Processes

26 pages, 6997 KB

Open AccessArticle

Ultimate Bearing Simulation of an 80 MN Compression–Shear–Torsion Multifunctional Bridge Bearing Testing Machine with a Plate-Column Composite Frame

by Shuzhen Mi, Mengting Chen, Tianyu Li and Jinggan Shao

Processes 2025, 13(10), 3331; https://doi.org/10.3390/pr13103331 - 17 Oct 2025

Abstract

Due to the existing shortcomings of small load and few functions in the current bridge bearing testing machine, a compression–shear–torsion multifunctional bridge bearing testing machine with a maximum vertical load of 80 MN is designed. It can enable five loading tests: static vertical [...] Read more.

Due to the existing shortcomings of small load and few functions in the current bridge bearing testing machine, a compression–shear–torsion multifunctional bridge bearing testing machine with a maximum vertical load of 80 MN is designed. It can enable five loading tests: static vertical compression, static double compression-shear, static single compression-shear, dynamic single compression-shear, and static compression-torsion. To ensure that the testing machine meets the strength and stiffness requirements under the above five ultimate loading conditions, a plate-column composite frame with lateral reaction plates is introduced. Next, the loading states of the bridge bearing and the testing machine under vertical compression, double compression-shear, single compression-shear, and compression-torsion are analyzed. On this basis, five ultimate loading simulations of this testing machine are carried out, respectively, and then compared with those of the traditional testing machine with a sole-column frame. The results show that because the lateral reaction plates increase the bearing area in the vertical direction and bear the load in the shear direction, the maximum stress position is successfully transferred from the high-cost columns to the low-cost lateral reaction plates, and both the maximum stress and the maximum displacement are decreased after introducing the lateral reaction plates. The lateral reaction plates have a great promoting effect on single compression-shear. During ultimate static single compression-shear and dynamic single compression-shear, the maximum total stress of the whole machine is reduced by 18.8% and 24.4%, respectively, and the maximum displacement of the whole machine is reduced by up to 72.5% and 75.0%, respectively. Under the five ultimate loading conditions, this testing machine meets the strength and stiffness requirements, indicating that it can bear the five ultimate loading tests and withstand an ultimate vertical load of 80 MN. Full article

(This article belongs to the Special Issue Finite Element Method and Computational Techniques for Industrial Processes)

14 pages, 3285 KB

Open AccessArticle

Enzymatic Characterisation of a Whole-Cell Biocatalyst Displaying Sucrase A from Bacillus subtilis in Escherichia coli

by Jorge Sánchez-Andrade, Víctor E. Balderas-Hernández and Antonio De Leon-Rodriguez

Processes 2025, 13(10), 3330; https://doi.org/10.3390/pr13103330 - 17 Oct 2025

Abstract

In this study, sucrase A (SacA) from Bacillus subtilis was successfully displayed on the outer membrane of Escherichia coli via fusion with the AIDA-I autotransporter from E. coli. The pAIDA-sacA plasmid was constructed by fusing sacA with the ctxB signal sequence [...] Read more.

In this study, sucrase A (SacA) from Bacillus subtilis was successfully displayed on the outer membrane of Escherichia coli via fusion with the AIDA-I autotransporter from E. coli. The pAIDA-sacA plasmid was constructed by fusing sacA with the ctxB signal sequence and the β-barrel domain of aida gene, enabling surface expression under both aerobic and anaerobic conditions. Functional expression of AIDA–SacA was confirmed by the appearance of reducing sugars in enzymatic assays of sucrose hydrolysis and by acid production on phenol red agar. Structural prediction suggested correct localisation of the catalytic domain on the extracellular surface. Enzymatic characterisation revealed that AIDA-SacA exhibits optimal activity at 40 °C and pH 7. The calculated Km for sucrose was 1.18 mM, while the corresponding Vmax was 2.32 U mL⁻¹. Thermal stability assays showed that the enzyme retained over 80% of its activity after 60 min at 45 °C, indicating notable resistance to moderate temperatures. Metal ion assays indicated that K⁺ enhanced enzymatic activity, while Zn²⁺, Cu²⁺, and Mg²⁺ were inhibitory. SDS-PAGE analysis confirmed the expression of the recombinant fusion protein, with a distinct band at approximately 114 kDa corresponding to the expected size. These results demonstrate the feasibility of employing the AIDA-I system for the surface display of SacA in E. coli, providing a functional platform for future applications in whole-cell biocatalysis. Full article

(This article belongs to the Special Issue Advances in Bioprocess Technology, 2nd Edition)

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28 pages, 1443 KB

Open AccessArticle

Towards Human-Centric, Traceable Negotiation Mechanisms for Sharing Autonomy in Multi-Agent Systems

by Sebastian Wallner, Richard Heininger and Christian Stary

Processes 2025, 13(10), 3329; https://doi.org/10.3390/pr13103329 - 17 Oct 2025

Abstract

Digitalization and the use of autonomous systems challenge companies in new ways, particularly concerning the automatic distribution of tasks. Various approaches are being discussed in research, specifically in the field of multi-agent systems. However, there does not seem to be a universally applicable [...] Read more.

Digitalization and the use of autonomous systems challenge companies in new ways, particularly concerning the automatic distribution of tasks. Various approaches are being discussed in research, specifically in the field of multi-agent systems. However, there does not seem to be a universally applicable and generally accepted solution. Due to the multitude of different approaches and the associated challenges, traceability is of particular interest and requires further analysis. This paper reviews the traceability of different negotiation-based approaches for task allocation in multi-agent systems. We conducted a structured literature review and implemented a prototype using an existing workflow engine for the approach we identified as most suitable, applying a weighted scoring model. The evaluation of the implemented demonstrator, performed both with and without restarting negotiations, indicates that a traceable distribution of tasks with a negotiation mechanism is feasible. Full article

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

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19 pages, 550 KB

Open AccessReview

Compositional Formulations for the Removal and Dissolution of Asphaltene–Resin–Paraffin Deposits in the Near-Wellbore Zone and Tubing Strings

by Nina Lyubchenko, Galina Boiko, Raushan Sarmurzina, Yelena Panova, Bagdaulet Kenzhaliyev and Uzakbay Karabalin

Processes 2025, 13(10), 3328; https://doi.org/10.3390/pr13103328 - 17 Oct 2025

Abstract

The concept of heating the near-wellbore zone (NWZ) using activated aluminum alloys offers a novel approach to enhancing oil recovery. This article reviews research on the development of hydrocarbon-based solvent formulations for removing asphaltene–resin–paraffin deposits (ARPD) in the NWZ and restoring well productivity. [...] Read more.

The concept of heating the near-wellbore zone (NWZ) using activated aluminum alloys offers a novel approach to enhancing oil recovery. This article reviews research on the development of hydrocarbon-based solvent formulations for removing asphaltene–resin–paraffin deposits (ARPD) in the NWZ and restoring well productivity. A comprehensive analysis of ARPD composition enabled the selection of solvent systems tailored to specific deposit types. The efficiency of ARPD removal from the NWZ, downhole equipment, and oil gathering systems in heavy and highly viscous Kazakhstani crude oils was evaluated using hydrocarbon solvent blends (e.g., hexane–toluene, gasoline–o-xylene, o-xylene–hexane–1-hexene) with surfactants (polyoxyethylene sorbitan–maleic anhydride esters), atactic polypropylene (APP), and activated aluminum alloys. The developed formulations accelerated ARPD breakdown and reduced energy consumption. It has been established that the optimal concentration of APP (0.5 wt.%) provides up to 100% cleaning efficiency and increases dissolving capacity by 25–30% compared to traditional binary systems. Cleaning efficiency is driven by a thermochemical reaction between water and the aluminum alloy, 2Al + 6H₂O → 2Al(OH)₃ + 3H₂↑ + 17 kJ, which depends on the alloy’s microstructure, grain boundary condition, and additive distribution. The exothermic effect of the reaction leads to the formation of a hot gas–steam–hydrogen mixture, where atomic hydrogen actively breaks down ARPD and increases the reservoir permeability by 2 to 4.5 times. Results show that a composite formulation of hexane–toluene–alloy–H₂O₂ (46.5:15:0.25:38.25) reduces the treatment time of ARPD-3 from 60 to 10 min while maintaining high efficiency at the level of 98.3%. Full article

(This article belongs to the Section Materials Processes)

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27 pages, 13228 KB

Open AccessArticle

A Hybrid Machine Learning Pipeline for Reliable Prediction of Potential HIV-1 Inhibitors

by Ciprian-Bogdan Chirila, Lucia Gradinaru and Luminita Crisan

Processes 2025, 13(10), 3327; https://doi.org/10.3390/pr13103327 - 17 Oct 2025

Abstract

The discovery of potent antiviral inhibitors remains a major challenge in combating viral infections. In this study, we present a hybrid computational pipeline that integrates machine learning for accurate prediction of small-molecule HIV-1 inhibitors. Five classification algorithms were trained on 7552 known inhibitors [...] Read more.

The discovery of potent antiviral inhibitors remains a major challenge in combating viral infections. In this study, we present a hybrid computational pipeline that integrates machine learning for accurate prediction of small-molecule HIV-1 inhibitors. Five classification algorithms were trained on 7552 known inhibitors from ChEMBL using five classes of molecular fingerprints. Among these, Random Forest (RFC) models consistently outperformed the others, achieving accuracy values of 0.9526 to 0.9932, while K-Nearest Neighbors (KNN) and Multilayer Perceptron (MLP) models, although slightly less accurate, still demonstrated robust performance, with accuracies ranging from 0.9170 to 0.9482 and 0.9071 to 0.9179 for selected descriptors, respectively. Based on model predictions, 4511 natural compounds from the COCONUT database were identified as potential inhibitors. After 3D shape similarity filtering (Tanimoto Combo > 1 and Shape Tanimoto > 0.8), eight top-ranked compounds were prioritized for further assessment of their physicochemical, ADMET, and drug-likeness properties. Two natural compounds, CNP0194477 and CNP0393067, were identified as the most promising candidates, showing low cardiotoxicity (hERG risk: 0.096 and 0.112), favorable hepatotoxicity and genotoxicity profiles, and good predicted oral absorption. This integrated workflow provides a robust and efficient computational strategy for the identification of natural compounds with antiviral potential, facilitating the selection of promising HIV-1 inhibitors for further experimental validation. Full article

(This article belongs to the Special Issue Natural and Synthetic Compounds: Processes, Challenges, and Opportunities for Human Health and Environmental Sustainability)

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21 pages, 8836 KB

Open AccessArticle

Strain-Softening-Based Elliptical Wellbore Model for Horizontal In-Situ Stress Prediction and Wellbore Stability Analysis in the Wujiaping Formation of Kaijiang-Liangping Block, Eastern Sichuan Basin, Sichuan Province

by Xinrui Yang, Qiang Wang, Ji Xu, Meng Li, Kanhua Su, Qian Li, Liangjun Xu, Qiang Pu, Guanghui Shi, Wen Tang, Chen Jing, Bo Xu and Qifeng Qin

Processes 2025, 13(10), 3326; https://doi.org/10.3390/pr13103326 - 17 Oct 2025

Abstract

Marine shale is highly prone to wellbore collapse due to its high pore pressure, propensity for hydration and swelling, distinct bedding planes, and low tensile strength. Horizontal in situ stress serves as a critical parameter for wellbore stability analysis; however, its accurate prediction [...] Read more.

Marine shale is highly prone to wellbore collapse due to its high pore pressure, propensity for hydration and swelling, distinct bedding planes, and low tensile strength. Horizontal in situ stress serves as a critical parameter for wellbore stability analysis; however, its accurate prediction is extremely challenging in complex geological environments. Conventional studies often simplify the wellbore as a circular shape, neglecting its natural elliptical deformation under non-uniform in situ stress, which leads to reduced predictive accuracy. To address this limitation, this study establishes an elliptical wellbore model that incorporates the strain-softening characteristics of shale. Theoretical models for stress distribution in both elastic and plastic zones were derived. The strain-softening behavior was validated through triaxial compression tests, providing a foundation for analytical solutions of stress distributions around circular and elliptical wellbores. Furthermore, an elliptical wellbore-based model was developed to derive a new prediction equation for horizontal in situ stress. Numerical programming was employed to compute stress distributions, and finite element simulations under various aspect ratios corroborated the theoretical results, showing excellent agreement. Results demonstrate that the elliptical wellbore model captures the near-wellbore stress state more accurately. As the aspect ratio increases, the extreme values of radial and tangential stresses increase significantly, with pronounced stress concentrations observed around the 180° and 360° positions. Predictions of horizontal in situ stress based on the proposed model achieved over 89% accuracy when verified against field data, confirming its reliability. This study overcomes the limitations inherent in the traditional circular wellbore assumption, providing a more precise analytical method for wellbore stability assessment in Marine shale under complex geological conditions. The findings offer a valuable theoretical basis for wellbore stability management and drilling engineering design. Full article

(This article belongs to the Special Issue Development of Advanced Drilling Engineering)

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22 pages, 6479 KB

Open AccessArticle

Spatiotemporal Modeling of Dissolved Oxygen in a Semi-Enclosed Water Body with a LSTM-GRU Hybrid Approach

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

Processes 2025, 13(10), 3325; https://doi.org/10.3390/pr13103325 - 17 Oct 2025

Abstract

The dynamic evolution of dissolved oxygen (DO) concentration is critical for aquatic ecosystem stability and biodiversity, serving as an important water quality indicator. Predicting DO distribution is challenging due to complex hydrodynamic conditions and environmental disturbances. While traditional experimental methods provide accurate short-term [...] Read more.

The dynamic evolution of dissolved oxygen (DO) concentration is critical for aquatic ecosystem stability and biodiversity, serving as an important water quality indicator. Predicting DO distribution is challenging due to complex hydrodynamic conditions and environmental disturbances. While traditional experimental methods provide accurate short-term data, they are limited in spatial coverage, costly, and lack real-time predictive capabilities. Computational fluid dynamics (CFD) simulations, though beneficial for mechanism modeling, suffer from high computational costs and reduced accuracy in long-term, nonlinear predictions. This study addresses these limitations by developing an LSTM-GRU Hybrid Model for predicting DO concentration in Shenzhen Bay. Combining Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), the model enhances time-memory capabilities and parameter efficiency. Results show that the LSTM-GRU Hybrid Model outperforms single neural networks with a correlation coefficient close to 0.99 and RMSE below 0.04 gO₂/m³. This study not only introduces a novel methodology for modeling dissolved oxygen in Shenzhen Bay, but also contributes to advancing predictive capabilities in earth systems environment and offers methodological insights applicable to similar semi-enclosed marine environments. Full article

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

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13 pages, 1426 KB

Open AccessArticle

Bayesian Neural Networks for Quantifying Uncertainty in Solute Transport Through Saturated Porous Media

by Seyed Kourosh Mahjour

Processes 2025, 13(10), 3324; https://doi.org/10.3390/pr13103324 - 17 Oct 2025

Abstract

Uncertainty quantification (UQ) is critical for predicting solute transport in heterogeneous porous media, with applications in groundwater management and contaminant remediation. Traditional UQ methods, such as Monte Carlo (MC) simulations, are computationally expensive and impractical for real-time decision-making. This study introduces a novel [...] Read more.

Uncertainty quantification (UQ) is critical for predicting solute transport in heterogeneous porous media, with applications in groundwater management and contaminant remediation. Traditional UQ methods, such as Monte Carlo (MC) simulations, are computationally expensive and impractical for real-time decision-making. This study introduces a novel machine learning framework to address these limitations. We developed a surrogate model for a 2D advection-dispersion solute transport model using a Bayesian Neural Network (BNN). The BNN was trained on a synthetic dataset generated by simulating solute transport across various stochastic permeability and dispersivity fields. Uncertainty was quantified through variational inference, capturing both data-related (aleatoric) and model-related (epistemic) uncertainties. We evaluated the framework’s performance against traditional MC simulations. Our BNN model accurately predicts solute concentration distributions with a mean squared error (MSE) of 9.8 ×

10^{- 5}

, significantly outperforming other machine learning surrogates. The framework successfully quantifies uncertainty, providing calibrated confidence intervals that align closely with the spread of the MC results. The proposed approach achieved a 98.5% reduction in computational time compared to a standard Monte Carlo simulation with 1000 realizations, representing a 65-fold speed-up. A sensitivity analysis revealed that permeability field heterogeneity is the dominant source of uncertainty in plume migration. The developed machine learning framework offers a computationally efficient and robust alternative for quantifying uncertainty in solute transport models. By accurately predicting solute concentrations and their associated uncertainties, our approach can inform risk-based decision-making in environmental and hydrogeological applications. The method shows promise for scaling to more complex, three-dimensional systems. Full article

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

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16 pages, 472 KB

Open AccessArticle

Integrating the I–S Model and FMEA for Process Optimization in Packaging and Printing Industry

by Shun-Hsing Chen and Huay-In Yan

Processes 2025, 13(10), 3323; https://doi.org/10.3390/pr13103323 - 16 Oct 2025

Abstract

This study investigates the determinants of service demand in the packaging and printing industry, identifying 19 key factors through expert evaluation. These factors were analyzed using the Importance–Satisfaction (I–S) Model to pinpoint areas requiring enhancement, with four elements classified within the improvement zone. [...] Read more.

This study investigates the determinants of service demand in the packaging and printing industry, identifying 19 key factors through expert evaluation. These factors were analyzed using the Importance–Satisfaction (I–S) Model to pinpoint areas requiring enhancement, with four elements classified within the improvement zone. Considering resource constraints, improvement priorities were established through a modified Risk Priority Number (RPN) framework derived from Failure Modes and Effects Analysis (FMEA), expressed as RPN = I × F × E. The highest-priority areas for improvement included product pricing, flexibility in meeting customer requirements, suppliers’ emergency response capabilities, and proactive communication regarding raw material price fluctuations. The findings indicate that consumers balance price against sustainability value, highlighting the necessity of setting prices that align with perceived value to sustain trust and meet expectations. Strengthening firms’ emergency response mechanisms and developing an online standard operating procedure (SOP) notification system for raw material price changes can enhance communication efficiency, increase transparency in pricing, and ultimately improve organizational competitiveness. Full article

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

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17 pages, 1483 KB

Open AccessArticle

Synergistic Promotion Strategies for Ni-Based Catalysts in Methane Dry Reforming: Suppressing Sintering and Carbon Deposition

by Xianghong Fang, Fuchu Qin, Lian Peng, Mengying Lv and Han Zeng

Processes 2025, 13(10), 3322; https://doi.org/10.3390/pr13103322 - 16 Oct 2025

Abstract

Methane dry reforming (DRM) represents a promising route for the simultaneous valorization of CH₄ and CO₂ into syngas; however, conventional Ni-based catalysts suffer from rapid deactivation due to sintering and carbon deposition. In this work, we present a synergistically engineered Ni-based [...] Read more.

Methane dry reforming (DRM) represents a promising route for the simultaneous valorization of CH₄ and CO₂ into syngas; however, conventional Ni-based catalysts suffer from rapid deactivation due to sintering and carbon deposition. In this work, we present a synergistically engineered Ni-based catalyst integrating hierarchical SiC confinement, Pd promotion via oleic-acid-assisted complexation, and MgO surface modification to overcome these challenges. Under optimized reaction conditions (CH₄/CO₂ = 1:1, 750 °C, GHSV = 36,000 mL g⁻¹ h⁻¹), the multifunctional NiPd/Si–xMg catalyst achieved steady-state conversions of 85% for CH₄ and 84% for CO₂, maintaining an H₂/CO ratio close to 1.0 over 100 h of continuous operation without noticeable deactivation. In contrast, the reference Ni/SiC and Ni/MgO catalysts exhibited initial conversions of 75–80% but declined by more than 50% within the same period, confirming the superior durability of the optimized system. Thermogravimetric analysis (TGA) revealed a drastic reduction in carbon deposition—from 119.0 mg C g⁻¹ for Ni/SiC to 81.4 mg C g⁻¹ for NiPd/Si-xMg—indicating enhanced coke resistance. Transmission electron microscopy (TEM) confirmed uniform Ni dispersion with an average particle size of 7.2 ± 1.8 nm, while H₂-TPR and CO₂-TPD analyses demonstrated improved reducibility and surface basicity. The combination of SiC confinement, Pd-induced hydrogen spillover, and MgO-mediated CO₂ activation effectively mitigated sintering and carbon accumulation, resulting in high activity, stability, and carbon tolerance. This integrated catalyst design provides a robust pathway toward industrially viable DRM systems for sustainable syngas production. Full article

(This article belongs to the Section Catalysis Enhanced Processes)

18 pages, 1809 KB

Open AccessArticle

Transformer Fault Diagnosis Method Based on Improved Particle Swarm Optimization and XGBoost in Power System

by Yuanhao Zheng, Chaoping Rao, Fei Wang and Hongbo Zou

Processes 2025, 13(10), 3321; https://doi.org/10.3390/pr13103321 - 16 Oct 2025

Abstract

Fault prediction and diagnosis are critical for enhancing the maintenance and reliability of power system equipment, reducing operational costs, and preventing potential failures. In power transformers, periodic oil sampling and gas ratio analysis provide valuable insights for predictive maintenance and life-cycle assessment. Machine [...] Read more.

Fault prediction and diagnosis are critical for enhancing the maintenance and reliability of power system equipment, reducing operational costs, and preventing potential failures. In power transformers, periodic oil sampling and gas ratio analysis provide valuable insights for predictive maintenance and life-cycle assessment. Machine learning methods, such as XGBoost, have proven to deliver more accurate results, especially when historical data is limited. However, the performance of XGBoost is highly dependent on the optimization of its hyperparameters. To address this, this paper proposes an improved Particle Swarm Optimization (IPSO) method to optimize the hyperparameters of XGBoost for transformer fault diagnosis. The PSO algorithm is enhanced by introducing topology optimization, adaptively adjusting the acceleration factor, dividing the swarm into master–slave particle groups to strengthen search capability, and dynamically adjusting inertia weights using a linear adaptive strategy. IPSO is applied to optimize key hyperparameters of the XGBoost model, improving both its diagnostic accuracy and generalization ability. Experimental results confirm the effectiveness of the proposed model in enhancing fault prediction and diagnosis in power systems. Full article

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

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21 pages, 7786 KB

Open AccessArticle

Engineered Mors1 Enzyme from the Antarctic Bacterium Moraxella TA144 for Enhanced Thermal Stability and Activity for Polyethylene Terephthalate Degradation

by Satyam Satyam and Sanjukta Patra

Processes 2025, 13(10), 3320; https://doi.org/10.3390/pr13103320 - 16 Oct 2025

Abstract

Plastic pollution, particularly from polyethylene terephthalate (PET), poses significant environmental concerns due to ecosystem persistence and extensive packaging use. Conventional recycling methods face inefficiencies, high costs, and limited scalability, necessitating sustainable alternatives. Biodegradation via PET hydrolases offers promising eco-friendly solutions, although most natural [...] Read more.

Plastic pollution, particularly from polyethylene terephthalate (PET), poses significant environmental concerns due to ecosystem persistence and extensive packaging use. Conventional recycling methods face inefficiencies, high costs, and limited scalability, necessitating sustainable alternatives. Biodegradation via PET hydrolases offers promising eco-friendly solutions, although most natural PET-degrading enzymes are thermophilic and require energy-intensive high temperatures. In contrast, psychrophilic enzymes function efficiently at extremely low temperatures but often lack stability under moderate conditions. Therefore, this study aimed to enhance the ability of the Mors1 enzyme from Moraxella TA144 to operate effectively under mesophilic conditions, which is closer to the optimal conditions for environmental application. Three strategic hydrophobic substitutions (K93I, E221I, and R235F) were introduced in loop regions, generating the mutant variant Mors1^MUT. Comparative characterization revealed that Mors1^MUT retained 98% of its activity at pH 9 and displayed greater resilience across both acidic and alkaline conditions than did the wild-type enzyme. Thermal stability assays revealed that Mors1^MUT preserved 61% of its activity at 40 °C and 14% at 50 °C, whereas the wild-type enzyme was fully inactivated at these temperatures. The enzymatic hydrolysis of PET films significantly improved with Mors1^MUT. Gravimetric analysis revealed weight losses of 0.83% for Mors1^WT and 3.46% for Mors1^MUT after a 12-day incubation period. This corresponds to a 4.16-fold increase in hydrolysis efficiency, confirming the enhanced catalytic performance of the mutant variant. The improvement was further validated by scanning electron microscopy (SEM), atomic force microscopy (AFM), and attenuated total reflectance–Fourier transform infrared (ATR-FTIR) analysis. Optimization of the reaction parameters through response surface methodology (enzyme load, time, pH, temperature, and agitation) confirmed increased PET hydrolysis under mild mesophilic conditions. These findings establish Mors1^MUT as a robust mesophilic PETase with enhanced catalytic efficiency and thermal stability, representing a promising candidate for sustainable PET degradation under environmentally relevant conditions. Full article

(This article belongs to the Special Issue Biochemical Processes for Sustainability, 2nd Edition)

23 pages, 6803 KB

Open AccessArticle

An Investigation of Water–Heat–Force Coupling During the Early Stage of Shaft Wall Pouring in Thick Topsoil Utilizing the Freezing Method

by Yue Yuan, Jianyong Pang, Jiuqun Zou and Chi Zhang

Processes 2025, 13(10), 3319; https://doi.org/10.3390/pr13103319 - 16 Oct 2025

Abstract

The freezing method is widely employed in the construction of a vertical shaft in soft soil and water-rich strata. As the construction depth increases, investigating the water–heat–force coupling effects induced by the hydration heat (internal heat source) of concrete is crucial for the [...] Read more.

The freezing method is widely employed in the construction of a vertical shaft in soft soil and water-rich strata. As the construction depth increases, investigating the water–heat–force coupling effects induced by the hydration heat (internal heat source) of concrete is crucial for the safety of the lining structure and its resistance to cracking and seepage. A three-dimensional coupled thermal–hydraulic–mechanical analysis model was developed, incorporating temperature and soil relative saturation as unknown variables based on heat transfer in porous media, unsaturated soil seepage, and frost heave theory. The coefficient type PDE module in COMSOL was used for secondary development to solve the coupling equation, and the on-site temperature and pressure monitoring data of the frozen construction process were compared. This study obtained the model-related parameters and elucidated the evolution mechanism of freeze–thaw and freeze–swelling pressures of a frozen wall under the influence of hydration heat. The resulting model shows that the maximum thaw depth of the frozen wall reaches 0.3576 m after 160 h of pouring, with an error rate of 4.64% compared to actual measurements. The peak temperature of the shaft wall is 73.62 °C, with an error rate of 3.76%. The maximum influence range of hydration heat on the frozen temperature field is 1.763 m. The peak freezing pressure is 4.72 MPa, which exhibits a 5.03% deviation from the actual measurements, thereby confirming the reliability of the resulting model. According to the strength growth pattern of concrete and the freezing pressure bearing requirements, it can provide a theoretical basis for quality control of the lining structure and a safety assessment of the freezing wall. Full article

(This article belongs to the Special Issue Optimization of Complex Engineering Systems and Application of Advanced Digital and Intelligent Technologies)

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13 pages, 5881 KB

Open AccessArticle

Numerical Simulation on the Propagation Behaviour of Hydraulic Fractures in Sandstone–Shale Interbeds

by Shasha Li, Yunyang Li and Wan Cheng

Processes 2025, 13(10), 3318; https://doi.org/10.3390/pr13103318 - 16 Oct 2025

Abstract

In the shale oil reservoirs, sandstone and shale often overlie each other. This significantly affects the vertical propagation of hydraulic fractures (HFs); however, the underlying mechanisms still remain unclear. This study employs Xsite software to investigate the influence of rock fracture toughness, tensile [...] Read more.

In the shale oil reservoirs, sandstone and shale often overlie each other. This significantly affects the vertical propagation of hydraulic fractures (HFs); however, the underlying mechanisms still remain unclear. This study employs Xsite software to investigate the influence of rock fracture toughness, tensile strength, elastic modulus, Poisson’s ratio, interlayer stress contrast, and the flow rate and viscosity of fracturing fluid on the propagation behaviour of HFs in sandstone–shale interbeds. As the type-I fracture toughness of the shale layer increases, the area of the vertical HF decreases and the average HF width becomes smaller. As the tensile strength of the sandstone layer increases, the distribution range of fluid pressure at the interface expands. The HF prefers to propagate in the softer rock rather than the harder one. A relatively narrower HF width is created in the layer with a higher elastic modulus resulting in a higher flow resistance to fracturing fluid. A shale layer with a high Poisson’s ratio is more likely to undergo a lateral expansion, causing stress at the fracture tip to be dispersed. When the effect of lithological interfaces is considered, an increasing interlayer stress contrast causes HFs to gradually transition from penetrating the interfaces to becoming confined between the two interfaces. When the influence of the lithological interface is not considered, an increasing interlayer stress contrast causes the HF to gradually transition from a penny-shaped fracture to a blade-shaped fracture. The HF penetrates the interfaces more easily at a higher injection rate and fluid viscosity, because most of the injected energy is used to create new fractures rather than leakoff into the interfaces. Understanding the influence of these factors on the HF propagation behaviour is of great significance for optimising hydraulic fracturing design. Full article

(This article belongs to the Special Issue Advances in Oil and Gas Reservoir Modeling and Simulation)

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12 pages, 3193 KB

Open AccessArticle

Phase Transformation of Fayalite from Copper Slag During Oxidation Roasting

by Xiaoxue Zhang, Yuqi Zhao, Huili Zhou, Xiangyu Wang, Zhonglin Gao and Hongyang Wang

Processes 2025, 13(10), 3317; https://doi.org/10.3390/pr13103317 - 16 Oct 2025

Abstract

The phase transformation of fayalite from copper slag during oxidation roasting was systematically studied in this work with an analysis using X-ray diffraction, X-ray photoelectron spectroscopy, vibrating sample magnetometer, scanning electronic microscope, and energy dispersive spectrometer. The results show that the oxidation of [...] Read more.

The phase transformation of fayalite from copper slag during oxidation roasting was systematically studied in this work with an analysis using X-ray diffraction, X-ray photoelectron spectroscopy, vibrating sample magnetometer, scanning electronic microscope, and energy dispersive spectrometer. The results show that the oxidation of fayalite occurs at ≥300 °C. Fayalite is first oxidized into amorphous Fe₃O₄ and SiO₂ during oxidation roasting. The former then converts into Fe₂O₃ while the latter converts into cristobalite solid solution with increasing temperature. Meanwhile, the specific saturation magnetization of roasted products increases from 9.43 emu/g at 300 °C to 20.66 emu/g at 700 °C, and then decreases to 7.31 emu/g at 1100 °C. The migration of iron in fayalite is prior to that of silicon during oxidation roasting. Therefore, the thickness of the iron oxide layer on the particle surface steadily increases with roasting temperature, from about 1.0 μm at 800 °C to about 5.0 μm at 1100 °C. This study has guiding significance for the iron grain growth in copper slag during the oxidation-reduction roasting process. Full article

(This article belongs to the Special Issue Non-ferrous Metal Metallurgy and Its Cleaner Production)

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16 pages, 4202 KB

Open AccessArticle

A Novel Intake Inflow Performance Relationship for Optimizing Pump Setting Depth in Low-Permeability Oil Wells

by Qionglin Shi, Junjian Li, Lei Wang, Bin Liu, Jin Shu, Yabo Li and Guoqing Han

Processes 2025, 13(10), 3316; https://doi.org/10.3390/pr13103316 - 16 Oct 2025

Abstract

The optimization of pump setting depth in low-permeability oil wells remains a persistent challenge, as conventional inflow performance relationship (IPR) curves fail to capture the coupled effects of downhole pump intake depth and reservoir productivity. To address this limitation, this study proposes a [...] Read more.

The optimization of pump setting depth in low-permeability oil wells remains a persistent challenge, as conventional inflow performance relationship (IPR) curves fail to capture the coupled effects of downhole pump intake depth and reservoir productivity. To address this limitation, this study proposes a novel Low-Permeability Intake Inflow Performance Relationship (LIIPR) framework. The method establishes a theoretical link between pump depth and production by integrating low-permeability reservoir inflow models with multiphase wellbore flow calculations. On this basis, a series of derivative concepts and analytical tools are introduced, including (i) a three-zone classification of inflow curves to distinguish effective, inefficient, and abnormal production regimes; (ii) a multi-pump-depth analysis to determine the feasible range and optimal boundaries of pump setting depth; and (iii) a three-dimensional deep-pumping limit map that couples inflow and outflow dynamics through nodal analysis, providing a comprehensive criterion for system optimization. The proposed LIIPR methodology enables accurate identification of optimal pump depth and intake pressure conditions, overcoming the ambiguity of traditional IPR-based approaches. Unlike previous IPR- or EIPR-based methods, LIIPR introduces for the first time a unified inflow–outflow coupling framework that quantitatively links pump intake depth with well productivity. This integration represents a novel theoretical and computational advance for deep-pumping optimization in low-permeability reservoirs. Applications for field cases in Shengli Oilfield confirm the theoretical findings and demonstrate the practical potential of the method for guiding efficient deep pumping operations in low-permeability reservoirs. Full article

(This article belongs to the Section Energy Systems)

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15 pages, 864 KB

Open AccessArticle

Prediction of Wax Deposition Rate of Waxy Crude Oil Based on Improved Elman Neural Network

by Wenbo Jin, Zhuo Chen, Kemin Dai, Qing Quan and Zongxiao Ren

Processes 2025, 13(10), 3315; https://doi.org/10.3390/pr13103315 - 16 Oct 2025

Abstract

The influencing factors of wax deposition are numerous and complex, and accurately predicting the wax deposition rate is of great practical significance for the safe operation of pipelines and the formulation of reasonable pigging schemes. On the basis of mastering the prediction steps [...] Read more.

The influencing factors of wax deposition are numerous and complex, and accurately predicting the wax deposition rate is of great practical significance for the safe operation of pipelines and the formulation of reasonable pigging schemes. On the basis of mastering the prediction steps of the Elman neural network (ENN), the arithmetic optimization algorithm (AOA) was introduced to improve the Elman neural network and an optimization model was established, and the differences in prediction results between improved models (AOA-ENN model, PSO-ENN model, GA-ENN model) and the traditional ENN model were compared and analyzed through examples. The prediction results of three examples showed that the average relative errors of the AOA-ENN model are 2.5470%, 1.4974%, and 2.3819 %, respectively, while the average relative errors of the traditional ENN model are 19.0313%, 9.1568%, and 11.4836%, respectively. Therefore, the arithmetic optimization algorithm used in this paper has good reliability. For the three improved models, the AOA-ENN model has the highest prediction accuracy, followed by the PSO-ENN model and the GA-ENN model. Overall, the Elman neural network improved by an arithmetic optimization algorithm can be used for predicting wax deposition rate, which can provide new ideas for accurate prediction of wax deposition rate. Full article

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

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27 pages, 43811 KB

Open AccessArticle

Development of a Chestnut Shell Bio-Adsorbent for Cationic Pollutants: Encapsulation in an Alginate Carrier for Application in a Flow System

by Atef Aljnin, Gorica Cvijanović, Bojan Stojadinović, Milutin Milosavljević, Katarina Simić, Aleksandar D. Marinković and Nataša Đ. Knežević

Processes 2025, 13(10), 3314; https://doi.org/10.3390/pr13103314 - 16 Oct 2025

Abstract

Melanin-based biosorbents (MiCS), derived from chestnut shells, were encapsulated in sodium alginate to obtain MiCS@Alg, useful in a column adsorption study. MiCS contains various acidic surface groups able to participate in the removal of cationic pollutants from aqueous solutions. The MiCS and MiCS@Alg [...] Read more.

Melanin-based biosorbents (MiCS), derived from chestnut shells, were encapsulated in sodium alginate to obtain MiCS@Alg, useful in a column adsorption study. MiCS contains various acidic surface groups able to participate in the removal of cationic pollutants from aqueous solutions. The MiCS and MiCS@Alg were characterized by Fourier-transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and Dynamic Light Scattering (DLS), while zeta potential and particle size analyses were performed to gain deeper insight into surface charge behavior. Batch adsorption experiments were carried out at three different temperatures, demonstrating that the adsorption kinetics followed a pseudo-second-order (PSO) model and that the Freundlich model best described the equilibrium data. The process was found to be endothermic and spontaneous, with maximum adsorption capacities of 300.2 mg g⁻¹ (BR2), 201.5 mg g⁻¹ (BY28) and 73.08 mg g⁻¹ (NH₃) on MiCS, and 189.3 mg g⁻¹ (BR2), 117.1 mg g⁻¹ (BY28) and 50.06 mg g⁻¹ (NH₃) on MiCS@Alg at 45 °C and compared with the unmodified chestnut shell. The MiCS and MiCS@Alg exhibited good adsorption performance, improved environmental compatibility, and greater reusability. Overall, these results highlight MiCS@Alg as a cost-effective, sustainable, and highly promising novel biosorbent for the removal of cationic pollutants (BR2, BY28, and NH₃) from water. Full article

(This article belongs to the Special Issue Physical and Chemical Removal of Recalcitrant Pollutants Using Novel Solids: Nanomaterials, Biochar, and Waste-Derived Biomaterials)

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25 pages, 12285 KB

Open AccessArticle

Integrated Geophysical Hydrogeological Characterization of Fault Systems in Sandstone-Hosted Uranium In Situ Leaching: A Case Study of the K₁b² Ore Horizon, Bayin Gobi Basin

by Ke He, Yuan Yuan, Yue Sheng and Hongxing Li

Processes 2025, 13(10), 3313; https://doi.org/10.3390/pr13103313 - 16 Oct 2025

Abstract

This study presents an integrated geophysical and hydrogeological characterization of fault systems in the sandstone-hosted uranium deposit within the K₁b² Ore Horizon of the Bayin Gobi Basin. Employing 3D seismic exploration with 64-fold coverage and advanced attribute analysis techniques (including [...] Read more.

This study presents an integrated geophysical and hydrogeological characterization of fault systems in the sandstone-hosted uranium deposit within the K₁b² Ore Horizon of the Bayin Gobi Basin. Employing 3D seismic exploration with 64-fold coverage and advanced attribute analysis techniques (including coherence volumes, ant-tracking algorithms, and LOW_FRQ spectral attenuation), the research identified 18 normal faults with vertical displacements up to 21 m, demonstrating a predominant NE-oriented structural pattern consistent with regional tectonic features. The fracture network analysis reveals anisotropic permeability distributions (31.6:1–41.4:1 ratios) with microfracture densities reaching 3.2 fractures/km² in the central and northwestern sectors, significantly influencing lixiviant flow paths as validated by tracer tests showing 22° NE flow deviations. Hydrogeological assessments indicate that fault zones such as F11 exhibit 3.1 times higher transmissivity (5.3 m²/d) compared to non-fault areas, directly impacting in situ leaching (ISL) efficiency through preferential fluid pathways. The study establishes a technical framework for fracture system monitoring and hydraulic performance evaluation, addressing critical challenges in ISL operations, including undetected fault extensions that caused lixiviant leakage incidents in field cases. These findings provide essential geological foundations for optimizing well placement and leaching zone design in structurally complex sandstone-hosted uranium deposits. The methodology combines seismic attribute analysis with hydrogeological validation, demonstrating how fault systems control fluid flow dynamics in ISL operations. The results highlight the importance of integrated geophysical approaches for accurate structural characterization and operational risk mitigation in uranium mining. Full article

(This article belongs to the Special Issue Exploration, Exploitation and Utilization of Coal and Gas Resources, 2nd Edition)

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14 pages, 1797 KB

Open AccessArticle

Identification of Key Parameters for Fracturing and Driving Oil in Low-Permeability Offshore Reservoirs Based on Fuzzy Analytic Hierarchy Process and Numerical Simulation

by Dianju Wang, Yanfei Zhou, Haixiang Zhang, Yan Ge, Lingtong Liu and Zhandong Li

Processes 2025, 13(10), 3312; https://doi.org/10.3390/pr13103312 - 16 Oct 2025

Abstract

The fracturing and driving oil technology used in shale oil provides a new approach for the development of offshore low-permeability reservoirs. However, the main control role of technical parameters is unclear, resulting in unsatisfactory accuracy and effectiveness of the enhanced oil recovery plan. [...] Read more.

The fracturing and driving oil technology used in shale oil provides a new approach for the development of offshore low-permeability reservoirs. However, the main control role of technical parameters is unclear, resulting in unsatisfactory accuracy and effectiveness of the enhanced oil recovery plan. For this reason, this study is based on the production and process data of five wells in the WZ oilfield. Fuzzy analytic hierarchical process analysis method (FAHP) was used to evaluate the parameter weights. Combined with numerical simulation technology, the evaluation results were verified by geological-engineering integration. The results show that in offshore low-permeability oilfields, the reservoir pressure coefficient has the greatest influence on the fracturing and oil repelling effect. The comprehensive weight reaches 0.450 compared to not adopting hydraulic fracturing oil displacement technology. This improves the recovery rate by 10.19% in 5 years. The surfactant concentration and the residual oil saturation of the reservoir rank are second, with a comprehensive weight of 0.219. Finally is the effective thickness of the reservoir, with a comprehensive weight of 0.113. In this study, the key parameters of fracturing and oil repelling in offshore low-permeability reservoirs are clarified. It provides theoretical basis and practical support for improving the success rate of well selection, layer selection and recovery capacity. Full article

(This article belongs to the Section Sustainable Processes)

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15 pages, 1079 KB

Open AccessArticle

A Multi-Granularity Random Mutation Genetic Algorithm for Steel Cold Rolling Scheduling Optimization

by Hairong Yang, Xiao Ji, Haiyan Sun, Yonggang Li and Weidong Qian

Processes 2025, 13(10), 3311; https://doi.org/10.3390/pr13103311 - 16 Oct 2025

Abstract

Cold rolling is the precision finishing stage in the steel production process, and its scheduling optimization is essential for enhancing production efficiency. To address the complex process constraints and objectives, this paper proposes a multi-granularity random mutation genetic algorithm (MGRM-GA) for cold rolling [...] Read more.

Cold rolling is the precision finishing stage in the steel production process, and its scheduling optimization is essential for enhancing production efficiency. To address the complex process constraints and objectives, this paper proposes a multi-granularity random mutation genetic algorithm (MGRM-GA) for cold rolling scheduling optimization. First, a multi-objective collaborative optimization model is established to integrate the production cost and process constraints. Then, high-quality initial solutions are generated based on greedy heuristic rules to fulfill the cold rolling constraints. Finally, four random mutation strategies are designed at different task granularities and unit levels to search diverse candidates. The standard flexible job shop scheduling problem (FJSP) datasets and practical cold rolling production data are studied to validate the feasibility and competitiveness of the MGRM-GA. Experimental results show that the MGRM-GA achieves a 94.2% improvement in objective function optimization, a 14.8-fold increase in throughput, and a 94.8% reduction in execution time on cold rolling data. Compared with the heuristic mutation algorithm, MGRM-GA increases population heterogeneity and avoids premature convergence, which enhances global search ability and scheduling performance. Full article

(This article belongs to the Special Issue Industrial IoT-Enabled Modeling and Optimization for the Process Industry—2nd Edition)

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18 pages, 6703 KB

Open AccessArticle

Three-Dimensional Study of Contact Melting of a Molten Material Crust Against a Stainless Steel Plate During a Severe Reactor Accident

by Junjie Ma, Yuqing Chen, Wenzhen Chen and Hongguang Xiao

Processes 2025, 13(10), 3310; https://doi.org/10.3390/pr13103310 - 16 Oct 2025

Abstract

In severe reactor accidents, molten corium solidifies within the core to form a corium crust. Under decay heat, the high-temperature corium crust induces contact melting of internal reactor components. Given the narrow and limited dimensions of these components, this study investigated the contact [...] Read more.

In severe reactor accidents, molten corium solidifies within the core to form a corium crust. Under decay heat, the high-temperature corium crust induces contact melting of internal reactor components. Given the narrow and limited dimensions of these components, this study investigated the contact melting of a corium crust against a stainless steel plate. A three-dimensional plate contact melting model for plate-shaped corium is proposed, with its validity demonstrated through experimental verification. The patterns and factors influencing contact melting were analyzed. The results indicate that under constant heat flux boundary conditions, the melting rate depends solely on the magnitude of the heat flux density, while the effects of the contact surface geometry and heat source mass on the melting rate are negligible. The thickness of the molten liquid film is proportional to both the heat flux density and contact surface area, yet inversely proportional to both the heat source mass and aspect ratio of the contact surface. When the aspect ratio exceeds six, the model can be simplified to two dimensions. Full article

(This article belongs to the Section Energy Systems)

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13 pages, 3471 KB

Open AccessArticle

Research on Stuck Pipe Prediction Based on Supervised and Unsupervised Ensemble Learning

by Boyi Xia, Yiwei Wang, Qihao Li, Xianzhi Song, Zhaopeng Zhu, Muchen Liu and Yanlong Yang

Processes 2025, 13(10), 3309; https://doi.org/10.3390/pr13103309 - 16 Oct 2025

Abstract

Stuck pipe is a common and serious accident in oil drilling processes, which may lead to huge economic losses and safety risks. In recent years, the rapid development of artificial intelligence technology has provided new ideas for stuck pipe prediction. Existing intelligent prediction [...] Read more.

Stuck pipe is a common and serious accident in oil drilling processes, which may lead to huge economic losses and safety risks. In recent years, the rapid development of artificial intelligence technology has provided new ideas for stuck pipe prediction. Existing intelligent prediction studies on stuck pipe mostly focus on the optimization and application of a single unsupervised or supervised algorithm, or the research on simple ensemble learning of these two types of algorithms. This paper proposes a stuck pipe prediction method based on mechanism constraints and a deep learning ensemble model. By integrating the advantages of mechanism constraints and various time-series data processing models, this method achieves accurate prediction of stuck pipe. The method first performs preprocessing, feature engineering, and mechanism constraints on multi-parameter time-series data during drilling, then constructs three models, namely, Autoencoder, BiLSTM, and Transformer, for feature extraction and preliminary prediction, respectively. Finally, it integrates the prediction results of multiple models through a meta-model to improve prediction accuracy. The experimental results show that after introducing mechanism constraints, the accuracy of each model increases by an average of 10%. For the stuck pipe prediction task, the accuracy and precision of the proposed ensemble model reach 90.1% and 95.9%, respectively. Compared with single models, the ensemble model achieves an optimal balance between the false alarm rate and missing alarm rate, which are 7.7% and 11.0%, respectively. Its comprehensive performance is significantly better than that of single models, which can provide effective risk early warning for drilling operations. Full article

(This article belongs to the Section AI-Enabled Process Engineering)

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18 pages, 5006 KB

Open AccessArticle

Hazardous Gas Emission Laws in Tunnels Based on Gas–Solid Coupling

by Yansong Li, Peidong Su, Li Luo, Yougui Li, Weihua Liu and Junjie Yang

Processes 2025, 13(10), 3308; https://doi.org/10.3390/pr13103308 - 16 Oct 2025

Abstract

This study investigates the mechanisms of hazardous gas outbursts in geologically complex non-coal tunnels. This is a critical safety concern during excavation, particularly at specific locations and during time-sensitive periods. To address this, a gas–solid coupled numerical model is established to simulate gas [...] Read more.

This study investigates the mechanisms of hazardous gas outbursts in geologically complex non-coal tunnels. This is a critical safety concern during excavation, particularly at specific locations and during time-sensitive periods. To address this, a gas–solid coupled numerical model is established to simulate gas seepage processes under such conditions. The simulations systematically reveal the spatiotemporal evolutionary patterns of the velocity and direction of the gas seepage and elucidate the migration mechanism driven by excavation-induced pressure gradients. The model specifically analyzes how geological structures, such as rock joints and fractures, control the seepage pathways. The model also demonstrates the dynamic variations in and enrichment behavior of the gas escape velocities near these discontinuities. Field measurements obtained from the Hongdoushan Tunnel validated the simulated emission patterns along jointed fissures. The findings clarify the intrinsic relationships between the outburst dynamics and key factors that include pressure differentials, geological structures, and temporal effects. This work provides a crucial theoretical foundation and practical strategy for the prediction and prevention of hazardous gas disasters in analogous tunnel engineering projects, thereby enhancing overall construction safety. Full article

(This article belongs to the Special Issue Numerical Simulation and Engineering Application of Rock Mechanics and Geotechnical Engineering (Second Edition))

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17 pages, 2524 KB

Open AccessArticle

Assessing Soil and Water Pollution: A Case Study of an Abandoned Coal Mine for Remediation and Repurposing in Mpumalanga Province, South Africa

by Nkanyiso Mlalazi, Charles Mbohwa, Shumani Ramuhaheli and Ngonidzashe Chimwani

Processes 2025, 13(10), 3307; https://doi.org/10.3390/pr13103307 - 15 Oct 2025

Abstract

Despite South Africa’s robust environmental legislation governing the mining industry, abandoned coal mines persist as a significant environmental concern, largely due to some companies evading accountability. This study assesses the level of contamination at an abandoned coal mine site in Mpumalanga, South Africa, [...] Read more.

Despite South Africa’s robust environmental legislation governing the mining industry, abandoned coal mines persist as a significant environmental concern, largely due to some companies evading accountability. This study assesses the level of contamination at an abandoned coal mine site in Mpumalanga, South Africa, and proposes preliminary remediation strategies and potential site repurposing options. The analysis included measuring parameters such as pH, electrical conductivity (EC), sulphates (SO₄), calcium (Ca), iron (Fe), manganese (Mn), magnesium (Mg), and lead (Pb) in both soil and water samples. Additionally, soil samples were analyzed for ammonia (NH₃), while water samples were analyzed to determine total suspended solids (TSSs) and total dissolved solids (TDSs). The results revealed that soil samples exceeded prescribed thresholds for SO₄ and Pb, according to Soil Screening Values 1 (SSV1) for protection of land and resources. Water samples also showed exceedances for several parameters, except for Mg and Pb, as per South African National Standards and guidelines. Water quality assessment using the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) yielded scores of 43.33 and 15.56, indicating poor quality for livestock watering and unsuitability for domestic use, respectively. These results suggest threatened water conditions, highlighting significant implications for human health and ecosystem. The study recommends a circular economy-driven approach to environmental remediation, where acid mine drainage is treated using passive systems like constructed wetlands, and phytomining is used to extract valuable metals or minerals. Invasive alien species are harvested and converted into compost, reducing waste and promoting sustainable land use. This approach not only restores the site but also generates economic opportunities through resource recovery, paving the way for sustainable post-mining land uses. Full article

(This article belongs to the Special Issue Advances in Heavy Metal Contaminated Soil and Water Remediation)

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16 pages, 4139 KB

Open AccessArticle

Comparing the Long-Term Stability and Measurement Performance of a Self-Made Integrated Three-in-One Microsensor and Commercial Sensors for Heating, Ventilation, and Air Conditioning (HVAC) Applications

by Chi-Yuan Lee, Jiann-Shing Shieh, Guan-Quan Huang, Chen-Kai Liu, Najsm Cox and Chia-Hao Chou

Processes 2025, 13(10), 3306; https://doi.org/10.3390/pr13103306 - 15 Oct 2025

Abstract

Building on our previous 310-h test of a larger MEMS sensor, this study develops and validates a miniaturized, lift-off-fabricated, and FPC-integrated three-in-one microsensor. In addition to extending the operation to 744 h, we introduce a wireless MQTT/Node-RED architecture to enable real-time IoT-level monitoring [...] Read more.

Building on our previous 310-h test of a larger MEMS sensor, this study develops and validates a miniaturized, lift-off-fabricated, and FPC-integrated three-in-one microsensor. In addition to extending the operation to 744 h, we introduce a wireless MQTT/Node-RED architecture to enable real-time IoT-level monitoring in factory HVAC ducts. The microsensor was fabricated using Micro-electro-mechanical systems (MEMS) technology and integrated with a flexible printed circuit (FPC) for improved mechanical compliance and ease of installation. To assess its durability and reliability, a 744-h long-term test was conducted in an industrial HVAC environment, where the performance of the microsensor was compared with that of two commercially available velocity sensors. The integrated sensor exhibited stable operation throughout the test and demonstrated effective measurement capabilities in the ranges of 10–40 °C for temperature, 60–90% RH for humidity, and 1.5–5.0 m/s for airflow velocity, with an overall accuracy of approximately ±3%. The results highlight the sensor’s potential for real-time environmental monitoring in factory HVAC systems, offering advantages in integration, adaptability, and cost-effectiveness compared to traditional single-function commercial sensors. Full article

(This article belongs to the Special Issue Micro/Nano Manufacturing Processes: Theories and Optimization Techniques (2nd Edition))

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19 pages, 3316 KB

Open AccessArticle

Tuning Whey Protein Properties: Ohmic Heating Effects on Interfacial Properties and Hydrophobic and Hydrophilic Interactions

by Israel Felipe dos Santos, Philippe Defáveri Bieler, Gabriel Oliveira Horta, Thais Caroline Buttow Rigolon, Adriano Gomes da Cruz, Paulo Cesar Stringheta, Evandro Martins and Pedro Henrique Campelo

Processes 2025, 13(10), 3305; https://doi.org/10.3390/pr13103305 - 15 Oct 2025

Abstract

Ohmic heating (OH) emerged as an alternative processing method for food preservation and has more recently been used to modify the functional properties of proteins. This study aimed to evaluate the effects of OH on the interfacial properties of whey proteins (WPC) and [...] Read more.

Ohmic heating (OH) emerged as an alternative processing method for food preservation and has more recently been used to modify the functional properties of proteins. This study aimed to evaluate the effects of OH on the interfacial properties of whey proteins (WPC) and its interactions with hydrophobic and hydrophilic compounds. WPC solutions (8% w/w) were subjected to electric field intensities ranging from 0 to 50 V·cm⁻¹ until reaching 80 °C. Structural and physicochemical parameters, including free sulfhydryl content, zeta potential, surface hydrophobicity, intrinsic fluorescence, and solubility, were analyzed. Protein–ligand interactions were also evaluated using β-carotene and caffeic acid as model compounds. The results indicated that moderate electric field intensities (30 V·cm⁻¹) promoted increased surface hydrophobicity and intrinsic fluorescence, suggesting protein unfolding and exposure of hydrophobic regions. Higher electric field intensities (40–50 V·cm⁻¹) led to aggregation, reducing solubility and binding affinity to β-carotene. Conversely, OH processing increased the interaction of WPC with caffeic acid due to enhanced exposure of hydrophilic binding sites. These findings provide insights into the modulation of whey protein interfacial properties through OH and highlight its potential for tailoring protein functionality in food formulations. Full article

(This article belongs to the Special Issue Applications of Non-Thermal and Thermal Technologies in Food Processing, 2nd Edition)

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26 pages, 5224 KB

Open AccessArticle

Modeling Anisotropic Permeability of Coal and Shale with Gas Rarefaction Effects, Matrix–Fracture Interaction, and Adsorption Hysteresis

by Lilong Wang, Zongyuan Li, Jie Zeng, Biwu Chen, Jiafeng Li, Huimin Jia, Wenhou Wang, Jinwen Zhang, Yiqun Wang and Zhihong Zhao

Processes 2025, 13(10), 3304; https://doi.org/10.3390/pr13103304 - 15 Oct 2025

Abstract

Permeability of fissured sorbing rocks, such as coal and shale, controls gas transport and is relevant to a variety of scientific problems and industrial processes. Multiple gas transport and rock deformation mechanisms affect permeability evolution, including gas rarefaction effects, gas-sorption-induced anisotropic matrix–fracture interaction, [...] Read more.

Permeability of fissured sorbing rocks, such as coal and shale, controls gas transport and is relevant to a variety of scientific problems and industrial processes. Multiple gas transport and rock deformation mechanisms affect permeability evolution, including gas rarefaction effects, gas-sorption-induced anisotropic matrix–fracture interaction, and anisotropic deformation induced by effective stress variation. In this paper, a generic anisotropic permeability model is proposed to address the impacts of the above mechanisms and effects. Specifically, the influence of matrix–fracture interactions on permeability evolution is depicted through the nonuniform matrix swelling caused by the gas diffusion process from fracture walls into the matrix. The following characteristics are also incorporated in this model: (1) anisotropic mechanical and swelling properties, (2) arbitrary box-shaped matrix blocks due to the anisotropic rock structure, (3) adsorbability variation of different matrix blocks because of complex rock compositions, (4) adsorption hysteresis, and (5) dynamic tortuosity. The directional permeability models are derived based on the anisotropic poroelasticity theory and anisotropic swelling equations considering adsorption hysteresis. We use a gas-invaded-volume ratio to describe the nonuniform swelling of matrix blocks. Additionally, swelling of blocks with different adsorption and mechanical properties are characterized by a volume-weighted function. Finally, the anisotropic tortuosity is defined as a power law function of effective porosity. The model is verified against experimental data. Results show that four-stage permeability evolution with time can be observed. Permeability evolution in different directions follows its own ways and depends on anisotropic swelling, mechanical properties, and structures, even when the boundary conditions are identical. Adsorption hysteresis controls the local shrinkage region. Tortuosity variation significantly affects permeability but has the smallest influence on the local swelling region. The existence of multiple matrix types complicates the permeability evolution behavior. Full article

(This article belongs to the Special Issue Advances in Enhancing Unconventional Oil/Gas Recovery, 2nd Edition)

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16 pages, 2438 KB

Open AccessArticle

Data-Driven Noise-Resilient Method for Wind Farm Reactive Power Optimization

by Zhen Pan, Lijuan Huang, Kaiwen Huang, Guan Bai and Lin Zhou

Processes 2025, 13(10), 3303; https://doi.org/10.3390/pr13103303 - 15 Oct 2025

Abstract

Accurate reactive power optimization in wind farms (WFs) is critical for optimizing operations and ensuring grid stability, yet it faces challenges from noisy, nonlinear, and dynamic Supervisory Control and Data Acquisition (SCADA) data. This study proposes an innovative framework, WBS-BiGRU, integrating three novel [...] Read more.

Accurate reactive power optimization in wind farms (WFs) is critical for optimizing operations and ensuring grid stability, yet it faces challenges from noisy, nonlinear, and dynamic Supervisory Control and Data Acquisition (SCADA) data. This study proposes an innovative framework, WBS-BiGRU, integrating three novel components to address these issues. Firstly, the Wavelet-DBSCAN (WDBSCAN) method combines wavelet transform’s time–frequency analysis with density-based spatial clustering of applications with noise (DBSCAN)’s density-based clustering to effectively remove noise and outliers from complex WF datasets, leveraging multi-scale features for enhanced adaptability to non-stationary signals. Subsequently, a Boomerang Evolutionary Optimization (BAEO) with the Seasonal Decomposition Improved Process (SDIP) synergistically decomposes time series into trend, seasonal, and residual components, generating diverse candidate solutions to optimize data inputs. Finally, a Bidirectional Gated Recurrent Unit (BiGRU) network enhanced with an attention mechanism captures long-term dependencies in temporal data and dynamically focuses on key features, improving reactive power forecasting precision. The WBS-BiGRU framework significantly enhances forecasting accuracy and robustness, offering a reliable solution for WF operation optimization and equipment health management. Full article

(This article belongs to the Special Issue Modern Machine Learning Applications in Control and Optimization of Energy Power and Storage Systems)

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21 pages, 1985 KB

Open AccessArticle

Bio-Solid Fuel from Wheat Straw via Microwave Torrefaction: Process Optimization and Environmental Assessment

by Yunji Pei, Zimo Liang, Xuexue Chen, Xinran Wang, Wenlin Zhou, Weiyu Lu and Li Jiang

Processes 2025, 13(10), 3302; https://doi.org/10.3390/pr13103302 - 15 Oct 2025

Abstract

There is a need to address the limitations of wheat straw (WS) as a raw biomass fuel, promote its valorisation into a high-quality renewable solid fuel, and enable this fuel to replace fossil fuels in applications such as power plants and industrial boilers. [...] Read more.

There is a need to address the limitations of wheat straw (WS) as a raw biomass fuel, promote its valorisation into a high-quality renewable solid fuel, and enable this fuel to replace fossil fuels in applications such as power plants and industrial boilers. This study focused on optimizing microwave torrefaction parameters to enhance key fuel properties. Optimal conditions were determined via the Box–Behnken design (BBD) within Response Surface Methodology (RSM) as 422.32 W of microwave power, 14.95 min of irradiation time, and a 15 g microwave absorber, resulting in a 69.12% mass yield, an 18.44 MJ/kg higher heating value (HHV) surpassing lignite at 16.76 MJ/kg, and a 25.50% Energy-Mass Co-efficiency Index (EMCI). Fourier transform infrared spectroscopy (FTIR) and thermogravimetric analysis/derivative thermogravimetric analysis (TG/DTG) were conducted to gain insights about chemical composition and thermal stability variations due to torrefaction. LCA showed that electricity produced from 1 ton of torrefied WS reduces CO₂ emissions by 259.26 kg CO₂eq compared to electricity generated from bituminous coal. From an economic perspective, the usage of torrefied WS for power generation lead to a net profit of CNY 435.19/ton. This scalable technology, by valorising agricultural waste for fuel production, delivers dual environmental and economic benefits, laying the groundwork for industrial deployment. Full article

(This article belongs to the Special Issue Biofuels Production Processes)

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