Processes

15 pages, 1167 KB

Open AccessArticle

Optimal Configuration of Transformer–Energy Storage Deeply Integrated System Based on Enhanced Q-Learning with Hybrid Guidance

by Zhe Li, Li You, Yiqun Kang, Daojun Tan, Xuan Cai, Haozhe Xiong and Yonghui Liu

Processes 2025, 13(10), 3267; https://doi.org/10.3390/pr13103267 (registering DOI) - 13 Oct 2025

Abstract

This paper investigates the multi-objective siting and sizing problem of a transformer–energy storage deeply integrated system (TES-DIS) that serves as a grid-side common interest entity. This study is motivated by the critical role of energy storage systems in generation–grid–load–storage resource allocation and the [...] Read more.

This paper investigates the multi-objective siting and sizing problem of a transformer–energy storage deeply integrated system (TES-DIS) that serves as a grid-side common interest entity. This study is motivated by the critical role of energy storage systems in generation–grid–load–storage resource allocation and the superior capability of artificial intelligence algorithms in addressing multi-dimensional, multi-constrained optimization challenges. A multi-objective optimization model is first formulated with dual objectives: minimizing voltage deviation levels and comprehensive economic costs. To overcome the limitations of conventional methods in complex power systems—particularly regarding solution quality and convergence speed—an enhanced Q-learning with hybrid guidance algorithm is proposed. The improved algorithm demonstrates strengthened local search capability and accelerated late-stage convergence performance. Validation using a real-world urban power grid in China confirms the method’s effectiveness. Compared to traditional approaches, the proposed solution achieves optimal TES-DIS planning through autonomous learning, demonstrating (1) 70.73% cost reduction and (2) 89.85% faster computational efficiency. These results verify the method’s capability for intelligent, simplified power system planning with superior optimization performance. Full article

(This article belongs to the Special Issue Applications of Smart Microgrids in Renewable Energy Development)

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16 pages, 7443 KB

Open AccessArticle

Study on the Organic Geochemical Characteristics of Jurassic Source Rocks from the Northern Tibetan Plateau Basin

by Yajun Shi, Li Xu, Xinmin Ma and Jiajia Guo

Processes 2025, 13(10), 3266; https://doi.org/10.3390/pr13103266 (registering DOI) - 13 Oct 2025

Abstract

The Northern Tibetan Plateau Basin is the most extensive and least explored Mesozoic marine basin in China and shows considerable potential for oil and gas exploration. This study systematically analyzed the abundance, type, hydrocarbon generation potential, and conversion rate of organic matter within [...] Read more.

The Northern Tibetan Plateau Basin is the most extensive and least explored Mesozoic marine basin in China and shows considerable potential for oil and gas exploration. This study systematically analyzed the abundance, type, hydrocarbon generation potential, and conversion rate of organic matter within three Jurassic drill core samples from the Biloucuo area of the Northern Tibetan Plateau Basin. The total organic carbon (TOC) content of these Jurassic source rocks was >4%, on average, permitting their classification as excellent source rocks. The average contents of sapropelinite, exinite, vitrinite, and inertinite in kerogen were 74%, 4%, 18%, and 4%, respectively. The H/C and O/C ratios of the kerogen mainly ranged from 0.8 to 1.3 and 0.06 to 0.11, respectively, indicative of type II₁ kerogen. The average S₁ + S₂ content was 15.0 mg/g rock, indicating a high hydrocarbon generation potential. On the basis of the relationship between the quantity of soluble hydrocarbons remaining in the strata and the S₂ and TOC contents, it can be inferred that the hydrocarbon generation conversion rate of these Jurassic source rocks was between 25% and 50%, and partial hydrocarbon expulsion has taken place. It is estimated that the maximum oil generation potential of the formation will reach 20 kg/t rock at a greater depth, which equates to good exploration potential. Full article

(This article belongs to the Special Issue Advances in Theory and Technology of Unconventional Oil and Gas Reservoirs)

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22 pages, 3254 KB

Open AccessArticle

Optimizing Steel Industry and Air Conditioning Clusters Using Coordination-Based Time-Series Fusion Transformer

by Xinyu Luo, Zhaofan Zhou, Bin Li, Yumeng Zhang, Chenle Yi, Kun Shi and Songsong Chen

Processes 2025, 13(10), 3265; https://doi.org/10.3390/pr13103265 (registering DOI) - 13 Oct 2025

Abstract

The steel industry, a typical energy-intensive sector, experiences significant load power fluctuations, particularly during peak periods, posing challenges to power-grid stability. Traditional studies often overlook its unique production characteristics, limiting a comprehensive understanding of power fluctuations. Meanwhile, air conditioning (AC), as a flexible [...] Read more.

The steel industry, a typical energy-intensive sector, experiences significant load power fluctuations, particularly during peak periods, posing challenges to power-grid stability. Traditional studies often overlook its unique production characteristics, limiting a comprehensive understanding of power fluctuations. Meanwhile, air conditioning (AC), as a flexible load, offers stable regulation with an aggregation effect. This study explores the potential for coordinated load dispatch between the steel industry and air conditioning clusters to enhance power system flexibility. A power characteristic model for steel loads was developed based on energy consumption patterns, while a physical ETP model aggregated air conditioning loads. To improve forecasting accuracy, a parallel LSTM-Transformer model predicts both steel and air conditioning loads. CEEMDAN-VMD decomposition reduces noise in steel-load data, and the QR algorithm computes confidence intervals for load responses. The study further examines interactions between electric-arc furnace control strategies and air conditioning demand response. Case studies using real-world data demonstrate that the proposed model enhances prediction accuracy, peak suppression, and variance reduction. These findings provide insights into steel industry power fluctuations and large-scale air conditioning load adjustments. Full article

(This article belongs to the Special Issue AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency)

15 pages, 3948 KB

Open AccessArticle

Study on the Preparation of Metallic Aluminum Powder by Nitrogen Atomization

by Xiaoyun Yu, Jiasheng Yang, Guozhi Wang, Qingchun Yu, Yong Deng and Weijin Yu

Processes 2025, 13(10), 3264; https://doi.org/10.3390/pr13103264 (registering DOI) - 13 Oct 2025

Abstract

Metal additive manufacturing represents the most promising application for Three-dimensional printing systems. Gas atomization is an important method for the preparation of metal powders. In this work, aluminum powders were prepared via vacuum induction atomization. Morphology and microstructure were investigated. Results demonstrated that [...] Read more.

Metal additive manufacturing represents the most promising application for Three-dimensional printing systems. Gas atomization is an important method for the preparation of metal powders. In this work, aluminum powders were prepared via vacuum induction atomization. Morphology and microstructure were investigated. Results demonstrated that optimal atomization occurred at 3 MPa with a 2.5 mm delivery tube diameter, yielding powders with an average particle size of 63.8 μm and a sphericity of more than 90%. Variations in droplet size caused differential cooling rates, enabling rapidly solidified small droplets to adhere to incompletely cooled large droplets and form satellite spheres. Quantitative analysis confirmed a positive correlation between metal powder particle size and surface satellite spheres. Large droplets exhibited long cooling times, resulting in more pronounced dendritic microstructures. This study provides theoretical insights for the preparation of metal powders. Full article

(This article belongs to the Special Issue Microstructure Properties and Characterization of Metallic Material)

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22 pages, 1565 KB

Open AccessArticle

Double-Layer Optimal Configuration of Wind–Solar-Storage for Multi-Microgrid with Electricity–Hydrogen Coupling

by Dong Yang, Gangying Pan, Jianhua Zhang, Jun He, Yulin Zhang and Chuanliang Xiao

Processes 2025, 13(10), 3263; https://doi.org/10.3390/pr13103263 (registering DOI) - 13 Oct 2025

Abstract

To address the collaborative optimization challenge in multi-microgrid systems with significant renewable energy integration, this study presents a dual-layer optimization model incorporating power-hydrogen coupling. Firstly, a hydrogen energy system coupling framework including photovoltaics, storage batteries, and electrolysis hydrogen production/fuel cells was constructed at [...] Read more.

To address the collaborative optimization challenge in multi-microgrid systems with significant renewable energy integration, this study presents a dual-layer optimization model incorporating power-hydrogen coupling. Firstly, a hydrogen energy system coupling framework including photovoltaics, storage batteries, and electrolysis hydrogen production/fuel cells was constructed at the architecture level to realize the flexible conversion of multiple energy forms. From a modeling perspective, the upper-layer optimization aims to minimize lifecycle costs by determining the optimal sizing of distributed PV systems, battery storage, hydrogen tanks, fuel cells, and electrolyzers within the microgrid. At the lower level, a distributed optimization framework facilitates energy sharing (both electrical and hydrogen-based) across microgrids. This operational layer maximizes yearly system revenue while considering all energy transactions—both inter-microgrid and grid-to-microgrid exchanges. The resulting operational boundaries feed into the upper-layer capacity optimization, with the optimal equipment configuration emerging from the iterative convergence of both layers. Finally, the actual microgrid in a certain area is taken as an example to verify the effectiveness of the proposed method. Full article

(This article belongs to the Section Energy Systems)

21 pages, 5270 KB

Open AccessArticle

Spatiotemporal Modeling of the Total Nitrogen Concentration Fields in a Semi-Enclosed Water Body Using a TCN-LSTM-Hybrid Model

by Xiaohui Yan, Hongyun Cheng, Shenshen Chi, Sidi Liu and Zuhao Zhu

Processes 2025, 13(10), 3262; https://doi.org/10.3390/pr13103262 (registering DOI) - 13 Oct 2025

Abstract

In the field of water process engineering, accurately predicting the total nitrogen (TN) concentration distribution in the Semi-Enclosed Bay area is of great importance for water quality assessment, pollution control, and scientific management. Due to the coupling of multiple influencing factors, the pollution [...] Read more.

In the field of water process engineering, accurately predicting the total nitrogen (TN) concentration distribution in the Semi-Enclosed Bay area is of great importance for water quality assessment, pollution control, and scientific management. Due to the coupling of multiple influencing factors, the pollution process is complex, and traditional monitoring methods struggle to achieve large-scale, long-term real-time observation. Although numerical simulations can reproduce TN transport processes, they are computationally expensive and have low prediction efficiency. To address this, this study develops a deep learning hybrid model that integrates a Temporal Convolutional Network (TCN) and a Long Short-Term Memory (LSTM) network, referred to as the TCN-LSTM-Hybrid Model, to predict the spatiotemporal distribution of TN concentration fields in Shenzhen Bay. Comparative experiments show that this model outperforms traditional models such as TCN, LSTM, GRU, and MLP in terms of prediction accuracy and spatial generalization, offering higher computational efficiency and breaking through the limitations of “point-based prediction” by achieving “field-based prediction,” thereby providing a new path for pollutant simulation in complex ocean environments, supporting more informed decision making in ocean and coastal management. Full article

(This article belongs to the Section Chemical Processes and Systems)

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26 pages, 5572 KB

Open AccessFeature PaperArticle

Targeting GPR55 with Cannabidiol Derivatives: A Molecular Docking Approach Toward Novel Neurotherapeutics

by Catalina Mares, Andra-Maria Paun, Maria Mernea, Cristina Matanie and Speranta Avram

Processes 2025, 13(10), 3261; https://doi.org/10.3390/pr13103261 (registering DOI) - 13 Oct 2025

Abstract

This study investigated the interaction between cannabidiol (CBD) derivatives and the GPR55 receptor using a bioinformatics-driven molecular docking approach. GPR55, implicated in central nervous system (CNS) pathologies, represents a promising target for novel therapeutics. Drug-likeness evaluation via SwissADME confirmed that all selected derivatives [...] Read more.

This study investigated the interaction between cannabidiol (CBD) derivatives and the GPR55 receptor using a bioinformatics-driven molecular docking approach. GPR55, implicated in central nervous system (CNS) pathologies, represents a promising target for novel therapeutics. Drug-likeness evaluation via SwissADME confirmed that all selected derivatives complied with Lipinski′s Rule of Five, exhibiting favorable physicochemical properties with molecular weights below 500 Da and acceptable logP values. Molecular docking simulations, performed using AutoDock Vina through PyRx, revealed strong binding affinities, with docking scores ranging from −9.2 to −7.2 kcal/mol, indicating thermodynamically feasible interactions. Visualization and interaction analysis identified a conserved binding pocket involving key residues, including TYR101, PHE102, TYR106, ILE156, PHE169, MET172, TRP177, PRO184, LEU185, LEU270 and MET274. Ligand clustering in this region further supports the presence of a structurally defined binding site. Molecular dynamics simulations of GPR55 in complex with the three top-scoring ligands (3″-HOCBD, THC, and CBL) revealed that all ligands remained stably bound within the cavity over 100 ns, with ligand-specific rearrangements. Predicted oral bioavailability was moderate (0.55), consistent with the need for optimized formulations to enhance systemic absorption. These findings suggest that CBD derivatives may act as potential modulators of GPR55, offering a basis for the development of novel CNS-targeted therapeutics. Full article

(This article belongs to the Section Biological Processes and Systems)

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23 pages, 2604 KB

Open AccessArticle

Flexible Job Shop Scheduling Optimization with Multiple Criteria Using a Hybrid Metaheuristic Framework

by Shubhendu Kshitij Fuladi and Chang Soo Kim

Processes 2025, 13(10), 3260; https://doi.org/10.3390/pr13103260 (registering DOI) - 13 Oct 2025

Abstract

The flexible job shop scheduling problem (FJSP) becomes significantly more complex when real-world factors such as due dates, sequence-dependent setup times, and processing times are considered as multiple criteria. This study presents a hybrid scheduling approach that combines a genetic algorithm (GA) and [...] Read more.

The flexible job shop scheduling problem (FJSP) becomes significantly more complex when real-world factors such as due dates, sequence-dependent setup times, and processing times are considered as multiple criteria. This study presents a hybrid scheduling approach that combines a genetic algorithm (GA) and variable neighborhood search (VNS), where several dispatching rules are used to create the initial population and improve exploration. The multiple objectives are to minimize makespan, total tardiness, and total setup time while improving overall production efficiency. To test the proposed approach, standard FJSP datasets were extended with due dates and setup times for two different environments. Due dates were generated using the Total Work Content (TWK) method. This study also introduces a dynamic scheduling framework that addresses dynamic events such as machine breakdowns and new job arrivals. A rescheduling strategy was developed to maintain optimal solutions in dynamic situations. Experimental results show that the proposed hybrid framework consistently performs better than other methods in static scheduling and maintains high performance under dynamic conditions. The proposed method achieved 6.5% and 2.59% improvement over the baseline GA in two different environments. The results confirm that the proposed strategies effectively address complex, multi-constraint scheduling problems relevant to Industry 4.0 and smart manufacturing environments. Full article

(This article belongs to the Section Manufacturing Processes and Systems)

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13 pages, 773 KB

Open AccessArticle

Convective Drying of Pirul (Schinus molle) Leaves: Kinetic Modeling of Water Vapor and Bioactive Compound Retention

by José Arturo Olguín-Rojas, Ariana Martinez-Candelario, Irving David Pérez-Landa, Paulina Aguirre-Lara, Maria Mariana González-Urrutia and Manuel González-Pérez

Processes 2025, 13(10), 3259; https://doi.org/10.3390/pr13103259 (registering DOI) - 13 Oct 2025

Abstract

Schinus molle L. is a tree commonly found in agricultural fields, deserts, and semi-arid areas of central Mexico. Its distinctive aroma makes it a source of essential oil, extracted mainly from the bark and fruits. The leaves contain phenolic compounds, and their extracts [...] Read more.

Schinus molle L. is a tree commonly found in agricultural fields, deserts, and semi-arid areas of central Mexico. Its distinctive aroma makes it a source of essential oil, extracted mainly from the bark and fruits. The leaves contain phenolic compounds, and their extracts have demonstrated antimicrobial activity. Obtaining these extracts requires a prior drying process. This study aimed to evaluate the effect of convective drying on phenolic compounds in pirul leaves and determine the thermodynamic properties of the process, including the effective diffusivity of water vapor (D) and activation energy (Ea). Drying kinetics were conducted at different air-drying temperatures (30, 40, and 50 °C) at a constant rate of 1 ms⁻¹, and the results were fitted to the second Fick’s law and semi-empirical models. After drying, a decrease in total flavonoid content was observed as the drying temperature increased, with losses of 37%, 49%, and 62% at 30, 40, and 50 °C, respectively. The final values ranged from 37.96 to 21.02 mg QE/100 g of dry leaf. The D varied between 1.32 × 10⁻¹² and 6.71 × 10⁻¹² m² s⁻¹, with an Ea of 66.06 kJ mol⁻¹. The fitting criteria (R², RMSE, AIC/BIC) indicated that the Logarithmic model best described the kinetics at 30–40 °C, while Page was adequate at 50 °C. These findings suggest an inverse relationship between drying temperature and flavonoid content, while higher temperatures accelerate water vapor diffusivity, reducing the processing time, as observed in plant matrices. Full article

(This article belongs to the Special Issue Pharmaceutical Potential and Application Research of Natural Products)

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18 pages, 1026 KB

Open AccessArticle

Research on the Optimization of the Volume Fracturing Shut-in and Drainage System of Unconventional Reservoirs in the Erlian Block

by Ning Li, Xinfang Ma, Liu Xu, Changjun Long, Guohua Liu, Shuzhi Xiu and He Ma

Processes 2025, 13(10), 3258; https://doi.org/10.3390/pr13103258 (registering DOI) - 13 Oct 2025

Abstract

Aiming at unclear imbibition replacement mechanisms and flowback/production strategies in unconventional reservoirs of the Erlian Block, this study proposes a systematic approach integrating “imbibition-flowback-productivity synergy” to optimize post-fracturing shut-in and production regimes. By developing numerical models incorporating geological and engineering factors, we analyzed [...] Read more.

Aiming at unclear imbibition replacement mechanisms and flowback/production strategies in unconventional reservoirs of the Erlian Block, this study proposes a systematic approach integrating “imbibition-flowback-productivity synergy” to optimize post-fracturing shut-in and production regimes. By developing numerical models incorporating geological and engineering factors, we analyzed fluid dynamics during both the shut-in and production phases. Concurrently, crude oil displacement-fracturing fluid imbibition replacement experiments were conducted to guide parameter optimization. The results indicate that optimized shut-in time and production rates substantially increase recovery efficiency while mitigating reservoir damage and proppant flowback. The well shut-in time of the Erlian Block can achieve the optimal shut-in replacement effect in about 20–25 days. The optimized flowback rate of the unconventional reservoir in the Erlian Block is 25–30 m³/d. The findings offer theoretical insights and practical recommendations for the efficient development of unconventional resources. Full article

(This article belongs to the Special Issue Numerical Simulation and Application of Flow in Porous Media)

23 pages, 5211 KB

Open AccessArticle

Towards Predictive Maintenance of SAG Mills: Developing a Data-Driven Prognostic Model

by Mehdi Dehghan, Gilmar Rios, Ximena Cubillos, Jean Franco, Vinícius Antunes, Eduardo Lima, Calequela Manuel, Christian da Rocha Iardino, Marco Reis, Fabio Reis Pereira and Layhon Santos

Processes 2025, 13(10), 3257; https://doi.org/10.3390/pr13103257 (registering DOI) - 13 Oct 2025

Abstract

Predictive maintenance of semi-autogenous grinding (SAG) mills reduces unplanned downtime and improves throughput. This study develops a data-driven prognostic model for production SAG mill using four years of operational data (temperature, voltage, current, motor speed, etc.). We follow a MATLAB (R2025a)-based prognostics and [...] Read more.

Predictive maintenance of semi-autogenous grinding (SAG) mills reduces unplanned downtime and improves throughput. This study develops a data-driven prognostic model for production SAG mill using four years of operational data (temperature, voltage, current, motor speed, etc.). We follow a MATLAB (R2025a)-based prognostics and health management (PHM) workflow: data cleaning and synchronization; feature engineering in time and frequency domains (statistical moments, spectral power, bandwidth); normalization and clustering to separate operating regimes; and labeling of run-to-failure sequences for a recurring electrical failure mode. A health indicator is derived by scoring candidate features for monotonicity, trendability, and prognosability and fusing them into a condition index. Using MATLAB Predictive Maintenance Toolbox, we train and validate multiple Remaining Useful Life (RUL) learners including similarity-based, regression, and survival models on run-to-failure histories, selecting the best via cross-validated error and prediction stability. On held-out sets, the selected model forecasts RUL consistent with observed failure dates, providing actionable lead time for maintenance planning. The results highlight the practicality of deploying a PHM pipeline for SAG mills using existing plant data and commercial toolchains. Full article

(This article belongs to the Section Process Control and Monitoring)

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17 pages, 2266 KB

Open AccessArticle

Comparative Analysis of Thermal Performance and Geometric Characteristics of Tubes with Rectangular and Triangular Fins

by Florent Bunjaku, Kastriot Buza and Risto V. Filkoski

Processes 2025, 13(10), 3256; https://doi.org/10.3390/pr13103256 (registering DOI) - 13 Oct 2025

Abstract

In thermal engineering applications, finned surfaces are extensively employed to enlarge the effective heat transfer area and thereby enhance the efficiency of heat exchangers. The present study examines cylindrical tubes externally fitted with rectangular and triangular fins under the condition of a constant [...] Read more.

In thermal engineering applications, finned surfaces are extensively employed to enlarge the effective heat transfer area and thereby enhance the efficiency of heat exchangers. The present study examines cylindrical tubes externally fitted with rectangular and triangular fins under the condition of a constant transverse cross-sectional area and total fin volume. For all five rectangular fin configurations analyzed, the cross-sectional area and total volume were kept constant, while for the five triangular fin configurations, these parameters were also maintained constant but were approximately half of those of the rectangular fins due to geometric characteristics. Both analytical calculations and numerical simulations using ANSYS Fluent were conducted to evaluate thermal performance across different fin thicknesses and heights. Results show that rectangular fins provide up to 9.75% higher heat flux than triangular fins at the optimal thickness; however, this improvement requires nearly a twofold increase in material consumption. The analysis further indicates that most heat transfer occurs near the fin base, where convective efficiency is highest, with effectiveness diminishing along the extended surface. These findings highlight the importance of selecting fin geometries that balance thermal performance with material economy. In conclusion, this study provides practical insights for designing more efficient and cost-effective heat exchangers. Full article

(This article belongs to the Section Energy Systems)

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25 pages, 1612 KB

Open AccessArticle

Interfacial Electrostatics of Low Salinity-Enhanced Oil Recovery: A Review of Theoretical Foundations, Applications and Correlation to Experimental Observations

by Adango Miadonye and Mumuni Amadu

Processes 2025, 13(10), 3255; https://doi.org/10.3390/pr13103255 (registering DOI) - 13 Oct 2025

Abstract

Low salinity-enhanced oil recovery has gained universal recognition regarding its ability to provide an environmentally friendly and low-cost method of improved oil recovery. Research findings so far based on experimentation and simulation suggest that the success of the scheme stems considerably from double [...] Read more.

Low salinity-enhanced oil recovery has gained universal recognition regarding its ability to provide an environmentally friendly and low-cost method of improved oil recovery. Research findings so far based on experimentation and simulation suggest that the success of the scheme stems considerably from double layer expansion and wettability enhancement, among others. However, while the double layer expansion and wettability effects have robust theoretical foundations that can be sought within the Mean Field Poisson–Boltzmann theory, there is hardly any published research work that has tackled this task. In this paper, we fill the knowledge gap by using the MFPB theory to calculate electric double layer (EDL) parameters as functions of salinity and to successfully correlate theoretical findings to literature-based experimental observations. Additionally, we have, for the first time integrated the concept of free energy of formation of the EDL in LSWFOR research, given its intimate relationship to EDL parameters. The theoretical findings are, therefore, indicators that theoretical foundations also provide reliable and alternative means of understanding and predicting the success of LSWFOR. Full article

(This article belongs to the Special Issue Design, Inspection and Repair of Oil and Gas Pipelines)

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19 pages, 3724 KB

Open AccessArticle

Mechanical Fault Diagnosis of High-Voltage Disconnectors via Multi-Domain Energy Features of Vibration Signals in Power Systems

by Shijian Zhu, Peilong Chen, Xin Li, Qichen Deng and Feiyue Yan

Processes 2025, 13(10), 3254; https://doi.org/10.3390/pr13103254 (registering DOI) - 13 Oct 2025

Abstract

To accurately diagnose the potential faults such as jamming and incomplete opening and closing of high-voltage disconnectors during long-term operation, this paper proposes a fault diagnosis method based on the fusion of time-frequency domain energy features of the body-side vibration signal. This method [...] Read more.

To accurately diagnose the potential faults such as jamming and incomplete opening and closing of high-voltage disconnectors during long-term operation, this paper proposes a fault diagnosis method based on the fusion of time-frequency domain energy features of the body-side vibration signal. This method extracts short-term energy in the time domain and the marginal spectral energy of the sub-signals processed by variational mode decomposition (VMD) as features in the frequency domain, and constructs a feature set that can effectively represent different states through feature fusion. This enables the distinction between six states, namely normal closing, normal opening, closing jam, opening jam, closing not in place, and opening not in place. On this basis, the particle swarm optimization (PSO) algorithm is adopted to optimize the hyperparameters of the support vector machine (SVM), and the fault diagnosis model is obtained. The fault simulation experiment was conducted on the ZF12B type disconnector, and the experimental results show that the recognition accuracy of the proposed method reaches 98.33%, which is superior to the compared method, verifying the effectiveness and superiority of the proposed method. Full article

(This article belongs to the Special Issue Hybrid Artificial Intelligence for Smart Process Control)

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11 pages, 547 KB

Open AccessFeature PaperArticle

Chloride Ion and Chemical Oxygen Demand on the Rust Generation of Metals in Cleaning

by Tsuyoshi Yoda

Processes 2025, 13(10), 3253; https://doi.org/10.3390/pr13103253 (registering DOI) - 13 Oct 2025

Abstract

Metal components that undergo ultrasonic cleaning are often stored in rinse water before drying; during this dwell period, surface corrosion can nucleate and grow. Here, we investigate how two easily monitored water-quality parameters—chloride ion concentration (Cl⁻) and chemical oxygen demand (COD), [...] Read more.

Metal components that undergo ultrasonic cleaning are often stored in rinse water before drying; during this dwell period, surface corrosion can nucleate and grow. Here, we investigate how two easily monitored water-quality parameters—chloride ion concentration (Cl⁻) and chemical oxygen demand (COD), a proxy for residual organic species—govern the initiation and propagation of corrosion on low-carbon steel. After ultrasonic cleaning in five representative cleaning solutions, test coupons were immersed for up to 72 h in the corresponding rinse water and the extent of corrosion was quantified by optical profilometry and mass loss. The surface area covered by corrosion scaled linearly with [Cl⁻] (0–150 mg L⁻¹) and COD (5–120 mg L⁻¹), with correlation coefficients of 0.92 and 0.88, respectively. When both parameters exceeded threshold values of 50 mg L⁻¹ (Cl⁻) and 30 mg L⁻¹ (COD), the corrosion rate doubled relative to the control. A two-step mitigation strategy—ion-exchange pretreatment followed by activated-carbon polishing—reduced Cl⁻ and COD below the thresholds and suppressed corrosion formation by >70%. These findings provide a simple water-quality guideline and a low-cost process retrofit for manufacturers that store steel parts in high-humidity environments. Full article

(This article belongs to the Special Issue Development of Materials and Processes for Integration of Reaction and Separation Operations)

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25 pages, 18664 KB

Open AccessArticle

Study on Lower Limb Motion Intention Recognition Based on PO-SVMD-ResNet-GRU

by Wei Li, Mingsen Wang, Daxue Sun, Zhuoda Jia and Zhengwei Yue

Processes 2025, 13(10), 3252; https://doi.org/10.3390/pr13103252 (registering DOI) - 13 Oct 2025

Abstract

This study aims to enhance the accuracy of human lower limb motion intention recognition based on surface electromyography (sEMG) signals and proposes a signal denoising method based on Sequential Variational Mode Decomposition (SVMD) optimized by the Parrot Optimization (PO) algorithm and a joint [...] Read more.

This study aims to enhance the accuracy of human lower limb motion intention recognition based on surface electromyography (sEMG) signals and proposes a signal denoising method based on Sequential Variational Mode Decomposition (SVMD) optimized by the Parrot Optimization (PO) algorithm and a joint motion angle prediction model combining Residual Network (ResNet) with Gated Recurrent Unit (GRU) for the two aspects of signal processing and predictive modeling, respectively. First, for the two motion conditions of level walking and stair climbing, sEMG signals from the rectus femoris, vastus lateralis, semitendinosus, and biceps femoris, as well as the motion angles of the hip and knee joints, were simultaneously collected from five healthy subjects, yielding a total of 400 gait cycle data points. The sEMG signals were denoised using the method combining PO-SVMD with wavelet thresholding. Compared with denoising methods such as Empirical Mode Decomposition, Partial Ensemble Empirical Mode Decomposition, Independent Component Analysis, and wavelet thresholding alone, the signal-to-noise ratio (SNR) of the proposed method was increased to a maximum of 23.42 dB. Then, the gait cycle information was divided into training and testing sets at a 4:1 ratio, and five models—ResNet-GRU, Transformer-LSTM, CNN-GRU, ResNet, and GRU—were trained and tested individually using the processed sEMG signals as input and the hip and knee joint movement angles as output. Finally, the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²) were used as evaluation metrics for the test results. The results show that for both motion conditions, the evaluation metrics of the ResNet-GRU model in the test results are superior to those of the other four models. The optimal evaluation metrics for level walking are 2.512 ± 0.415°, 1.863 ± 0.265°, and 0.979 ± 0.007, respectively, while the optimal evaluation metrics for stair climbing are 2.475 ± 0.442°, 2.012 ± 0.336°, and 0.98 ± 0.009, respectively. The method proposed in this study achieves improvements in both signal processing and predictive modeling, providing a new method for research on lower limb motion intention recognition. Full article

(This article belongs to the Special Issue Intelligent Robotics: Autonomous Control, Perception, and Collaboration)

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26 pages, 2445 KB

Open AccessArticle

Image-Based Deep Learning Approach for Drilling Kick Risk Prediction

by Wei Liu, Yuansen Wei, Jiasheng Fu, Qihao Li, Yi Zou, Tao Pan and Zhaopeng Zhu

Processes 2025, 13(10), 3251; https://doi.org/10.3390/pr13103251 (registering DOI) - 13 Oct 2025

Abstract

As oil and gas exploration and development advance into deep and ultra-deep areas, kick accidents are becoming more frequent during drilling operations, posing a serious threat to construction safety. Traditional kick monitoring methods are limited in their multivariate coupling modeling. These models rely [...] Read more.

As oil and gas exploration and development advance into deep and ultra-deep areas, kick accidents are becoming more frequent during drilling operations, posing a serious threat to construction safety. Traditional kick monitoring methods are limited in their multivariate coupling modeling. These models rely too heavily on single-feature weights, making them prone to misjudgment. Therefore, this paper proposes a drilling kick risk prediction method based on image modality. First, a sliding window mechanism is used to slice key drilling parameters in time series to extract multivariate data for continuous time periods. Second, data processing is performed to construct joint logging curve image samples. Then, classical CNN models such as VGG16 and ResNet are used to train and classify image samples; finally, the performance of the model on a number of indicators is evaluated and compared with different CNN and temporal neural network models. Finally, the model’s performance is evaluated across multiple metrics and compared with CNN and time series neural network models of different structures. Experimental results show that the image-based VGG16 model outperforms typical convolutional neural network models such as AlexNet, ResNet, and EfficientNet in overall performance, and significantly outperforms LSTM and GRU time series models in classification accuracy and comprehensive discriminative power. Compared to LSTM, the recall rate increased by 23.8% and the precision increased by 5.8%, demonstrating that its convolutional structure possesses stronger perception and discriminative capabilities in extracting local spatiotemporal features and recognizing patterns, enabling more accurate identification of kick risks. Furthermore, the pre-trained VGG16 model achieved an 8.69% improvement in accuracy compared to the custom VGG16 model, fully demonstrating the effectiveness and generalization advantages of transfer learning in small-sample engineering problems and providing feasibility support for model deployment and engineering applications. Full article

(This article belongs to the Section Energy Systems)

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22 pages, 3842 KB

Open AccessArticle

Application of Hybrid SMA (Slime Mould Algorithm)-Fuzzy PID Control in Hip Joint Trajectory Tracking of Lower-Limb Exoskeletons in Multi-Terrain Environments

by Wei Li, Xiaojie Wei, Daxue Sun, Zhuoda Jia, Zhengwei Yue and Tianlian Pang

Processes 2025, 13(10), 3250; https://doi.org/10.3390/pr13103250 (registering DOI) - 13 Oct 2025

Abstract

This paper addresses the challenges of inadequate trajectory tracking accuracy and limited parameter adaptability encountered by hip joints in lower-limb exoskeletons operating across multi-terrain environments. To mitigate these issues, we propose a hybrid control strategy that synergistically combines the slime mould algorithm (SMA) [...] Read more.

This paper addresses the challenges of inadequate trajectory tracking accuracy and limited parameter adaptability encountered by hip joints in lower-limb exoskeletons operating across multi-terrain environments. To mitigate these issues, we propose a hybrid control strategy that synergistically combines the slime mould algorithm (SMA) with fuzzy PID control, thereby improving the trajectory tracking performance in such diverse conditions. Initially, we established a dynamic model of the hip joint in the sagittal plane utilizing the Lagrange method, which elucidates the underlying motion mechanisms involved. Subsequently, we designed a fuzzy PID controller that facilitates dynamic parameter adjustment. The integration of the slime mould algorithm (SMA) allows for the optimization of both the quantization factor and the proportional factor of the fuzzy PID controller, culminating in the development of a hybrid control framework that significantly enhances parameter adaptability. Ultimately, we performed a comparative analysis of this hybrid control strategy against conventional PID, fuzzy PID, and PSO-fuzzy PID controls through MATLABR2023b/Simulink simulations as well as experimental tests across a range of multi-terrain scenarios including flat ground, inclines, and stair climbing. The results indicate that in comparison to PID, fuzzy PID, and PSO-fuzzy PID controls, our proposed strategy significantly reduced the adjustment time by 15.06% to 61.9% and minimized the maximum error by 39.44% to 72.81% across various terrains including flat ground, slope navigation, and stair climbing scenarios. Additionally, it lowered the steady-state error ranges by an impressive 50.67% to 90.75%. This enhancement markedly improved the system’s response speed, tracking accuracy, and stability, thereby offering a robust solution for the practical application of lower-limb exoskeletons. Full article

(This article belongs to the Special Issue Design and Control of Complex and Intelligent Systems)

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18 pages, 5841 KB

Open AccessArticle

Supercritical Water Oxidation of Nuclear Cation Exchange Resins: Process Optimization and Reaction Mechanism

by Tiantian Xu, Yanhui Li, Shuzhong Wang, Donghai Xu, Qian Zhang, Yabin Jin and Wenhan Song

Processes 2025, 13(10), 3249; https://doi.org/10.3390/pr13103249 (registering DOI) - 13 Oct 2025

Abstract

This study conducted a systematic investigation of the degradation pathway and process optimization of strong acid cation exchange resins subjected to SCWO. Controlled experiments evaluated the effects of operating temperature, oxidant stoichiometry, initial organic concentration, and residence time. RSM was utilized to refine [...] Read more.

This study conducted a systematic investigation of the degradation pathway and process optimization of strong acid cation exchange resins subjected to SCWO. Controlled experiments evaluated the effects of operating temperature, oxidant stoichiometry, initial organic concentration, and residence time. RSM was utilized to refine the operating parameters, and a second-order regression model (R² = 0.9951) was established to predict COD removal (R_COD), valid within experimental ranges: reaction temperature 400–500 °C, oxidant stoichiometry 80–150%, initial COD 10,000–100,000 mg·L⁻¹, and residence time 1–10 min. COD-dependent NaOH addition could enhance degradation efficiency. The R_COD was sensitive to operating temperature, oxidant stoichiometry, and residence time. Under the optimized conditions of 472 °C, oxidant stoichiometry of 137%, initial COD of 77,216 mg·L⁻¹, and residence time of 4.9 min with the addition of 1.74 wt% NaOH, the R_COD reached 99.92%, which was in close agreement with model predictions. GC-MS analysis of intermediates revealed that sulfonic groups dissociated early, followed by aromatic compounds, particularly phenol, undergoing ring-opening and oxidation to small carboxylic acids and aliphatic species, which were ultimately mineralized to CO₂ and H₂O. These findings provide mechanistic insight into resin decomposition and offer a scientific basis for the safe treatment of radioactive waste resins using SCWO. Full article

(This article belongs to the Special Issue Supercritical Fluid Technology and Its Applications in Separation Processes)

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