CPL), a unipolar Battery Energy Storage System (BESS), and local [...] Read more.
This paper presents the validation of a voltage balancing converter for a bipolar DC microgrid designed to ensure reliable operation in both grid-connected and islanded modes. This microgrid includes unipolar constant power loads (CPL), a unipolar Battery Energy Storage System (BESS), and local PV generation. The BESS converter employs a V–I droop strategy using only inductor current feedback, reducing sensing requirements while maintaining plug-and-play capability and ensuring smooth transitions between connected and islanded modes. In such a microgrid, the voltage balancing converter regulates the differential voltages under severe unbalanced load conditions and during transients caused by changes in unipolar loads and sources. The experimental results validate the voltage balancing strategy across various scenarios in a small-scale prototype. The results show tight voltage regulation under unbalanced conditions, and smooth transitions during load transients and unintentional islanding, even if there is no dc voltage source in one of the poles of the bipolar dc bus. For both conditions, the imbalance between the unipolar voltages is less than 0.5% of the total bipolar voltage. Full article
(This article belongs to the Special Issue Advances in Power Converters in Energy and Microgrid Systems)
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17 pages, 2012 KB  
Article
A TRIZ-Based Experimental Design Approach to Enhance Wave Soldering Efficiency in Electronics Manufacturing
by Chia-Nan Wang, Nai-Chi Shiue, Van-Thanh Phan and Dang-Quy Hong
Processes 2025, 13(11), 3733; https://doi.org/10.3390/pr13113733 - 19 Nov 2025
Viewed by 320
Abstract
Wave soldering is a technological process that allows for the simultaneous soldering of multiple locations on the same circuit board. Its major defects, such as tin bridging and insufficient tin filling, continue to challenge manufacturers, resulting in increased rework, labor, and operational costs. [...] Read more.
Wave soldering is a technological process that allows for the simultaneous soldering of multiple locations on the same circuit board. Its major defects, such as tin bridging and insufficient tin filling, continue to challenge manufacturers, resulting in increased rework, labor, and operational costs. Therefore, reducing errors in wave soldering is crucial to ensure the best quality for customers and achieve cost savings for the company. This study aims to enhance wave soldering performance by using an integrated approach that combines Teoriya Resheniya Izobreatatelskikh Zadatch (TRIZ) and Design of Experiment (DOE) for empirical improvement in an Original Equipment Manufacturer (OEM) factory, a subsidiary of a global OEM company. The results are sound: we eliminated tin till bridge defects by 88%, achieved a 33% reduction in manpower, and increased production volumes by 6%. This proposed framework can be utilized in other electronics manufacturing factories and related industries. Full article
(This article belongs to the Special Issue Recent Trends in Advanced Manufacturing Technologies for Materials Processing and Production)
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19 pages, 4050 KB  
Article
Experimental and Simulation Research on Straight-Through Cyclone Water Separator: Effects of Structural and Operational Parameters on Separation Performance
by Yihan Chen, Xingjuan Zhang, Chao Wang and Han Yang
Processes 2025, 13(11), 3732; https://doi.org/10.3390/pr13113732 - 19 Nov 2025
Viewed by 373
Abstract
The aircraft air-cycle system (ACS) provides cabin cooling, dehumidification, and pressurization. As a key component, the water separator removes free moisture from the air, preventing turbine icing/blockage under high humidity and avoiding humidity-induced electronics failures, thus ensuring reliable ACS operation. Existing studies focus [...] Read more.
The aircraft air-cycle system (ACS) provides cabin cooling, dehumidification, and pressurization. As a key component, the water separator removes free moisture from the air, preventing turbine icing/blockage under high humidity and avoiding humidity-induced electronics failures, thus ensuring reliable ACS operation. Existing studies focus mainly on oil and chemical applications, with limited work for aircraft ACS. To address this research gap, this study investigates a straight-through cyclone water separator for aircraft ACS applications. We built a test platform to measure separation efficiency and conducted experiments at swirl angles of 20°, 30°, and 40°. A simulation model based on the Reynolds Stress turbulence model and a discrete phase model was established, and its simulation efficiency agreed with experiments within 4.1%. Simulation on water separator under high-pressure and low-pressure conditions were conducted, revealing internal flow fields and droplet dynamics. Results show each swirl angle has a distinct high-efficiency operating range, enabling selection according to system parameters across air mass flow rates; under varying humidification rate, the 40° swirl generator performed best. Simulations further indicate that higher operating pressure markedly improves performance: pressure loss decreased from 4.5 kPa to 0.7 kPa, while separation efficiency increased by 30.7%. Full article
(This article belongs to the Section Process Control and Monitoring)
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34 pages, 1873 KB  
Review
Artificial Intelligence in Rice Quality and Milling: Technologies, Applications, and Future Prospects
by Benjamin Ilo, Abraham Badjona, Yogang Singh, Alex Shenfield and Hongwei Zhang
Processes 2025, 13(11), 3731; https://doi.org/10.3390/pr13113731 - 19 Nov 2025
Viewed by 942
Abstract
The global demand for high-quality rice necessitates advancements in milling technologies and quality assessment techniques that are rapid, accurate, and scalable. Traditional methods of rice evaluation are time-consuming and subjective, and are increasingly being replaced by artificial intelligence driven solutions that offer non-destructive, [...] Read more.
The global demand for high-quality rice necessitates advancements in milling technologies and quality assessment techniques that are rapid, accurate, and scalable. Traditional methods of rice evaluation are time-consuming and subjective, and are increasingly being replaced by artificial intelligence driven solutions that offer non-destructive, real-time monitoring capabilities. This review presents a comprehensive synthesis of current AI applications including machine vision, deep learning, spectroscopy, thermal imaging, and hyperspectral imaging for the assessment and classification of rice quality across various stages of processing. Major emphasis is put on the recent advances in convolutional neural networks (CNNs), YOLO architectures, and Mask R-CNN models, and their integration into industrial rice milling systems is discussed. Additionally, the review highlights next steps, notably designing lean AI architectures suitable for edge computing, hybrid imaging systems, and the creation of open-access datasets. Across recent rice-focused studies, classification accuracies for grading and varietal identification are typically ≥90% using machine vision and CNNs, while NIR–ANN models for physicochemical properties (e.g., moisture/protein proxies) commonly report strong fits (R2≈0.90–0.99). End-to-end detectors/segmenters (e.g., YOLO/YO-LACTS) achieve high precision suitable for near real-time inspection. These results indicate that AI-based approaches can substantially outperform conventional evaluation in both accuracy and throughput. Full article
(This article belongs to the Special Issue Machine Learning, Control, and Optimization in Manufacturing and Industry 4.0)
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18 pages, 1335 KB  
Article
Impact of Oil on the Bacterial Community of the Sierozems of the ‘Daulet Asia’ Landfill in Southern Kazakhstan
by Roza Narmanova, Yanina Delegan, Yulia Kocharovskaya, Alexander Bogun, Irina Puntus, Lenar Akhmetov, Anna Vetrova, Angelina Baraboshkina, Nelly Chayka, Svetlana Kuzhamberdieva, Nurzhan Suleimenov, Saken Kanzhar, Dinara Niyazova, Indira Yespanova, Bekhzan Alimkhan, Meruert Tolegenkyzy, Klara Darmagambet, Karima Arynova, Nurbol Appazov and Andrey Filonov
Processes 2025, 13(11), 3730; https://doi.org/10.3390/pr13113730 - 19 Nov 2025
Viewed by 380
Abstract
In the Republic of Kazakhstan (one of the top 10 oil-producing countries in the world), the remediation of oil pollution found in unproductive soils under the conditions of a sharply continental arid climate is a highly relevant problem. The aims of this work [...] Read more.
In the Republic of Kazakhstan (one of the top 10 oil-producing countries in the world), the remediation of oil pollution found in unproductive soils under the conditions of a sharply continental arid climate is a highly relevant problem. The aims of this work are to study the biodegradation capacity of the gray soil of the ‘Daulet Asia’ landfill, assess the degradative potential of indigenous oil-degrading strains and changes in the composition of the soil microbial community. Analytical chemistry methods, distillation and chromatographic mass spectrometry were used for oil analysis; gravimetry and IR spectroscopy were used to evaluate oil degradation. Standard microbiological techniques were employed to isolate and cultivate microorganisms and metagenomic sequencing was carried out using Oxford Nanopore technology. Raw data processing and subsequent analysis were performed using modern software packages. Three isolated strains of interest were identified based on the analysis of 16S rRNA gene fragment sequences. The studied soil has low biodegradation capacity (oil loss was 6.2% on day 60), possibly due to the low abundance and weak activity of indigenous hydrocarbon-oxidizing microorganisms. The taxonomic composition of the microbiome in the studied soil suggests some potential for oil degradation. Assessment of the effectiveness of oil degradation by the indigenous microbiome indicates that this potential can be realized only marginally in situ. Isolated oil-degrading strains were identified as belonging to the Rhodococcus and Kocuria genera. Effective oil removal from the studied soil requires the introduction of active microorganisms (e.g., as part of biopreparations). Considering the characteristics of the hot arid climate, for bioremediation of contaminated sierozems of Southern Kazakhstan, it is advisable to use halotolerant oil-degrading microorganisms with a wide temperature range that are capable of degrading hydrocarbons under moisture deficiency. Full article
(This article belongs to the Section Environmental and Green Processes)
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15 pages, 2412 KB  
Article
Isolation of Bioactive Metabolites from Fusarium fujikuroi: GC-MS Profiling and Bioactivity Assessment
by Zainab Farooq, Sobia Nisa, Eman Y. Santali, Ruwida M. K. Omar and Ashraf Ali
Processes 2025, 13(11), 3729; https://doi.org/10.3390/pr13113729 - 19 Nov 2025
Viewed by 332
Abstract
In the present study, the endophytic fungus Fusarium fujikuroi was isolated from the medicinal plant Debregeasia salicifolia and cultivated for the extraction of bioactive metabolites. The crude extract was fractionated via gravity column chromatography using solvents of increasing polarity (n-hexane, n-hexane/chloroform 1:1 v [...] Read more.
In the present study, the endophytic fungus Fusarium fujikuroi was isolated from the medicinal plant Debregeasia salicifolia and cultivated for the extraction of bioactive metabolites. The crude extract was fractionated via gravity column chromatography using solvents of increasing polarity (n-hexane, n-hexane/chloroform 1:1 v/v, chloroform, ethyl acetate, and methanol) to isolate bioactive compounds. The antimicrobial activity of these fractions was evaluated against pathogenic bacteria (Bacillus subtilis, Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli). Most extracts exhibited significant antimicrobial activity, with the n-hexane/chloroform fraction (HCF) showing the highest efficacy (18 mm inhibition zone), followed by the n-hexane fraction while Ciprofloxacin was used as a positive control. Fractions were tested in triplicate; antibacterial activities (p < 0.05) were highest in the HCF. Bioactive compounds from the most potent fractions were further purified and analyzed using gas chromatography-mass spectrometry (GC-MS). The GC-MS profiling revealed the presence of diverse bioactive metabolites, including polycyclic aromatic hydrocarbons (PAHs), phenols, and fatty acids. Notably, several of these compounds have not been previously reported in Fusarium fujikuroi, highlighting the potential for novel antimicrobial agents from this endophytic strain. In silico toxicity prediction using the ProTox-II tool indicated that the major compounds possess low to moderate toxicity profiles, supporting their potential safety for further biological evaluation. Full article
(This article belongs to the Special Issue Technologies for Production, Processing, and Extraction of Natural Products, 3rd Edition)
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24 pages, 850 KB  
Article
Spatio-Temporal Artificial Intelligence for Multi-Hazard-Aware Renewable Energy Site Selection Using Integrated Geospatial and Climate Data
by Katleho Moloi, Kwabena Addo and Ernest Mnkandla
Processes 2025, 13(11), 3728; https://doi.org/10.3390/pr13113728 - 19 Nov 2025
Viewed by 465
Abstract
The siting of renewable energy systems (RESs) in regions vulnerable to multiple climate hazards presents a critical challenge for sustainable infrastructure planning. Traditional approaches, primarily driven by static assessments of solar and wind potential, often neglect the compounded risks posed by floods, droughts, [...] Read more.
The siting of renewable energy systems (RESs) in regions vulnerable to multiple climate hazards presents a critical challenge for sustainable infrastructure planning. Traditional approaches, primarily driven by static assessments of solar and wind potential, often neglect the compounded risks posed by floods, droughts, and windstorms, resulting in investments that are operationally vulnerable and economically unsustainable. This study proposes a novel spatio-temporal artificial intelligence (AI) framework for multi-objective RES deployment that integrates satellite-derived resource maps, high-resolution hazard data, and dynamic climate time series into a unified optimization pipeline. The methodology employs a gated recurrent unit (GRU)-based encoder to capture temporal hazard dynamics, combined with a multi-objective evolutionary algorithm (NSGA-II) to balance energy yield and resilience. A case study in South Africa’s Vhembe District demonstrates the framework’s effectiveness: the proposed model reduces the average hazard exposure by 31.6% while preserving 96.4% of the baseline energy output. Attention-based saliency analysis reveals that flood and windstorm hazards are the dominant drivers of site exclusion. Compared to conventional siting methods, the proposed framework achieves superior trade-offs between performance and risk, ensuring alignment with South Africa’s Just Energy Transition and Climate Adaptation strategies. The results confirm the value of spatio-temporal embeddings and hazard-aware multi-objective optimization in guiding resilient, data-driven energy infrastructure development. This model offers direct benefits to energy planners, climate adaptation agencies, and policymakers seeking to implement resilient, data-driven renewable energy strategies in hazard-prone regions. Full article
(This article belongs to the Section Energy Systems)
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20 pages, 3728 KB  
Article
A Multi-Source Fusion-Based Material Tracking Method for Discrete–Continuous Hybrid Scenarios
by Kaizhi Yang, Xiong Xiao, Yongjun Zhang, Guodong Liu, Xiaozhan Li and Fei Zhang
Processes 2025, 13(11), 3727; https://doi.org/10.3390/pr13113727 - 19 Nov 2025
Viewed by 355
Abstract
Special steel manufacturing involves both discrete processing events and continuous physical flows, forming a representative discrete–continuous hybrid production system. However, due to the visually homogeneous surfaces of steel products, the highly dynamic production environment, and frequent disturbances or anomalies, traditional single-source tracking approaches [...] Read more.
Special steel manufacturing involves both discrete processing events and continuous physical flows, forming a representative discrete–continuous hybrid production system. However, due to the visually homogeneous surfaces of steel products, the highly dynamic production environment, and frequent disturbances or anomalies, traditional single-source tracking approaches struggle to maintain accurate and consistent material identification. To address these challenges, this paper proposes a multi-source fusion-based material tracking method tailored for discrete–continuous hybrid scenarios. First, a state–event system (SES) is constructed based on process rules, enabling interpretable reasoning of material states through event streams and logical constraints. Second, on the visual perception side, a YOLOv8-SE detection network embedded with the squeeze-and-excitation (SE) channel attention mechanism is designed, while the DeepSORT tracking framework is improved to enhance weak feature extraction and dynamic matching for visually similar targets. Finally, to handle information conflicts and cooperation in multi-source fusion, an improved Dempster–Shafer (D-S) evidence fusion strategy is developed, integrating customized anomaly handling and fault-tolerance mechanisms to boost decision reliability in conflict-prone regions. Experiments conducted on real special steel production lines demonstrate that the proposed method significantly improves detection accuracy, ID consistency, and trajectory integrity under complex operating conditions, while enhancing robustness against modal conflicts and abnormal scenarios. This work provides an interpretable and engineering-feasible solution for end-to-end material tracking in hybrid manufacturing systems, offering theoretical and methodological insights for the practical deployment of multi-source collaborative perception in industrial environments. Full article
(This article belongs to the Section Manufacturing Processes and Systems)
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