Next Issue
Volume 14, July-2
Previous Issue
Volume 14, June-2
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.7
3.4
Journals
Processes
Volume 14
Issue 13
share announcement

Processes, Volume 14, Issue 13 (July-1 2026) – 197 articles

Cover Story (view full-size image): Detonation-based combustion is an option for higher efficiency in gas turbines (GTs) as it introduces a pressure rise during the heat addition process compared to conventional deflagrative combustion. In the thermodynamic cycle, an additional compressor is required for the secondary air system (SAS) to correctly feed the cooling circuits at the turbine blades as well as to deliver high-pressure air for cooling the combustion system. Focusing on a 335 MW F-class gas turbine, a pressure gain combustion GT cycle is simulated, where the main compressor operates at a lower pressure ratio compared to the Brayton–Joule case. Based on the proposed parametric analysis, the power of the SAS compressor ranges from 16 to 22 MW, depending on the air-cooling demands. In addition, a preliminary sizing of this compressor leads to a centrifugal architecture. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
25 pages, 1884 KB  
Review
Carbon Monoxide Purification Technologies for Diesel-Powered Mining Equipment: A Review
by Chenghao Hou, Yun Lei, Chengbing Liu and Cong Li
Processes 2026, 14(13), 2225; https://doi.org/10.3390/pr14132225 - 7 Jul 2026
Viewed by 436
Abstract
Diesel-powered equipment is widely used in underground coal mines for auxiliary transportation, material handling, and equipment relocation because of its long operating endurance, convenient refueling, and strong adaptability to complex operating conditions. However, carbon monoxide (CO) emissions from such equipment can accumulate locally [...] Read more.
Diesel-powered equipment is widely used in underground coal mines for auxiliary transportation, material handling, and equipment relocation because of its long operating endurance, convenient refueling, and strong adaptability to complex operating conditions. However, carbon monoxide (CO) emissions from such equipment can accumulate locally under restricted ventilation, idling, and frequent start–stop operation, thereby threatening occupational health and mine safety. This review focuses on CO purification technologies for diesel-powered mining equipment. The operating characteristics and influencing factors are analyzed, and different technical routes are compared, including in-cylinder control, wet scrubbing, adsorption, non-thermal plasma (NTP), and catalytic oxidation. Recent advances in noble-metal catalysts, transition-metal and CeO2-based reducible oxide catalysts, and single-atom catalyst (SAC) design strategies are summarized. Research progress in exhaust aftertreatment systems is also discussed. Overall, CO purification for diesel-powered mining equipment requires coordinated optimization of low-temperature activity, safety-oriented thermal management, flow resistance, and long-term operational stability. Future research should focus on structured catalytic units, durability under coupled exhaust conditions, online monitoring, and field validation to improve the compatibility of CO purification systems with underground mining conditions. Full article
(This article belongs to the Section Energy Systems)
►▼ Show Figures

Figure 1

28 pages, 9183 KB  
Article
Evolution of Mechanical Properties and Damage of Deep Coal Under CO2 Foam Treatment
by Changjiang Duan, Xin Jin, Dong Han, Xuefeng Shi, Longgang Zhou, Lijun Gao, Chengzhen Liu, Wenjun Xu and Chen Hao
Processes 2026, 14(13), 2224; https://doi.org/10.3390/pr14132224 - 7 Jul 2026
Viewed by 288
Abstract
CO2 foam fracturing has emerged as a promising stimulation technology for enhancing permeability and improving production performance in deep coalbed methane (CBM) reservoirs while providing additional potential for carbon utilization. However, the multiscale relationship between local mechanical degradation and macroscopic mechanical deterioration [...] Read more.
CO2 foam fracturing has emerged as a promising stimulation technology for enhancing permeability and improving production performance in deep coalbed methane (CBM) reservoirs while providing additional potential for carbon utilization. However, the multiscale relationship between local mechanical degradation and macroscopic mechanical deterioration and fracture instability induced by CO2 foam treatment remains insufficiently understood. In this study, four candidate coal samples originating from the Carboniferous–Permian No. 8+9 coal seam system were first comparatively characterized. Based on petrographic characteristics, mineralogical composition, and specimen integrity, representative bright coal and semi-dull coal samples from the Lüliang mining area were selected for subsequent multiscale mechanical investigations. Based on petrographic characteristics, mineralogical composition, and specimen integrity, representative bright coal and semi-dull coal samples from the Lüliang mining area were selected for petrographic analysis, X-ray diffraction (XRD), nanoindentation, conventional triaxial compression, and cracked chevron-notched Brazilian disc (CCNBD) fracture toughness tests. Coal specimens were immersed in CO2 foam under reservoir-relevant conditions (50 °C, 20 MPa, foam quality of 65%) for different durations (0–6 days), and the coupled evolution of micromechanical properties, macroscopic mechanical behavior, and fracture resistance was evaluated. The results indicate that both coal types exhibit pronounced heterogeneity in maceral composition and mineral distribution. Bright coal is characterized by high vitrinite content and low mineral abundance, whereas semi-dull coal contains higher proportions of inertinite and minerals. Nanoindentation results reveal that mineral-rich regions possess significantly higher Young’s modulus and hardness than organic-matter-rich regions, highlighting pronounced micromechanical heterogeneity within the coal matrix. With increasing immersion time, the micromechanical properties of both coals exhibit a two-stage evolution characterized by rapid initial deterioration followed by a gradual stabilization trend. After 6 days of immersion, the average Young’s modulus and hardness of bright coal decreased by 40% and 30%, respectively, whereas those of semi-dull coal decreased by 30% and 17%. Simultaneously, macroscopic mechanical properties and fracture resistance continuously declined, with fracture toughness reductions of 74% and 55% for bright coal and semi-dull coal, respectively. Compared with semi-dull coal, bright coal exhibited higher damage sensitivity, evolving from dominant single-fracture failure to granular fragmentation, whereas semi-dull coal maintained a multi-crack composite shear failure mode. Combined micromechanical and macroscopic observations suggest that the observed mechanical deterioration may be associated with coupled effects of fluid–coal interaction, matrix softening, and progressive damage evolution. Although pore and crack evolution were not directly observed, the results suggest that coal structure plays an important role in governing damage transfer across scales and thereby influences fracture behavior and mechanical weakening. These findings provide insight into the multiscale mechanical response of coal under CO2 foam treatment and may support the optimization of stimulation strategies for deep CBM reservoirs. Full article
(This article belongs to the Special Issue Emerging Technologies for Unconventional Reservoir Development and Geothermal Resource Utilization)
►▼ Show Figures

Figure 1

17 pages, 1879 KB  
Article
Influence of Albanian Spring Water Mineral Composition on Fermentation Performance and Physicochemical Characteristics of Pale Ale Beer
by Julian Karaulli, Onejda Kycyk, Fatbardha Lamce, Mamica Ruci, Nertil Xhaferaj, Bruno Testa, Albert Kopali and Massimo Iorizzo
Processes 2026, 14(13), 2223; https://doi.org/10.3390/pr14132223 - 7 Jul 2026
Viewed by 428
Abstract
Water composition is a key factor influencing brewing performance and beer quality due to its impact on mash chemistry, fermentation kinetics, and fermentation-derived metabolites. This study evaluated the effect of four Albanian spring waters (Bogova, Germenji, Selita, and Lajthiza), each with distinct mineral [...] Read more.
Water composition is a key factor influencing brewing performance and beer quality due to its impact on mash chemistry, fermentation kinetics, and fermentation-derived metabolites. This study evaluated the effect of four Albanian spring waters (Bogova, Germenji, Selita, and Lajthiza), each with distinct mineral compositions, on the fermentation behaviour and physicochemical characteristics of Pale Ale beer produced under standardised brewing conditions. All beers were brewed using the same malt formulation, hopping regime, yeast strain, and fermentation parameters, with water source as the sole experimental variable. The produced worts showed only moderate differences in pH, colour, extract, free amino nitrogen (FAN), bitterness, and density, whereas alcoholic fermentation proceeded efficiently in all treatments and was completed within seven days. Final alcohol contents ranged from 5.56 to 5.70% v/v, confirming comparable fermentation performance among treatments. More pronounced differences were observed in acidity-related parameters and fermentation-derived compounds. Volatile acidity ranged from 0.19 to 0.93 g/L, with the highest values in beers produced with Selita and Lajthiza waters. Glycerol concentrations varied from 0.88 to 1.24 g/L, with Germenji beer showing the highest value, whereas acetaldehyde ranged from 3.16 to 6.04 mg/L, with the lowest concentration in Germenji beer. Pearson correlation analysis and exploratory principal component analysis (PCA) identified associations between water mineralisation and selected physicochemical and fermentation-derived beer parameters. Calcium, magnesium, conductivity, and hardness were positively associated with glycerol concentration, whereas bicarbonate concentration was associated with beer pH and acidity-related parameters. The first two principal components explained 87.7% of the total variance. Overall, the results indicate that Albanian spring waters are suitable for Pale Ale production and show that differences in water mineral composition were associated with variations in the physicochemical and fermentation-derived characteristics of the final beers. These findings highlight that brewing water should not be regarded as a neutral processing medium but rather as an important technological factor associated with differences in the physicochemical characteristics of beer, while supporting the valorisation of Albanian spring waters for geographically distinctive craft brewing applications. Full article
(This article belongs to the Section Food Process Engineering)
►▼ Show Figures

Figure 1

19 pages, 1162 KB  
Article
Spatially Constrained Evacuation Route Optimization for LPG Leakage Accidents in Chemical Industrial Parks
by Xinhui Wang
Processes 2026, 14(13), 2222; https://doi.org/10.3390/pr14132222 - 7 Jul 2026
Viewed by 304
Abstract
In chemical industrial parks, evacuation during LPG tank leakage is governed not only by travel distance but by the loss of safe corridors, failed exits, and congestion induced by spatially coupled vapor exposure, explosion overpressure, and thermal radiation. Existing consequence assessment studies usually [...] Read more.
In chemical industrial parks, evacuation during LPG tank leakage is governed not only by travel distance but by the loss of safe corridors, failed exits, and congestion induced by spatially coupled vapor exposure, explosion overpressure, and thermal radiation. Existing consequence assessment studies usually delineate hazardous zones, while evacuation models often optimize routes on a fixed network with available exits and simplified capacity constraints; the coupling between multi-hazard consequence fields and capacity-constrained evacuation assignment remains insufficient. This study proposes a spatially constrained, congestion-aware evacuation optimization framework. ALOHA-derived AEGL exposure, vapor cloud explosion overpressure, and jet fire radiation zones are mapped onto the plant network to identify unsafe nodes, unavailable links, and failed exits. A capacity-constrained model is then established to minimize system-level RSET under an ASET constraint, and a congestion-aware ant colony algorithm balances evacuees among available exits by incorporating risk and density penalties. In a petrochemical plant case with 717 evacuees and 74 nodes, Gate 3 failure makes the nearest-exit strategy infeasible, whereas the proposed strategy reduces RSET from 560.8 to 504.9 s. The framework links accident consequence assessment with actionable evacuation routing for chemical parks. Full article
(This article belongs to the Section Process Control, Modeling and Optimization)
►▼ Show Figures

Figure 1

21 pages, 1065 KB  
Article
Multi-Objective Parameter Matching of Servo Pump-Controlled Units for Electric Loaders Using Sobol Sensitivity Analysis and NSGA-II Optimization
by Huibing Zhao, Gexin Chen, Keyi Liu, Kai Zheng, Jiaqing Zhang, Yuchu Dong, Shuo Tang, Boyuan Li, Jianghui Chen, Yinpin Liu, Yaou Zhang, Tianyi Jia and Xiaolong Yu
Processes 2026, 14(13), 2221; https://doi.org/10.3390/pr14132221 - 7 Jul 2026
Viewed by 289
Abstract
With the development of electrification and green technologies in construction machinery, servo pump-controlled technology has shown great potential in electric loaders due to its high efficiency and energy-saving characteristics. However, the complex coupling among multiple parameters makes it difficult to simultaneously optimize dynamic [...] Read more.
With the development of electrification and green technologies in construction machinery, servo pump-controlled technology has shown great potential in electric loaders due to its high efficiency and energy-saving characteristics. However, the complex coupling among multiple parameters makes it difficult to simultaneously optimize dynamic response and energy efficiency. To address this issue, a multi-objective parameter optimization method for servo pump-controlled units based on Sobol sensitivity analysis and the NSGA-II algorithm is proposed. First, an electro-hydraulic coupling model of the servo pump-controlled unit is established. Subsequently, Sobol global sensitivity analysis is employed to identify sensitive parameters affecting system performance, and a multi-objective optimization model is constructed with dynamic performance and energy efficiency as optimization objectives. Finally, the NSGA-II algorithm is adopted for coordinated parameter optimization, and the results are validated through MATLAB/Simulink simulations and experiments. Results show that the optimized system achieves improved dynamic response, stability, and energy efficiency, demonstrating the effectiveness of the proposed method for the parameter optimization of electric loader servo pump-controlled systems. Full article
(This article belongs to the Section Process Control, Modeling and Optimization)
►▼ Show Figures

Figure 1

28 pages, 29122 KB  
Article
Assessment of Mine Subsidence Using Finite Element-Based 3-D Numerical Modelling: A Case Study from an Underground Metal Mine
by Avinash Singh and Mohammad Soyeb Alam
Processes 2026, 14(13), 2220; https://doi.org/10.3390/pr14132220 - 7 Jul 2026
Viewed by 312
Abstract
This paper assesses mining-induced surface deformation in Mine-A of the Khetri Copper Belt, India, using a three-dimensional (3-D) numerical model based on geological, geotechnical, mine layout, and in situ stress data. 3-D numerical models were developed for the virgin state, current mining state, [...] Read more.
This paper assesses mining-induced surface deformation in Mine-A of the Khetri Copper Belt, India, using a three-dimensional (3-D) numerical model based on geological, geotechnical, mine layout, and in situ stress data. 3-D numerical models were developed for the virgin state, current mining state, next 5 years of mining, and next 10 years of mining, and their corresponding strain and displacement were analysed in different directions. For the mine lease boundary, the strain increment from the virgin to current mining state shows maximum surface strain of 2.41 mm/m, 1.93 mm/m, and 2.35 mm/m in the XX, YY, and ZZ directions, respectively, and the displacement increment from the virgin to current mining state shows maximum surface displacement of 0.003 m, 0.002 m, and 0.003 m in the X, Y, and Z directions, respectively. The results indicate that the model-predicted surface deformation response for the current, next 5 years, and next 10 years of mining states is mainly concentrated around already disturbed zones, while the incremental deformation outside such zones remains comparatively limited under the simulated mining sequence. The spatial concentration of deformation within the mining-influenced zone is further supported by available Total Station monitoring data. From a mine planning perspective, the validated modelling framework is useful for identifying locations requiring focused subsidence monitoring, slope stability assessment, and future model refinement. Full article
(This article belongs to the Section Energy Systems)
►▼ Show Figures

Figure 1

18 pages, 11008 KB  
Article
Air-Breathing Microfluidic Fuel Cell Stack Powered by Tequila: Experimental Evaluation and Computational Fluid Dynamics Simulation of Series-Parallel Configurations Effect
by Andrés Dector, Irma Lucía Vera Estrada, Juan Manuel Olivares-Ramírez, José Eli Eduardo González-Duran, Jocelyne Estrella-Nuñez and Juvenal Rodríguez Reséndiz
Processes 2026, 14(13), 2219; https://doi.org/10.3390/pr14132219 - 7 Jul 2026
Viewed by 299
Abstract
Microfluidic fuel cells offer a promising route for portable power generation; however, scaling paper-based systems remains challenging because capillary-driven flow can limit fuel distribution and electrochemical performance. This work investigates the experimental performance and computational fluid dynamics (CFD) behavior of air-breathing paper-based microfluidic [...] Read more.
Microfluidic fuel cells offer a promising route for portable power generation; however, scaling paper-based systems remains challenging because capillary-driven flow can limit fuel distribution and electrochemical performance. This work investigates the experimental performance and computational fluid dynamics (CFD) behavior of air-breathing paper-based microfluidic fuel cell (μFCs) stacks powered directly by commercial tequila (35 vol.% ethanol). Single cells with electrode areas ranging from 0.5 cm × 0.5 cm to 3 cm × 3 cm were evaluated to determine the optimal design, followed by the construction of 4-cell and 6-cell series-parallel stacks. The smallest electrode (0.5 cm × 0.5 cm) achieved the highest power density (0.142 mW cm−2) and open-circuit voltage (0.92 V). Scaling to a 6-cell stack increased the maximum power density to 3.20 mW cm−2 and the voltage to 1.39 V, outperforming the 4-cell configuration (1.09 mW cm−2 and 1.07 V). Computational Fluid Dynamics simulations revealed that fuel velocity decreased from 2.8 × 10−2 m s−1 near the inlet to approximately 1.0 × 10−6 m s−1 in the final cells because of porous-medium resistance, explaining the observed mass-transport limitations. The results demonstrate that tequila can be directly used as a sustainable fuel source and that optimized stack architectures significantly enhance power generation in paper-based microfluidic fuel cells. Full article
(This article belongs to the Special Issue Recent Advances in Fuel Cells: Materials, Simulation, and AI-Based Applications)
►▼ Show Figures

Figure 1

27 pages, 3592 KB  
Article
Mitigating Particle Erosion in Axial-Flow Turbines Through Air Injection at the Inlet Rotor Section
by José Gustavo Coelho, Rafael de Almeida, Hermeson Conceição Wanzeler and André Luiz Amarante Mesquita
Processes 2026, 14(13), 2218; https://doi.org/10.3390/pr14132218 - 7 Jul 2026
Viewed by 333
Abstract
This study presents a computational analysis of degradation caused by cavitation and hydro-abrasive erosion in a low-head axial microturbine (H=4m), incorporating strategic air injection as a passive mitigation technique. Using Computational Fluid Dynamics (CFD) within ANSYS CFX 2025 [...] Read more.
This study presents a computational analysis of degradation caused by cavitation and hydro-abrasive erosion in a low-head axial microturbine (H=4m), incorporating strategic air injection as a passive mitigation technique. Using Computational Fluid Dynamics (CFD) within ANSYS CFX 2025 R2, the study investigates hydrodynamic performance and the spatial distribution of surface wear across the runner blades. The turbine geometry was developed from aerofoil profiles mapped onto cylindrical coordinates, using a structured three-dimensional mesh with localized refinement to ensure grid independence. Physical modeling employed the Shear Stress Transport (SST) turbulence model, with cavitation dynamics governed by the Rayleigh–Plesset equation and sediment transport modeled using a Lagrangian framework incorporating the Finnie erosion model. The numerical framework showed good agreement with reference characteristic curves, confirming its predictive accuracy. The results indicate that vapor cavities form predominantly on the suction side, whereas solid particle erosion highly concentrated on the pressure side of the blades, where the outer 20% of the span accounts for over 91% of the total erosion intensity. Parametric assessments of controlled air injection revealed a highly non-linear mitigation response, identifying IAVF 2 as the optimal air-injection case. This configuration reduced integrated erosion by 0.95% and maximum localized erosion by 6.17%. In contrast, excessive air volumes accelerated material removal due to localized flow distortion. The findings indicate that carefully controlled air injection is a viable strategy for extending the operational lifespan of small-scale hydropower assets. Full article
(This article belongs to the Special Issue CFD Simulation of Fluid Machinery)
►▼ Show Figures

Figure 1

20 pages, 9002 KB  
Article
Investigation into the Heat Transfer Mechanism via Mixed Coherent Structures Induced by Vortex Generators Punched with Multi-Holes
by Kai Liu and Jiangbo Wang
Processes 2026, 14(13), 2217; https://doi.org/10.3390/pr14132217 - 7 Jul 2026
Viewed by 302
Abstract
Perforated vortex generators have been widely investigated as a passive heat transfer enhancement technique due to their ability to modify local flow structures through perforation-induced bleed flows. However, their thermo-hydraulic performance is strongly dependent on geometric and flow conditions, and a consistent enhancement [...] Read more.
Perforated vortex generators have been widely investigated as a passive heat transfer enhancement technique due to their ability to modify local flow structures through perforation-induced bleed flows. However, their thermo-hydraulic performance is strongly dependent on geometric and flow conditions, and a consistent enhancement effect has not been universally observed. In this study, the mechanism of heat transfer enhancement as well as flow behaviors associated with perforation-induced bleed flows are elucidated through an analysis of the generation and interference behaviors of mixed coherent structures induced by vortex generators punched with multi-holes (PMHVGs). The results showed that the beveled edges of the PMHVGs are responsible for initiating the formation of mixed coherent structures, while local fluid-pressure gradients are identified as the primary driving factor behind their development. Once formed, the perforation-induced bleed flows exert interference on other coherent structures, thereby reducing both their formation intensity and interaction strength. After their generation, the mixed coherent structures contribute to thermal energy transport within the flow through their near-wall ejection and sweep motions. Full article
►▼ Show Figures

Figure 1

25 pages, 18553 KB  
Article
Analysis of Key Factors Controlling Fractured Wells Productivity in Tight Gas Condensate Reservoirs Based on Machine Learning Surrogate Models and SHAP
by Xinyu Chen, Gang Luo, Yan Dong, Jiacheng Dang, Guoquan Zhu and Shaoyang Geng
Processes 2026, 14(13), 2216; https://doi.org/10.3390/pr14132216 - 7 Jul 2026
Viewed by 345
Abstract
To address the complex factors affecting the productivity of fractured horizontal wells in tight condensate gas reservoirs, as well as the high computational costs and opaque mechanism interpretation associated with traditional numerical simulations, this study proposes and implements a quantitative evaluation method for [...] Read more.
To address the complex factors affecting the productivity of fractured horizontal wells in tight condensate gas reservoirs, as well as the high computational costs and opaque mechanism interpretation associated with traditional numerical simulations, this study proposes and implements a quantitative evaluation method for the main productivity-controlling factors. This method integrates a machine learning surrogate model with the Shapley additive explanations (SHAP) interpretability framework. First, based on 3D geological modeling and fracture propagation simulation, a high-dimensional parameter set encompassing reservoir geology, artificial fractures, and fluid properties was constructed. Subsequently, representative samples were generated through an orthogonal experimental design. On this basis, machine learning algorithms, including Support Vector Machines (SVM), Random Forests (RF), and eXtreme Gradient Boosting (XGBoost), were utilized to construct low-cost, high-precision surrogate models targeting initial productivity and Estimated Ultimate Recovery (EUR). These surrogate models effectively substituted the computationally expensive fully coupled numerical simulations. Furthermore, SHAP values were applied to the trained surrogate models to conduct both global and local interpretability analyses. This approach not only quantifies the magnitude and direction of each input parameter’s contribution to the productivity predictions, but also reveals their non-linear mechanisms and interaction effects. The results indicate that reservoir properties and gas saturation are the fundamental factors determining the productivity of fractured horizontal wells, while fracture conductivity and fracture half-length are the key engineering factors. Furthermore, there exist significant synergistic or antagonistic effects between the geological and engineering parameters. The integrated “parametric modeling–surrogate model construction—SHAP interpretability analysis” workflow established in this study provides a highly efficient, transparent, and physically insightful novel approach for the rapid optimization of fracturing designs and the mechanistic analysis of main productivity-controlling factors in tight condensate gas reservoirs. Full article
(This article belongs to the Special Issue Intelligent Reservoir Characterization Technology and Numerical Simulation)
►▼ Show Figures

Figure 1

20 pages, 3715 KB  
Article
An Identification Method for Coal and Gas Outburst Based on Stacking Ensemble Learning
by Yuhan Liu, Xueqi Qu, Kai Cui, Shaohan Yu, Riyuan Chen, Yanlei Guo and Jian Chen
Processes 2026, 14(13), 2215; https://doi.org/10.3390/pr14132215 - 7 Jul 2026
Viewed by 372
Abstract
Coal and gas outburst is a major mine disaster affected by complex coupled factors, bringing obstacles to disaster prevention. To address low accuracy and poor generalization of traditional single-algorithm prediction models, this paper constructs a two-layer Stacking ensemble learning identification model for outburst [...] Read more.
Coal and gas outburst is a major mine disaster affected by complex coupled factors, bringing obstacles to disaster prevention. To address low accuracy and poor generalization of traditional single-algorithm prediction models, this paper constructs a two-layer Stacking ensemble learning identification model for outburst risk. RF, SVM and AdaBoost serve as base models, and WOA-LightGBM acts as the meta-model. Based on measured data of a Shanxi coal mine, Spearman correlation analysis and RF dimensionality reduction remove redundant features; Borderline-SMOTE balances imbalanced samples with few severe-risk data. Accuracy, macro-precision, recall and F1-score evaluate model performance after parameter optimization. Results show that the proposed Stacking model reaches 0.9770 accuracy, outperforming single machine learning models and other intelligent algorithms. It presents minor index fluctuations with strong stability and correctly identifies all eight practical engineering cases. Combining feature engineering and Stacking learning effectively captures nonlinear relations between influencing factors and risk levels. The model owns high precision and robustness, offering reliable technical support for coal and gas outburst prediction and control. Full article
(This article belongs to the Section AI-Enabled Process Engineering)
►▼ Show Figures

Figure 1

22 pages, 15111 KB  
Article
Study on the Mechanism of Gas-Phase Space in Liquid Hydrogen Cylinders Under Different Filling Rates
by Hui Wu, Zuolei Xiao, Chaoyang Hao, Zengming Feng and Zheng Cao
Processes 2026, 14(13), 2214; https://doi.org/10.3390/pr14132214 - 7 Jul 2026
Viewed by 295
Abstract
To ensure that the necessary gas-phase safety space is retained after the filling of a liquid hydrogen cylinder and to reduce the risk of rapid pressure rise caused by overfilling, a 34 L vehicle-mounted liquid hydrogen cylinder was taken as the research object. [...] Read more.
To ensure that the necessary gas-phase safety space is retained after the filling of a liquid hydrogen cylinder and to reduce the risk of rapid pressure rise caused by overfilling, a 34 L vehicle-mounted liquid hydrogen cylinder was taken as the research object. A non-isothermal two-dimensional numerical model considering gas–liquid two-phase flow, heat transfer, and phase change was established. The dynamic and thermal characteristics of cylinders with and without a gas phase space were compared under different filling rates. The results show that, during liquid hydrogen filling, the liquid phase first accumulates at the bottom of the main chamber. Then, the liquid level rises and compresses the upper gas phase, and part of the liquid hydrogen enters the gas-phase space in the later stage. The gas-phase space can delay the occupation of the safety gas cushion by liquid hydrogen, allowing a certain volume of compressible gas to remain during overfilling. The pressure variation presents three stages: a rapid increase in the initial stage, a slower increase in the middle stage, and another rapid increase in the final stage. These stages are related to liquid hydrogen flash evaporation, gas-phase cooling and condensation, and compression of the remaining gas, respectively. The tank temperature generally shows a rapid decrease followed by a slower decrease. As the filling rate increases, the liquid level rises faster, the gas–liquid interface disturbance becomes stronger, the liquid hydrogen enters the gas-phase space earlier, the pressure rise rate increases, and the buffering effect weakens. The results indicate that the gas-phase space structure can improve the safety margin in the final stage of liquid hydrogen cylinder filling, but the filling rate should still be reasonably controlled in actual filling processes. Full article
(This article belongs to the Section Energy Systems)
►▼ Show Figures

Figure 1

22 pages, 7832 KB  
Article
Influence of Auxiliary Emulsifier Ionic Characteristics on Interfacial Film Stability and Performance of Water-in-Oil Emulsions for Oil-Based Drilling Fluids
by Gang Li, Lei Pu and Dunqing Liu
Processes 2026, 14(13), 2213; https://doi.org/10.3390/pr14132213 - 7 Jul 2026
Viewed by 438
Abstract
Under high-temperature and high-salinity drilling conditions, maintaining the stability of water-in-oil emulsions is critical for oil-based drilling fluids, while the roles of auxiliary emulsifiers with different ionic characteristics remain unclear. In this study, Span 80 was used as the primary emulsifier, and nonionic [...] Read more.
Under high-temperature and high-salinity drilling conditions, maintaining the stability of water-in-oil emulsions is critical for oil-based drilling fluids, while the roles of auxiliary emulsifiers with different ionic characteristics remain unclear. In this study, Span 80 was used as the primary emulsifier, and nonionic OP-4, anionic SDBS, zwitterionic EAB40, and cationic CTAB were introduced as auxiliary emulsifiers to construct blended emulsifier systems. The HLB value was controlled at 5.2–5.4, and the total emulsifier concentration was fixed at 6.0 wt%. The effects of auxiliary emulsifier type on interfacial tension, rheological behavior, electrical stability, droplet morphology, and thermal stability were systematically investigated. The Span80/OP-4 system exhibited the lowest interfacial tension, smallest droplet size, and best overall emulsion stability. In contrast, the Span80/SDBS system showed poor electrical stability due to weakened effective interfacial adsorption in Ca2+ brine. After aging at 120 °C, EAB40 promoted interfacial rearrangement, whereas CTAB weakened interfacial order. Further verification in 1.50 g/cm3 weighted oil-based drilling fluids showed that the Span80/OP-4 system maintained high electrical stability, low HTHP filtrate volume, and good sedimentation stability after aging at 140 °C. Full article
(This article belongs to the Special Issue Whole Petroleum System Theory and Multi-Scale Modeling of Hydrocarbon Generation, Migration and Enrichment)
►▼ Show Figures

Figure 1

27 pages, 11113 KB  
Article
Numerical Simulation and Field Testing of Coal Seam Drilling Hole Gas Discharge Characteristics Based on Fluid–Solid Interaction
by Chong Liu, Junfeng Wang, Zhifan Lu, Zhiyu Dong, Kaiwen Ren and Yu Bai
Processes 2026, 14(13), 2212; https://doi.org/10.3390/pr14132212 - 7 Jul 2026
Viewed by 361
Abstract
The effectiveness of gas discharge depends on the geological conditions and drilling parameters. Investigating gas seepage behavior near boreholes under fluid–solid coupling conditions can provide theoretical support for scientifically determining the effective discharge radius (EDR) and ensuring mining safety. In this study, taking [...] Read more.
The effectiveness of gas discharge depends on the geological conditions and drilling parameters. Investigating gas seepage behavior near boreholes under fluid–solid coupling conditions can provide theoretical support for scientifically determining the effective discharge radius (EDR) and ensuring mining safety. In this study, taking Xinyuan Coal Mine as the engineering background, a fluid–solid coupled model describing gas migration was developed. The effects of the discharge duration, borehole diameter, permeability, and borehole layout on the spatiotemporal evolution of gas around boreholes and EDR were investigated. The results indicate that gas pressure around the borehole continuously decreases with time, and the affected zone expands elliptically. The EDR exhibits a power-law relationship with time. Increasing the borehole diameter enlarges the EDR, with the effect being particularly significant in the initial stage of gas discharge. After 5 h of gas discharge, the EDR in high-permeability coal seams is approximately twice that in low-permeability coal seams. Compared to the triple-flower patterns, the square pattern produces a larger EDR at the same time. The EDR calculated based on the measured values of the drill cuttings volume S value and drill cuttings desorption gas volume K1 value shows a high degree of consistency with the simulation results. After 5 h of gas discharge using the square pattern, the gas volume fraction at the upper corner of the working face dropped to the safe level of 6%, enabling mining to resume. Full article
(This article belongs to the Topic Advances in Coal Mine Disaster Prevention Technology)
►▼ Show Figures

Figure 1

20 pages, 5578 KB  
Article
Energy-Efficient Microwave Drying and Shelf-Life Prediction of Soybean Residue Powder: Sorption Isotherm Modeling and Bakery Application
by Shu-Chin Wang, Meng-Jen Tsai, Chih-Hong Tung and Po-Hua Wu
Processes 2026, 14(13), 2211; https://doi.org/10.3390/pr14132211 - 7 Jul 2026
Viewed by 367
Abstract
Soybean residue (Okara), a major by-product of soybean product processing, is highly susceptible to spoilage due to its high moisture content (approximately 78% w.b.), which creates both environmental and resource-related challenges. This study aimed to develop energy-efficient drying technologies and value-added application models [...] Read more.
Soybean residue (Okara), a major by-product of soybean product processing, is highly susceptible to spoilage due to its high moisture content (approximately 78% w.b.), which creates both environmental and resource-related challenges. This study aimed to develop energy-efficient drying technologies and value-added application models to improve its storage stability. The effects of heat pump drying, microwave drying, and two-stage drying on the drying kinetics, energy consumption, and product quality of okara were systematically compared. The experimental results indicated that Heat Pump Drying (HPD) at 65 °C required a prolonged drying time of 360 min. In contrast, Microwave Drying (MWD) at 2.0 W/g significantly accelerated the process, achieving the shortest drying time of 50 min (an 86.1% reduction) and lowering the specific energy consumption (SEC) to 2.2 kWh/kg (a 42.1% energy saving compared to HPD). Meanwhile, the HPD–MWD two-stage drying process offered a balanced alternative, requiring 190 min and reducing thermal risk while maintaining high efficiency. The dried okara powder contained a total dietary fiber content of 47.78%, while its water activity was maintained below 0.60. Dynamic Dew Point Isotherm (DDI) analysis confirmed a critical water activity (awc) of 0.66, with mathematical modeling predicting a shelf life of up to 389 days under barrier packaging conditions. In value-added application experiments, muffins containing 10% okara powder achieved sensory scores above 6 on a 9-point scale and demonstrated significantly better flavor acceptability (p = 0.0093). In summary, this study established an efficient drying and value-added application approach for okara, providing a feasible strategy for the circular use and sustainable utilization of agricultural by-products. Full article
(This article belongs to the Section Food Process Engineering)
►▼ Show Figures

Graphical abstract

19 pages, 3609 KB  
Article
A Physically Constrained Deep Learning Method for Shale Gas Well Production Forecasting
by Cheng Chang, Fanxiang Xu, Hongbin Liang, Huangben Zeng, Xiaojing Ji, Ze Wanyan and Ziqi Qiu
Processes 2026, 14(13), 2210; https://doi.org/10.3390/pr14132210 - 6 Jul 2026
Viewed by 347
Abstract
Shale gas production is governed by complex geological and engineering factors, and its production dynamics are often highly variable. Conventional methods, which can incorporate only a limited number of production-related variables, often struggle to provide accurate forecasts under fluctuating operating conditions. Focusing on [...] Read more.
Shale gas production is governed by complex geological and engineering factors, and its production dynamics are often highly variable. Conventional methods, which can incorporate only a limited number of production-related variables, often struggle to provide accurate forecasts under fluctuating operating conditions. Focusing on the natural flowing stage of shale gas wells, this study proposes a probabilistic forecasting framework that integrates physical decline characteristics with dynamic production data. A dual-branch TCN–LSTM network constrained by decline features is constructed, and Student’s t-distribution is introduced to quantify the uncertainty caused by short-term production fluctuations. The results show that embedding physical decline constraints into the deep learning architecture helps bridge the gap between conventional models with limited parameter representation and purely data-driven models with insufficient interpretability. The proposed method improves forecasting accuracy while preserving the physical meaning of the predictions, and it can generate noise-robust confidence intervals with stable coverage. This method provides decision support for short-term production tracking and production-regime adjustment in shale gas wells. Full article
(This article belongs to the Special Issue Advances in Enhancing Unconventional Oil/Gas Recovery, 3rd Edition)
►▼ Show Figures

Figure 1

25 pages, 5008 KB  
Article
A Comparative Study of a Single-Phase Immersion-Cooled Server with a Pin-Fin Heat Sink for Mitigation of the Flow Bypass Effect
by Shau-Wai Cheng, Yong-Dong Zhang, Li-Hung Chien and Chi-Chuan Wang
Processes 2026, 14(13), 2209; https://doi.org/10.3390/pr14132209 - 6 Jul 2026
Viewed by 414
Abstract
Single-phase oil immersion is a promising alternative to air cooling for high-power servers, but the high viscosity of dielectric fluids amplifies the bypass flow around the CPU heat sink via the adjacent random-access memory (RAM) channels, degrading thermal performance. A simplified hydraulic-thermal analysis [...] Read more.
Single-phase oil immersion is a promising alternative to air cooling for high-power servers, but the high viscosity of dielectric fluids amplifies the bypass flow around the CPU heat sink via the adjacent random-access memory (RAM) channels, degrading thermal performance. A simplified hydraulic-thermal analysis shows that this bypass penalty cannot be eliminated by reducing the fin pitch of a rectangular-fin heat sink alone. A staggered pin-fin heat sink is therefore proposed, with pin diameter D, longitudinal pitch Sd, and transverse pitch St optimized by three-dimensional CFD using PAO-6. The optimum geometry is D = 2.8 mm, St = 6 mm, Sd = 8.45 mm. The heat sink is fabricated and tested in a commercial server at oil inlet temperatures of 30–45 °C and flow rates of 3–6 LPM. At 3 LPM, the pin-fin immersion server reduces the CPU thermal resistance by 22.29% relative to a rectangular-fin immersion server using the same oil, and by 38.37% relative to an air-cooled server. The partial Power Usage Effectiveness (pPUE) reaches 1.015, an 88.09% improvement over the air-cooled baseline (pPUE = 1.126), confirming that pin-fin geometries effectively mitigate the bypass penalty in single-phase oil immersion cooling. Full article
(This article belongs to the Special Issue Advances in Fluid Flow and Transport Processes for Energy and Environmental Systems)
►▼ Show Figures

Figure 1

10 pages, 818 KB  
Communication
Data-Driven Quantification of Temperature-Induced Mechanical Property Variations in 5Cr–0.5Mo Steel Using Artificial Neural Networks
by Muhammad Ishtiaq, Ha Jae Hong and Nagireddy Gari Subba Reddy
Processes 2026, 14(13), 2208; https://doi.org/10.3390/pr14132208 - 6 Jul 2026
Viewed by 271
Abstract
This study presents the quantitative estimation of the effect of temperature on the mechanical properties of 5Cr–0.5Mo steels using an artificial neural network (ANN) model. The developed ANN model predicts yield strength (YS, MPa), ultimate tensile strength (UTS, MPa), elongation (El, %), and [...] Read more.
This study presents the quantitative estimation of the effect of temperature on the mechanical properties of 5Cr–0.5Mo steels using an artificial neural network (ANN) model. The developed ANN model predicts yield strength (YS, MPa), ultimate tensile strength (UTS, MPa), elongation (El, %), and reduction in area (RA, %) at different service temperatures. Predictions were validated against experimental data at critical temperatures of 450 °C and 700 °C and found to show high accuracy. Predicted results show minimal errors of 3.84%, 2.3%, 2.2%, and 0.42% for YS, UTS, El, and RA, respectively at 450 °C, and 3.7%, 0.45%, 1.88%, and 0.19%, respectively at 700 °C. Furthermore, ten-fold cross-validation confirmed the generalization capability of the developed model, yielding high coefficients of determination and correlation coefficients together with low normalized prediction errors across all output variables. Despite the absence of explicit metallurgical descriptors, the ANN model successfully quantified the influence of temperature from 25 to 700 °C, demonstrating its effectiveness as a predictive tool for high-temperature Cr–Mo steels. Furthermore, a user-friendly graphical interface was developed to facilitate rapid property estimation, demonstrating the potential of the framework as a supportive tool for the preliminary assessment of high-temperature Cr–Mo steels. Full article
(This article belongs to the Special Issue Recent Advances in the Effects of Thermal Processes on Alloy Microstructure and Mechanical Properties)
►▼ Show Figures

Figure 1

13 pages, 2327 KB  
Article
Molecular Docking Assessment of Tarragon Essential Oil Constituents Toward OATP1B1 and OATP1B3
by Andrijana Pujicic, Diana-Larisa Roman and Adriana Isvoran
Processes 2026, 14(13), 2207; https://doi.org/10.3390/pr14132207 - 6 Jul 2026
Viewed by 240
Abstract
Tarragon (Artemisia dracunculus) essential oil contains bioactive phytochemicals that may interact with hepatic transporters involved in drug disposition. This study used molecular docking and interaction analysis to evaluate the binding potential of compounds identified in tarragon essential oil samples from the [...] Read more.
Tarragon (Artemisia dracunculus) essential oil contains bioactive phytochemicals that may interact with hepatic transporters involved in drug disposition. This study used molecular docking and interaction analysis to evaluate the binding potential of compounds identified in tarragon essential oil samples from the Romanian market toward organic anion transporters OATP1B1 and OATP1B3, using multiple cryo-EM structures representing distinct conformational states. All investigated compounds were predicted to bind within the cavities of OATP1B1 and OATP1B3, exhibiting moderate predicted binding scores ranging from –3.956 to –6.583 kcal/mol, whereas the reference ligands resolved in the experimental structures showed binding scores ranging from –7.152 to –11.212 kcal/mol. Eugenol and its oxygenated derivatives exhibited relatively higher scores, likely due to their ability to form both hydrophobic and hydrogen-bonding interactions, whereas monoterpene hydrocarbons relied mainly on hydrophobic contacts. Interaction profiling predicted for both transporters binding environments dominated by aromatic and hydrophobic residues, alongside key polar residues contributing to hydrogen bonding. Binding patterns varied across OATP1B1 conformations, indicating state-dependent ligand recognition. Overall, the results suggest that tarragon essential oil constituents may interact with OATP1B1 and OATP1B3. Experimental studies are required to confirm the functional and clinical relevance of these findings. Full article
(This article belongs to the Special Issue From Plant to Product: Process-Centric Advances in Essential Oils—Extraction, Modeling, Standardization, and Use)
►▼ Show Figures

Graphical abstract

17 pages, 4567 KB  
Article
Experimental Study on Atomization Characteristics of Droplet Field in the Downstream Region of Hydraulic Nozzles Under Co-Flow Disturbance
by Zhirong Wu, Wen Li, Yongping Chen, Shiqiang Chen and Chunyu Liu
Processes 2026, 14(13), 2206; https://doi.org/10.3390/pr14132206 - 6 Jul 2026
Viewed by 285
Abstract
Hydraulic nozzles are widely utilized for dust removal, cooling, and waste heat recovery in mining production. Nevertheless, the influence of co-flow disturbance on the atomization characteristics within the downstream region of droplet fields remains inadequately understood. In this study, three typical hydraulic nozzles [...] Read more.
Hydraulic nozzles are widely utilized for dust removal, cooling, and waste heat recovery in mining production. Nevertheless, the influence of co-flow disturbance on the atomization characteristics within the downstream region of droplet fields remains inadequately understood. In this study, three typical hydraulic nozzles were selected, and the atomization characteristics of the downstream region under different co-flow disturbance intensities were experimentally investigated. The results reveal that increasing co-flow disturbance velocity does not intensify the reduction in sauter mean diameter (SMD), but markedly reduces the dispersed phase fraction (DPF). Under four co-flow disturbance velocities (1.5, 3.0, 4.5, and 6.0 m/s), the relative reduction rates of mean SMD are 6.77%, 3.27%, 4.42% and 2.60%, while those of mean DPF are 13.86%, 35.85%, 52.88%, and 61.86% (e.g., hollow-cone nozzle), respectively. The variation in SMD is achieved through the redistribution of cumulative volume among CV1, CV2, CV3, and CV4. As the velocity increases from 0 to 3 m/s, the mean SMD of the three hydraulic nozzles exhibits a decreasing trend, which can be directly attributed to the continuous increase in the total cumulative volume of CV1 and CV2, and the continuous decrease in those of CV3 and CV4. For the hollow-cone and solid square-cone nozzles, the SMD first decreases and then increases, with the turning point occurring at 3.0 m/s, consistent with the variation trend of cumulative volume fractions. In contrast, for the solid-cone nozzle, the SMD continues to decrease at velocities exceeding 3.0 m/s. This work provides both a fundamental understanding of atomization characteristics in the downstream region of hydraulic nozzles under co-flow disturbance and practical guidance for velocity control in mine spray systems. Full article
(This article belongs to the Section Energy Systems)
►▼ Show Figures

Figure 1

15 pages, 3567 KB  
Article
Rheological Properties of Film-Forming Gels Based on Collagen from Octopus maya By-Products and Food-Grade Polysaccharides
by María Fernanda Acosta-Pacheco, Élida Gastélum-Martínez, Juan Valerio Cauich-Rodríguez, Ingrid Mayanin Rodríguez-Buenfil and Manuel Octavio Ramírez-Sucre
Processes 2026, 14(13), 2205; https://doi.org/10.3390/pr14132205 - 6 Jul 2026
Viewed by 355
Abstract
Octopus maya is a fast-growing species from the Yucatán Peninsula with high economic relevance, accounting for a major share of regional fishery production. However, a significant fraction of the organism, rich in type I collagen, is discarded as by-products, representing a promising and [...] Read more.
Octopus maya is a fast-growing species from the Yucatán Peninsula with high economic relevance, accounting for a major share of regional fishery production. However, a significant fraction of the organism, rich in type I collagen, is discarded as by-products, representing a promising and underutilized source for sustainable biomaterials. This study evaluated, through a 32 factorial design, the effect of two factors on the rheological and dynamic mechanical properties of film-forming solutions (FFS). The first factor was the type of food-grade polysaccharide: chitosan (Ch), hydroxypropyl methylcellulose (HPMC), or starch (S). The second factor was the proportion of each polysaccharide blended with ultrasound-extracted Octopus maya insoluble collagen (CIPM), using polysaccharide ratios of 30:70, 50:50, and 70:30 (w/w). This approach aims to valorize octopus by-products through the recovery and functional utilization of collagen. Rheological properties were determined by rotational and oscillatory rheometry at 25 °C, with flow curves fitted to the Carreau-Yasuda model. All formulations exhibited pseudoplastic behavior (n < 1), with viscosity decreasing as shear rate increased. Pure CIPM showed high viscosity (190.36 Pa·s at 1 s−1), which decreased (0.3–10.44 Pa·s) in HPMC and chitosan systems, suggesting their potential suitability for applications requiring fluidity, such as spray coatings or film-forming solutions, based on their rheological properties. In contrast, starch-based systems exhibited higher viscosities (33.54–197.53 Pa·s) and a more structured viscoelastic profile (G′ > G″), suggesting potential suitability for thick coatings or gels requiring structural stability, although these applications were not experimentally validated. These results demonstrate that CIPM-polysaccharide systems enable tunable rheological properties, supporting the use of Octopus maya collagen as a sustainable functional material for advanced food and biomaterial design. Full article
(This article belongs to the Special Issue Applications of Ultrasound and Other Technologies in Food Processing)
►▼ Show Figures

Graphical abstract

18 pages, 2159 KB  
Article
Prediction Model for Harmful Gas Risk Levels in Non-Coal Strata Tunnels Based on SSA-CatBoost
by Zengchan Mao, Wenpin Luo, Jianhua Wu, Peidong Su, Xiaojin Wang and Peng Yang
Processes 2026, 14(13), 2204; https://doi.org/10.3390/pr14132204 - 6 Jul 2026
Viewed by 302
Abstract
Harmful gas is a major hazard in underground engineering construction, and accurate prediction of its risk level is essential for tunnel safety. Existing prediction methods for harmful-gas risk in non-coal strata tunnels are limited by empirical scoring, subjective indicator assignment, and insufficient quantitative [...] Read more.
Harmful gas is a major hazard in underground engineering construction, and accurate prediction of its risk level is essential for tunnel safety. Existing prediction methods for harmful-gas risk in non-coal strata tunnels are limited by empirical scoring, subjective indicator assignment, and insufficient quantitative characterization of reservoir performance. To address these limitations, this study proposes an SSA-CatBoost prediction model for harmful-gas risk levels in non-coal strata tunnels. Eight influencing indicators were selected as input variables. Among them, reservoir performance was quantitatively characterized by measured porosity and permeability, while the other six indicators were quantified using engineering-based scoring criteria. A database containing 138 real harmful-gas tunnel cases was constructed, and CatBoost was used as the base classifier. The Sparrow Search Algorithm was introduced to optimize the hyperparameters of CatBoost. The proposed SSA-CatBoost model achieved an average accuracy of 93.63% in five-fold cross-validation and an accuracy of 92.86% on the independent test set. Compared with CatBoost, SSA-SVM, and SSA-XGBoost, the proposed model showed the highest cross-validation accuracy. Engineering validation further showed that all selected validation samples were correctly classified. In addition, replacing empirical reservoir-performance scoring with measured porosity and permeability improved the recognition performance of adjacent risk levels, with the F1-scores of Level III and Level IV increasing from 0.667 and 0.727 to 0.909, respectively. The novelty of this study lies in integrating measured reservoir-performance parameters into a machine-learning-based harmful-gas risk prediction framework, thereby reducing the subjectivity of conventional scoring systems and improving the quantitative characterization of non-coal strata tunnel gas hazards. Full article
(This article belongs to the Section AI-Enabled Process Engineering)
►▼ Show Figures

Figure 1

20 pages, 5646 KB  
Review
CO2 Trapping Mechanisms in Geological Carbon Sequestration: A Critical Review of Multiscale Processes and Storage Security
by Anurag Banerjee and Tathagata Acharya
Processes 2026, 14(13), 2203; https://doi.org/10.3390/pr14132203 - 6 Jul 2026
Viewed by 476
Abstract
Geological carbon sequestration is a critical strategy for reducing atmospheric CO2 emissions and mitigating climate change; however, its long-term effectiveness depends on a robust understanding of subsurface trapping mechanisms. This review synthesizes recent advances in evidence-based CO2 trapping by systematically examining [...] Read more.
Geological carbon sequestration is a critical strategy for reducing atmospheric CO2 emissions and mitigating climate change; however, its long-term effectiveness depends on a robust understanding of subsurface trapping mechanisms. This review synthesizes recent advances in evidence-based CO2 trapping by systematically examining four primary mechanisms—structural/stratigraphic, residual (capillary), solubility, and mineral trapping—using insights from experimental studies, field observations, and numerical modeling. The analysis highlights that structural trapping provides immediate containment controlled by caprock integrity and reservoir geometry, while residual trapping immobilizes CO2 at the pore scale through capillary forces and multiphase flow dynamics. Over longer timescales, solubility trapping enhances storage security via dissolution and density-driven convection, whereas mineral trapping offers the most permanent form of sequestration through geochemical conversion to stable carbonates, albeit with slower kinetics. Recent findings emphasize the strong coupling among trapping mechanisms, the influence of wettability, heterogeneity, and flow regimes, and the growing role of engineered injection strategies and enhanced mineralization approaches. Overall, the review demonstrates that secure and scalable CO2 storage requires an integrated, multiscale understanding of these interacting processes, supported by improved monitoring, modeling, and experimental validation to reduce uncertainty and optimize storage performance. Full article
(This article belongs to the Special Issue Unconventional Energy, Clean Energy and Carbon Sequestration: Progress in Technology)
►▼ Show Figures

Figure 1

23 pages, 3282 KB  
Article
Influence of Soil Properties and Soil Aeration Design on Subsurface Methane Removal During Soil Aeration Operations
by Jui-Hsiang Lo, J. R. R. Navodi Jayarathne, Daniel J. Zimmerle and Kathleen Smits
Processes 2026, 14(13), 2202; https://doi.org/10.3390/pr14132202 - 6 Jul 2026
Viewed by 325
Abstract
Soil aeration is a widely used field method to remove subsurface methane (CH4) following natural gas (NG) pipeline leaks, reducing safety risks and enabling site recovery. However, conventional aeration practices often rely on generalized guidance and do not explicitly account for [...] Read more.
Soil aeration is a widely used field method to remove subsurface methane (CH4) following natural gas (NG) pipeline leaks, reducing safety risks and enabling site recovery. However, conventional aeration practices often rely on generalized guidance and do not explicitly account for site-specific soil conditions, resulting in inefficient CH4 removal and prolonged cleanup times. This study investigated the influence of soil properties and aeration system design on CH4 removal using controlled field-scale experiments and a validated multiphase transport model. Six field-scale aeration experiments and 39 numerical simulations were conducted across representative soil types, soil moisture conditions, vacuum pressures, and bar hole configurations. Results show that CH4 removal occurs in two distinct stages: an initial advection-dominated removal phase followed by a slower diffusion-controlled phase. More than 50% of the residual CH4 mass was removed within the first 10 min of aeration in permeable soils, while greater than 90% removal was achieved within 30 min under favorable conditions. Increasing vacuum pressure improved CH4 removal by approximately 15 percentage points after 60 min and increased the effective radius of influence of individual bar holes. Soil permeability exerted a primary control on performance, with high-permeability soils exhibiting substantially faster CH4 removal and larger treatment zones than lower-permeability soils. Bar hole configuration was equally important; properly spaced bar holes improved plume coverage and removal efficiency, whereas excessive overlap reduced aeration effectiveness through airflow interference. Overall, the results demonstrate that CH4 removal during NG soil aeration is governed by coupled interactions among soil properties, moisture conditions, vacuum pressure, and bar hole deployment. Incorporating these factors into aeration system design can improve removal efficiency, reduce aeration duration, and provide utilities with a quantitative basis for safer and more effective NG leak mitigation. Full article
(This article belongs to the Section Process Control, Modeling and Optimization)
►▼ Show Figures

Figure 1

34 pages, 11900 KB  
Article
Wellbore Size Effect and Borehole Instability Response Characteristics of Fractured Sandstone in SP Gas Storage
by Zhi Chang, Tian’en Liu, Hengyu Song, Hong Zhang, Xinglong Cao, Jilong Ma and Yingjian Xiao
Processes 2026, 14(13), 2201; https://doi.org/10.3390/pr14132201 - 6 Jul 2026
Viewed by 330
Abstract
The SP Gas Storage is situated in the SP-Xingcheng structural belt, where volcanic gas reservoirs are widely distributed and characterized by abundant primary microfractures and pore structures. The developed pores and fractures degrade the petrophysical properties of reservoirs and render volcanic basement rocks [...] Read more.
The SP Gas Storage is situated in the SP-Xingcheng structural belt, where volcanic gas reservoirs are widely distributed and characterized by abundant primary microfractures and pore structures. The developed pores and fractures degrade the petrophysical properties of reservoirs and render volcanic basement rocks highly abrasive. In addition, pore-fracture systems alter the internal stress field of formations, which substantially increases the risk of wellbore instability and the collapse of injection and production wells. This poses great challenges to drilling operations and the safe running of the gas storage in this block. To systematically clarify the wellbore instability mechanism of large-diameter wellbores and address the drilling engineering problems in the study area, a dedicated experimental scheme for large-diameter wellbore stability was designed in this work. Laboratory true triaxial tests were conducted on wellbore stability with different borehole sizes, and basic mechanical parameter tests of reservoir rocks were also completed. This study systematically investigates the evolution of rock mechanical parameters and the surrounding stress-reconstruction mechanism induced by pore-forming unloading and identifies the dominant internal mechanism of wellbore instability under large-diameter conditions. A clear distinction is made between the formation stress redistribution caused by stratum exposure and unloading during drilling and formation stress evolution during the subsequent injection and production of the gas storage. On this basis, the fracture initiation threshold, propagation paths, and morphological evolution in thin interbedded sandstone–mudstone reservoirs are further analyzed. Combined with rock mechanical parameters and in situ stress balance conditions, criteria and quantitative evaluation methods for wellbore instability discrimination are finally established. Full article
(This article belongs to the Special Issue Environmentally Friendly Production of Energy from Natural Gas Hydrates, 2nd Edition)
►▼ Show Figures

Figure 1

27 pages, 3381 KB  
Article
Effect of Regenerative Evaporative Cycle on Performance and NOx Formation of a Micro Gas Turbine
by Daniel R. López, Edywin G. C. Oliveira, Manuel P. Ojeda, Kamal A. R. Ismail, Jorge R. Henriquez, Alvaro A. V. Ochoa, José Ângelo P. da Costa and Gustavo N. P. Leite
Processes 2026, 14(13), 2200; https://doi.org/10.3390/pr14132200 - 6 Jul 2026
Viewed by 455
Abstract
Micro gas turbines are small-scale systems based on the Brayton cycle and represent a viable solution for distributed generation. However, the main limitation to extending their application range is the cycle efficiency. Numerical and experimental analyses of power plants are important for evaluating [...] Read more.
Micro gas turbines are small-scale systems based on the Brayton cycle and represent a viable solution for distributed generation. However, the main limitation to extending their application range is the cycle efficiency. Numerical and experimental analyses of power plants are important for evaluating the energy performance of different cycle configurations. Another issue is the formation of pollutants, including nitrogen oxide emissions. The humidified gas turbine cycle is one alternative to address these problems. Among wet cycles, the regenerative evaporative cycle offers a means to improve gas turbine efficiency. However, this configuration is less commonly discussed in the literature, which focuses more on steam injection cycles. Therefore, this paper presents an energy, exergetic, and nitrogen oxide formation analysis of an evaporative regenerative cycle for a 30 kW micro gas turbine across the gas turbine load range to define the most suitable operational system regime. The novelty of this study lies in an integrated assessment that simultaneously covers the operation of the micro gas turbine at full and part load under different conditions of water injection into the evaporator. The analyses conducted show that, for the micro turbine operating at full load, the benefits in terms of energy and pollutant formation are positive for all fractions of injected water. However, decreases in cycle performance are reported at power outputs below 19 kW compared with the dry cycle. Although nitrogen oxide formation decreases with increasing water injection, the reduction is less pronounced at lower microturbine power levels. Full article
(This article belongs to the Special Issue Data-Driven Modeling, Optimization and Control of Complex Thermal Processes)
►▼ Show Figures

Figure 1

15 pages, 2475 KB  
Article
Multi-Objective Vibration Reduction for Robotic Planetary Gears with an Improved MOPSO Algorithm
by Dehai Zhang, Peihua Zhu, Weizhen Chen, Yanqin Li, Fei Ren, Shengmao Zhou and Huijuan Zhang
Processes 2026, 14(13), 2199; https://doi.org/10.3390/pr14132199 - 6 Jul 2026
Viewed by 312
Abstract
As a commonly used component in robot joints, helical planetary gear system is restricted from further application in the robotics industry due to excessive maximum subsurface shear stress and vibration amplitude during their meshing motion. Tooth modification can effectively reduce the maximum subsurface [...] Read more.
As a commonly used component in robot joints, helical planetary gear system is restricted from further application in the robotics industry due to excessive maximum subsurface shear stress and vibration amplitude during their meshing motion. Tooth modification can effectively reduce the maximum subsurface shear stress and vibration amplitude of gears, making it particularly important to conduct research on the modification of helical planetary gear trains. In this study, a lumped mass method is first adopted to establish a bending to rsionaxial coupling dynamic model of the helical planetary gear train. Subsequently, multi-objective optimization modification research on the left tooth flank of the planetary gear is carried out using both traditional empirical formulas and an improved Multi-Objective Particle Swarm Optimization (MOPSO) algorithm featuring physics-informed search boundaries and an automated optimal selection mechanism, respectively. Then, the finite element method is employed to analyze the maximum subsurface shear stress of planetary gears under three scenarios: unmodified, traditionally modified, and modified with the improved MOPSO. Finally, the 4th-order Runge Kutta method is used to solve the bending to rsionaxial coupling dynamic model of the helical planetary gear train system, thereby obtaining the vibration amplitude of the sun gear under the three scenarios. The research results show that the empirical formula method and the improved MOPSO reduce the maximum subsurface shear stress of the planetary gear by 12.629% and 30.107%, respectively, and decrease the vibration amplitude of the sun gear by 10.26% and 19.29%, respectively. This study provides theoretical and data support for the development of helical planetary gear modification and promotes its further application in the robotics industry. Full article
(This article belongs to the Special Issue Innovations in Manufacturing Processes and Systems for Sustainable Practices (2nd Edition))
►▼ Show Figures

Figure 1

26 pages, 1527 KB  
Review
A Review of Digital Twin Applications in Distribution Network Simulation
by Guohang Zhang, Chengxi Liu, Shuoyang Li, Yuneng Wang and Bo Peng
Processes 2026, 14(13), 2198; https://doi.org/10.3390/pr14132198 - 6 Jul 2026
Viewed by 559
Abstract
The large-scale connection of distributed energy resources, electric vehicles, and flexible loads, together with expanding low-voltage monitoring and edge sensing, is turning distribution networks into active cyber-physical systems. Conventional offline simulation cannot fully support the online state tracking, short-term scenario analysis, operational risk [...] Read more.
The large-scale connection of distributed energy resources, electric vehicles, and flexible loads, together with expanding low-voltage monitoring and edge sensing, is turning distribution networks into active cyber-physical systems. Conventional offline simulation cannot fully support the online state tracking, short-term scenario analysis, operational risk assessment, and closed-loop decision support now expected in network operation. Digital twins offer a way to address this gap by linking network models to operational data and revising those models as system conditions change. After systematically searching Scopus and the Web of Science, six application areas for digital twin applications in distribution network simulations are summarized: model construction, simulation and validation platforms; asset, equipment and spatial digitalization; DER (distributed energy resource), PV, EV (electric vehicle) and prosumer integration; operation, monitoring and situational awareness; protection, fault diagnosis and resilience; and optimization, control and planning. The review examines the architectures, enabling technologies, and applications reported across this evidence base. The literature indicates a gradual shift from conceptual digital representations toward real-time simulation, hardware-in-the-loop validation, data-driven model updating, and distribution-side decision support. Persistent gaps concern low-voltage observability, data governance, model credibility assessment, standardized interfaces, cybersecurity, and closed-loop control. Full article
(This article belongs to the Special Issue Development and Implementation of Digital Twins for Industrial Processes)
►▼ Show Figures

Figure 1

14 pages, 461 KB  
Article
A Novel Grey Prediction Framework for Integrating Fault Detection and Correction in Software Reliability Estimation
by Xiaomei Liu, Haoyu Yu, Fanghong Jian and Xiaozhong Tang
Processes 2026, 14(13), 2197; https://doi.org/10.3390/pr14132197 - 6 Jul 2026
Viewed by 285
Abstract
During software testing phases, fault detection and correction processes are carried out simultaneously. However, the fault correction process is not considered in the grey software reliability growth model (SRGM). To solve this problem, this paper proposed a new grey SRGM framework named the [...] Read more.
During software testing phases, fault detection and correction processes are carried out simultaneously. However, the fault correction process is not considered in the grey software reliability growth model (SRGM). To solve this problem, this paper proposed a new grey SRGM framework named the grey SRGM integrating correction process (ICP) that considers both fault detection and correction processes. In the new model framework, the corrected faults are directly incorporated into the grey SRGM, and a one-step iterative calculation method is used to estimate the model. Numerical experiments involving four specific models tested on two real datasets validate the effectiveness of grey ICP-SRGM compared to the original grey model framework. Furthermore, the predictive performance of the new model framework is compared with various other prediction methods, including the Brown exponential smoothing model, Holt exponential smoothing model, autoregressive integrated moving average model, support vector regression model, and feedforward neural network model. Comparative analysis demonstrates that the new model framework exhibits superior adaptability when dealing with small samples and highlights its potential for practical applications where data availability is limited. Full article
(This article belongs to the Section AI-Enabled Process Engineering)
►▼ Show Figures

Figure 1

20 pages, 2426 KB  
Article
Transmission Line Fault Diagnosis Based on Time–Frequency-Domain Recurrence Plots and CNN-BiGRU-Attention
by Fei Long, Long Hong and Zhenman Gao
Processes 2026, 14(13), 2196; https://doi.org/10.3390/pr14132196 - 6 Jul 2026
Viewed by 312
Abstract
Rapid and accurate identification of various faults occurring in transmission lines is essential for restoring normal line operation. However, existing transmission line fault diagnosis methods still face challenges in terms of noise immunity and diagnostic accuracy. To address these issues, this paper proposes [...] Read more.
Rapid and accurate identification of various faults occurring in transmission lines is essential for restoring normal line operation. However, existing transmission line fault diagnosis methods still face challenges in terms of noise immunity and diagnostic accuracy. To address these issues, this paper proposes a deep learning method based on recurrence plots and a convolutional neural network–bidirectional gated recurrent unit–attention mechanism model. The voltage and current signals of transmission lines are transformed into recurrence plots in both the time and frequency domains. Parallel convolutional neural networks are then employed to extract local features from the two domains, while bidirectional gated recurrent units are used to capture temporal dependencies. Furthermore, multi-head self-attention and cross-attention mechanisms are introduced to enhance key features within each domain and achieve adaptive fusion of inter-domain feature information. A transmission line model is established in Simulink to collect data under various fault conditions and influencing factors, thereby verifying the effectiveness and adaptability of the proposed method. Experimental results show that the proposed method achieves fault recognition accuracies of 99.63%, 96.68%, and 75.38% under NL1, NL2, and NL3 Gaussian-noise conditions, respectively, and maintains accuracies of 99.02%, 95.93%, and 72.43% under mixed-noise conditions. Compared with other deep learning models, the proposed method demonstrates higher diagnostic accuracy and stronger robustness. Full article
(This article belongs to the Section Process Control, Modeling and Optimization)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-197
Previous Issue
Next Issue
Back to TopTop