Processes doi: 10.3390/pr14101585

Authors: Yanjing Zhang Gang Chen Liang Guo Kunhua Liu Shufang Zhou

With the continuously increasing proportion of renewable energy integration, the structure of power grid networks has become increasingly complex. Under extreme weather conditions such as typhoons and hail, faults like line breaks or information disruptions can occur in the power grid, imposing significant burdens and risks on the economic and reliable operation of the power system. However, existing methods still focus on the allocation of human repair teams, with insufficient utilization of flexible resources within the system, resulting in low efficiency in restoring power supply to the power system. To address this challenge, this paper proposes a resilience enhancement strategy for the power system under typhoon scenarios. It leverages active resources on the grid side and fully exploits the flexibility of both the supply and demand sides to enhance the resilience of the power system. Firstly, this paper aims at the economic operation of the power system, taking into account the physical and operational constraints of both the supply and demand sides, including power flow constraints, mobile energy storage system (MESS) transfer constraints, and phase-shifting transformer (PST) regulation constraints. Meanwhile, an improved grasshopper optimization algorithm is introduced to achieve efficient and rapid problem-solving. Finally, the effectiveness and feasibility of the proposed method are demonstrated through validation using an improved IEEE-33 bus test system. Through analysis, the total system load loss was reduced by 75.6%, with the maximum load loss during the typhoon decreasing by 72.4%. The approach enables real-time response to the dynamic impacts of typhoons, swiftly stabilizes load fluctuations, and significantly enhances the resilience of the power system.

Processes doi: 10.3390/pr14101583

Authors: Ziyang Zhang Qingsong An

Addressing the challenges of fluctuating renewable energy integration and stable green ammonia production, this study develops and optimizes a deeply integrated system comprising Solid Oxide Electrolysis Cells (SOEC), Liquid Air Energy Storage (LAES), Air Separation Units (ASU), and Haber–Bosch (HB) synthesis. We constructed a simulation model in Aspen Plus incorporating Ru/C catalyst kinetic parameters to analyze key subsystem parameters and optimize operating conditions based on maximized economy and efficiency. At the integrated system level, a parametric analysis of ammonia condensation temperature was further conducted to investigate the coupling characteristics. Using real power output data from Inner Mongolia, we formulated a dynamic energy scheduling strategy satisfying 24-h self-balancing constraints. Results indicate that a system producing 1415 tons of ammonia per day achieves a maximum hourly integrated profit of 69,838 CNY under optimal conditions: a hydrogen-to-nitrogen ratio of 2.98:1, operating pressure of 169 bar, reactor inlet temperature of 380 °C, and ammonia condensation temperature of −9 °C. Increasing the LAES throttle valve outlet pressure from 1 bar to 9 bar improved round-trip efficiency from 52.65% to 72.18%. The integrated-level parametric analysis reveals that the specific electricity consumption per unit mass of ammonia exhibits a non-monotonic trend with a minimum of 8.67 kWh/kg at −10 °C, reflecting the trade-off between refrigeration power consumption and cold energy recovery. In dynamic scheduling scenarios, the system maintains a maximum constant load of 45.78 MW with a steady-state liquid ammonia output of 6543 kg/h. This work optimizes both economic performance and system stability, providing a significant reference for the large-scale development of green ammonia systems.

Processes doi: 10.3390/pr14101582

Authors: Meiqi Yu Hang Li Haochuan Zhang Chang Zhai Long Liu Hongliang Luo Qing Wu Yoichi Ogata Liqiu Wang

In this study, three-dimensional (3D) and one-dimensional (1D) models of port fuel injection (PFI) hydrogen-oxygen internal combustion engine (H2-O2 ICE) were established. Firstly, the experiment of PFI-H2 ICE was conducted to validate the accuracy of the simulation models. Then the effects of the oxygen equivalence ratio (ΦO2) and engine speed in the combustion process were analyzed. Results show that two threshold values in the H2-O2 ICE combustion can be deduced. When ΦO2 = 0.20, the combustion process is violent with extremely high temperatures and pressure, called excessively intense combustion. When ΦO2 = 0.10, the flame propagation is slow, suggesting it is difficult to ignite at a smaller oxygen equivalence ratio. Moreover, the influence of engine speed on performance parameters is analyzed in a 1D simulation. Results show that the fluctuation of brake thermal efficiency with engine speed becomes more obvious with the decrease in the oxygen equivalence ratio.

Processes doi: 10.3390/pr14101581

Authors: Hong Xiao Jingshu Shi Wei He Wenfeng Guo Ruikuo Liu Yanjun Liu Xianbo Lu Depeng Hu

Ensuring frequency security is of vital importance for power systems. As the penetration of renewable energy generation (REG) continues to increase, its impact on frequency stability cannot be neglected. The motion equation model is a suitable modeling method for REGs, which can derive a model with a structure similar to synchronous generators. However, when using the motion equation models for frequency dynamic analysis, only the impact paths related to active power disturbance are retained. Adding that there are different types of motion equation models, it is important to discuss which type is more appropriate. This paper takes the doubly fed induction generator-based wind turbine as an example; two types of motion equation models are first derived and verified. Then, by looking back at the assumptions of the average system frequency model, the adaptability of each model is judged. By comparing the frequency dynamics between different motion equation models and the electromagnetic transient model, the results indicate that the motion equation model employing active power and terminal voltage dynamics as input variables is more suitable for average frequency analysis. Lastly, by utilizing these models in a power system, the analysis is verified through MATLAB/Simulink R2021a.

Processes doi: 10.3390/pr14101580

Authors: Qian Zhu Li Xu Guizhen Li Wei Xu Yuxin Wang Wen Zhang

To mitigate the gas–liquid mass-transfer bottleneck in anion-exchange membrane water electrolysis (AEMWE), a 3D multiphysics numerical model was developed to systematically investigate the regulatory effects of gradient porosity (GPD) and gradient pore-size distribution (GPSD) on anode reaction kinetics and cell polarization. Single-factor analysis reveals that increasing the GPD/GPSD from the membrane side toward the flow channel side effectively reduces activation overpotential due to the high specific surface area of small pores near the membrane, while simultaneously lowering mass-transfer resistance through high porosity and large pores near the flow channel. Conversely, a decreasing gradient leads to localized gas stagnation and uneven mass transfer, deteriorating cell performance. Furthermore, an innovative synergistic design is proposed featuring a simultaneous linear increase in porosity (0.6 to 0.9) and pore diameter (0.11 to 0.17 mm). This configuration achieves a cell voltage of only 1.812 V at 1100 mA/cm2 (1 mol/L KOH, 80 °C), approximately 40 mV lower than that of conventional uniform structures, thereby significantly reducing energy consumption at high current densities. This study provides a mechanistic framework for the precise architectural design of high-performance AEMWE electrodes, highlighting the importance of spatial heterogeneity in optimizing two-phase transport.

Processes doi: 10.3390/pr14101579

Authors: Yingzhe Ding Zhijun Yang Jingrui Zhang

Active Distribution Networks (ADNs) rely on the precise coordination of flexible resources to mitigate the stochasticity of high-penetration renewables. However, the hierarchical and partitioned nature of modern ADNs transforms the day-ahead scheduling problem into a high-dimensional many-objective optimization task, typically involving conflicting objectives across multiple regions. Standard evolutionary algorithms often struggle with the “curse of dimensionality” in such scenarios. To address this limitation, this study formulates a hierarchical partition-based scheduling model for many-objective optimization and introduces a novel adaptive MOEA/D algorithm. Specifically, a double-layer weight generation method and an adaptive neighborhood adjustment strategy are introduced to balance global search capability with local convergence speed. The methodology is validated using a practical 47-node ADN case study in Panzhihua, China. Comprehensive analysis of evaluation metrics (e.g., Hypervolume and IGD) indicates that the proposed algorithm achieves enhanced performance at the expense of a marginal increase in cost. Furthermore, it demonstrates strong competitiveness against advanced heuristic algorithms in solving high-dimensional scheduling problems, effectively balancing economic efficiency and voltage stability under renewable uncertainty.

Processes doi: 10.3390/pr14101578

Authors: Yun Zhang Xiaofan Chen Kuanguo Li Yifan Zou

Purely data-driven machine-learning methods are currently limited by weak physical interpretability; meanwhile, the sparsity of well-site data in oil and gas fields further degrades the prediction performance of deep learning models for reservoir seepage simulation. To overcome this bottleneck, this study embeds Darcy’s law-based seepage equations as physical constraints into the loss function of a deep learning framework, thereby constructing a physics-informed neural network (PINN) for seepage flow in porous media of oil and gas reservoirs. Numerical simulations are performed in heterogeneous porous media to compare the predictive performance of the proposed PINN against conventional purely data-driven approaches, via evaluation metrics including the coefficient of determination (R2) and root mean square error (RMSE). The results show that both models achieve comparable predictive accuracy with sufficient training samples. In contrast, the PINN retains high predictive accuracy even with a reduced number of samples, and it delivers prominent superiority under conditions of sparse well data and strong reservoir heterogeneity. This study clarifies the applicable scenarios of the two aforementioned methods (physics-informed neural networks and purely data-driven machine-learning models) for fluid flow simulation in porous media and provides a solid theoretical and technical foundation for the accurate prediction of reservoir seepage fields and the optimization of oil and gas reservoir development. This work also offers a validated physics-constrained deep learning framework to guide the deployment of intelligent algorithms in practical subsurface flow engineering.

Processes doi: 10.3390/pr14101577

Authors: Yafei Liu Yinuo Fei Yumeng Su Qing Zhou Peiming Liu Hanliang Guan

This study investigated rhamnolipid synthesis in Pseudomonas aeruginosa ATCC 27853. Two constitutive promoters, PrpsJ and PoprL, were isolated and cloned upstream of the rhlABRI and rmlBDAC gene clusters to evaluate their impact on rhamnolipid titers. The overexpression of rhlB, driven by the PrpsJ promoter, significantly enhanced rhamnolipid production. Subsequent glycine-scanning mutagenesis of RhlB identified an optimal variant (RhlBM328G), which increased the titer 1.82-fold (to 24.6 g·L−1) compared to the wild type, achieving a product yield of 0.39 g·g−1. Characterization of the extracted rhamnolipids revealed a critical micelle concentration of 1 mg/L, a corresponding surface tension of 53.9 mN/m, and a hydrophilic–lipophilic balance (HLB) value of 14. This HLB value indicated that the synthesized rhamnolipids possess superior hydrophilicity, robust oil-in-water emulsifying capabilities, and excellent solubilization and dispersion properties. Furthermore, molecular docking and molecular dynamics simulations demonstrated that in the RhlBM328G mutant, the nucleophilic attack distances between the substrates and the catalytic moiety are optimized for catalysis, thereby boosting rhamnolipid production.

Processes doi: 10.3390/pr14101575

Authors: Yingjie Liu Wenhao Xian Yuheng Zhang Yongbo Cai Zuo Sun Chao Xu Chi Li

Coal mine dust is a critical hazard that can trigger explosions and cause pneumoconiosis, thereby severely threatening mine safety and occupational health. Although long-forcing and short-exhausting ventilation are commonly adopted in long-distance heading faces, their parameters are often determined empirically, leading to suboptimal dust control efficiency. This study utilizes numerical simulations via FLUENT to investigate dust migration patterns under five key ventilation parameters in the 2-2 Upper Coal Working Face of the Xintai Taigemiao Mining Area. The results reveal a zonal distribution of dust: a high-concentration accumulation zone within 0–15 m, a medium-concentration transition zone between 15 and 35 m, and a low-concentration settling zone beyond 35 m. Diffusion rates vary significantly across zones under different ventilation settings. The optimized parameters for the 20 m2 cross-section roadway in this study include: exhausting duct set 0.3 m from the return-side wall, exhausting inlet at a distance of 4 m (0.9√A, A is the roadway cross-sectional area) from the face, forcing inlet at 20 m (4.5√A) from the face, duct installation height of 0.75 times the roadway height, and a forcing-to-exhausting air volume ratio between 1.2 and 1.6. Compared with the non-optimized scheme, this configuration reduces the average dust concentration in the breathing zone (1.2 m height) by up to 62.3%, and restricts 85% of the high-concentration dust within 0–15 m from the heading face, effectively suppressing dust dispersion to the rear roadway. This study provides a quantitative reference and theoretical strategy for engineering applications of dust prevention in similar large-section long-distance heading faces within the scope of numerical simulation.

Processes doi: 10.3390/pr14101576

Authors: Donghui Xiao Jianliang Xie Shiyang Liu Dinglue Wu Yucai Zhang Yibo Tan Benhua Liu

Crystallization and blockage in tunnel drainage systems represent a major challenge in the operation and maintenance of tunnels in karst regions. This study focuses on a tunnel in Guilin, Guangxi, employing a combined approach of field investigation, laboratory characterization, and molecular dynamics (MD) simulations to explore the atomic-scale mechanism of CaCO3 crystallization within the drainage system. Field investigations reveal that the groundwater is dominated by Ca2+ and HCO3− ions, and the crystalline products consist primarily of high-crystallinity single-phase calcite, characterized by typical rhombohedral geometric structures and heterogeneous stacking. Molecular dynamics simulations indicate that the CaCO3 nucleation process is accompanied by the desolvation of Ca2+, while background electrolyte ions exert distinct regulatory effects on the nucleation kinetics. SO42− participates in cluster construction through strong coordination, inducing the formation of loose, chain-like aggregates; conversely, Cl− delays cluster coalescence primarily through charge shielding and steric hindrance effects. Additionally, Na+ influences the overall solution dynamics and the stability of pre-nucleation clusters by constructing stable hydration shells and providing charge neutralization. This research reveals the formation mechanism of tunnel crystallization from a microscopic perspective, providing theoretical support for the prevention and control of crystallization in tunnel drainage systems.

Processes doi: 10.3390/pr14101574

Authors: Zhanhong Su Zijian Li Lin Li Yifeng Qiu Shenglang Liang Ziming Hao Fanghui Guo Chaochuang Xu Dongxu Zhou Wen Lin Haochong Huang

Addressing the challenges of high-dimensional redundancy, noise interference, and parameter missing in the net present value prediction of shale gas wells, an intelligent prediction model PCA-DFNN integrating Principal Component Analysis and Deep Feedforward Neural Network is proposed. Based on actual data from 48 shale gas wells, Principal Component Analysis is first performed on 19 input features to reduce dimensionality, extracting 9 core principal components, which achieve a cumulative variance contribution rate of 88.05%. Subsequently, a deep neural network model is constructed for comparative modeling. The results indicate that the PCA-DFNN model achieves a coefficient of determination on the independent test set that improves from 0.6439 in the original model to 0.6882, an increase of 0.0443, or approximately 6.9%, with faster training convergence and superior generalization ability. The research confirms that the proposed method can effectively eliminate feature redundancy, filter noise, and circumvent the uncertainty of missing value imputation, providing a more reliable technical tool for the early economic evaluation of shale gas.

Processes doi: 10.3390/pr14101572

Authors: Longyi Wang Xizhe Li Ya’na Chen Mengfei Zhou Zan Huang Nijun Qi Sijie He Liangji Jiang Yuhang Zhou Ziyang Zhao

Currently, pore fractal characteristics of deep marine–terrestrial transitional coal-measure mudstones in the central Sichuan Basin remain poorly understood. To clarify the pore fractal characteristics and their controlling factors, seven representative deep mudstone samples were collected from the Longtan Formation of Well NT1H in the Suining area, central Sichuan Basin. These samples were subjected to total organic carbon (TOC) content determination, vitrinite reflectance (Ro) measurement, X-ray diffraction (XRD) analysis of whole-rock and clay minerals, and low-pressure nitrogen adsorption (LPN2A) experiments. Pore fractal dimensions were calculated based on the Frenkel–Halsey–Hill (FHH) theoretical model. The influences of mineral composition, organic geochemical characteristics, and pore structural parameters on pore fractal dimensions were analyzed. The results indicate that shale pores in the study area are predominantly developed as mesopores, exhibiting dual fractal characteristics; fractal dimension D1 (structural fractal dimension at high-pressure segment) ranges from 2.6662 to 2.7366, and fractal dimension D2 (surface fractal dimension at low-pressure segment) ranges from 2.5895 to 2.6363. Mineral composition exerts differential control over pore fractal dimensions. The effects of organic matter content and thermal evolution degree on fractal dimensions exhibit stage-dependent characteristics. Correlations between pore structural parameters and fractal dimensions indicate that small-aperture pores (micropores and mesopores) constitute the primary factor controlling pore heterogeneity in shales. These findings provide a theoretical basis for “reservoir evaluation” and “sweet spot” optimization of deep marine–terrestrial transitional coal-measure shales in central Sichuan Basin.

Processes doi: 10.3390/pr14101571

Authors: Jesús Rodríguez-Miranda Betsabé Hernández-Santos María G. Lozano-Aguirre Juan G. Torruco-Uco Rebeca G. Tejeda Enrique Ramírez-Figueroa

This study evaluated the use of Michigan bean protein concentrate (PC) and maltodextrin (MD) as alternative wall materials for the microencapsulation of gallic acid, selected as a model phenolic compound due to its well-defined structure and suitability for assessing encapsulation efficiency, stability, and matrix–polyphenol interactions. Increasing the inlet temperature enhanced microencapsulation yield (37.25–55.56%) and total color difference (5.23–13.20), but reduced DPPH• radical inhibition from 90.92% to 54.04%, ABTS•+ radical inhibition from 99.43% to 79.98%, moisture content from 4.98% to 3.51%, and water activity from 0.35 to 0.30. Higher PC concentrations increased efficiency (86.45–99.26%), microencapsulation retention (38.06–100%), moisture content (3.51–4.98%), Aw (0.301–0.358), water absorption capacity (0–2.38 g/g), oil absorption capacity (3.31–3.67 g/g), and emulsifying capacity (0–2.2%). The interaction between temperature and PC content significantly improved yield, antioxidant capacity, and moisture content. Optimal conditions were achieved at a PC:MD ratio of 51:49 and a temperature of 116 °C. Under these conditions, yield, efficiency, microencapsulation retention, total phenolic content, and DPPH• radical inhibition were higher than the values predicted by the model. Morphological analysis revealed that the microcapsules exhibited irregular shapes with dents and particle sizes ranging from 5.43 to 10.19 µm. These findings demonstrate that Michigan bean protein concentrate, when combined with maltodextrin, exhibits strong potential as a wall material for gallic acid microencapsulation, achieving high retention and microencapsulation efficiency.

Processes doi: 10.3390/pr14101573

Authors: Sojin Kim Meera Kweon

High-amylose rice starch (HARS) complexation with fatty acids and emulsifiers was evaluated using differential scanning calorimetry (DSC), Rapid Visco Analyzer (RVA) pasting, X-ray diffraction (XRD), and in vitro digestibility. DSC confirmed amylose–lipid complex formation for both additive types, with emulsifiers more effective than fatty acids. Lysolecithin produced the largest amylose–lipid complex endotherm with no detectable uncomplexed melting peak. Complexation increased up to 2.5% (w/w) and then plateaued, accompanied by a reduced gelatinization endotherm. Fatty-acid effects depended on chain length and included overlapping melting from uncomplexed lipids; higher additive levels generally increased complex stability. RVA results indicated that emulsifiers, but not fatty acids, increased pasting temperature by approximately 10 °C and delayed pasting. Lysolecithin markedly reduced viscosity breakdown, suggesting restricted granule swelling due to stabilized complexes. Myristic acid and lysolecithin caused the greatest changes in thermal and pasting parameters. XRD patterns shifted from mixed A + V to predominantly V-type reflections, confirming V-amylose complex formation. In vitro digestion showed decreases of 7.5–15.8% in rapidly digestible starch and increases of 4.7–12.3% in slowly digestible starch and 2.4–39.5% in resistant starch, with the strongest effects for lysolecithin (and myristic acid). These results support emulsifier-assisted production of RS5 from HARS, enhancing its utilization.

Processes doi: 10.3390/pr14101570

Authors: Lifu Wan Hongchen Song Ai-Guo Wang Meng Li Zhili Zhang Kanhua Su Jiangen Xu Lulin Kong Yan Yan Gui Hu Guohui Zhang

Aiming at the engineering challenges of low Rate of Penetration (ROP), high rock-breaking energy consumption, and the difficulty in realizing collaborative optimization of efficiency improvement and consumption reduction through traditional drilling parameter regulation in deep interbedded sandstone and mudstone formations of the Southwest Oil and Gas Field, a study on multi-objective optimization of drilling parameters is carried out. First, the traditional Teale Mechanical Specific Energy (MSE) model is modified, with the introduction of the effective energy utilization coefficient of the drill bit and the downhole torque calculation method, to establish an improved MSE model that fits the actual field drilling conditions. Second, a hybrid TCN-LSTM model fused with an additive attention mechanism is constructed to realize high-precision dynamic prediction of ROP. Finally, taking the on-site real-time controllable Weight on Bit (WOB) and rotary speed as decision variables, the synergy–conflict boundary between the dual objectives of ROP maximization and MSE minimization is clarified, a dual-objective coupled optimization model is established, and the genetic algorithm is used to complete the global optimization. Case verification is carried out based on the actual drilling data of 12 deep wells in the study block, and the results show that the Coefficient of Determination R2 of the established ROP prediction model on the test set reaches 0.91, and the prediction accuracy is significantly better than that of BP, CNN and single LSTM models; after optimization, the ROP of the target well interval is increased by 13.1% compared with the on-site actual drilling value, and the improved MSE is reduced by 23.5%, which simultaneously realizes drilling efficiency improvement and rock-breaking energy saving within the safe operation boundary. The stability of the model and the effectiveness of each module are verified through five-fold inter-well cross-validation and module ablation experiments. The research results can provide a theoretical basis and technical support for the precise regulation of drilling parameters in deep formations of the target block.

Processes doi: 10.3390/pr14101569

Authors: Bingxu Yan Mingjin Cai Haocheng Sun Qingyu Li Tengyi Long Guojun Zhang Jianing Hu Yachao Bai

The distribution and volume of karst caves are the core parameters for the development of fractured cave reservoirs. In this paper, the single-phase seepage equation is adopted to describe the pressure variation in the fracture system, the wave equation is introduced to characterize the pressure dynamics in the cave, and based on the discrete technology of unstructured grid and finite volume method, the numerical simulation algorithm of fractured caved reservoirs is realized. This framework uniquely enables the inversion of physically meaningful karst cave parameters—specifically volume and location—directly from interference/pulse test data, bridging a significant gap between conventional statistical multi-porosity models and practical reservoir characterization needs. Using this algorithm program, a simulation study was conducted on the impulse well test responses of active wells and observation wells in fractured caved reservoirs. Studies show that the volume of karst caves with connectivity and the distance between the active well and the karst cave are the key factors affecting the pressure response of the observation well: the larger the volume of the karst cave, the smaller the variation range of the pressure of the observation well; The greater the distance between the active well and the cave, the smaller the variation range of the observation well pressure. Based on the above rules, this paper proposes for the first time to use the pressure response and derivative historical fitting method of the observation well in pulse well testing to inversely explain key parameters such as the volume and location of the karst cave. This research provides a theoretical basis for the application of pulse well testing technology in the evaluation of fractured caved reservoirs.

Processes doi: 10.3390/pr14101568

Authors: Nur Beliz Döğer Erva Parıldı Semih Latif İpek Osman Kola

Xylitol is a leading low-calorie sugar alcohol worldwide. Industrial production efficiency has become crucial given the increasing global demand. The catalytic hydrogenation of xylose to produce xylitol is a leading method. In this study, xylitol production using a Raney nickel catalyst was optimized in a pressurized batch reactor. The catalyst amount, xylose concentration, hydrogen pressure, and temperature were used as the process variables, and the maximum yield (%) was determined. The experimental design, created using a central composite design (CCD), was optimized using Response Surface Methodology (RSM), and the obtained values were evaluated using ANOVA. The linear regression model was found to be statistically significant. The linear model indicated that temperature had no effect on the model. The optimal conditions determined by the RSM were 40 g of catalyst, 25% xylose, and 60 bar at 400 rpm stirring. The yield was 86.61%, which is quite close to the predicted value of 87.2557%, demonstrating the accuracy of the model. The product was analyzed using HPLC, XRD, DSC, TGA, and ICP-MS to confirm product quality. Under these optimized chemical conditions, increasing the stirring speed to 800 rpm as a post-optimization step led to a further increase in yield up to 98.01%, indicating the presence of mass transfer limitations at lower mixing intensities.

Processes doi: 10.3390/pr14101567

Authors: Kai Wang Xin Nie Xiaojiang Wang Fei Wang Jianxun Liu Tong Wang Fan Yong

In situ stress field inversion is a fundamental challenge in geothermal resource development, oil and gas exploration, and mine safety assessment. To address the non-uniqueness and limited accuracy of traditional single-data-source inversion approaches, this study proposes a two-dimensional in situ stress field inversion method constrained by multi-source data, based on integrated well-seismic fault identification. By incorporating dynamic and static mechanical parameters from well logs and employing both a combined spring model and an anisotropic model, a fault-constrained stress field inversion framework is established. Deep learning and optimization algorithms are utilized to integrate the vertical constraints from well logging data with the lateral continuity characteristics of seismic data, enabling high-resolution reconstruction of the in situ stress field. Taking the complex fault-developed geothermal field in the Xiong’an New Area of the Jizhong Depression, Bohai Bay Basin, as a case study, the proposed method demonstrates a marked reduction in inversion error and a substantial improvement in both fault localization accuracy and stress characterization reliability.

Processes doi: 10.3390/pr14101566

Authors: Yunnan Teng Liyang Xie

To improve machining accuracy, a multi-axis linkage dimensional error calculation model was established based on multi-body system theory and validated by the experiment. A comprehensive sensitivity analysis for a CNC machining center was conducted. The results reveal that multi-axis machining errors depend not only on the machine’s structural dimensions but also dynamically vary with the axes’ motion positions. Specifically, X-axis machining errors are most sensitive to δx(z), δx(y), δx(x), εy(y). Y-axis machining error is more sensitive to δy(z), δy(y), and δy(x). Z-axis machining error is more sensitive to δz(z), δz(y), and δz(x). The practical implications of these findings identify the specific dimensional errors that most significantly impact overall accuracy, providing a targeted theoretical reference to prioritize the compensation of key dimensional errors and effectively enhance machine tool precision.

Processes doi: 10.3390/pr14101565

Authors: Guodong Yao Qiuxia Zhu Limin Jin Ningzheng Zhu Yangyuan Zhou Jianfu Zhao

The capture and utilization of carbon dioxide (CO2) from CO2-rich off-gas streams play a vital role in mitigating carbon emissions. Chemical absorption, as a well-established and commercially deployed CO2 capture technology, has attracted sustained research interest. Current efforts in this field are primarily directed toward developing cost-effective absorbents and energy-efficient processes. Meanwhile, industrial operations generate significant quantities of by-product alkaline salts, which often consist of mixtures containing two or more components such as sodium carbonate, sodium hydroxide, sodium sulfate, and sodium chloride. The use of these alkaline by-products for CO2 absorption to produce value-added chemicals presents a promising and economically attractive carbon capture and utilization (CCU) strategy. In this work, an industrial salt by-product from a petrochemical plant, rich in sodium carbonate and sodium sulfate, was employed as an absorbent to simulate a CCU process. By controlling the mass transfer and crystallization behavior of CO2 within the multicomponent system during carbon capture, it was feasible to produce sodium bicarbonate from CO2. Concurrently, industrial-grade sodium sulfate was separated based on solubility differences. This study proposes an innovative integrated approach that combines the utilization of alkaline by-product salts, CO2 capture from off-gas, and resource recovery in a single process.

Processes doi: 10.3390/pr14101564

Authors: Petko Denev Manol Ognyanov Mariya Pimpilova Desislava Teneva

The increasing global demand for natural bioactive compounds in the food, nutraceutical, and pharmaceutical sectors highlights the need for sustainable extraction technologies capable not only of efficiently valorizing crop biomass and agro-waste but also of producing reproducible and standardized botanical extracts. Subcritical water extraction (SWE), which utilizes pressurized hot water at temperatures between 100 °C and 374 °C to modify solvent properties, has emerged as a promising green alternative to conventional organic solvent-based extraction methods. Despite its advantages in terms of environmental compatibility, extraction efficiency and tunable selectivity, the industrial application of SWE remains limited, and strategies for obtaining standardized extracts using this technology are still insufficiently explored. This review provides a comprehensive overview of SWE in the context of natural product extraction and the development of standardized plant extracts. The fundamental principles of SWE are discussed, including temperature-dependent changes in water polarity, solvent–solute interactions, and the influence of key process parameters such as temperature, pressure, extraction time, and particle size. Particular emphasis is placed on how these factors affect extraction selectivity, phytochemical composition, and reproducibility, which are critical aspects for extract standardization. Mechanistic insights into plant cell disruption, compound stability, and hydrothermal transformations under SWE conditions are also examined. Recent applications of SWE for the extraction of phenolics, flavonoids, terpenoids, alkaloids, and other pharmacologically relevant compounds are reviewed, highlighting the relationship between extraction conditions and extract quality. Finally, current challenges and future perspectives for integrating SWE into the production of standardized botanical extracts suitable for food, nutraceutical, and pharmaceutical applications are discussed, paving the way for the wider industrial adoption of this environmentally friendly technology.

Processes doi: 10.3390/pr14101563

Authors: Oscar Danilo Montoya Luis Fernando Grisales-Noreña Javier Rosero-García

Three-phase induction motors account for nearly two-thirds of industrial electricity consumption, making accurate parameter identification essential for efficiency optimization, predictive maintenance, and digital twin calibration. This paper introduces the stochastic spheric navigator algorithm (SSNA) for estimating the equivalent circuit parameters (stator and rotor resistances, leakage reactances, and magnetizing reactance) of induction motors by minimizing the normalized squared error between manufacturer-provided torque characteristics (starting, peak, and full-load) and their analytical counterparts derived from the steady-state Thévenin model. The SSNA employs an adaptive spherical search mechanism with a decaying radius schedule that progressively narrows the exploration neighborhood, enabling a balanced transition from global exploration to local refinement. Validated on 5 hp and 25 hp motors against the genetic algorithm (GA), particle swarm optimizer (PSO), hybrid GA-PSO, and sine–cosine algorithm (SCA), the SSNA demonstrates distinct advantages. For the 5 hp motor, it achieves the lowest errors in maximum torque (1.34×10−4%) and full-load torque (5.08×10−4%). For the previously unreported 25 hp motor, the SSNA yields an objective function value of 4.68×10−12—six orders of magnitude lower than the SCA—and reduces magnetizing reactance estimation error from 46.55% (SCA) to 16.18%. Statistical analysis over 100 independent runs reveals that the SSNA uniquely combines the lowest minimum (best) value, the lowest maximum (worst) value, and the lowest standard deviation, demonstrating superior accuracy, reliability, and consistency. These results position the SSNA as a highly competitive optimization framework for induction motor parameter identification, with particular suitability for applications demanding high precision and robust performance.

Processes doi: 10.3390/pr14101562

Authors: Zhenhua Li Li Chen Hengbo Mao Qingzhao Cao Baoqiang Ma Hongyao Zheng Wenke Liu Ying Tang Ya Wu

Phthalate acid esters (PAEs) are persistent organic pollutants (POPs) widely prevalent in industrial wastewater, posing significant threats to both ecological environments and human health. Although Advanced Oxidation Processes (AOPs) are recognized as efficient technologies for PAE degradation, conventional synergistic systems typically employ a simultaneous dosing mode. This approach often leads to the instantaneous quenching of excess radicals, low oxidant utilization, and imbalanced degradation kinetics. Despite its critical role in determining efficiency and costs, the dosing strategy remains an overlooked factor in current research. In this study, dimethyl phthalate (DMP) was selected as the target pollutant to evaluate a synergistic FeSO4/H2O2/K2S2O8 system. An innovative continuous dosing strategy was implemented to optimize radical utilization. A laboratory-scale continuous flow apparatus was developed to simulate industrial onsite conditions, enabling a systematic comparison of degradation kinetics, mineralization characteristics, and radical evolution between the two dosing modes. Results indicated that the degradation rate constant for the continuous dosing system reached 0.659 h−1, representing a 21.1% increase over the simultaneous dosing system (0.544 h−1). Electron Paramagnetic Resonance (EPR) analysis confirmed that the continuous dosing mode maintains a sustained and stable radical flux (•OH and SO4•−) during the critical mid-stage of the degradation, effectively mitigating radical–radical quenching. When applied to real industrial wastewater (salinity: 2083 mg/L), the continuous dosing system achieved a Total Organic Carbon (TOC) removal efficiency of 86.0% at ambient temperature and initial raw water pH, outperforming the simultaneous dosing system (82.0%). GC-MS analysis further confirmed the thorough mineralization of complex organic compounds, especially those containing ester groups and aromatic rings. This research addresses a critical gap in dosing strategy studies, providing an efficient, cost-effective, and industrially viable solution for recalcitrant wastewater treatment while establishing a theoretical foundation for large-scale continuous dosing applications.

Processes doi: 10.3390/pr14101561

Authors: Jelena Vukosavljević Sara Hourani Uroš Gašić Jan Turan Boris M. Popović Branimir Pavlić

The increasing generation of agri-food waste represents a significant environmental challenge, but also a valuable source of bioactive compounds with potential industrial applications. In this study, selected minimally processed agri-food wastes from the food processing industry were evaluated as potential sources of bioactive compounds and antioxidants. Seven types of agri-food waste were investigated: green bean cutting waste, yellow bean cutting waste, sweet corn waste from the air selector, edamame pods, pepper seed by-products, potato peels, and potato waste from the air selector. Solid–liquid extraction was performed using ethanol at different concentrations (20, 40, 60, 80, and 96%, w/w) as a green solvent. Total polyphenol content (TPC) and antioxidant activity (DPPH, FRAP, and ABTS assays) were determined. The results demonstrated significant differences among the investigated raw materials, with the highest antioxidant activity observed in the potato peel extracts. Specifically, the strongest activity was recorded using 40% ethanol, yielding values of 3.9596 mg TE/g DW for DPPH and 11.4555 mg TE/g DW for ABTS assays. In contrast, the highest FRAP value (2.3970 mg Fe2+/g DW) was obtained with 60% ethanol. The highest TPC was detected in pepper seed by-products, reaching 6.7829 mg GAE/g DW when extracted with 20% ethanol. Furthermore, selected extracts were subjected to LC-MS analysis to obtain a preliminary characterization of their chemical profiles. Untargeted LC-MS analysis identified 115 metabolites belonging to different chemical classes, highlighting agri-food waste as a rich source of bioactive compounds, particularly flavonoids and phenolic acids. These findings demonstrate agri-food wastes as sustainable sources of bioactive compounds and support their valorization within circular economy and green processing frameworks.

Processes doi: 10.3390/pr14101560

Authors: Shikang Xiao Xiaojun Deng Yuanhao Sun

In ceramic cup manufacturing, manual inspection is prone to missed detections and false positives, particularly for small surface defects. To address these challenges, this study presents an effective and efficient YOLOv11m-based detection framework, termed CEL-YOLOv11m, for precise identification of small-scale defects on ceramic surfaces. Specifically, a multi-scale convolution module (EMSC) is introduced to enhance the backbone feature extraction structure. By integrating convolution kernels of varying sizes, the module improves multi-scale feature representation, while grouped convolution is employed to reduce computational overhead. In the feature aggregation stage, a CRGseg-based structure is incorporated, and a refinement component (RCM) is designed to strengthen fine-grained information for small targets. Additionally, a cross-scale feature fusion strategy is applied to improve contextual representation across different resolutions. For the detection stage, a Layer-shared Detail-Enhanced Convolutional Detection Head (LSDECD) is adopted to improve fine-grained localization while improving computational efficiency through parameter sharing. Experiments conducted on a self-constructed ceramic defect dataset and the VisDrone2019 benchmark show that the proposed framework achieves competitive performance compared with representative methods. The model attains an mAP@50(%) of 54.8% with an inference speed of 89.9 FPS, providing a favorable trade-off between detection accuracy and computational efficiency while maintaining strong precision in small defect detection.

Processes doi: 10.3390/pr14101559

Authors: Hao Peng Cuimei Li

Shallow lakes in the Yangtze River Delta are characterized by fragile ecosystems, strong sediment–water interactions, and poor resistance to pollution shocks; they are prone to shift from macrophyte-dominated clear-water states to phytoplankton-dominated turbid states under intensive human disturbance. To improve the efficacy of aquatic ecological restoration, this study takes a typical shallow urban lake—Kuilei Lake in Kunshan—as the research object, and establishes a two-dimensional hydrodynamic and water quality model to simulate the temporal and spatial variations in flow fields, flow circulations, and water quality indicators (TP, NH3-N, CODMn) throughout the year. The results are as follows: (1) The hydrodynamic regime of Kuilei Lake is dominated by wind-driven currents, with seasonal flow circulations regulating pollutant migration and the suitability for submerged macrophyte growth; (2) Intense circulations in summer (July–September) enhance sediment resuspension and endogenous nutrient release, which are unfavorable for submerged plant colonization; (3) April–June is the optimal window for ecological restoration, with a mean flow velocity of 2.0–2.5 cm/s, TP ≤ 0.06 mg/L, NH3-N ≤ 0.20 mg/L, CODMn ≤ 3.0 mg/L, and water temperature of 15–25 °C, providing favorable thresholds for submerged macrophyte recovery. This study reveals the coupled hydrodynamic–water environmental thresholds for shallow lake restoration, and offers a scientific basis for flow field regulation and ecological reconstruction of shallow lakes in the Yangtze River Delta.

Processes doi: 10.3390/pr14101558

Authors: Xiankang Xin Zhao Xie Saijun Liu Gaoming Yu Jing Cao

Accurate prediction of reservoir production dynamics remains a key challenge in the oil and gas industry, especially for complex, high-dimensional time-series data. Conventional models fail to capture temporal dependencies, while existing hybrid models suffer from high parameter complexity and lack automated hyperparameter tuning, increasing training difficulty. To address these issues, this study proposes a novel hybrid model, TCN-LSTM-AVOA, combining a temporal convolutional network (TCN) with a long short-term memory network (LSTM) and incorporating the African Vulture Optimization Algorithm (AVOA) to enhance forecasting accuracy. The model not only captures complex temporal relationships and nonlinear features in reservoir data but also facilitates automated tuning of critical hyperparameters (e.g., the number of TCN kernels, LSTM units, batch size, and learning rate), which significantly enhances its robustness. Compared to eight benchmark models (back propagation neural network (BPNN), LSTM, convolutional neural network(CNN)-LSTM, TCN-LSTM, LSTM-AVOA, CNN-AVOA, TCN-AVOA), TCN-LSTM-AVOA achieves superior performance on a two-dimensional, three-phase heterogeneous reservoir, yielding a root mean square error (RMSE) of 7.0806, mean absolute error (MAE) of 3.4780, coefficient of determination (R2) of 0.9975, and mean absolute percentage error (MAPE) of 1.81%. This work demonstrates a more accurate and efficient methodology for reservoir production prediction, with significant potential for oilfield production optimization and resource management.

Processes doi: 10.3390/pr14101557

Authors: Yunjia Ma Ying Zhang Qi Zeng Yi Li Meng Xu Donghua Zou

Droplets impacting heated porous surfaces trigger a complex process involving liquid and vapor penetration, as well as the growth and rupture of internal bubbles. In the current paper, four types of sintered porous substrates with different permeability and surface roughness are used. The droplet impact process on heated porous surfaces is visualized by high-speed photography and image processing algorithms. The boiling phase transition characteristics of propylene glycol–propanetriol binary droplets impact on different heating surfaces and the variation pattern of the number and diameter of secondary droplets splashed during the boiling process were investigated. The results show that the surface properties of the porous medium and the composition of the droplet solution have a large effect on the boiling state of the droplets as well as the number and diameter of the secondary droplets. An elevated proportion of propanetriol in solution makes it difficult for droplets to penetrate porous substrates, and it is more difficult for droplets on substrates with large pore size and roughness to undergo film boiling, with more secondary droplets erupting during boiling.

Processes doi: 10.3390/pr14101556

Authors: Shitong Wei Xinran Zhang Di Liang Shoujun Yang

Anaerobic digestion is a key technology for chicken manure valorization, but ammonia accumulation often causes system instability. In this study, a 100-day continuous stirred tank reactor (CSTR) experiment was conducted under mesophilic conditions to investigate the mechanisms of ammonia inhibition in chicken manure at total solids (TS) contents of 8% (T1), 12% (T2), and 16% (T3). Compared to T1, the peak TAN concentrations in T2 and T3 were 64.28% and 73.82% higher. After 100 days, pH in T2 and T3 dropped by 5.19% and 7.65% relative to T1. Volatile fatty acid (VFA) accumulation increased by 4.6- and 6.5-fold, while the TS-based methane yield decreased by 52.94% and 73.11%, respectively. Metagenomic analysis revealed the mechanisms of ammonia inhibition: high-ammonia conditions not only directly suppressed the gene abundance of methanogenic pathways but also systematically reduced the abundance of hydrolytic bacteria and acidogenic fermentative bacteria, leading to a disruption in the supply chain of methanogenic precursors, while ammonia-tolerant microbiota became competitively enriched. This study elucidates the multi-level mechanism of ammonia inhibition in high-TS chicken manure digestion at the functional gene level, providing a theoretical basis for the precise regulation of ammonia stress and improvement of system stability.

Processes doi: 10.3390/pr14101555

Authors: Song Wang Linfeng Gou Jianfeng Wang Bo Lu Yabin Liu Yahui Gao

To address the loss of closed-loop nozzle control caused by failures in the nozzle throat angle sensor or the nozzle oil separator valve displacement sensor, a fault-tolerant control method for the aeroengine nozzle actuator based on dynamic control loop reconfiguration is proposed. When either sensor fails, the control structure is reconfigured by removing the corresponding servo loop that can no longer form a closed loop, thereby preserving the remaining effective control loops and maintaining nozzle controllability. The proposed method is validated through full-digital simulation, hardware-in-the-loop simulation, and bench testing. The results show that the method ensures the stable operation of the digital control system over the representative operating conditions across the flight envelope and maintains satisfactory steady-state and dynamic performance under sensor fault conditions. The steady-state fluctuations before and after faults remain comparable. Although transient overshoot and droop increase after fault occurrence, the deterioration is limited: the fan speed overshoot and droop remain within 3% and 4%, respectively, while those of the compressor speed remain within 1% and 3%, respectively. Overall, the proposed method mitigates post-fault performance degradation and improves the fault tolerance of the nozzle control system. These results show that the method provides a feasible technical approach for enhancing the reliability of aeroengine digital control systems.

Processes doi: 10.3390/pr14101546

Authors: Xinlei Li Ranfeng Wang Xiang Fu Longkang Li Shunqiang Wang Gan Luo Hanchi Ren

Aiming at the characteristics of multivariable coupling, pronounced nonlinearity, time-varying behavior, and time delay effects in clean coal ash content during dense medium cyclone separation, which make it difficult for traditional control methods to achieve high-precision and stable regulation, this paper proposes an intelligent control method based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. First, a data-driven environment model based on Long Short-Term Memory (LSTM) is constructed using historical operational data from a coal preparation plant to enable offline training of the reinforcement learning policy. Second, the state space, action space, and multi-objective reward function are designed for the ash content control task. On this basis, the standard TD3 algorithm is improved by introducing a hierarchical experience replay mechanism to enhance the utilization efficiency of critical samples, and a gated feature attention enhancement network to strengthen state representation under complex operating conditions. Experimental results demonstrate that the proposed method achieves the best overall performance among the compared approaches, with a mean absolute error (MAE) of 0.1190 and a root mean square error (RMSE) of 0.1938. The compliance rates within the target intervals of ±0.2 and ±0.5 reach 83.29% and 97.14%, respectively. Compared with Model Predictive Control (MPC), the proposed method improves the compliance rate under strict constraints by approximately 9.58 percentage points, indicating superior fine control capability. In addition, the proposed method outperforms the benchmarks in terms of error distribution, fluctuation suppression, and steady-state maintenance. These results verify that the improved TD3 method can effectively enhance the accuracy and stability of clean coal ash content control, providing a feasible solution for intelligent optimization control of quality indicators in complex industrial processes.

Processes doi: 10.3390/pr14101552

Authors: Qiang Pu Ji Xu Xuefen Zhao Qifeng Qin Yong Qing Juan Fu Zhiwen Fan Yangang Wang Hong Liu Xia Sheng

This study evaluates the corrosion evolution of N80 steel in H2S/CO2 environments simulating Carbon Capture, Utilization, and Storage-Enhanced Gas Recovery (CCUS-EGR) processes. High-pressure autoclave experiments were conducted to analyze the impacts of CO2/H2S partial pressure ratios (2.9–67.4), temperature (40–80 °C), and flow rate. Grey relational analysis indicates that the CO2/H2S partial pressure ratio dominates uniform corrosion (γ = 0.880), while flow rate and temperature primarily govern pitting behavior (γ > 0.85). Increasing the ratio from 2.9 (H2S-dominated) to 67.4 (CO2-dominated) doubled the uniform corrosion rate to 1.042 mm/y but reduced pitting by 46%. Mechanistically, the semiconductor conductivity of FeS (∼10−1 S/cm) drives deep pitting via “large cathode–small anode” galvanic effects. Additionally, fluid shear stress selectively erodes porous FeCO3, enriching surface FeS and creating differential corrosion patterns. A comprehensive evolution model describing the transition from a H2S-dominated regime to mixed control and finally to a CO2-dominated regime is established, providing a theoretical foundation for wellbore integrity management throughout the CCUS-EGR lifecycle.

Processes doi: 10.3390/pr14101553

Authors: Haruki Tanaka Yuma Morita Zizhen An Mingcong Deng

In recent years, microreactors have attracted increasing attention as next-generation chemical reactors, enabling rapid and highly efficient reactions, while requiring precise control against temperature variations. In this paper, a research platform for a microreactor process close to practical implementation is constructed using a distributed control system (DCS) and wireless communication. By establishing such a research platform, not only the effectiveness of control methods but also discussions on system configuration, including operation and maintenance, can be verified and optimized at an early stage. Moreover, operator-based multi-dimensional nonlinear control strategies have been applied in existing studies of channel temperature control. In contrast, this paper extends such strategies by integrating an operator-based feedback scheme with state estimation via particle filters, which simultaneously accounts for unknown communication delay compensation and the nonlinear characteristics of microreactors. Finally, the feasibility and effectiveness of the proposed research platform are verified through real-world experiments.

Processes doi: 10.3390/pr14101554

Authors: Tamy Carolina Herrera-Rodríguez Vianny Parejo-Palacio Ángel Darío González-Delgado

The Colombian Caribbean is a strategic area for avocado production, not only because of its favorable climatic conditions, but also because of the availability of varieties with a high content of compounds of industrial interest. The Creole-Antillean avocado grown in Montes de María represents a significant source of raw material with potential for processing, both because of the lipid fraction of its pulp and the chemical composition of its seed. However, the use of this resource has been limited by low technology incorporation and poor coordination of agro-industrial chains that would allow its valorization beyond fresh consumption. In view of this situation, the design of a plant for the simultaneous production of oil and biochar is proposed, with the aim of migrating from a linear model to a comprehensive biomass valorization scheme. The study analyzes the performance of the process from a thermodynamic perspective, applying an exergy analysis that allows for the evaluation of the quality of the energy used and the quantification of irreversibilities at each stage. The results indicate that the highest exergy destruction occurs during seed washing (12.37%), oil extraction and centrifugation (19.71%), distillation and condensation (20.64%), and pyrolysis with by-product separation (28.72%). Although the seed washing stage showed high exergy efficiency (99.81%) when integrated into biochar production, stage 12 recorded a significant loss of 2438.52 MJ/h, associated with the non-use of the volatile gases generated in pyrolysis. Overall, the exergy efficiency of the system reached 30.07%, reflecting the high thermodynamic demands involved in transforming the seed into a high-value product such as biochar. This type of assessment not only identifies critical points of exergy destruction, but also establishes technical bases for optimizing energy consumption, reducing losses, and moving towards a more efficient and sustainable process.

Processes doi: 10.3390/pr14101551

Authors: Alessandro Colletti Marzia Pellizzato María Celeste Ruiz-Aracil Giancarlo Cravotto

Background: Functional dyspepsia (FD) is a disorder of gut–brain interaction characterized by heterogeneous pathophysiological mechanisms, including altered gastric motility, visceral hypersensitivity, low-grade inflammation, impaired mucosal defence, and oxidative stress. Multi-target botanical strategies may represent a rational approach for addressing this complexity. Methods: This study evaluated the mechanistic rationale supporting a botanical formulation containing Zingiber officinale, Cynara scolymus, and Citrus limon extracts, here referred to as DyspepCyn®. A focused narrative review was conducted to summarize the available mechanistic and clinical evidence for the three botanicals. In addition, the formulation was characterized through solubility testing in aqueous and biorelevant simulated gastrointestinal media, together with antioxidant assessment using ORAC and DPPH assays. Results: DyspepCyn® showed favourable dispersion and solubility behaviour across simulated gastrointestinal conditions, with complete solubilization up to approximately 700 mg/100 mL in water and up to approximately 800 mg/100 mL in simulated intestinal fluids. No precipitation was observed in the tested media. The formulation also showed measurable antioxidant activity, with an ORAC value of 365 µmol Trolox equivalents/g and a DPPH radical scavenging EC50 of 32 µg/mL. Conclusions: DyspepCyn® combines botanicals with complementary actions on gastric motility, postprandial digestive processes, mucosal protection, and oxidative stress. The observed physicochemical stability and antioxidant capacity support the mechanistic rationale for this multi-target botanical strategy in FD. Clinical studies are required to confirm its efficacy in patients with FD.

Processes doi: 10.3390/pr14101550

Authors: Rahmani Ala eddine Kouadri Ramzi Souhil Mouassa Lucian Toma

The stability and dynamic performance of power systems can be significantly improved by regulating the terminal voltage of synchronous generators using an automatic voltage regulator (AVR). However, the effectiveness of the AVR largely depends on the optimal tuning of the proportional integral derivative (PID) controller parameters. In this paper, a recently developed metaheuristic optimization technique, namely, the red-tailed hawk (RTH) algorithm, is employed to determine the optimal PID controller parameters for the AVR system. This proposed algorithm aims to minimize a multi-objective performance index that combines the time-weighted squared error (ITSE) and Zwe-Lee Gaing (ZLG) criterion, in order to improve voltage regulation performance, enhance system stability, and achieve superior transient response characteristics. The effectiveness of the proposed RTH-PID controller is validated through extensive simulations and comparative analyses with several well-established optimization PID tuning methods under the same constraints. The obtained results demonstrate that the proposed controller significantly improves dynamic performance by reducing overshoot, settling time, and rise time and enhancing system robustness. These findings confirm the superiority and reliability of the RTH-PID controller for AVR systems.

Processes doi: 10.3390/pr14101545

Authors: Ranran Li Hecheng Yuan Jianing Zhang Qiushuang Li Jiarui Li Wanying Li Zhengsen Ji

Different types of Virtual Power Plants (VPPs) play distinct roles within power systems. To scientifically evaluate the operational benefits of VPPs, this paper constructs a comprehensive evaluation framework based on combined weighting and the Evaluation based on Distance from Average Solution (EDAS) method. First, an evaluation index system is established encompassing four dimensions: economic, environmental, social, and technical. Subsequently, a hybrid model integrating DEMATEL, CRITIC, Game Theory, and EDAS is proposed. Specifically, the DEMATEL method is employed to analyze the causal relationships among indicators and determine subjective weights, while the CRITIC method is used to calculate objective weights. Game Theory is then applied to optimize the combination of weights, and the EDAS method is utilized to rank the alternatives. Empirical analysis of five VPP scenarios indicates that the renewable energy accommodation rate and hardware investment costs are the core driving factors affecting operational benefits. Specifically, the renewable-energy accommodation rate exhibits the highest combined weight of 0.08, and the hardware investment cost reaches 0.07. Among the scenarios, a wind-solar-storage hybrid VPP demonstrates the optimal comprehensive performance. The results are consistent with comparative methods such as TOPSIS, verifying the reliability of the proposed framework and providing a scientific reference for VPP investment decision-making.

Processes doi: 10.3390/pr14101548

Authors: Hangju Yu Bin Zhao

The reconstruction of acoustic (AC) logging curves is of great significance for reservoir evaluation, lithology identification, and velocity modeling, particularly in the presence of missing or degraded logging data. However, conventional reconstruction methods and existing deep learning models often suffer from limited feature representation capability and rely heavily on manual hyperparameter tuning, leading to suboptimal performance. To address these challenges, this study proposes a reinforcement learning-based optimization framework for AC logging curve reconstruction. Specifically, a hybrid deep learning architecture integrating convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM), and an attention mechanism is developed to effectively capture local spatial features, long-range temporal dependencies, and key feature contributions from multi-logging data. Furthermore, a Q-learning-based optimization strategy is introduced to adaptively tune model hyperparameters by formulating the optimization process as a Markov Decision Process (MDP), enabling dynamic and data-driven parameter adjustment. To validate the effectiveness of the proposed method, comparative experiments are conducted using several baseline and optimized models, including CNN–BiLSTM, CNN–BiLSTM–Attention, particle swarm optimization (PSO)-optimized CNN–BiLSTM–Attention, and genetic algorithm (GA)-optimized CNN–BiLSTM–Attention. The results demonstrate that the proposed approach achieves superior reconstruction accuracy for AC curves, with improved convergence efficiency and model stability. In addition, it exhibits stronger robustness and generalization capability under limited data conditions, effectively mitigating the risk of overfitting and local optima. This study provides a novel reinforcement learning-driven solution for AC logging curve reconstruction and offers practical value for intelligent reservoir characterization in complex geological environments.

Processes doi: 10.3390/pr14101549

Authors: Yuanchi Wang Zhenhua Zhao Langyue Chen Binglu Teng Zhirui Qin Wenqing Zhang Jiayuan Cheng

Microbial remediation technologies have emerged as a promising strategy for polycyclic aromatic hydrocarbon (PAH) removal; however, single strains are often constrained by limited metabolic pathways and PAH toxicity, and the response patterns of bacterial consortia to interacting environmental drivers remain poorly elucidated. Herein, culturable individual bacterial consortia were enriched from soils of three typical contaminated sites, and culturable mixed bacterial consortia were constructed via binary and ternary combinations. The phenanthrene (a representative PAH) degradation performance and community characteristics of these consortia under specific environmental conditions were systematically evaluated by orthogonal experimental design; range and variance analyses were employed to identify the primary influencing factors. Results revealed that mixed consortia (83.69%) exhibited stronger phenanthrene degradation efficiency compared to individual consortia (50.55%). Statistical analysis further identified phenanthrene concentration and temperature as primary factors influencing phenanthrene degradation efficiency and bacterial diversity. Meanwhile, elevated phenanthrene concentration increased OTU counts, while phylum-level and genus-level compositions exhibited greater sensitivity to temperature. Functionally, metabolic dominated KEGG pathways of Level 1, with higher temperature inhibiting the abundance of functional genes by phenanthrene degradation. Overall, the culturable mixed bacterial consortia constructed in this study are capable of effectively removing phenanthrene from wastewater under specific environmental conditions, laying a solid theoretical and practical foundation for PAH remediation.

Processes doi: 10.3390/pr14101547

Authors: Shang Wang Linjie Li Yajuan Zhang

Accurate prediction of mechanical properties is essential for quality control in hot strip rolling (HSR), where the relationships among chemical composition, process parameters, and mechanical properties are highly nonlinear under industrial conditions. In this work, a data-driven framework was established for the prediction and interpretation of yield strength (YS), tensile strength (TS), and elongation (EL) of hot-rolled strips based on industrial production data. A high-quality dataset was constructed through data collection, outlier removal, and feature selection. Six machine learning (ML) models were developed and compared, and particle swarm optimization (PSO) was employed for hyperparameter tuning. The results showed that random forest (RF) achieved the best overall predictive performance, with R2 values of 0.979, 0.986, and 0.959 for YS, TS, and EL, respectively. In addition, faster convergence and better optimization performance were obtained by PSO than by genetic algorithm (GA) and artificial bee colony (ABC). Shapley additive explanations (SHAP) were further introduced to reveal both global feature importance and local feature contributions. The proposed framework provides an effective approach for mechanical property prediction and alloy design in HSR.

Processes doi: 10.3390/pr14101544

Authors: Baosheng Liu Qianhua Liao Lei Zhang Yuchen Zhang Guowei Zhu Guohua Wang Qiang Zheng Yanwei Sun

With continued expansion of offshore oil and gas development, the number of high-inclination wells has increased rapidly. During drilling of such wells, vibration transmission from the bottom drillstring to the wellhead is significantly attenuated. Therefore, even when severe vibration occurs at the bit, surface monitoring may not accurately reflect downhole conditions. To analyze axial and lateral vibration behavior, this study considers drillstring–wellbore contact and bit–formation interaction. Based on the Lagrange equation and the S–N fatigue curve, a dynamic model of the drillstring in offshore high-inclination wells is developed using the beam element method. A dynamic safety evaluation model is then constructed using the calculated dynamic characteristics, forming a mechanical analysis and optimization approach for drillstrings in these wells. The technique was applied in a branch well in the Bohai Oilfield. Drillstring vibration under different wellbore trajectories and drilling parameters was examined, and limit drilling parameters were selected through fatigue life analysis. The recommended configuration includes 24 drill collars, a weight on bit of 100 kN, and a rotation speed of 60 r/min. These optimization guidelines support improved drilling efficiency and help ensure drillstring safety in offshore high-inclination well applications.

Processes doi: 10.3390/pr14101543

Authors: Yuchan Ahn

This study presents an integrated environmental assessment of plastic waste-to-hydrogen systems with varying CO2 utilization ratios, combining process-level simulation with life-cycle assessment (LCA). The environmental impacts are evaluated across key categories, including global warming potential (GWP), fine particulate matter formation (PM), fossil resource scarcity (FRC), and water consumption (WC). The results reveal a non-linear relationship between CO2 utilization and environmental impacts. As the CO2 utilization ratio increases from the N2 baseline to moderate levels (CO2-40 to CO2-50), environmental impacts decrease due to improved carbon utilization and reduced direct CO2 emissions. However, further increases in CO2 utilization lead to a reversal of this trend, with environmental burdens rising significantly due to increased energy and utility demand associated with intensified CO2 recycling. Process contribution analysis shows that the dominant impact drivers shift from direct CO2 emissions to utility-related contributions, particularly heat (steam) and electricity, at higher utilization levels. A trade-off analysis between direct CO2 emissions and utility-related impacts identifies an optimal environmental operating range around CO2-50. An integrated comparison with techno-economic performance, represented by the minimum hydrogen selling price (MHSP), reveals a divergence between environmental and economic optima. While environmental impacts are minimized at CO2-40 to CO2-50, the economic optimum occurs at higher utilization levels (CO2-60 to CO2-70). These results highlight that CO2 utilization acts as a key design variable governing the trade-off between carbon efficiency and energy demand. An optimal compromise region is identified around CO2-50 to CO2-60, providing a balanced operating window for both environmental and economic performance. This study demonstrates that maximizing CO2 utilization is not necessarily optimal from a system-level sustainability perspective and provides practical insights for the design and optimization of integrated plastic waste-to-hydrogen systems.

Processes doi: 10.3390/pr14101542

Authors: Shayuan Yang Laibin Zhang Jingtian Qin Wei Chen Yu Song Lei Li Kun Jiang Gengchen Li Xiaoxiao Zhu

To address the severe shielding of conventional electromagnetic signals by metallic pipelines and the inherent design trade-off under fixed-voltage excitation, whereby increasing coil size suppresses current and limits magnetic field intensity, this study proposes an independently parallel-fed multistage coil enhancement scheme for the transmitter of through-wall magnetic induction communication. Based on electromagnetic theory and COMSOL6.3 simulations, a coupled analysis framework for multistage coils was established to systematically evaluate the effects of axial partitioning, radial partitioning, nonuniform turn allocation, and magnetic-core loading on branch-current amplitude and phase consistency as well as spatial magnetic field intensity. The results show that, under in-phase, equal-frequency excitation, the resultant magnetic field intensity increases approximately linearly with the number of partitions. The partition scheme significantly alters the mutual inductance distribution among sub-coils, thereby affecting current synchronization and magnetic field synthesis efficiency. The introduction of a high-permeability magnetic core markedly improves the amplitude and phase consistency of the radially partitioned structure and enhances output stability. Considering magnetic field output, current synchronization, and engineering feasibility, the axial–radial hybrid four-partition structure with a magnetic core was identified as the preferred configuration. These findings provide a theoretical basis and structural guidance for transmitter design in low-frequency through-wall magnetic induction communication under metallic shielding conditions.

Processes doi: 10.3390/pr14101541

Authors: Tucker Graff Eric W. Maichel Sajid Alavi

Energy consumption and different methods for determining energy efficiency were evaluated for drying of extruded rainbow trout feed pellets using a pilot-scale, heated air, integrated conveyor dryer and cooler. Impact of drying parameters on product quality, especially final moisture and pellet durability index (PDI), was also studied. From an initial moisture of 21.2 to 22.1% wet basis (wb), the drying–cooling process reduced the pellet moisture to 3.5 to 5.0% wb. Dryer throughput (82–121 kg/h) did not have statistically significant impact on final moisture (p = 0.0965) although the highest throughput corresponded to highest moisture; but increase in drying temperature from 93 to 115 °C led to a significant decrease in final moisture (p = 0.0285). Increase in dryer throughput led to a significant increase in PDI from 82.8 to 88.0% (p = 0.0003), while increase in drying temperature resulted in a slight decrease in PDI from 84.3 to 83.6%, although not statistically significant (p = 0.0811). Sorghum-based aquatic feed had a slightly lower PDI than wheat-based feed (82.8 versus 83.7%, respectively), but the difference was not statistically significant (p = 0.3009). Differences in pellet durability were attributed primarily to structural weakness induced by product shrinkage during drying, which in turn was impacted by drying rates. Specific energy consumption (SEC) during drying decreased from 136.3 to 101.1 MJ/kg-water with increase in throughput and increased from 122.5 to 150.1 MJ/kg-water with increase in drying temperature. An inverse trend was observed for various measures of dryer energy efficiency, with increase in efficiency for higher throughput and decrease for higher temperature. Sorghum-based aquatic feed had a higher drying SEC as compared to wheat-based feed and a lower energy efficiency. Overall, the results highlighted trade-offs between throughput, drying efficiency and pellet quality during drying of aquatic feeds.

Processes doi: 10.3390/pr14101540

Authors: Quanbin Wang Deli Jia Jun Fu Chuan Yu Mujie Luo Xiuyuan Chen

Injection operations are critical in subsurface energy engineering, where wellbores endure complex thermo-hydro-mechanical (THM) coupling under high-temperature and high-pressure conditions, impacting tubing string stability and wellbore long-term safety. Current tubing string THM research relies on simplified assumptions, focusing on single/dual-field coupling without full-scale modeling, failing to accurately characterize comprehensive multi-field behaviors or actual structural stress distributions. This paper presents a full-scale THM coupled numerical model for actual injection conditions, taking real wellbore structures as the object to realize unified modeling of tubing, packer, casing, cement sheath and formation, covering the entire well section and synergistically describing fluid flow, heat conduction and structural mechanical response. It considers fluid pressure/temperature effects on tubing axial load, thermal stress and deformation, as well as nonlinear boundary conditions like packer-casing contact and friction. The governing equations are discretized via the finite element method and solved by Newton iteration. Benchmark verification shows the maximum relative errors of casing inner/outer wall Mises stress vs. analytical solutions are 2.43% and 4.98%, confirming high accuracy. Systematic analysis of displacement, axial force, stress and temperature responses under typical conditions is conducted, providing reliable theoretical and technical support for wellbore structure optimization, injection parameter regulation and long-term wellbore integrity evaluation.

Processes doi: 10.3390/pr14101539

Authors: Weifu Wang Guoqiang Zhang

Large-scale shell-and-tube heat exchangers operate for extended periods, critically affecting the energy efficiency and safety of hydrogen production processes. However, online condition monitoring on industrial distributed control systems (DCSs) is often hindered by an engineering trilemma: high-fidelity mechanistic models incur prohibitive computational latency; static constant-parameter models suffer from severe systematic bias; and purely data-driven models risk yielding non-physical predictions under out-of-distribution scenarios such as variable-load operations. To address these challenges, this study proposes a physics-guided adaptive digital twin tailored to high-noise industrial DCS environments. Energy conservation and the counterflow logarithmic mean temperature difference (LMTD) relation are embedded as hard constraints in a lightweight reduced-order model (ROM). On this basis, a closed-loop online adaptation strategy—comprising physical-bound checking, window-wise inverse estimation, anomaly rollback, and exponentially weighted moving average (EWMA) smoothing—treats the overall heat transfer coefficient U as an equivalent time-varying parameter that co-evolves with operating regimes. Validation on real plant DCS data under variable-load conditions shows that, compared with a conservative fixed-U baseline, the proposed online update eliminates massive systematic overestimations (up to tens of degrees Celsius) and suppresses inversion oscillations caused by small cold-side temperature differences and sensor noise. Relative to an overfitting-prone data-driven baseline, the framework retains millisecond-level inference latency while enforcing thermodynamic feasibility, thereby establishing a dynamic healthy baseline. This baseline provides a proxy indicator for distinguishing load-induced reversible variations from potential degradation-related residual trends.

Processes doi: 10.3390/pr14101538

Authors: Tianliang Duan Hui Jiang Zhengwei Luo Tao Xie Wenhua Geng

Supercritical water gasification (SCWG) is an essential technology for achieving low-carbon and high-value utilization of coal; however, traditional gasification methods face challenges such as high reaction temperatures, low product selectivity, and a lack of rational catalyst design. In this study, we first quantified inherent metallic elements in coal and identified that K, Fe, and Ca show statistically significant positive correlations with H2 and CH4 yields. Based on this feedstock-adapted guideline, a novel K–Fe–Ca/Al ternary catalyst was rationally designed and synthesized via a stepwise impregnation method. Characterizations confirmed the successful loading and uniform dispersion of active components on the γ-Al2O3 support. The ternary catalyst exhibited remarkable synergistic effects and significantly boosted H2 and CH4 yields, far superior to those of physical mixtures. Mechanistic analysis revealed the integrated functions of K for gasification promotion, Fe for reversible redox cycles, and Ca for in-situ CO2 capture and anti-deactivation performance. This work provides a new paradigm for data-driven catalyst design in coal SCWG and offers a theoretical foundation for efficient and clean coal conversion. This rational, data-driven research design ensures the reliability and scientific rigor of the catalyst development for coal SCWG.

Processes doi: 10.3390/pr14101534

Authors: Yuping Sun Hao Wang Hang Yuan Mingqiang Wei Qiaojing Li

Inter-well interference in multi-well pad development is a critical factor influencing the recovery efficiency of shale gas reservoirs. This study presents a comprehensive semi-analytical model to characterize the transient pressure behavior and interference mechanisms of multi-well multi-stage fractured horizontal wells (MFHWs). Utilizing point source functions and the principle of superposition, the model accounts for complex shale gas transport mechanisms, including gas desorption, diffusion, and real-gas compressibility via pseudo-pressure transformation. The proposed model is validated against the industrial standard numerical simulator KAPPA-Saphir, showing an excellent match across most flow regimes, with a maximum relative error of 3.2% and an average relative error of less than 1% across the entire production period. The results identify five distinct flow stages: fracture linear flow, fracture radial flow, compound linear flow, compound radial flow, and boundary-dominated flow. Sensitivity analysis reveals that decreasing the inter-well spacing significantly shortens the fracture radial flow duration, while longitudinal staggering of wellbore centers effectively mitigates early-time interference and promotes more uniform reservoir drainage. Furthermore, it is observed that in multi-well systems, inner wells suffer from more severe energy competition and faster pressure depletion than peripheral wells. Based on these findings, it is proposed that the inter-well spacing should exceed four times the fracture half-length, and a staggered fracture arrangement (the relative positions in the x-direction of the fractures between the wells are not the same) should be prioritized. This work provides a robust theoretical framework and practical guidelines for optimizing well spacing and infill drilling strategies in shale gas reservoirs.

Processes doi: 10.3390/pr14101536

Authors: Yizheng Li Haiqiong Yi Xiao Yang Shichang Cui Xueying Wang Yihan Liu Xinying Zhou

Accurate quantification of uncertainty risks is pivotal for enhancing the reliability of generation maintenance scheduling (GMS) in power systems. However, existing risk quantification methods predominantly focus on the aggregate impact of uncertainty on system-wide operational risks, failing to identify critical risk sources. This limitation hinders the secure and efficient operation of power systems with high penetration of renewable energy. To address this issue, we propose a risk-averse GMS approach for power systems based on contagious value-at-risk (CoVaR). Specifically, we first introduce the CoVaR theory to identify dominant risk sources affecting the secure operation of the system and derive a general analytical expression for CoVaR that incorporates integral terms of uncertain variables. Subsequently, a scenario-based linearization reconstruction strategy is developed to discretize these integral terms, and the complex CoVaR model is reformulated into a computationally tractable mixed-integer linear programming (MILP) model. On this basis, a new risk-averse GMS model embedded with CoVaR constraints is constructed. This model achieves precise identification of critical risk sources by quantifying and comparing the impacts of different risk sources on both system operational costs and risk costs. Finally, simulation results on the modified IEEE 24-bus power system and IEEE 118-bus power system demonstrate the effectiveness and superiority of the proposed approach.

Processes doi: 10.3390/pr14101535

Authors: Bing Li Yunwei Kang Pengcheng Hao Weiping Zhu Pengbo He Huaibin Zhen Dong Xu Kunsen Bai Yi Liu Yuchuan Wang Zixi Guo

Deep coalbed methane reservoirs are characterized by complex geological conditions, strong heterogeneity, and significant variations in in situ stress, posing challenges for accurate three-dimensional in situ stress prediction. To address the issues of strong dependence on rock mechanical parameters in traditional physical models, as well as the lack of physical constraints and poor generalization capability under small-sample conditions in purely data-driven methods, this paper proposes a LightGBM prediction model that integrates physical information and data clustering. A total of 1289 fracturing clusters in the DJ block are selected as the research objects. First, the K-means algorithm is used to divide the reservoir into three categories to reduce the impact of heterogeneity. Then, a LightGBM model is constructed for each category, and physical constraints based on Huang’s model and stress–gravity equilibrium are incorporated into the loss function to ensure that the prediction results conform to mechanical laws. Taking the fracturing clusters in Category I as an example, the proposed model achieves an MAPE of 2.78% and an R2 of 0.89 on the test set. Comparative experiments show that the proposed model outperforms BP neural networks, random forests, and Transformers in prediction accuracy. Ablation experiments verify the independent contributions and synergistic effects of the clustering module and the physical information constraints. Transfer experiments demonstrate that the model has good applicability to blocks with similar geological conditions. This study provides an effective method for predicting in situ stress in deep coalbed methane reservoirs, balancing accuracy and physical interpretability.

Processes doi: 10.3390/pr14101537

Authors: Chaoyue Li Qikang Xiao Yutao Zhang Sha Liu Guannan Liu

Modern aircraft fuel tank explosion protection relies critically on inerting efficiency. This study presents and investigates a novel scrubbing deoxygenation strategy utilizing mixed inert gas (MIG) generated by oxygen-consuming inerting systems for high-vapor-pressure RP5 aviation fuel. A high-fidelity computational fluid dynamics (CFD) numerical framework was established using the Eulerian–Eulerian two-fluid model coupled with Higbie’s penetration theory, with experimental validation ensuring computational accuracy (maximum errors for ullage oxygen concentration and dissolved oxygen in fuel controlled within 4.11% and 5.23%, respectively). The research systematically elucidates the influence mechanisms of bubble diameter, MIG temperature, and superficial gas velocity on mass transfer characteristics (oxygen mass transfer coefficient and volumetric mass transfer coefficient). Key findings reveal that reducing bubble diameter achieves localized polarization of mass transfer intensity in the central plume region through an “area-velocity” synergistic effect, with the oxygen volumetric mass transfer coefficient at 1.0 mm diameter increasing by 51.3% compared to 2.5 mm. The performance enhancement from superficial gas velocity primarily stems from the “area multiplication effect” triggered by surging gas holdup. Notably, MIG temperature exhibits a unique three-stage reversal characteristic of “kinetically dominated early stage, thermodynamically controlled late stage” on deoxygenation performance. These results provide critical physical foundations for the forward design of next-generation multifunctional onboard inerting systems.

Processes doi: 10.3390/pr14101533

Authors: Timur Semenov Evgenii Epifanov Gennady Mishakov Vladimir Rovenko Anton Vorobei Ivan Goryachuk Alexander Lazarev Nikita Minaev Evgenii Mareev

Using an in situ method of time-resolved Mie scattering indicatrix registration, the dynamics of micro- and nanoparticle formation during the explosive boiling of a solution of ibuprofen in subcritical carbon dioxide (T0 = 302 K, P0 = 71 bar) were investigated. The process is found to exhibit multistage behavior. At the jet front, ibuprofen microaggregates with a mean radius of 1.4 ± 0.2 μm are formed, maintaining a stable size over the initial ~100 ms. Subsequent reduction in boiling intensity results in a decrease in the particle radius to 650 ± 100 nm. In the following stage, nanoscale CO2 clusters (20–50 nm) are detected by the Mie scattering technique. The findings indicate that the final size of the resulting ibuprofen particles is governed not only by the initial thermodynamic conditions but also by the boiling dynamics of the ibuprofen-saturated CO2 solution during the pulsed ejection process.

Processes doi: 10.3390/pr14101532

Authors: María del Rosario Cárdenas-Aquino Danna Lorena Ovalle-Ayala José Guadalupe Ávila-Hernández Enrique Ramírez-Chávez Agustino Martínez-Antonio Alberto Camas-Reyes Lisset Herrera-Isidrón

Lemongrass (Cymbopogon citratus) essential oil is mainly composed of the acyclic monoterpene aldehydes geranial (α-citral) and neral (β-citral), collectively known as citral, which exhibit documented cytotoxic activity against cancer cell lines, as well as geraniol and limonene, among other monoterpenoids. In a previous study we reported that the constituents of the essential oil (EO) composition of lemongrass in vitro plants were modulated by different types of cytokinins (CKs) exogenously added to the culture medium. However, in that work, EO components were detected as volatile headspace compounds by SPME-GC/MS rather than as bulk oil extracts directly injected to GC/MS. Therefore, in this study, EOs were extracted by hydrodistillation from plants micropropagated with different CKs (BAP or 2iP) under different osmotic conditions (MS 3/3 and MS 5/5) and subsequently established in a greenhouse. Analysis of EO in C. citratus plants showed that plants grown on MS-3/3 BAP had more α-citral, and plants grown on MS-5/5 2iP had more limonene. This study demonstrates the impact of various CKs on EO production in lemongrass. The findings showed that 5/5 2iP produced the highest limonene yield, indicating a potential yield of 100 mL from 8719 plants. Similarly, 101 plants under the 5/5 Ctrl treatment are required for 100 mL of citral, and 34 plants under the 5/5 Ctrl treatment are required for 100 mL of geranyl acetate. The 5/5 2iP requires 816 plants to produce 100 mL of geraniol, and it takes 11,340 plants to produce 100 mL of β-caryophyllene from the 3/3 2iP treatment.

Processes doi: 10.3390/pr14101530

Authors: Maria Fernanda Rios-Mercado Viviana Sanchez-Torres German Zafra Delia Rueda-López Nelson Rodriguez-Lopez Cristian Rodriguez Jonathan Blanco Karen Vides Jessica Vargas Edgar Ricardo Oviedo-Ocaña

Biochar and zeolite are promising additives for improving composting; however, their effects during the co-composting of agricultural waste have not yet been sufficiently studied. This study evaluated their influence on the composting of green onion residues and chicken manure under psychrophilic conditions on a pilot scale using 200 kg piles. Three treatments were evaluated: a control, 5% biochar, and 2% zeolite. Both amendments increased the maximum composting temperature by approximately 3 °C and improved the germination index, with increases of around 10% for biochar and 26% for zeolite compared to the control. Biochar increased the relative abundance of the amoA gene, associated with ammonia oxidation and nitrification, suggesting greater biochemical potential for nitrification. During maturation, zeolite reduced pH and electrical conductivity, indicating greater compost stability. In fast-growing crops, compost from zeolite treatment did not significantly affect plant growth when applied alone, but improvements were observed when combined with synthetic fertilizer. Overall, both additives improved composting performance and compost quality, with zeolite showing the most consistent effects.

Processes doi: 10.3390/pr14101531

Authors: Nevena Ilić Anja Antanasković Jelena Filipović Tričković Miona Miljković Ana Milivojević Marija Milić Katarina Mihajlovski

This study addresses the need for sustainable approaches in textile wastewater treatment by investigating laccase production with the white-rot fungus Schizophyllum commune TMF3 using agro-industrial waste as a substrate. Laccase was produced via solid-state fermentation on brewery spent grain under optimized conditions (1.75 g malt extract, 75% moisture, 7 days, 25 °C), reaching a maximum activity of 21.06 IU/g dry substrate. The crude enzyme was applied for the decolorization of azo and triphenylmethane dyes (50 mg/L). Decolorization efficiencies above 80% were achieved within 60 min without redox mediators, while chemical oxygen demand (COD) was reduced by more than 50% for all tested dyes. HPLC analysis showed parent dye peaks decreasing and the transformation products’ appearance. Antimicrobial activity testing showed no increase in inhibitory effects against Escherichia coli, Lactobacillus rhamnosus, Candida albicans, and Saccharomyces cerevisiae, while slight growth stimulation was observed in selected cases. Phytotoxicity assays using Triticum aestivum showed no inhibitory effects, with germination index values of 77–124%. Cytotoxicity assessment showed no effects for azo dyes, while cytotoxicity of the triphenylmethane dye decreased by 30% after treatment. These findings support the potential of agro-industrial laccase production as an effective approach for dye removal in sustainable wastewater strategies.

Processes doi: 10.3390/pr14101529

Authors: Itumeleng Kohitlhetse Harry Chiririwa

The steel sector is one of the main contributors to carbon dioxide emissions among the industrial activities. It is mostly the use of carbon-rich blast furnaces and natural gas direct reduction processes that cause this. Hydrogen-based direct iron reduction (H-DRI) is a demonstrated method of lowering steel production carbon emissions by using hydrogen rather than carbon monoxide as the reducing agent; therefore, water vapor is released instead of carbon dioxide. This work offers a detailed analysis of the trends, operating concepts, industrial-scale trials, difficulties, and advantages of H-DRI. It is well supported by both energetic and reaction rate considerations that hydrogen is an efficient agent for the reduction of iron oxides to iron metal, giving metallization rates up to those of the traditional processes and at the same time significantly reducing GHG emissions. Moreover, industrial trials confirm that the method is technically feasible on a large scale, which is not yet realized because green hydrogen is very expensive, infrastructure needs are high, and there are still hurdles to be overcome in process optimization, such as water vapor management, pellet quality, and reactor design. According to the studies of product life cycles, if the hydrogen is extracted from renewable sources of energy, then the reduction in CO can be as high as 90%. The article also discusses different aspects of the economy, environment, and law that are already there and the ones that need to be developed so that research, technological breakthroughs, and industrial harmonization can be directed to the right spots. Practical deployment requires control of hydrogen supply, optimizing reduction processes, integrating renewable energy, and regulatory support. The results offer operational insights to the steel industry, policymakers, and academia on the path to sustainable, energy-efficient, and carbon-neutral steel production while retaining the metallurgical quality and industrial scale of the steelmaking processes.

Processes doi: 10.3390/pr14101528

Authors: Yupeng Li Shengjie Sui Yinao Jiao Cheng Pan Haitao Yang

The application of lignin-based films is often restricted by traditional processing methods that rely on toxic organic solvents and harsh chemical reagents, which result in poor compatibility with the polymer matrix and difficulty balancing transparency, barrier, and toughness. Here, lignin was green-modified by ternary deep eutectic solvent (choline chloride-lactic acid-ethanol), and ZnO hybrids with cellulose nanocrystals (CNC) as anchor points were introduced to realize the stability and uniform dispersion of ZnO in the polyvinyl alcohol (PVA) matrix. The prepared composite film maintains a transmittance of about 78% at 800 nm while achieving a wide spectrum of ultraviolet shielding. The barrier properties of the film were markedly improved: the water vapor permeability (WVP) decreased to 0.24 × 10−7 g·m−1·h−1·Pa−1, and the oxygen permeability (OTR) to 6.98 cm3·m−2·24 h−1·0.1 MPa−1. In addition, the mechanical flexibility and durability of the material were significantly improved, as evidenced by a tensile strain of 109%. In the insurance experiment, compared with the blank film, the browning degree and weight loss of the composite film were relatively low. The scalable and low-solvent consumption route provides a practical idea for the application of lignin in food preservation.

Processes doi: 10.3390/pr14101527

Authors: Mukhtar Tultabayev Tamara Tultabayeva Madina Sultanova Aigerim Saduakas Akerke Kamali Nurtore Akzhanov

In the context of increasing consumer interest in functional food products and the use of vegetable oils with enhanced biological value, the development of mayonnaise emulsions with improved structural, rheological, and performance characteristics is highly relevant. Safflower oil is of considerable interest as a fat base for mayonnaise due to its high content of polyunsaturated fatty acids and antioxidant components. However, its application in emulsion systems requires scientifically substantiated optimization of formulation parameters to ensure product stability and the desired rheological properties. The aim of this study was to optimize the formulation parameters of mayonnaise based on safflower oil using response surface methodology. The independent variables were the mass fraction of safflower oil, the content of egg powder, and skimmed milk powder. The response functions included apparent viscosity (Y1), consistency coefficient (Y2), and emulsion stability (Y3), which comprehensively characterize the structural and rheological behavior and stability of mayonnaise emulsions. Experimental studies were carried out using a rotatable central composite design. The rheological properties of the mayonnaise emulsions were determined by rotational rheometry, and emulsion stability was assessed by centrifugation. Based on the experimental data, second-order quadratic regression models were developed, adequately describing the effects of formulation factors and their interactions on the studied parameters. It was established that the apparent viscosity, consistency coefficient, and emulsion stability of mayonnaise emulsions depend nonlinearly on the formulation factors and are determined by their combined effect. The maximum response values are achieved at an optimal ratio of fat and protein phases rather than at the extreme concentrations of individual components. As a result of optimization, a mayonnaise formulation based on safflower oil was proposed, ensuring high emulsion stability and balanced rheological characteristics. The developed technological scheme confirms the practical feasibility of the optimized formulation and its potential application in the production of functional mayonnaise sauces.

Processes doi: 10.3390/pr14101526

Authors: Quanhong Li Shuli Liu Zhuofan Gao Dongdong Cui Zheng Li He Qin Zhuo Huang

Conventional physicochemical parameter-based monitoring fails to provide real-time early warning of microbial risks in drinking water sources, highlighting the critical necessity to unravel the inherent associations between microbial activity and water quality dynamics. This study integrated a domestically developed microbial enzymatic analyzer with multivariate analyses to assess real-time microbial activity in a complex reservoir. Measurements of microbial indicators and physicochemical parameters were conducted at eight sites during dry and wet seasons, plus six months of continuous monitoring at a polluted tributary site. Key findings: (1) Water quality showed clear spatiotemporal variation, worse in the wet season than the dry season, with tributaries more polluted than mainstream, and downstream better than upstream; (2) Microbial activity exhibited spatial heterogeneity, with a significant positive correlation between E. coli and Enterococcus (EC); (3) Microbial activity responded to key water quality indicators, and both E. coli and EC correlated positively with NH3-N. EC also correlated positively with TP. In summary, this study reveals a mechanism-based link between microbial activity and key water quality parameters, providing a theoretical foundation for a microbial-response-centered early warning model. This marks a shift from conventional reactive monitoring to proactive risk management for drinking water safety, offering a new paradigm with ecological indication and practical value.

Processes doi: 10.3390/pr14101525

Authors: Hao Qin Kaidong Lin Guangbin Wu Shijian Zhang

To address the challenges of nonlinearity, strong temporal dependence, and accuracy degradation caused by sudden disturbances in power grid customer service telephone traffic forecasting, this paper proposes a novel forecasting method based on an ensemble model pairing Gated Recurrent Unit (GRU) with Correntropy loss (CL) (called EnsCL-GRU). First, to overcome the sensitivity of the traditional Mean Squared Error (MSE) loss to abnormal spikes and its difficulty in capturing the overall trend consistency of the sequence, a CL is introduced as the loss function for the GRU model. This loss function calculates the normalized Correntropy coefficient between the predicted sequence and the true sequence in the time-delay domain, guiding the model to focus on the overall shape matching of the time series data rather than point-wise error fitting. Furthermore, the gated memory mechanism of the GRU can capture long-term dependencies in the time series, while the CL constrains the consistency of the predicted dynamic trends from the sequence level. This preserves the GRU’s temporal modeling capability while enhancing the model’s response accuracy to sudden disturbances and trend changes. Second, to improve the generalization ability of a single GRU model, an ensemble strategy is employed to train multiple CL-enhanced GRU base models serially. By adaptively adjusting sample weights, the fitting capability for difficult samples (such as telephone traffic spikes) is improved, further improving the model’s robustness. Finally, Bayesian optimization is introduced to automatically search for the optimal hyperparameters of the ensemble model, efficiently approximating the global optimal configuration within a limited number of evaluations. Experimental results demonstrate that the proposed method outperforms traditional approaches. Specifically, compared with the standard GRU model, the proposed method reduces MAPE from 29.15% to 22.61%. It also consistently outperforms the ensemble baseline EnsGRU, achieving a MAPE reduction of 4.73 percentage points. The results indicate that the proposed model significantly improves forecasting accuracy and robustness, particularly under scenarios with nonlinear fluctuations and sudden disturbances, providing reliable support for optimal resource allocation in power grid customer service systems.

Processes doi: 10.3390/pr14101524

Authors: Feng Deng Jin Wu Chengyong Li Yi Liu Shuo Zhai Shaoyang Geng Liuting Chen Yang Zeng

The commercial development of deep shale gas reservoirs depends mainly on hydraulic fracturing. Concurrently, the accurate prediction of rock mechanical parameters is critical to informed decision-making and the optimization of hydraulic fracturing parameters. Traditional methods for developing deep shale reservoirs primarily rely on empirical formulas based on compressional and shear-wave velocities. However, acquiring shear wave data is challenging, and the accuracy of such formulas is often low. Conventional machine learning algorithms exhibit limited predictive accuracy and generalization due to the significant heterogeneity of rock-mechanical data. To efficiently predict rock mechanical parameters with high precision in deep shale formations and to improve guidance for optimizing hydraulic fracturing designs in the deep shale reservoirs of Western Chongqing, this study integrates logging data with laboratory rock-mechanical test data. This research moves beyond simplistic model stacking and utilizes a customized architectural design tailored to the three core characteristics of deep shale in the Western Chongqing Block: high pressure, high organic content, and high brittle mineral content. Specifically, the Variational Mode Decomposition (VMD) is utilized to isolate high-frequency logging fluctuations induced by the strong heterogeneity of high brittle mineral content. The Convolutional Neural Network (CNN) acts as a spatial feature extractor to capture the intricate spatial distribution patterns associated with high organic content. Subsequently, the Bidirectional Long Short-Term Memory (BiLSTM) network models the long-range sequential depth dependencies, reflecting the continuous geomechanical evolution under high-pressure compaction gradients. Finally, the Attention (AT) mechanism dynamically prioritizes the most sensitive logging responses to rock mechanical properties under these complex geological constraints. The proposed VMD-CNN-BiLSTM-AT model was then used to estimate Young’s modulus and Poisson’s ratio in the Western Chongqing Block. The testing phase yielded strong predictive performance, achieving R2 scores of 0.966 and 0.96 for Young’s modulus and Poisson’s ratio, respectively, which were approximately 10% higher than those of other conventional models. Therefore, the proposed model supports data-driven fracturing optimization in deep shale plays by precisely predicting mechanical properties.

Processes doi: 10.3390/pr14101523

Authors: Zhaokun Li Xuebin Su Fuxin Zheng Xinghao Li Yang Qiu Yangquan Jiao

The CO2+O2 in-situ leaching (ISL) mining process has been widely applied in the exploitation of sandstone-type uranium deposits; however, evaluating leaching efficiency remains a challenging issue. In this study, a sandstone-type ISL uranium deposit was selected, and based on comprehensive investigations of hydrogeological conditions and mineral geochemistry, a multi-physics coupled numerical model of uranium solute reactions during CO2+O2 leaching was established. The model fully accounts for variations in the groundwater flow field between injection and production wells and, on this basis, couples the chemical reaction field between the ore and the leaching solution. The model simulates the evolution of uranium concentration in the leaching solution and further calculates the leaching efficiency of the ore. The results indicate that groundwater flow velocity is highest between injection and production wells, where groundwater dynamics are strongest, and gradually decreases toward the interwell zones as hydrodynamic intensity weakens. Uranium concentration in the leaching solution is closely related to the groundwater flow field. In the early stage, high-uranium-concentration zones are mainly concentrated between injection and production wells. As time progresses, ore reactions in high-flow regions become more complete, leading to a decline in uranium concentration, while residual uranium ions within the formation diffuse outward under concentration gradients, causing high-concentration zones to expand outward. Sensitivity analysis shows that increasing CO2 and O2 concentrations significantly enhances uranium leaching concentrations, with increases of approximately 22.1% and 11.3%, respectively. Lower injection-production flow rates reduce dilution and promote more complete reactions, but may also introduce risks such as ore layer clogging. These results provide a theoretical basis and scientific guidance for flow-field regulation in situ leaching uranium mining.

Processes doi: 10.3390/pr14101520

Authors: Yaan Wang Mingqian Cao Jianyi Chen Xiaojian Wu Xuebin Su

In situ leaching (ISL) of uranium faces challenges in solid–liquid separation of pregnant leaching solution, with conventional bag filters showing suboptimal performance. This study investigates wellbore and ore-bearing layer clogging in neutral ISL uranium mining, characterizing particle size distribution in the leaching solution. Results show that leaching solution particles consist mainly of clay and silt-grade debris (<200 μm). A novel hybrid separation system integrating an optimized hydro cyclone with a bag filter was developed using theoretical fluid mechanics and CFD simulations. The optimized hydro cyclone with a novel swirl chamber and conical inverted wire mesh collector achieves complete separation of particles >60 μm and 99.9% efficiency for particles >50 μm. The hybrid system significantly reduces operating pressure and filter bag replacement frequency from three times to once weekly, mitigating ore-bearing layer clogging. This research provides insights into particle migration mechanisms and offers an efficient solid–liquid separation solution for uranium mining operations.

Processes doi: 10.3390/pr14101521

Authors: Ruixue Ma Yiwei Dong Jinxiao Shen Zhengcang Chen Dongping Zhao

Mobile robots operating in structured indoor environments face significant challenges including the “kidnapped robot” problem, sensor accumulation errors, and environmental perceptual ambiguity, which collectively lead to slow convergence and inadequate accuracy in relocalization. To address these critical issues, this paper proposes an Adaptive Fusion Relocalization Algorithm (AFRA) based on a likelihood-field measurement model. The core innovations of AFRA include the construction of a refined likelihood-field model that effectively integrates LiDAR and Ultra-Wideband (UWB) data, significantly enhancing the accuracy of observation likelihood through probabilistic modeling of hybrid noise. Furthermore, a particle filter framework incorporating dynamic particle scheduling and adaptive resampling mechanisms is developed to achieve an optimal balance between precision and computational efficiency. The Experimental results demonstrate that AFRA maintains relocalization errors within ±0.035 m, improving accuracy by 45.3% compared to the best-performing single sensor, while achieving a 40.7% acceleration in convergence speed. These advancements substantially enhance the robustness and real-time performance of mobile robot localization in complex scenarios.

Processes doi: 10.3390/pr14101522

Authors: Guo Qiang Zhao Shuang Liu Juan Wang Zhimin Chen Wen Zhang Xiuzhao Chen Jing Yu Dong

Interregional renewable-dominant power systems are increasingly constrained by limited transmission capacity and insufficient support capability in weak-grid areas. This paper proposes a coordinated planning method for transmission expansion and grid-forming energy storage. A two-stage stochastic mixed-integer optimization model is developed in which candidate corridor expansion and grid-forming storage siting and sizing are jointly determined in the planning stage, while conventional generation dispatch, renewable accommodation, storage charging and discharging, and interregional power flows are optimized in the operation stage under multiple load and renewable scenarios. Planning-level support constraints and operational support availability constraints are introduced to represent the structural and operational support roles of grid-forming storage in weak-grid areas. Case studies show that the optimal investment structure combines reinforcement of key transmission corridors with grid-forming storage deployment at critical nodes. Compared with the baseline scheme, the coordinated scheme reduces the annual total cost from CNY 6.842 billion to CNY 6.078 billion, decreases annual renewable curtailment from 426 thousand MWh to 121 thousand MWh, reduces annual unserved energy from 12.8 thousand MWh to 0.5 thousand MWh, and changes the planning-level support margins of weak-grid regions from negative to positive. Additional tail-risk stress tests under high-load and low-renewable conditions further show that the coordinated scheme preserves lower unserved energy and positive support margins. The results indicate that transmission expansion mainly improves interregional resource allocation, whereas grid-forming storage mainly enhances local support capability and operational flexibility. The twn o resources therefore exhibit strong complementarity across both spatial and temporal dimensions. The proposed method provides systematic decision support for renewable energy delivery, backbone grid reinforcement, and grid-forming storage planning.

Processes doi: 10.3390/pr14101519

Authors: Daniel Ros Valladolid Gianmaria Pio Alessandro Zambon

The agri-food sector faces the dual challenge of reducing waste and meeting the growing demand for sustainable plant-based ingredients. This study investigated the valorization of rejected peas through two alternative pathways, pea flour production and pea protein production, with the aim of identifying the most suitable option for industrial implementation. A two-stage methodology was adopted. First, a preliminary screening comparison was carried out using qualitative criteria related to process complexity, implementation time, sustainability, expected material yield, market relevance, investment requirements, and operating costs. This initial assessment identified pea flour as the more feasible route under the industrial conditions considered. Second, the selected pea flour pathway was investigated in greater detail through process mapping, mass and energy balances, preliminary equipment sizing, and early-stage economic assessment. The results show that pea flour production can be organized as a flexible process combining seasonal upstream preprocessing with stabilized downstream conversion supported by freezing and controlled thawing. Under the base case considered, the route achieves an annual packaged-flour recovery of 46.5% relative to the recoverable rejected peas. Overall, the study indicates that pea flour represents a practical and scalable first-step valorization strategy, whereas pea protein remains a potentially valuable but more complex alternative requiring further dedicated development and quantitative assessment.

Processes doi: 10.3390/pr14101518

Authors: Raquel A. de L. Dias Newton C. Santos Raphael L. J. Almeida Virgínia M. de A. Silva Thalis L. B. de Lima Alexmilde Fernandes da Silva Mércia M. de A. Mota Ana F. S. Coelho Severina de Sousa Josivanda P. Gomes Ana P. T. Rocha Romário O. de Andrade Victor H. de A. Ribeiro Hanndson A. Silva Priscila S. Souza

In this study, bean starch films were developed and treated with dielectric barrier discharge (DBD) cold plasma (5 min (DBD5), 10 min (DBD10), and 15 min (DBD15)) and pulsed light (PL) radiation (4 J cm−2 (PL4), 8 J cm−2 (PL8), and 12 J cm−2 (PL12)), and the effects of these treatments on the physical, barrier, mechanical, morphological, and structural properties were evaluated, as well as the practical application of the films in bread storage for 7 days. Both treatments significantly modified the film properties (p < 0.05). Film thickness decreased from 95 µm (control) to 87 µm (PL12), while solubility was reduced from 39.40% (control) to 25.32% (PL12), indicating improved water resistance. Reductions in water vapor permeability (WVP) were also observed, with a more pronounced effect for PL12 (approximately 55% reduction compared to the control). The contact angle increased from 58.30° (control) to 67.76° (PL12), indicating a moderate increase in surface hydrophobicity. The DBD cold plasma treatment increased tensile strength (up to 16.05 MPa in DBD15) and reduced elongation (44.72%), whereas PL, especially at PL8, increased flexibility (60.36%). Morphological analyses indicated increased surface roughness for DBD-treated films, while structural analyses suggested subtle changes in molecular organization rather than the formation of well-defined crystalline domains. During bread storage, the treated films, particularly PL12, were significantly more effective than the control in delaying bread staling (final firmness of 6.67 N vs. 11.82 N), reducing mass loss (5.66% vs. 12.66%), and maintaining higher water activity, thereby better preserving product quality. Overall, both treatments showed potential for tailoring film properties: DBD was more effective in enhancing mechanical strength, while PL promoted improvements in barrier properties and practical performance. Therefore, physical treatments, particularly PL, represent promising strategies to overcome intrinsic limitations of starch-based films and to develop packaging materials with potential applications in bakery product preservation.

Processes doi: 10.3390/pr14101517

Authors: Xiaojing Feng Shutong Guo Dong Duan Weiheng Guo Zhiduo Fu Minggang Chang

During underground mining, the stability of in-seam gas drainage boreholes is jointly affected by multiple factors, including the in situ stress state and borehole structure. Borehole instability can reduce gas drainage efficiency and increase underground safety risks. Among these factors, confining pressure plays a decisive role in the damage evolution of the coal surrounding the borehole. To clarify the damage evolution characteristics of the coal surrounding the borehole under different confining pressure conditions, conventional triaxial graded cyclic loading–unloading numerical simulations were conducted on borehole-containing specimens using PFC2D software (version 6.0). The effects of confining pressure on acoustic emission (AE) ringing counts, microcrack propagation, crack angle distribution, damage evolution, and failure characteristics were systematically analyzed. The results show that, under graded cyclic loading–unloading, the peak AE ringing count of the borehole-containing specimens first increases and then decreases with increasing confining pressure, whereas the cumulative ringing count continues to increase. The spatial distribution of microcracks gradually evolves from dispersed development to concentration around the borehole, and the crack propagation path changes from single-path dominance to coordinated multi-path propagation. The angular distribution of tensile cracks exhibits a non-monotonic evolution pattern, namely, dispersion, concentration, and weakening, with increasing confining pressure, whereas the distributions of shear cracks and total cracks show a gradually broadened unimodal pattern with enhanced connectivity between angular intervals. At the final failure stage, both the tensile damage ratio and the shear damage ratio increase with increasing confining pressure, and their difference increases from 0.24% to 0.90%, indicating that increasing confining pressure further strengthens the dominant role of shear damage. The failure mode gradually evolves from tensile–shear mixed failure toward relatively shear-dominated failure. The results provide a theoretical basis for analyzing borehole instability and failure characteristics under different confining pressure conditions, as well as for optimizing grouting-based borehole protection parameters.

Processes doi: 10.3390/pr14101516

Authors: Yangnan Shangguan Jinghua Wang Kang Tang Hua Guan Futeng Feng Yun Bai Qi Wang Rui Huang Guowei Yuan Tuo Liang

Low-permeability reservoirs at the high-water-cut stage commonly suffer from dominant water channel development, poor sweep of weakly connected zones, and inefficient mobilization of remaining oil. Existing profile control or oil displacement agents can improve either flow diversion or microscopic oil displacement, but their single-agent evaluation does not fully explain the coupled process of sweep expansion and remaining oil mobilization. To address this issue, this study focuses on a previously optimized HK-0417/ALT-603 composite system and investigates its synergistic behavior at pore, core, and well group scales. Microscopic visualization displacement experiments were used to identify streamline redistribution and remaining oil evolution. Natural core experiments were conducted to evaluate injectivity adaptability and plugging persistence. Under slug injection conditions, the Box–Behnken design was employed to optimize the injection parameters. Finally, the field pilot response was analyzed based on production data from test wells in the Changqing Oilfield. The results show that the combination system simultaneously achieves streamline expansion and residual oil reduction: the injected fluid is redistributed toward weakly swept zones, large continuous oil bodies are fragmented and dispersed, and both sweep efficiency and oil displacement efficiency are superior to those of individual agents. Natural core experiments indicate that the injection pressure difference is generally controllable in cores with permeabilities ranging from 1.76 to 7.02 mD, and the plugging rate during subsequent water flooding reaches 75.47–80.54%. Response surface optimization yields the following optimal parameter combination: profile control slug volume = 0.41 pore volume (PV), oil displacement slug volume = 0.61 PV, injection rate = 0.19 mL/min, with a corresponding predicted enhanced oil recovery (EOR) of 18.52%. In the field pilot, the cumulative injection volumes of the two injectors are 41,898 kg and 61,472 kg, respectively. The injection pressure in the well group increases from 5.8 MPa to 7.0 MPa, the comprehensive water cut decreases from 90.6% to 85.3%, and the monthly decline rate is reduced from 0.5% to 0.2%. The proposed system mainly acts by increasing flow resistance and redirecting flow in high-water-cut channels, while it enhances oil detachment through interfacial tension reduction in oil-bearing pores. After optimizing the slug parameters, the field pilot exhibits a clear phased response and promising application potential.

Processes doi: 10.3390/pr14101515

Authors: Ahmed Abdulhamid Mahmoud Bassam Mohsen Alzayer George Panagopoulos Paschalia Kiomourtzi Panagiotis Kirmizakis Salaheldin Elkatatny Pantelis Soupios

In the original publication [...]

Processes doi: 10.3390/pr14101514

Authors: Xinguo Qiu Haodong Lu Jiahui Wang

Under high-frequency commutation conditions, the Single-Piston Two-Dimensional Electro-Hydraulic Pump suffers from severe reverse flow and pressure pulsation, which limit its volumetric efficiency and operational stability. To address this issue, this study proposes a surrogate-assisted multi-objective optimization framework for the pump distribution structure. First, a dynamic model is established to analyze the influence of triangular damping groove geometry on flow and pressure characteristics, and four key parameters are selected as design variables. Then, sample data generated from AMESim simulations are used to train a Genetic Algorithm-optimized Backpropagation neural network surrogate model. Finally, the surrogate model is integrated with NSGA-II to minimize the peak reverse flow and pressure pulsation amplitude simultaneously. The results show that the GA-BP model predicts reverse flow and pressure pulsation with mean relative errors of 2.72% and 2.99%, respectively. Compared with the initial design, the optimized structure reduces the peak reverse flow by 27.6% and decreases the pressure pulsation amplitude from 0.78 MPa to 0.41 MPa. These results indicate that, within the parameter ranges and operating conditions considered in this study, the proposed framework provides an effective tool for the coordinated optimization of damping groove parameters for the Single-Piston Two-Dimensional Electro-Hydraulic Pump.

Processes doi: 10.3390/pr14101513

Authors: Hakan Elçi Murat Yılmaz Ramazan Hacımustafaoğlu Ali Bozdağ

This study explains how an extremely low electrical earthing resistance was achieved in challenging gravelly soil conditions. In the existing soil, a resistance of 5 ohms was measured using traditional earthing techniques. After excavating and removing the granular soil, it was replaced with fine-grained, sandy-silty clay, then compacted after moistening, reducing earthing resistance to 2.5 ohms. The goal was to achieve a resistance below 0.5 ohms, which is necessary for the precise operation of robotic welding machines. To achieve this, a hybrid strategy was employed, combining deep earthing by drilling with ground-enhancing compounds in the gravelly soil. In İzmir-Torbalı, a 40 m-deep borehole was drilled to install a copper electrode in water-saturated clay below the groundwater level. To increase the conductivity of the granular soil and ensure contact with the electrode, the borehole was filled with graphite powder. As a result, the earthing resistance reached only 0.28 ohms, proving the effectiveness of this method in high-resistance soils.

Processes doi: 10.3390/pr14101512

Authors: Xiaomin Yu Yingchuan Gong Maoyin Chen

Incipient fault detection in industrial processes remains challenging, particularly for notorious faults 3, 9, and 15 in a chemical process benchmark, namely the Tennessee Eastman process (TEP). This paper proposes a novel unsupervised framework, namely self-modulated KAN-enhanced direct cross-attention (SMK-DCA). It constructs heterogeneous features by integrating raw data, process-aware features, and sliding-window singular values. Kolmogorov–Arnold networks (KAN) enhance nonlinear expressiveness before a cyclic DCA mechanism enables comprehensive interactions among heterogeneous features. A feature-wise linear modulation (FiLM) adaptively calibrates representations, while a sparse autoencoder with multi-target reconstruction amplifies subtle fault signatures. By leveraging KAN’s superior approximation capability and cyclic multi-view fusion, the proposed method effectively captures incipient fault-induced variations often overlooked by conventional approaches. Extensive experiments on TEP demonstrate that SMK-DCA effectively detects incipient faults 3, 9, and 15, while obtaining the best average detection rate across all faults among the compared MSPM and deep learning methods. Furthermore, validation on real-world data from an IGBT power system confirms the generalization capability of the proposed method across different industrial domains.

Processes doi: 10.3390/pr14101510

Authors: Timilehin Martins Oyinloye Won Byong Yoon

Surimi manufacturing involves complex, multi-step operations in which small changes in raw material condition, formulation, and heating history can markedly alter texture, water retention, and visual quality. This review critically examines peer-reviewed studies that apply artificial intelligence to surimi and surimi-based products, focusing on work validated directly in surimi systems. Current evidence mainly supports non-destructive quality evaluation and integrity screening using imaging and vibrational spectroscopy. These applications include deep learning for classifying gel surface images, as well as chemometric and machine learning analysis of infrared, near-infrared, and hyperspectral data for quality prediction and adulteration detection. Process-linked monitoring during thermal treatment is also beginning to emerge, with one time-resolved hyperspectral imaging study demonstrating quality tracking during heating. Major barriers to industrial adoption include limited and narrowly sampled datasets, batch effects and validation designs that may overestimate predictive performance, and practical deployment challenges such as stable sensing in wet environments, instrument drift, and calibration transfer across devices and sites. The review also outlines forward-looking directions, including digital twins, adaptive control strategies, and automation, and identifies data standardization, external validation, and maintenance strategies as priorities for translating laboratory demonstrations into reliable industrial applications.

Processes doi: 10.3390/pr14101511

Authors: Dejun Miao Yijian Ren Qingshun Ge

A coal mine gas explosion is a systematic failure caused by the interaction of multiple factors. In previous studies, most research determined the key causes based on practical experience or a single static indicator. This study puts forward a comprehensive method that integrates complex network theory and a genetic algorithm. By analyzing the explosion mechanism, a network model with 43 causal factors as nodes and their relationships as edges was established, thus capturing the overall structure of the accident system. Subsequently, the genetic algorithm was employed to optimize the identification of key nodes in the network. At present, most of the research on accident risk assessment relies on static topological analysis, failing to take into account the synergistic effects resulting from the simultaneous removal of multiple nodes, and is prone to getting stuck in local optimal solutions. The purpose of this study is to be able to search for the most influential node set and reduce the reliance on static indicators. The results show that both random attacks and deliberate attacks can reduce network efficiency. Meanwhile, when attacking the key cause combinations identified through searching, the network efficiency drops most rapidly. This indicates that the network is more vulnerable in more targeted attacks. This method encourages us to transition from a single-dimensional risk assessment to a comprehensive and multi-dimensional analysis framework.

Processes doi: 10.3390/pr14101509

Authors: Cheng Bi Weiqiang Zhu Guomin Chen Liang Fang Shang Li Hongyun Wang

Ti-6Al-4V miniature screws are widely used in medical implants, where surface quality strongly affects biocompatibility and service life. However, their threaded geometry, small radius of curvature, and poor machinability make high-quality finishing difficult. To address this challenge, a roller-type ultrasonic-assisted magnetorheological finishing (R-UMRF) process was proposed for Ti-6Al-4V miniature screws (M2.5 × 4 mm). A three-pole magnetic field generator was designed and optimized by finite element analysis to establish a stable finishing zone and a weak-field renewal zone for flexible-brush regeneration. Guided by the process mechanism, single-factor experiments were first performed to evaluate the effects of finishing time, spindle speed, roller speed, ultrasonic amplitude, and applied current on surface roughness (Ra). A four-factor, three-level orthogonal experiment was then conducted to optimize Ra and the change rate of surface roughness (ΔRa). The applied current showed the strongest effect on finishing performance, followed by roller speed; however, excessive current or ultrasonic amplitude deteriorated the surface because of over-aggressive abrasive action. The optimal parameters were a spindle speed of 260 r/min, a roller speed of 140 r/min, an ultrasonic amplitude of 10 μm, and an applied current of 4 A. Under these conditions, Ra decreased from 1.208 μm to 0.081 μm after 120 min, corresponding to a reduction of 93.29%. These results demonstrate that R-UMRF is an effective low-damage finishing method for Ti-6Al-4V miniature screws with complex threaded surfaces.

Processes doi: 10.3390/pr14101508

Authors: Jiaqi Lian Yunjing Jiao Xianchao Rao Xinpeng Liu

To accurately describe the combustion process of ammonia/diesel dual-fuel, this paper develops a simplified kinetic mechanism for ammonia/diesel dual-fuel based on the decoupling method and a modular approach, comprising 212 components and 620 elementary reactions. The diesel component is represented by four components: n-heptane, n-hexadecane, isohexadecane, and α-methylnaphthalene. The mechanism was validated using shock tube experimental data. The results indicate that the developed mechanism can accurately predict key parameters such as ignition delay time and laminar flame speed under different ammonia-blending ratios, showing good agreement with experimental values. Single-component ignition delay prediction error ≤ 6%; laminar flame speed deviation error ≤2%; CFD validation metrics (e.g., peak cylinder pressure error within 1.35%) Furthermore, the mechanism was coupled with 3D CFD software to validate the cylinder pressure and heat release rates, using a six-cylinder, heavy-duty diesel engine with a bore of 114 mm, a stroke of 145 mm, a displacement of 8.9 L, and a compression ratio of 16.6 as the study subject. Based on the validation of the model and the feasibility of the mechanism, further studies were conducted on combustion and emission patterns under different load conditions and ammonia substitution rates. The results indicate that at low ammonia substitution rates, as the load decreases, the combustion rate slows down and thermal efficiency declines, while the indicated thermal efficiency first decreases and then increases; load primarily influences ignition and combustion processes by altering the thermodynamic state within the cylinder. At ammonia substitution rates of 20–60%, the heat release rate exhibits a “bimodal” pattern under different load conditions. NO, NO2, and N2O emissions first increase and then decrease with increasing ammonia substitution rate, peaking in the 40–60% range; CO2 emissions gradually decrease as the ammonia substitution rate increases.

Processes doi: 10.3390/pr14101506

Authors: Xianwei Zhang Lin Hou Lingquan Liu Yixuan Li Shaobing Hu

In magnetized casing environments, casing-induced magnetic interference can significantly reduce azimuth calculation accuracy and compromise the reliability of downhole tool spatial localization. To address this issue, this study proposes a magnetic-field-based spatial localization method for downhole tools in magnetized casing environments. First, a joint azimuth correction framework is developed. This framework combines ellipse fitting for radial distortion correction, single-axis multi-station analysis (MSA) for axial interference suppression, and a radial basis function neural network (RBFNN) for residual nonlinear error compensation. Subsequently, based on corrected azimuth information, the magnetic field distribution around the magnetized casing is analyzed through theoretical modeling and finite element simulation, and a cosine-type azimuthal response model is established. On this basis, a minimum-residual localization model is constructed to invert the measurement-point height and radial distance. The results show that the proposed correction framework effectively improves azimuth calculation accuracy, with the average RMSE reduced to 0.0371° after RBFNN compensation. In addition, the inverted height and radial distance show good consistency with the experimental values, demonstrating the effectiveness of the proposed localization method. This study provides an effective approach for spatial localization in complex downhole magnetic environments.

Processes doi: 10.3390/pr14101507

Authors: Khan Jamil Yao Haq Ullah Shah Khan

Early Jurassic organic-rich shales deposited in fluvio-deltaic settings serve as important hydrocarbon source rocks, particularly in the highly petroliferous basins of the Middle East. The Lower to Middle Jurassic sedimentary succession of the frontier Kohat Basin, Pakistan, comprises thick sequences of shale, coaly, and carbonate rocks deposited along the northwestern margin of the Indian Plate, adjacent to the eastern Tethys Ocean, and records a crucial paleoenvironmental transition from fluvio-deltaic to shallow marine settings. Despite the economic significance of the Jurassic succession as a potential hydrocarbon source in the Kohat Basin and surrounding regions, their organic geochemical characteristics and role in the regional petroleum system remain poorly understood. This study presents an integrated organic geochemical and carbon isotopic evaluation of Jurassic source rocks using well cuttings and outcrop samples, focusing on organic matter (OM) input, depositional environment, hydrocarbon generation potential, thermal maturity, and oil–source rock correlation. Source rock characterization indicates that the Shinawari and Datta formations possess fair-to-excellent generative potential, whereas the Samana Suk Formation exhibits poor-to-marginal potential. Biomarker and isotopic evidence indicate that the Shinawari Formation is dominated by algal-derived OM, characterized by higher aquatic OM deposited under relatively reducing marine to marginal marine conditions. The relatively more depleted bulk and individual fraction δ13C values for the Shinwari Formation are also consistent with a stronger marine influence. In contrast, the Datta Formation shows mixed OM inputs with a greater terrestrial influence and suggests deposition in more oxic lacustrine to marginal marine settings. The thermal maturity-related parameters for both formations indicate early to peak oil window thermal maturity. The geochemical correlation of Jurassic source rock extracts with Kohat crude oils, based on published data, suggests that the Kohat oils differ significantly, exhibiting stronger terrestrial organic matter signatures, more oxic depositional conditions, and slightly higher maturity, thereby indicating no direct genetic linkage with the Jurassic source rocks. Overall, the Jurassic formations are unlikely to represent the primary source rocks for Kohat oils but may have contributed locally to a multi-source petroleum system. This underscores the need for integrated geochemical investigations combining biomarker and isotopic analyses, supported by broader source rock and crude oil datasets, to resolve uncertainties in oil–source correlations, source contributions, and hydrocarbon migration pathways, thereby better constraining the petroleum system framework.

Processes doi: 10.3390/pr14101505

Authors: Jorge Lara Mauricio Samper Delia Graciela Colomé

The growing need to enhance observability in distribution networks has driven the development of load pseudomeasurement generation methods, particularly under partial smart meter (SM) penetration. This paper proposes a load pseudomeasurement framework that builds representative daily load profiles (load curves) from hourly SM time series using clustering techniques, with and without weather information. Markov chain models are then used to capture day-to-day dynamics by predicting the most likely next-day profile to be assigned to customers without SM. To enable this transfer, a hierarchical grouping scheme based on monthly energy consumption is introduced to map behaviors from SM-equipped customers to customers without SM measurement. The methodology is validated with real residential data from the Low-Carbon London project under multiple observability scenarios including different SM availability levels, where SM measurements are withheld from the inputs to emulate customers without SM measurement, and the resulting pseudomeasurements are benchmarked against the original measurements. The results show that the Euclidean representative curve method achieved the most robust overall performance, with a minimum MAE of 1.65 in the Reduced × 75% SM configuration. The best-performing configuration depended on the observability level: Reduced was the most robust option under medium-to-high observability, whereas Temp_reduced with a 21-day window performed best under the lowest-observability condition. In addition, the Euclidean method showed low practical deviation in the Reduced × 25% SM case, with a bias of 0.63 and Cohen’s d = 0.27. Overall, the proposed approach accurately reproduces the hourly load shape and captures inter-day variability under partial observability conditions.

Processes doi: 10.3390/pr14101502

Authors: Michelle Silva Santos André Bacelar Rodrigues Bruna de Oliveira Barros Paulo Gibson Farias Bezerra Lucas Pedroza de Souza Lina Bufalino Karina Cardoso Valverde

Biocoagulants derived from Moringa oleifera Lam seeds are a sustainable alternative for water clarification, but require preparation adapted to the treatment of the dark waters of the Amazon River. This study compared the effectiveness of three coagulant preparation methods from moringa seeds: powder (B1), saline solution (B2), and aqueous solution (B3) in the clarification of samples collected on the shore of Macapá (Amapá—AP), Brazil. The tests were performed using a jar test (fast mixing of 100 rpm for 3 min and slow mixing of 20 rpm for 15 min), with dosages of 20 to 200 mg·L−1, and sedimentation times between 10 and 60 min. The optimized conditions were: 80 mg·L−1/20 min (B1), 40 mg·L−1/30 min (B2), and 40 mg·L−1/40 min (B3). The maximum removals achieved by clarification were as follows: apparent color (92.6%), turbidity (79.4%), chloride (70.9%), ammonia (81.2%), aluminum (99.1%), copper (85.4%), iron (85.8%), and manganese (100.0%). The saline solution was the most efficient. Filtration brought additional improvements to the treated water. It was found that the moringa coagulant was effective in removing contaminants from the waters of the Amazon River, standing out as a green, sustainable, and low-cost technology. However, disinfection would be necessary to improve its microbiological quality.

Processes doi: 10.3390/pr14101504

Authors: Xiaoran Kong Hanxuan Huang Maoyin Chen

This study addresses the problem of incipient fault detection in industrial processes by developing an enhanced dynamic inner principal component analysis (DiPCA) framework. This framework integrates a Transformer attention mechanism and a masked linear autoregressive moving average (ARMA) structure. In contrast to the original DiPCA, this approach can effectively characterize complex dynamic processes while maintaining the theoretical integrity of the original framework. Here, the sliding-window singular value decomposition is chosen to construct the dynamic feature matrix. The ARMA-Transformer network can capture the multi-step prediction features. A DiPCA-based dual monitoring system can then be used to establish dynamic and static residual statistics. The simulations of the Tennessee Eastman Process (TEP) demonstrate that the proposed method yields fault detection rates (FDRs) of 77.80% and 69.50% for incipient faults 3 and 9. These are higher than those of traditional DiPCA, ARMA, PCA, and DPCA by more than 40% on average. Validation on the Case Western Reserve University (CWRU) bearing dataset further shows an average FDR of 94.74% across 13 fault conditions. It thus offers an effective approach for early anomaly identification and safety monitoring in complex industrial processes.

Processes doi: 10.3390/pr14101503

Authors: Zhiyuan Zhu Haoxi Zheng Hao Li Rui Yuan

This paper presents a novel approach for Cu-Cu bonding processes, incorporating microfluidic technology into chip-level metal bonding to precisely and effectively control the temperature on the bonding layer surface. To achieve effective bonding, fluidic channels with a specific design were created on the backside of the chip, enabling temperature gradient bonding across multiple pairs of bonding surfaces by controlling the fluid velocity at the microchannel inlets. Finite element simulations demonstrate that this method can establish a controlled thermal gradient across the bonding interface, with a maximum temperature difference exceeding 100 °C across a single bonding plane. The results indicate that this technique is not only suitable for copper–copper metal bonding but can also be applied to the bonding of other metal materials, offering a versatile solution for metal bonding in chip fabrication.

Processes doi: 10.3390/pr14091501

Authors: Marta Lilia Eraña-Díaz Juana Enríquez-Urbano Beatriz Martínez-Bahena Jazmin Yanel Juárez-Chávez Alfonso D’Granda-Trejo Javier De-la-Rosa-Mondragon

The design of differentiated fiscal instruments for industrial sustainability requires robust, data-driven tools capable of capturing the heterogeneity of environmental performance across manufacturing units—a challenge that conventional econometric approaches address only partially, given the non-linear nature of operational–environmental interactions in reconfigurable production systems. This study introduces a two-phase computational framework that integrates unsupervised machine learning and supervised classification to generate evidence-based sustainability profiles for fiscal policy targeting. Its principal contribution is the combination of K-Means clustering with a binary artificial neural network (ANN) classifier, operationalized through an accessible decision-support interface that enables differentiated incentive allocation without requiring programming expertise from policymakers. A dataset of 1000 manufacturing records comprising seven operational and technological input variables—material usage, production capacity, reconfiguration time, downtime, AI optimization, IoT connectivity, and predictive maintenance—and three environmental output indicators—energy consumption, carbon emissions, and waste generation—was analyzed. In Phase One, K-Means segmentation with k = 6, selected through multi-criteria convergence (Silhouette = 0.102; Elbow, Davies–Bouldin, and Calinski–Harabasz indices), identified six distinct sustainability profiles with marked environmental differentiation. In Phase Two, a binary ANN classifier (architecture: 7 → 64 → 32 → 1 neurons; ReLU and sigmoid activations) was trained to distinguish the reference cluster C0 (low environmental impact: energy 145.1 kWh, emissions 45.2 CO2-eq) from the high-impact cluster C1 (emissions 67.8 CO2-eq, waste 41.5 kg). The trained classifier achieved an overall accuracy of 75.4% and an AUC-ROC of 0.774 on the held-out test set, with a macro-averaged F1-score of 0.753 and a Cohen’s kappa coefficient of 0.508, indicating moderate-to-substantial agreement beyond chance. Class C1 (high-impact establishments) achieved a precision of 0.794 and a recall of 0.730, supporting reliable identification of manufacturing units that would most benefit from targeted fiscal support. The framework is deployed through a Gradio-based graphical interface incorporating a traffic-light sustainability classification (green/yellow/red), enabling direct and interactive application by tax authorities and industrial policymakers. The modular architecture supports adaptation to larger or sector-specific datasets, making it transferable across industrial policy contexts.

Processes doi: 10.3390/pr14091500

Authors: Zhiren Rong Dongzhi Hou Chao Chen Guoqi Song Zhuoyue Du Jiongtong Li Houyuan Zhang Wanming Lin Lei Chen Guoyu Qian

This study experimentally investigated, using a water-model hydrodynamic analogue, the effects of crystallizer rotational speed and baffle configuration on the flow-field structure, mass transfer, and mixing behavior inside the crucible of a rotational segregation model system relevant to silicon processing. Three configurations were examined: no baffle, straight baffles, and inclined baffles. Flow visualization and stimulus–response tracer experiments were conducted at 200 and 300 rpm to compare their effects on the main flow pattern and mixing characteristics. The results showed that, without baffles, a complete annular main flow formed, and the fluid moved downward spirally along the crystallizer wall. Mixing was relatively fast, indicating limited potential for local tracer retention. With straight baffles, the main flow was strongly obstructed and redistributed, and the mixing time in local bottom regions, especially in front of the 90° baffle, was markedly prolonged. This behavior suggested a more favorable hydrodynamic environment for local retention and accumulation in the model system, and the effect was most evident at 200 rpm. With inclined baffles, transport in the upper region was enhanced, whereas bottom flow was weakened. Although the tracer could move downward along the baffle surface, it was rapidly swept away after reaching the bottom, indicating reduced stability of local accumulation. Increasing the rotational speed from 200 to 300 rpm strengthened the overall flow and shortened the mixing time under all conditions. Overall, straight baffles, particularly at 200 rpm, produced the strongest tendency for local retention in the present model system. These results provide preliminary hydrodynamic insight into flow regulation and transport behavior in rotational segregation systems.

Processes doi: 10.3390/pr14091496

Authors: Shuchao Li Haiyue Zhou Xin Wang Yuling Wang Xian Wu Jingjing Li Wentao Jiang Longnv Li Gaojia Zhu

Low-voltage (LV) alternating current (AC) power distribution systems are widely used, where phase-to-neutral short-circuit faults are a major cause of electrically induced fires. Prior to a circuit breaker interruption, arc discharges may develop between conductors, leading to intense localized heating of the cable insulation and a potential ignition risk. In this study, a magnetohydrodynamic (MHD) model of 220 V AC short-circuit arcs is established to investigate the coupled electrical and thermal behavior of arc discharges and their induced heating effects on conductor insulation. The transient temperature distribution in the arc region and insulation layer is numerically analyzed under different tripping currents and tripping times, and insulation ignition risk is evaluated based on characteristic thermal thresholds. To validate the simulations, a controllable 220 V AC short-circuit experimental platform is developed using a motor-driven wire contact mechanism. Circuit breakers rated at 20 A, 32 A, and 63 A are tested, and short-circuit current and voltage waveforms are recorded. The results indicate that insulation ignition risk is jointly governed by short-circuit current magnitude and breaker tripping time. Delayed interruption significantly increases insulation temperature and ignition susceptibility, whereas rapid interruption effectively suppresses arc-induced heating.

Processes doi: 10.3390/pr14091499

Authors: Jian Sun Xingyu Liang Yongdi He Yonghai Tian Lianfeng Li

Bent-pipe intake distortion restricts the stable flow range (SFR) and degrades the aerodynamic performance of centrifugal compressors. To expand the SFR while minimizing efficiency loss, this study carries out multi-objective optimization on a self-recirculating casing treatment (SRCT). Numerical simulations were performed at 65,000 rpm based on a four-factor, three-level orthogonal test design, focusing on four key geometric parameters: recirculation angle (α), downstream slot width (br), axial passage height (hb), and axial passage width (bb). The specific effects of these parameters on the SFR, isentropic efficiency (η), and a comprehensive stability index (ΔSFR/Δη) were systematically analyzed. Three optimal designs were obtained through this optimization approach, tailored to different operational requirements, namely CasingSFR, Casingη, and CasingOpt. The results indicate that the comprehensive optimal model (CasingOpt) achieves an optimal balance between SFR expansion and efficiency retention, extending the SFR by 28.67% with only a 10.84% reduction in isentropic efficiency. Flow field analysis further verifies that the optimized SRCT can effectively modulate tip leakage flow via low-energy fluid suction and reinjection, correct deviated inlet incidence, thereby mitigating the severe leading-edge flow separation and high-entropy generation induced by distorted inflow.

Processes doi: 10.3390/pr14091498

Authors: Ke Zhang Zhigang Que Jie Luo Yinxuan Fu Xiaodi Cheng Rong Huang Fan Gu Xianhua Qiu

Biodiesel is a promising green and renewable fuel that can replace fossil fuels and reduce greenhouse gas emissions. The effects of reaction temperature (200–290 °C), residence time (0–75 min), and methanol-to-oleic acid molar ratio (6:1–35:1) on the esterification of oleic acid with supercritical methanol were investigated in a batch reactor. Furthermore, orthogonal experiments were designed to explore the optimal reaction conditions. and the influences of H2O (0–33.3 wt%) and Fe (0–20.0 wt%) contents were examined. Results showed that the conversion of oleic acid to methyl oleate exhibited a volcano-type dependence on both temperature and molar ratio, peaking at 250 °C and a ratio of 15:1, respectively. Conversion initially increased with residence time, then plateaued around 30 min. Under the optimal conditions of 250 °C, 30 min, and a 15:1 molar ratio, the conversion reached 76.8%. Both additives enhanced conversion at low loadings (≤5.0 wt%). However, higher water content inhibited conversion, whereas the promotional effect of Fe saturated beyond 5.0 wt%. The co-addition of 5.0 wt% water and 5.0 wt% Fe yielded a positive but sub-additive effects: conversion exceeded that with water alone but remained lower than with Fe alone. These findings contribute to advancing the high-efficiency and low-cost production of biodiesel.

Processes doi: 10.3390/pr14091497

Authors: Mario Acosta-Flores Moisés Montiel-González Mario Limón-Mendoza Maura Casales-Díaz

The experimental analysis of stresses in thermo-mechanical problems is fundamental for the design and evaluation of the mechanical behavior of structures, frames and various machine elements that operate under mechanical and thermal loads. It is also essential for complementing and validating analytical and numerical studies. This paper proposes an experimental methodology that allows the determination of plane states of stress—mechanical, thermal, and thermo-mechanical—based on the experimental measurement of thermo-mechanical deformation states at a surface point. Based on the theory of linear mechanical elasticity and applying the principle of superposition, plane thermo-mechanical constitutive models are developed, and methods are proposed that allow the thermal and mechanical variables in the models to be experimentally decoupled. The methodology was validated by thermal, mechanical and thermo-mechanical tests carried out on test specimens made of three materials: steel, aluminum and brass. The results show effectiveness in decoupling and solving the analytical models corresponding to the plane, mechanical, thermal, and thermo-mechanical states of stress. The maximum deviations obtained between the stresses provided by the formulated models and the experimental results were a maximum of 4% in most cases.

Processes doi: 10.3390/pr14091495

Authors: Yueping Luo Yongmao Xiao

The journal retracts the article titled “A Full-State Reliability Analysis Method for Remanufactured Machine Tools Based on Meta Action and a Markov Chain Using an Exercise Machine (EM) as an Example” [...]

Processes doi: 10.3390/pr14091494

Authors: Tianjiang Wu Teng Wang Hong He Baoqiang Wu Jiajun Chen Zhuojun Liu

The water huff-n-puff imbibition oil recovery technique has been recognized as an important approach to supplementing formation energy and recovering the remaining oil, attracting increasing attention. To further improve imbibition efficiency, a surfactant-aided huff-n-puff imbibition technique under high pressure was proposed. However, the imbibition mechanisms under high pressure, particularly under variable pressurization modes, remain insufficiently understood. In this study, the effects of different pressurization methods (constant vs. variable pressure) and surfactant types on imbibition behavior were systematically investigated. The results show that, compared with spontaneous imbibition, high-pressure imbibition increases oil recovery by 7–10% and the imbibition rate by 1–2 times, with the variable pressurization mode demonstrating a more pronounced enhancement. Surfactant selection should not pursue ultra-low interfacial tension (IFT) alone; instead, the wettability alteration ability is more critical. An optimal IFT–wettability synergy window is identified, through which the best imbibition performance is achieved when the IFT ranges from 10−2 to 10−1 mN/m and the contact angle ranges from 30° to 60°. Furthermore, the slug injection mode provides a synergistic effect with high-pressure variable pressurization and surfactant action. Compared with high-pressure formation water imbibition, surfactant-aided imbibition increases oil recovery by 10.44% and the imbibition rate by three times. These findings provide a deeper understanding of the key factors governing imbibition behavior and support the application of surfactant-aided huff-n-puff imbibition under high pressure in tight sandstone reservoirs.

Processes doi: 10.3390/pr14091493

Authors: Qiuli Zhang Jiasen He Huaiyu Zhao Jing Zhang Chengbin Shen Lei Wu

The liquid-only transfer dividing wall column (LDWC) eliminates the difficulty of controlling the vapor-phase distribution ratio; however, it involves numerous structural and operating parameters, resulting in high initialization difficulty and convergence challenges. This paper proposes a Matlab-SaDE-Aspen Plus (Aspen Plus V14) framework that reformulates the convergence problem through a multi-parameter simultaneous optimization approach, thereby enabling the efficient design of the LDWC. Building upon this framework, two intermediate reboiler intensification schemes (IR-LDWC1 and IR-LDWC2) are proposed based on CGCC analysis, and four key parameters are simultaneously optimized using the Matlab-SaDE-Aspen Plus framework to eliminate the cumulative errors inherent in independent sequential parameter optimization. The results indicate that, compared with conventional distillation sequences, the LDWC achieves reductions of 17.62% in total energy consumption, 19.35% in total annual cost, and 16.53% in CO2 emissions, with the most significant improvement observed in exergy efficiency. Among the intensified configurations, IR-LDWC2 exhibits the best overall performance, with total energy consumption, TAC, and CO2 emissions further reduced by 30.15%, 33.17%, and 31.24%, respectively.

Processes doi: 10.3390/pr14091492

Authors: Jimmy Núñez-Pérez Osmar J. Cornejo-Lucero Rosario C. Espin-Valladares Pedro Barba Hortensia M. Rodríguez Cabrera José-Manuel Pais-Chanfrau

The accumulation of petroleum-based plastics demands sustainable alternatives such as polyhydroxyalkanoates (PHAs), biodegradable polyesters synthesised by numerous prokaryotes. However, high feedstock costs limit their commercialisation. This study evaluated cocoa mucilage, an underutilised by-product of the Ecuadorian cacao sector, as a low-cost carbon source for PHA production by a wild-type strain isolated from cocoa fruit residues. Bacteria were recovered from cocoa mucilage and pod shell fractions and screened for PHA accumulation by Sudan Black B staining with UV–Vis spectrophotometric confirmation. A single PHA-positive isolate, designated Priestia aryabhattai strain NBP01-UTN (GenBank accession OR567321.1; 99.88% 16S rRNA gene sequence identity to the type strain B8W22T), was recovered from the cocoa shell surface—representing, to the best of our knowledge, the first report of a PHA-producing P. aryabhattai from cacao fruit residues. Fermentation conditions were optimised using the response surface methodology with a central composite design evaluating temperature, pH, and ammonium sulphate concentration. The fitted quadratic model was highly significant (R2 = 0.978, p < 0.0001), indicating that temperature and nitrogen limitation were the dominant factors. Optimal conditions (40 °C, pH 7.30, 0 g·L−1 (NH4)2SO4) yielded 0.496 g·L−1 PHA at 24 h (productivity ≈ 20.7 mg·L−1·h−1). Notably, no external nitrogen supplementation was required, as the endogenous nitrogen in cocoa mucilage sufficed to sustain growth whilst triggering the nutrient imbalance needed for PHA biosynthesis. FTIR and DSC analyses provided spectroscopic and thermal evidence consistent with poly(3-hydroxybutyrate) (PHB), although definitive monomer-level confirmation requires GC–MS or NMR spectroscopy. These results demonstrate the feasibility of coupling a locally isolated wild-type strain with cocoa mucilage to produce bioplastic within a circular bioeconomy framework.

Processes doi: 10.3390/pr14091491

Authors: Jiahua Li Yue You Xiang Qiu Xiang Zheng Miaomiao Jin Haifang Mao

This study investigates the continuous-flow hydrolysis reaction of propylene oxide (PO) in a spiral microchannel reactor, integrating experiments, computational fluid dynamics (CFD) simulations, and response surface methodology (RSM). To the best of our knowledge, experimentally determined apparent Arrhenius parameters for PO hydrolysis under microscale continuous-flow conditions remain rarely reported, and afterwards they were incorporated into CFD-based numerical simulations. This combined experimental–numerical framework provides a robust methodology for quantifying and optimizing liquid-phase kinetics in microscale flow environments. Subsequently, CFD simulations were employed to examine key process parameters, including reaction system temperature, inlet flow rate, and reactor length. Finally, RSM was utilized to identify the optimal process conditions (reaction system temperature of 298.15 K, inlet flow rate of 6 × 10−3 m·s−1, and reactor length of 4 m), achieving a predicted PO conversion rate of 81.68%. The study provides a reference for designing and optimizing spiral microchannel reactors for PO hydrolysis.

Processes doi: 10.3390/pr14091489

Authors: Yimo Ma Yanzhen Wang Ming Wang Shu Jiang Guozheng Ma Xuexue Jiang Wenfei Yang Xuanhe Tang

In the ultra-high water cut stage, unconsolidated sandstone reservoirs suffer from severe reservoir property time-variation, streamline solidification, and inefficient water circulation. To tackle these problems, this study takes Chengdao Oilfield Block 1G as an example and establishes a dynamic geological model considering permeability time-varying characteristics based on logging, core, and production data. The flow field intensity index and streamline solidification rate are introduced to quantitatively characterize the preferential flow channels and high water-consumption zones. Results show that long-term water flooding increases the average permeability by 26.88% and expands the interlayer permeability ratio from 10.33 to 19.00. The streamline solidification rate reaches 75%, forming obvious “short-circuit” circulation. Three remaining oil enrichment patterns are identified, which are mainly controlled by sedimentary microfacies, structural highs, and well pattern control. A differential regulation strategy including 3D well pattern reconstruction and streamline diversion is proposed. Field prediction indicates that the cumulative incremental oil can reach 410,000 tons and the recovery factor is enhanced by 1.3%. This study not only reveals the dynamic evolution mechanism of flow field under water-rock coupling effects but also provides a practical technical system for flow field regulation and remaining oil tapping in similar offshore ultra-high water-cut unconsolidated sandstone reservoirs.

Processes doi: 10.3390/pr14091490

Authors: Giselle Giovanna do Couto de Oliveira Maurillo de Nez Souza João Victor Ribeiro Bizarri Ana Paula Peron Kassiely Zamarchi Cristiane Mengue Feniman Moritz Otávio Akira Sakai

According to the National Cancer Institute, approximately 3.9 billion people worldwide suffer from non-communicable oral diseases, with head and neck cancer patients experiencing exacerbated oral mucositis primarily from radiotherapy. This condition manifests as painful, debilitating mucosal lesions, necessitating effective antimicrobial interventions. This study developed and characterized stable mouthwash formulations containing green-synthesized silver nanoparticles (AgNPs) derived from Baccharis trimera (carqueja) extract for the management of oral mucositis, evaluating their physicochemical stability, antimicrobial efficacy, and biosafety. AgNPs formation was confirmed by color change to brown and a surface plasmon resonance band at 407 nm (UV-Vis), with dynamic light scattering revealing a monomodal hydrodynamic diameter of ~25 nm and stable dispersion; scanning electron microscopy showed spherical particles of 25–35 nm. Four formulations (22–85 ppm AgNPs) in a commercial vehicle exhibited excellent stability over 60 days at 5 °C and 25 °C, maintaining near-neutral pH (~7), low surface tension (<5 mN/m), and unchanged spectral profiles, with no phase separation under centrifugation or thermal stress (up to 70 °C). Antimicrobial assays via broth microdilution demonstrated broad-spectrum activity for the 85 ppm formulation: MICs of 125 µg/mL (S. epidermidis, E. faecalis), 62.5 µg/mL (E. coli, P. aeruginosa), and 250 µg/mL (S. aureus), with MBC of 125 µg/mL (bactericidal) against P. aeruginosa; no activity against C. albicans (MIC > 500 µg/mL). Against human oral microbiota (n = 4 volunteers), it reduced bacterial growth by 14–156% relative to controls (e.g., −5% to 156% inhibition). Cytogenotoxicity tests (A. cepa) confirmed non-toxicity (mitotic index 79–93% of control, low cellular alteration index). These findings establish the carqueja-mediated instant green AgNPs mouthwash as a stable, potent antimicrobial agent, poised to mitigate mucositis-related infections and enhance the quality of life of cancer patients.

Processes doi: 10.3390/pr14091488

Authors: Liang Guo Ziping Peng Junjie Zhang Yi Zheng Shufang Zhou

In practical power system operation scenarios, extreme natural weather conditions and fluctuations at both the supply and demand sides pose significant challenges to the stable operation and the formulation of operational decision-making for power systems. Particularly in extreme scenarios involving faults, it may lead to power supply–demand imbalances and instability in the power system. To address this issue, this paper proposes a decision-making approach for the secure and stable operation of power systems using a multi-scenario-based optimization model. Initially, a joint scenario set is generated using historical operational data to accurately depict multiple complex scenarios. Building on this, a multi-scenario-based optimization model is constructed, with responses facilitated by flexible adjustment resources within the system. Considering the non-convex and nonlinear characteristics of the model, an improved Harris Hawks Optimization (HHO) algorithm is employed to search for the global optimal solution. Finally, a modified IEEE-33 bus test system is utilized to demonstrate the feasibility and effectiveness of the proposed method.

Processes doi: 10.3390/pr14091487

Authors: Wei-Dong Gao Wen-Long Mo Xiao-Qin Yang Wei-Qiang Yang Ya-Ya Ma Gui-Han Zhao Shu-Pei Zhang Zhi-Qiang Yang

Fly ash (FA) from Zhundong coal combustion features high alkali/calcium content and a low Si/Al ratio, limiting its potential for conventional utilization. To enable its high-value application, six size-fractionated samples (FA1–FA6) were characterized via laser particle sizing, SEM-EDS, XRF, XRD, FT-IR, and TGA, to elucidate particle-size-dependent physicochemical and thermal properties. The results show that the size distribution centered at 48–150 μm (~71%). With decreasing size, the morphology shifted from irregular aggregates to smooth vitreous spheres. The chemical composition exhibits significant elemental segregation; the SiO2 content decreases with decreasing particle size, while active components such as CaO, MgO, and Fe2O3 are significantly enriched in fine particles. The thermal conversion behavior is regulated by particle size: The combustion reaction under an air atmosphere conforms to the second-order kinetic model, with the activation energy decreasing from 192.73 kJ·mol−1 for coarse particles (>150 μm) to 63.53 kJ·mol−1 for fine particles (<43 μm); under a nitrogen atmosphere, the weight loss originates from the removal of structural water and the decomposition of carbonates, and fine particles exhibit a higher pyrolysis activation energy (504.15 kJ·mol−1) in the high-temperature stage (850–940 °C) due to being rich in high-crystallinity carbonates. The results of this study elucidate the structure–activity relationship of “particle size-composition-activity” for Zhundong coal fly ash and propose a graded utilization scheme where coarse fractions are suitable for low-grade building fillers, while fine fractions can be used as feedstocks for coal pyrolysis catalysts and functional adsorbents, providing a theoretical basis for its targeted resource utilization based on particle size fractionation.

Processes doi: 10.3390/pr14091486

Authors: Mingyu Li Desheng Wang Lingyun Wang Yuhan Wang Jinhao Xie Yun Liang Jian Kang Hao Wang

Air pollution has drawn increasing attention. The channel-type structure, as an ideal energy-saving and resistance-reducing strategy for air filters, can effectively lower filtration resistance. However, current commercial channel-type filters generally exhibit only medium or low filtration efficiency, and the use of plant fibers as raw material limits their application in high-efficiency filters. In this study, high-efficiency glass fiber filter paper was combined with a channel-type structure, and the formulation and processing techniques suitable for the channel-type design were systematically investigated, leading to the fabrication of channel-type high-efficiency filters. The optimal formulation was determined to be a blend of glass wool fibers and 6 mm Tencel fibers in a 6:4 ratio, coated with a thermosetting resin, which yielded filter paper suitable for wave-pleating. The resulting filter paper demonstrated a filtration efficiency of 99.9624%, a pressure drop of 265.6 Pa, and a pleat aspect ratio of 0.209. Using this formulation, pilot-scale filter paper was produced and wave-pleated under processing conditions including a roller speed of 5 m/min, a roller gap of 0.4 mm, and a roller temperature of 160 °C, which was then used to fabricate channel-type high-efficiency filters. The finished channel-type filters achieved a filtration efficiency of 99.9940% with a pressure drop of 164.0 Pa. Compared to traditional pleated filters of the same volume and efficiency rating, the channel-type filter exhibited a 49.53% larger filtration area, a 33.13% lower face velocity, and a 31.67% reduction in pressure drop. This work offers a novel approach to reducing resistance and enhancing efficiency in air filtration systems.

Processes doi: 10.3390/pr14091485

Authors: M. Jafari A. Andarz G. Bagnato K. Ghasemzadeh

The rapid accumulation of plastic and biomass waste has emerged as a major environmental and resource management challenge, driven by increasing global consumption, low recycling efficiency, and the long-term persistence of waste in natural ecosystems. Conventional valorization routes such as pyrolysis, gasification, reforming, and fermentation provide promising pathways for converting waste into fuels and chemicals, yet their industrial deployment remains constrained by thermodynamic limitations, tar formation, catalyst deactivation, high energy demand, and complex downstream separation requirements. Despite increasing research activity, a comprehensive review that systematically addresses membrane reactor (MR) mechanisms, configurations, and their specific applications in the valorization of both plastic and biomass waste remains lacking in the current literature. In recent years, MR technology has attracted increasing attention as a platform for process intensification, integrating reaction and selective separation within a single unit. By enabling in situ product removal, MRs shift reaction equilibria toward higher conversion, selectivity improvement, and a reduction in separation severity and overall energy consumption. This critical review provides a unified and systematic assessment of MR technologies for the valorization of plastic and biomass waste. Reactor configurations, membrane materials, transport mechanisms, and catalytic systems are comprehensively examined, with particular emphasis on hydrogen-selective, oxygen-permeable, and water-selective membranes and their roles in reforming, tar mitigation, and syngas upgrading. The techno-economic and environmental implications of MR integration are critically discussed, together with current technology readiness levels (TRLs) and scale-up challenges. Overall, this review highlights MRs as a versatile and enabling platform for next-generation waste-to-value technologies and outlines their potential role in supporting the transition toward circular, low-carbon fuel and chemical production.