Next Issue
Volume 13, August
Previous Issue
Volume 13, June
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
2.8
Journals
Processes
Volume 13
Issue 7
share announcement

Processes, Volume 13, Issue 7 (July 2025) – 374 articles

Cover Story (view full-size image): The rapid spike in municipal solid waste (MSW) arising from urbanization and industrialization poses significant environmental and socioeconomic concerns. Conventional disposal methods, such as landfilling and incineration, contribute to greenhouse gas emissions and environmental pollution in general. Gasification offers a cleaner, more efficient alternative by converting MSW into syngas for energy and chemical production while minimizing emissions. Aligned with global circular economy principles and sustainability goals, gasification stands out for its lower environmental impact, high energy recovery, and versatility in handling various waste types, making it a promising solution for sustainable waste management. 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, 7040 KiB  
Review
Fluid–Structure Interactions in Pump-Turbines: A Comprehensive Review
by Linmin Shang, Jianfeng Zhu, Xingxing Huang, Shenjie Gao, Zhengwei Wang and Jian Liu
Processes 2025, 13(7), 2321; https://doi.org/10.3390/pr13072321 - 21 Jul 2025
Viewed by 679
Abstract
With the global transition towards renewable energy, pumped storage has become a pivotal technology for large-scale energy storage, playing an essential role in peak load regulation, frequency control, and ensuring the stability of modern power systems. As the core equipment of pumped storage [...] Read more.
With the global transition towards renewable energy, pumped storage has become a pivotal technology for large-scale energy storage, playing an essential role in peak load regulation, frequency control, and ensuring the stability of modern power systems. As the core equipment of pumped storage power stations, pump-turbines operate under complex and frequently changing conditions. These units are required to switch repeatedly between pumping, generating, and transitional modes, giving rise to significant fluid–structure interactions (FSIs). Such interactions have a profound impact on the operational performance and stability of the units. This review provides a comprehensive summary of current research on FSIs in pump-turbines, encompassing both experimental investigations and numerical simulations. Key topics discussed include internal flow dynamics, vibration and acoustic characteristics, and structural responses such as runner deformation and stress distribution. Various numerical coupling strategies for FSI modeling are also examined in detail. Despite progress in this field, several challenges remain, including the complexity of multidisciplinary coupling, the difficulty in developing and solving accurate models, and limitations in predictive capabilities. This review highlights the critical requirements for advancing FSI research in pump-turbines and identifies gaps in the current literature that warrant further investigation. Full article
(This article belongs to the Section Process Control and Monitoring)
►▼ Show Figures

Graphical abstract

23 pages, 6480 KiB  
Article
Mechanism Analysis and Evaluation of Formation Physical Property Damage in CO2 Flooding in Tight Sandstone Reservoirs of Ordos Basin, China
by Qinghua Shang, Yuxia Wang, Dengfeng Wei and Longlong Chen
Processes 2025, 13(7), 2320; https://doi.org/10.3390/pr13072320 - 21 Jul 2025
Viewed by 494
Abstract
Capturing CO2 emitted by coal chemical enterprises and injecting it into oil reservoirs not only effectively improves the recovery rate and development efficiency of tight oil reservoirs in the Ordos Basin but also addresses the carbon emission problem constraining the development of [...] Read more.
Capturing CO2 emitted by coal chemical enterprises and injecting it into oil reservoirs not only effectively improves the recovery rate and development efficiency of tight oil reservoirs in the Ordos Basin but also addresses the carbon emission problem constraining the development of the region. Since initiating field experiments in 2012, the Ordos Basin has become a significant base for CCUS (Carbon capture, Utilization, and Storage) technology application and demonstration in China. However, over the years, projects have primarily focused on enhancing the recovery rate of CO2 flooding, while issues such as potential reservoir damage and its extent have received insufficient attention. This oversight hinder the long-term development and promotion of CO2 flooding technology in the region. Experimental results were comprehensively analyzed using techniques including nuclear magnetic resonance (NMR), X-ray diffraction (XRD), scanning electron microscopy (SEM), inductively coupled plasma (ICP), and ion chromography (IG). The findings indicate that under current reservoir temperature and pressure conditions, significant asphaltene deposition and calcium carbonate precipitation do not occur during CO2 flooding. The reservoir’s characteristics-high feldspar content, low carbon mineral content, and low clay mineral content determine that the primary mechanism affecting physical properties under CO2 flooding in the Chang 4 + 5 tight sandstone reservoir is not, as traditional understand, carbon mineral dissolution or primary clay mineral expansion and migration. Instead, feldspar corrosion and secondary particles migration are the fundamental reasons for the changes in reservoir properties. As permeability increases, micro pore blockage decreases, and the damaging effect of CO2 flooding on reservoir permeability diminishes. Permeability and micro pore structure are therefore significant factors determining the damage degree of CO2 flooding inflicts on tight reservoirs. In addition, temperature and pressure have a significant impact on the extent of reservoir damage caused by CO2 flooding in the study region. At a given reservoir temperature, increasing CO2 injection pressure can mitigate reservoir damage. It is recommended to avoid conducting CO2 flooding projects in reservoirs with severe pressure attenuation, low permeability, and narrow pore throats as much as possible to prevent serious damage to the reservoir. At the same time, the production pressure difference should be reasonably controlled during the production process to reduce the risk and degree of calcium carbonate precipitation near oil production wells. Full article
(This article belongs to the Section Energy Systems)
►▼ Show Figures

Figure 1

16 pages, 1006 KiB  
Article
Spray Drying of Jackfruit (Artocarpus heterophyllus Lam.) Seeds Protein Concentrate: Physicochemical, Structural, and Thermal Characterization
by Dulce María de Jesús Miss-Zacarías, Montserrat Calderón-Santoyo, Victor Manuel Zamora-Gasga, Gabriel Ascanio and Juan Arturo Ragazzo-Sánchez
Processes 2025, 13(7), 2319; https://doi.org/10.3390/pr13072319 - 21 Jul 2025
Viewed by 465
Abstract
Jackfruit seeds (Artocarpus heterophyllus Lam.) are a viable option for supporting a sustainable protein supply. The objective was to obtain protein powder from jackfruit seeds protein concentrate (JSPC) by spray drying. A central composite design was used; the independent variables were inlet [...] Read more.
Jackfruit seeds (Artocarpus heterophyllus Lam.) are a viable option for supporting a sustainable protein supply. The objective was to obtain protein powder from jackfruit seeds protein concentrate (JSPC) by spray drying. A central composite design was used; the independent variables were inlet temperature (110, 115, and 120 °C) and the solids of the JSPC solution (5, 7.5, and 10%). With the desirability function, the optimal drying parameters to maximize the process yield and achieve a low moisture content were 7.5% solids in the JSPC solution and an inlet temperature of 115 °C, resulting in a process yield of 71.51 ± 1.21%. Moisture (5.33 ± 0.11%), water activity (0.15 ± 0.02), bulk density (0.40 ± 0.01 g/mL), and color (L*: 70.56 ± 0.38, a*: 7.80 ± 0.11 and b*: 15.18 ± 0.15) were measured; these parameters are within the allowed ranges for stable food powders. Hydrosolubility (82.46 ± 1.68%), foaming capacity (48.33 ± 1.66%), and emulsifying activity (105.74 ± 10.20 m2/g) were evaluated. Glass transition temperature (129.49 °C) of the JSPC powder enables the establishment of optimal storage and processing conditions for the protein. JSPC powder could be applied to the elaboration of food products with nutritional and functional value. Full article
(This article belongs to the Section Food Process Engineering)
►▼ Show Figures

Graphical abstract

10 pages, 4132 KiB  
Article
Numerical Simulation on Carbon Dioxide Geological Storage and Coalbed Methane Drainage Displacement—A Case Study in Middle Hunan Depression of China
by Lihong He, Keying Wang, Fengchu Liao, Jianjun Cui, Mingjun Zou, Ningbo Cai, Zhiwei Liu, Jiang Du, Shuhua Gong and Jianglun Bai
Processes 2025, 13(7), 2318; https://doi.org/10.3390/pr13072318 - 21 Jul 2025
Viewed by 323
Abstract
Based on a detailed investigation of the geological setting of coalbed methane by previous work in the Xiangzhong Depression, Hunan Province, numerical simulation methods were used to simulate the geological storage of carbon dioxide and displacement gas production in this area. In this [...] Read more.
Based on a detailed investigation of the geological setting of coalbed methane by previous work in the Xiangzhong Depression, Hunan Province, numerical simulation methods were used to simulate the geological storage of carbon dioxide and displacement gas production in this area. In this simulation, a 400 m × 400 m square well group was constructed for coalbed methane production, and a carbon dioxide injection well was arranged in the center of the well group. Injection storage and displacement gas production simulations were carried out under the conditions of original permeability and 1 mD permeability. At the initial permeability (0.01 mD), carbon dioxide is difficult to inject, and the production of displaced and non-displaced coalbed methane is low. During the 25-year injection process, the reservoir pressure only increased by 7 MPa, and it is difficult to reach the formation fracture pressure. When the permeability reaches 1 mD, the carbon dioxide injection displacement rate can reach 4000 m3/d; the cumulative production of displaced and non-displaced coalbed methane is 7.83 × 106 m3 and 9.56 × 105 m3, respectively, and the average daily production is 1430 m3/d and 175 m3/d. The displacement effect is significantly improved compared to the original permeability. In the later storage stage, the carbon dioxide injection rate can reach 8000 m3/d, reaching the formation rupture pressure after 3 years, and the cumulative carbon dioxide injection volume is 1.17 × 107 m3. This research indicates that permeability has a great impact on carbon dioxide geological storage. During the carbon dioxide injection process, selecting areas with high permeability and choosing appropriate reservoir transformation measures to enhance permeability are key factors in increasing the amount of carbon dioxide injected into the area. Full article
(This article belongs to the Special Issue Comprehensive Evaluation and Utilization of Coal Measure Mineral Resources)
►▼ Show Figures

Figure 1

14 pages, 2616 KiB  
Article
Evaluation Model of Water Production in Tight Gas Reservoirs Considering Bound Water Saturation
by Wenwen Wang, Bin Zhang, Yunan Liang, Sinan Fang, Zhansong Zhang, Guilan Lin and Yue Yang
Processes 2025, 13(7), 2317; https://doi.org/10.3390/pr13072317 - 21 Jul 2025
Viewed by 296
Abstract
Tight gas is an unconventional resource abundantly found in low-porosity, low-permeability sandstone reservoirs. Production can be significantly reduced due to water production during the development process. Therefore, it is necessary to predict water production during the logging phase to formulate development strategies for [...] Read more.
Tight gas is an unconventional resource abundantly found in low-porosity, low-permeability sandstone reservoirs. Production can be significantly reduced due to water production during the development process. Therefore, it is necessary to predict water production during the logging phase to formulate development strategies for tight gas wells. This study analyzes the water production mechanism in tight sandstone reservoirs and identifies that the core of water production evaluation in the Shihezi Formation of the Linxing block is to clarify the pore permeability structure of tight sandstone and the type of intra-layer water. The primary challenge lies in the accurate characterization of bound water saturation. By integrating logging data with core experiments, a bound water saturation evaluation model based on grain size diameter and pore structure index was established, achieving a calculation accuracy of 92% for the multi-parameter-fitted bound water saturation. Then, based on the high-precision bound water saturation, a gas–water ratio prediction model for the first month of production, considering water saturation, grain size diameter, and fluid type, was established, improving the prediction accuracy to 87.7%. The bound water saturation evaluation and water production evaluation models in this study can achieve effective water production prediction in the early stage of production, providing theoretical support for the scientific development of tight gas in the Linxing block. Full article
(This article belongs to the Topic Exploitation and Underground Storage of Oil and Gas)
►▼ Show Figures

Figure 1

14 pages, 2161 KiB  
Article
Inferential Online Measurement of 3D Fractal Dimension of Spray Fluidized Bed Agglomerates
by Jialin Men, Aisel Ajalova, Evangelos Tsotsas and Andreas Bück
Processes 2025, 13(7), 2316; https://doi.org/10.3390/pr13072316 - 21 Jul 2025
Viewed by 311
Abstract
In this work, a model-based approach to inferentially obtaining information about the 3D fractal dimension of agglomerates produced in spray fluidized beds is presented. The method utilizes high-detail but scarce offline information from X-ray microcomputed tomography for establishing and training an inferential relationship [...] Read more.
In this work, a model-based approach to inferentially obtaining information about the 3D fractal dimension of agglomerates produced in spray fluidized beds is presented. The method utilizes high-detail but scarce offline information from X-ray microcomputed tomography for establishing and training an inferential relationship with online information that is easy and fast to obtain. The online measurement information is the geometric roundness of the single agglomerate. To investigate the interpolation capability of the inferential approach, three different strategies are evaluated: correlation with individual process conditions; correlation with parameters adjusted to process parameters; and correlation with respect to a range of process conditions. It is shown that the approach incorporating process conditions provides sufficient accuracy over a wide range of conditions. The inferential evaluation of single agglomerate 3D fractal dimension is achieved in 5 ms on average. This enables the measurement of the distribution of 3D fractal dimension in an online setting for product quality monitoring and control. Several examples illustrate the capabilities of the approach, as well as current limitations. Full article
(This article belongs to the Section Particle Processes)
►▼ Show Figures

Figure 1

7 pages, 203 KiB  
Editorial
Innovations in Manufacturing Processes and Systems for Sustainable Practices
by Raul D. S. G. Campilho and Flávia B. Barbosa
Processes 2025, 13(7), 2315; https://doi.org/10.3390/pr13072315 - 21 Jul 2025
Viewed by 431
Abstract
In recent years, manufacturing has undergone significant transformation triggered by pressing societal demand for productivity and sustainability, made possible by accelerating technological innovation [...] Full article
(This article belongs to the Special Issue Innovations in Manufacturing Processes and Systems for Sustainable Practices)
28 pages, 5208 KiB  
Article
ORC System Temperature and Evaporation Pressure Control Based on DDPG-MGPC
by Jing Li, Zexu Gao, Xi Zhou and Junyuan Zhang
Processes 2025, 13(7), 2314; https://doi.org/10.3390/pr13072314 - 21 Jul 2025
Viewed by 353
Abstract
The organic Rankine cycle (ORC) is a key technology for the recovery of low-grade waste heat, but its efficient and stable operation is challenged by complex kinetic coupling. This paper proposes a model partitioning strategy based on gap measurement to construct a high-fidelity [...] Read more.
The organic Rankine cycle (ORC) is a key technology for the recovery of low-grade waste heat, but its efficient and stable operation is challenged by complex kinetic coupling. This paper proposes a model partitioning strategy based on gap measurement to construct a high-fidelity ORC system model and combines the setting of observer decoupling and multi-model switching strategies to reduce the coupling impact and enhance adaptability. For control optimization, the reinforcement learning method of deep deterministic Policy Gradient (DDPG) is adopted to break through the limitations of the traditional discrete action space and achieve precise optimization in the continuous space. The proposed DDPG-MGPC (Hybrid Model Predictive Control) framework significantly enhances robustness and adaptability through the synergy of reinforcement learning and model prediction. Simulation shows that, compared with the existing hybrid reinforcement learning and MPC methods, DDPG-MGPC has better tracking performance and anti-interference ability under dynamic working conditions, providing a more efficient solution for the practical application of ORC. Full article
(This article belongs to the Section Energy Systems)
►▼ Show Figures

Figure 1

17 pages, 3415 KiB  
Article
A Hybrid Multi-Step Forecasting Approach for Methane Steam Reforming Process Using a Trans-GRU Network
by Qinwei Zhang, Xianyao Han, Jingwen Zhang and Pan Qin
Processes 2025, 13(7), 2313; https://doi.org/10.3390/pr13072313 - 21 Jul 2025
Viewed by 353
Abstract
During the steam reforming of methane (SRM) process, elevated CH4 levels after the reaction often signify inadequate heat supply or incomplete reactions within the reformer, jeopardizing process stability. In this paper, a novel multi-step forecasting method using a Trans-GRU network was proposed [...] Read more.
During the steam reforming of methane (SRM) process, elevated CH4 levels after the reaction often signify inadequate heat supply or incomplete reactions within the reformer, jeopardizing process stability. In this paper, a novel multi-step forecasting method using a Trans-GRU network was proposed for predicting the methane content outlet of the SRM reformer. First, a novel feature selection based on the maximal information coefficient (MIC) was applied to identify critical input variables and determine their optimal input order. Additionally, the Trans-GRU network enables the simultaneous capture of multivariate correlations and the learning of global sequence representations. The experimental results based on time-series data from a real SRM process demonstrate that the proposed approach significantly improves the accuracy of multi-step methane content prediction. Compared to benchmark models, including the TCN, Transformer, GRU, and CNN-LSTM, the Trans-GRU consistently achieves the lowest root mean squared error (RMSE) and mean absolute error (MAE) values across all prediction steps (1–6). Specifically, at the one-step horizon, it yields an RMSE of 0.0120 and an MAE of 0.0094. This high performance remains robust across the 2–6-step predictions. The improved predictive capability supports the stable operation and predictive optimization strategies of the steam reforming process in hydrogen production. Full article
(This article belongs to the Section Chemical Processes and Systems)
►▼ Show Figures

Figure 1

24 pages, 1722 KiB  
Article
Design and Construction of an Aerated Accumulation Bioreactor for Solid Waste Treatment
by Margarita Ramírez-Carmona, Leidy Rendón-Castrillón, Carlos Ocampo-López and Valentina Álvarez-Flórez
Processes 2025, 13(7), 2312; https://doi.org/10.3390/pr13072312 - 21 Jul 2025
Viewed by 485
Abstract
Aerated accumulation bioreactors represent a promising alternative for the aerobic bioremediation of solid contaminated substrates. However, achieving homogeneous mixing and effective air distribution remains a key design challenge in solid-phase systems. This study presents the design and construction of a novel pilot-scale aerated [...] Read more.
Aerated accumulation bioreactors represent a promising alternative for the aerobic bioremediation of solid contaminated substrates. However, achieving homogeneous mixing and effective air distribution remains a key design challenge in solid-phase systems. This study presents the design and construction of a novel pilot-scale aerated bioreactor equipped with an angled-paddle agitation system, specifically developed to improve solid mixing and aeration. To evaluate the geometric configuration, a series of simulations were performed using the Discrete Element Method (DEM), with particle dynamics analyzed through the Lacey Mixing Index (LMI). Four paddle angles (0°, 15°, 45°, and 55°) were compared, with the 45° configuration achieving optimal performance, reaching LMI values above 0.95 in less than 15 s and maintaining high homogeneity at a filling volume of 70%. These results confirm that the paddle angle significantly influences mixing efficiency in granular media. While this work focuses on engineering design and DEM-based validation, future studies will include experimental trials to evaluate biodegradation kinetics. The proposed design offers a scalable and adaptable solution for ex situ bioremediation applications. This work reinforces the value of integrating DEM simulations early in the bioreactor development process and opens pathways for further optimization and implementation in real-world environmental remediation scenarios. Full article
(This article belongs to the Special Issue Bioreactor Design and Optimization Process)
►▼ Show Figures

Figure 1

16 pages, 3339 KiB  
Article
Impact of Spectral Irradiance Control on Bioactive Compounds and Color Preservation in Solar-Dried Papaya
by Diana Paola García-Moreira, Erick César López-Vidaña, Ivan Moreno and Lucía Delgadillo-Ruiz
Processes 2025, 13(7), 2311; https://doi.org/10.3390/pr13072311 - 20 Jul 2025
Viewed by 1442
Abstract
The quality effects of spectral irradiance conditions during papaya (Carica papaya L.) drying were investigated using three different dryers: a solar dryer with dynamic irradiance control (SDIC), a cylindrical solar dryer (CSD), and a solar simulator dryer (SSD). This study builds upon [...] Read more.
The quality effects of spectral irradiance conditions during papaya (Carica papaya L.) drying were investigated using three different dryers: a solar dryer with dynamic irradiance control (SDIC), a cylindrical solar dryer (CSD), and a solar simulator dryer (SSD). This study builds upon previous PDLC film applications in solar drying by specifically examining its impact on phytochemical preservation and color degradation, addressing gaps in spectral-specific effects on food quality parameters. The drying conditions were as follows: a temperature of 50 °C for each method, 700 w/m2 for both SDIC and solar simulator dryers (SSD), and full solar irradiance for the cylindrical solar dryer (CSD). The cylindrical solar dryer exhibited 210 min of drying time due to higher solar irradiance than SDIC (300 min), while SSD lasted 180 min. Drying rates were highest for CSD (0.056 g H2O/g d.m. min−1), followed by SDIC (0.027 g H2O/g d.m. min−1). Color analysis revealed that CSD resulted in the most significant color degradation, followed by SSD and SDIC. This was attributed to the varying spectral composition of radiation in each method. The CSD, with a full solar spectrum, including higher UV and visible radiation, induced more pronounced color changes than SDIC, which received lower intensity radiation in these ranges. Chemical analyses showed that SSD samples had the highest antioxidant activity (1432.91 µmol TE/g dw by ABTS) and phenolic content (58.92 mg GAE/100 g), suggesting simulated conditions may better preserve certain phytochemicals. SDIC maintained better carotenoid-related color parameters while showing intermediate antioxidant levels (1084.09 µmol TE/g dw). These results demonstrate that irradiance control significantly impacts drying efficiency and quality parameters. Full article
(This article belongs to the Special Issue Processes in Agri-Food Technology)
►▼ Show Figures

Figure 1

22 pages, 848 KiB  
Article
Modeling Prediction of Physical Properties in Sustainable Biodiesel–Diesel–Alcohol Blends via Experimental Methods and Machine Learning
by Kaan Yeşilova, Özgün Yücel and Başak Temur Ergan
Processes 2025, 13(7), 2310; https://doi.org/10.3390/pr13072310 - 20 Jul 2025
Viewed by 525
Abstract
This study investigated the production of biodiesel from canola oil, the formulation of sustainable ternary fuel blends with diesel and alcohol (ethanol or propanol), and the experimental and machine learning-based modeling of their physical properties, including density and viscosity over a temperature range [...] Read more.
This study investigated the production of biodiesel from canola oil, the formulation of sustainable ternary fuel blends with diesel and alcohol (ethanol or propanol), and the experimental and machine learning-based modeling of their physical properties, including density and viscosity over a temperature range of 10 °C to 40 °C. Biodiesel was synthesized via alkali-catalyzed transesterification (6:1 methanol-to-oil molar ratio, 0.5 wt % NaOH of oil) and blended with diesel and alcohols (ethanol and propanol) in varying volume ratios. The experimental results revealed that blend density decreased from 0.8622 g/cm3 at 10 °C to 0.8522 g/cm3 at 40 °C for a blend containing ethanol. Similarly, the viscosity showed a significant reduction with temperature, e.g., the blend exhibited a viscosity decline from 8.5 mPa·s at 10 °C to 7.2 mPa·s at 40 °C. Increasing the alcohol or diesel content further reduced density and viscosity due to the lower intrinsic properties of these components. The machine learning models, Gaussian process regression (GPR), support vector regression (SVR), artificial neural networks (ANN), and decision tree regression (DTR), were applied to predict the properties of these blends. GPR demonstrated the best predictive performance for both density and viscosity. These findings confirm the strong potential of GPR for the accurate and reliable prediction of fuel blend properties, supporting the formulation of alternative fuels optimized for diesel engine performance. These aspects contribute new insights into modelling strategies for sustainable fuel formulations. Full article
(This article belongs to the Section AI-Enabled Process Engineering)
►▼ Show Figures

Figure 1

27 pages, 4366 KiB  
Article
Fuzzy Logic-Based Optimization for Pseudocereal Processing: A Case Study on Buckwheat
by Mariana-Liliana Păcală, Anca Șipoș, Otto Ketney and Alexandrina Sîrbu
Processes 2025, 13(7), 2309; https://doi.org/10.3390/pr13072309 - 20 Jul 2025
Viewed by 546
Abstract
In response to the increasing consumer interest in the health benefits of plant-based foods, in this study, fuzzy logic modeling (FLM) was used to optimize the lactic fermentation process of several buckwheat (Fagopyrum esculentum)-based substrates (B-bSs), which were bio-prospected [...] Read more.
In response to the increasing consumer interest in the health benefits of plant-based foods, in this study, fuzzy logic modeling (FLM) was used to optimize the lactic fermentation process of several buckwheat (Fagopyrum esculentum)-based substrates (B-bSs), which were bio-prospected for the development of pseudocereal-based fermented foodstuffs. The experimental methodology involved obtaining B-bSs, either green or roasted, under various milling conditions and subjecting them to two different types of thermal treatment. This experimental design allowed us to obtain a set of experimental data, based on which a fuzzy system was developed and calibrated. The main physicochemical characteristics (pH, total titratable acidity, dynamic viscosity, and color) and sensory attributes (appearance, color, aroma, taste, texture or mouthfeel, and overall acceptability) of B-bSs were evaluated. The fuzzy logic approach proved useful for monitoring the evolution of lactic fermentation and for the rapid and accurate identification of situations that require technological interventions, acting as a reliable tool for the ongoing optimization of fermentation processes. Our study’s results showed that the optimal technological variants identified using FLM corresponded to green buckwheat milled with a 0.12 mm gap disk and a hammer mill and subjected to ultrasonic water bath treatment. The hedonic descriptive sensory evaluation also validated this conclusion. Full article
(This article belongs to the Special Issue Processing Foods: Process Optimization and Quality Assessment, 3rd Edition)
►▼ Show Figures

Figure 1

23 pages, 7547 KiB  
Article
Internal Flow Characteristics in a Prototype Spray Tower Based on CFD
by Xin Li, Hui-Fan Huang, Xiao-Wei Xu and Yu-Liang Zhang
Processes 2025, 13(7), 2308; https://doi.org/10.3390/pr13072308 - 20 Jul 2025
Viewed by 380
Abstract
To investigate the mechanisms by which inlet water velocity and rotational speed affect spray tower performance, computational fluid dynamics (CFD) was employed to analyze key performance indicators, including outlet flow velocity, flow rate, and the ratio of internal to external outlet flow rates. [...] Read more.
To investigate the mechanisms by which inlet water velocity and rotational speed affect spray tower performance, computational fluid dynamics (CFD) was employed to analyze key performance indicators, including outlet flow velocity, flow rate, and the ratio of internal to external outlet flow rates. The results show that outlet flow rate is strongly positively correlated with rotational speed, while inlet water velocity demonstrates nonlinear effects on internal flow velocity. Significant parameter interaction exists—the correlation between inlet velocity and outlet velocity varies with rotational speed (R = −0.9831 to 0.5229), and the outlet flow rate ratio shows a strong negative correlation with rotational speed (R = −0.9918). The gray model demonstrated superior robustness with minimal error fluctuations, whereas the partial least squares regression model exhibited significantly increased errors under extreme conditions. This study provides a theoretical foundation and data support for spray tower parameter optimization. Full article
(This article belongs to the Section Automation Control Systems)
►▼ Show Figures

Figure 1

14 pages, 1705 KiB  
Article
Ultrasonic-Assisted Enzymatic Extraction: An Innovative Technique for the Obtention of Betalains and Polyphenols from Dragon Fruit Peel
by Cristhel Guadalupe Puc-Santamaria, Rosa Us-Camas, Emanuel Hernández-Núñez, Luis Alfonso Can-Herrera, Dany Alejandro Dzib-Cauich, Adán Cabal-Prieto, Nattha Pensupa and Julio Enrique Oney-Montalvo
Processes 2025, 13(7), 2307; https://doi.org/10.3390/pr13072307 - 19 Jul 2025
Viewed by 695
Abstract
Dragon fruit peel is a by-product rich in bioactive compounds, such as polyphenols and betalains. In this study, ultrasound-assisted enzyme extraction (UAEE) was proposed to exploit this, combining the advantages of the enzymatic hydrolysis and ultrasound extraction. The effect of extraction time, temperature, [...] Read more.
Dragon fruit peel is a by-product rich in bioactive compounds, such as polyphenols and betalains. In this study, ultrasound-assisted enzyme extraction (UAEE) was proposed to exploit this, combining the advantages of the enzymatic hydrolysis and ultrasound extraction. The effect of extraction time, temperature, and enzyme quantity were evaluated using a Box–Behnken design. Total betalains and polyphenol contents were determined spectrophotometrically. The results show that the extraction of total polyphenols was significantly affected (p ≤ 0.05) by the enzyme quantity, while temperature had a significant effect (p ≤ 0.05) on the extracted betalains. The optimal conditions for the extraction of total betalains and polyphenols were a temperature of 20 °C, an extraction time of 20 min, and an enzyme/substrate ratio of 400 mg/g. Under optimized conditions, the extraction efficiency reached 565.6 ± 12.9 µg/g for total betalains and 14.9 ± 2.4 mg/g for total polyphenols. In addition, UAEE showed the best extraction yields compared to other methodologies, such as microwave, ultrasound, and enzymatic hydrolysis extraction (p ≤ 0.05). This study helps us to understand how the temperature, time, and amount of enzymes affect the extraction of total polyphenols and betalains present in the peel of the dragon fruit using the UAEE technique. Full article
(This article belongs to the Special Issue Applications of Ultrasound and Other Technologies in Food Processing)
►▼ Show Figures

Figure 1

15 pages, 2776 KiB  
Article
A Novel Fluorescent Probe AP for Highly Selective and Sensitive Detection of Hg2+ and Its Application in Environmental Monitoring
by Zhi Yang, Chaojie Lei, Qian Wang, Yonghui He and Senlin Tian
Processes 2025, 13(7), 2306; https://doi.org/10.3390/pr13072306 - 19 Jul 2025
Viewed by 391
Abstract
Mercury is a highly toxic heavy metal that poses serious threats to human health and environmental safety, highlighting the critical importance of accurate Hg2+ detection. In this study, a novel fluorescent probe AP was synthesized by conjugating fluorescein, serving as the luminescent [...] Read more.
Mercury is a highly toxic heavy metal that poses serious threats to human health and environmental safety, highlighting the critical importance of accurate Hg2+ detection. In this study, a novel fluorescent probe AP was synthesized by conjugating fluorescein, serving as the luminescent group, with pyridine-2-carboxaldehyde to enable selective Hg2+ detection. Hg2+ binds to AP in a 1:2 stoichiometric ratio, inducing the opening of the spiro-lactam ring and resulting in a significant fluorescence enhancement. The probe exhibited excellent selectivity and sensitivity toward Hg2+. A strong linear correlation was observed between its fluorescence intensity and Hg2+ concentration (R2 = 0.99952), with a detection limit of as low as 9.75 × 10−8 mol/L. The average recoveries of Hg2+ across various water matrices ranged from 95.23% to 103.40%, with relative standard deviations (RSDs) below 3.07%. These results indicate that the probe performs effectively in real water-sample testing. Full article
(This article belongs to the Section Environmental and Green Processes)
►▼ Show Figures

Figure 1

18 pages, 886 KiB  
Review
Research Status and Prospect of Coal Spontaneous Combustion Source Location Determination Technology
by Yongfei Jin, Yixin Li, Wenyong Liu, Xiaona Yang, Xiaojiao Cheng, Chenyang Qi, Changsheng Li, Jing Hui and Lei Zhang
Processes 2025, 13(7), 2305; https://doi.org/10.3390/pr13072305 - 19 Jul 2025
Viewed by 405
Abstract
The spontaneous combustion disaster of coal not only causes a waste of resources but also affects the safe production of coal mines. In order to accurately detect the range and location of the spontaneous combustion source of coal, this paper studies and summarizes [...] Read more.
The spontaneous combustion disaster of coal not only causes a waste of resources but also affects the safe production of coal mines. In order to accurately detect the range and location of the spontaneous combustion source of coal, this paper studies and summarizes previous research results, and based on the principles and research and development progress of existing detection technologies such as the surface temperature measurement method, ground temperature measurement method, wellbore temperature measurement method, and infrared remote sensing detection method, it briefly reviews the application of various detection technologies in engineering practice at this stage and briefly explains the advantages and disadvantages of each application. Research shows that the existing technologies are generally limited by the interference of complex environmental conditions (such as temperature measurement deviations caused by atmospheric turbulence and the influence of rock layer structure on ground temperature conduction) and the implementation difficulties of geophysical methods in mining applications (such as the interference of stray currents in the ground by electromagnetic methods and the fast attenuation speed of waves detected by geological radar methods), resulting in the insufficient accuracy of fire source location and difficulties in identifying concealed fire sources. In response to the above bottlenecks, the ”air–ground integrated” fire source location determination technology that breaks through environmental constraints and the location determination method of a CSC fire source based on a multi-physics coupling mechanism are proposed. By significantly weakening the deficiency in obtaining parameters through a single detection method, a new direction is provided for the detection of coal spontaneous combustion fire sources in the future. Full article
(This article belongs to the Special Issue Monitoring, Process Control and Prevention Measures for Safety Problems in Coal Mining)
►▼ Show Figures

Figure 1

19 pages, 4674 KiB  
Article
Flow Field Optimization for Enhanced SCR Denitrification: A Numerical Study of the Chizhou Power Plant Retrofit
by Wendong Wang, Zongming Peng, Sanmei Zhao, Bin Li, Haihua Li, Zhongqian Ling, Maosheng Liu and Guangxue Zhang
Processes 2025, 13(7), 2304; https://doi.org/10.3390/pr13072304 - 19 Jul 2025
Viewed by 368
Abstract
Denitrification technology in thermal power plants plays a critical role in reducing nitrogen oxide (NOx) emissions, thereby improving air quality and mitigating climate change. This study conducts a numerical simulation of the SCR (Selective Catalytic Reduction) system at the Chizhou Power Plant to [...] Read more.
Denitrification technology in thermal power plants plays a critical role in reducing nitrogen oxide (NOx) emissions, thereby improving air quality and mitigating climate change. This study conducts a numerical simulation of the SCR (Selective Catalytic Reduction) system at the Chizhou Power Plant to optimize its flow field configuration. The original system exhibited severe flow non-uniformity, with local maximum velocities reaching 40 m/s and a velocity deviation coefficient of 28% at the inlet of the first catalyst layer. After optimizing the deflector design, the maximum local velocity was reduced to 21 m/s, and the velocity deviation coefficient decreased to 14.1%. These improvements significantly enhanced flow uniformity, improved catalyst efficiency, and are expected to extend equipment service life. The findings provide a practical reference for the retrofit and performance enhancement of SCR systems in similar coal-fired power plants. Full article
(This article belongs to the Special Issue Advances in Combustion Processes: Fundamentals and Applications)
►▼ Show Figures

Figure 1

22 pages, 7906 KiB  
Article
Trajectory-Integrated Kriging Prediction of Static Formation Temperature for Ultra-Deep Well Drilling
by Qingchen Wang, Wenjie Jia, Zhengming Xu, Tian Tian and Yuxi Chen
Processes 2025, 13(7), 2303; https://doi.org/10.3390/pr13072303 - 19 Jul 2025
Viewed by 394
Abstract
The accurate prediction of static formation temperature (SFT) is essential for ensuring safety and efficiency in ultra-deep well drilling operations. Excessive downhole temperatures (>150 °C) can degrade drilling fluids, damage temperature-sensitive tools, and pose serious operational risks. Conventional methods for SFT determination—including direct [...] Read more.
The accurate prediction of static formation temperature (SFT) is essential for ensuring safety and efficiency in ultra-deep well drilling operations. Excessive downhole temperatures (>150 °C) can degrade drilling fluids, damage temperature-sensitive tools, and pose serious operational risks. Conventional methods for SFT determination—including direct measurement, temperature recovery inversion, and artificial intelligence models—are often limited by post-drilling data dependency, insufficient spatial resolution, high computational costs, or a lack of adaptability to complex wellbore geometries. In this study, we propose a new pseudo-3D Kriging interpolation framework that explicitly incorporates real wellbore trajectories to improve the spatial accuracy and applicability of pre-drilling SFT predictions. By systematically optimizing key hyperparameters (θ = [10, 10], lob = [0.1, 0.1], upb = [20, 200]) and applying a grid resolution of 100 × 100, the model demonstrates high predictive fidelity. Validation using over 5.1 million temperature data points from 113 wells in the Shunbei Oilfield reveals a relative error consistently below 5% and spatial interpolation deviations within 5 °C. The proposed approach enables high-resolution, trajectory-integrated SFT forecasting before drilling with practical computational requirements, thereby supporting proactive thermal risk mitigation and significantly enhancing operational decision-making on ultra-deep wells. Full article
(This article belongs to the Special Issue Experiments and Numerical Simulations for Heat Transfer and Flow of Supercritical Fluids)
►▼ Show Figures

Figure 1

21 pages, 3359 KiB  
Article
Carbonisation of Quercus spp. Wood: Temperature, Yield and Energy Characteristics
by Juan Carlos Contreras-Trejo, Artemio Carrillo-Parra, Maginot Ngangyo-Heya, José Guadalupe Rutiaga-Quiñones, Jorge Armando Chávez-Simental and José Rodolfo Goche-Télles
Processes 2025, 13(7), 2302; https://doi.org/10.3390/pr13072302 - 19 Jul 2025
Viewed by 492
Abstract
Energy production is a global concern, encouraging the search for sustainable alternatives such as charcoal, a promising solid biofuel. This study evaluated the effects of temperature and carbonisation time on charcoal produced from Quercus wood. Carbonisation was carried out at 550 °C for [...] Read more.
Energy production is a global concern, encouraging the search for sustainable alternatives such as charcoal, a promising solid biofuel. This study evaluated the effects of temperature and carbonisation time on charcoal produced from Quercus wood. Carbonisation was carried out at 550 °C for 30 min, 700 °C for 30 min and under two progressive heating profiles: one starting at 550 °C for 30 min and increasing to 700 °C for a further 30 min, and another starting at 300 °C for 2 h and rising to 1000 °C for 10 min. Mass and volumetric yield, bulk density, proximate analysis, calorific value, energy yield and fuel ratio were determined. The results showed that carbonisation temperature affected charcoal properties. Mass and volumetric yields were highest at 550 °C (30.10% and 4.81 m3 t−1) in Q. convallata and Q. urbanii. At higher temperatures, bulk density (0.56 g cm−3), fixed carbon (91.51%) and calorific value (32.82 MJ kg−1) increased in Q. urbanii. Lower temperatures led to lower moisture levels (2.46%) and a higher energy yield (48.02%). Overall, temperatures above 700 °C improved energy properties, while those below 550 °C favoured higher yields. Species’ characteristics also influenced charcoal quality. These findings offer valuable insights into optimising the carbonisation of Quercus species and supporting the development of more efficient, sustainable charcoal production methods. Full article
(This article belongs to the Special Issue Research on Conversion and Utilization of Waste Biomass)
►▼ Show Figures

Figure 1

31 pages, 1275 KiB  
Article
The Operational Nitrogen Indicator (ONI): An Intelligent Index for the Wastewater Treatment Plant’s Optimization
by Míriam Timiraos, Antonio Díaz-Longueira, Esteban Jove, Óscar Fontenla-Romero and José Luis Calvo-Rolle
Processes 2025, 13(7), 2301; https://doi.org/10.3390/pr13072301 - 19 Jul 2025
Viewed by 514
Abstract
In the context of wastewater treatment plant optimization, this study presents a novel approach based on a virtual sensor architecture designed to estimate total nitrogen levels in effluent and assess plant performance using an operational indicator. The core of the system is an [...] Read more.
In the context of wastewater treatment plant optimization, this study presents a novel approach based on a virtual sensor architecture designed to estimate total nitrogen levels in effluent and assess plant performance using an operational indicator. The core of the system is an intelligent agent that integrates real-time sensor data with machine learning models to infer nitrogen dynamics and anticipate deviations from optimal operating conditions. Central to this strategy is the operational nitrogen indicator (ONI), a weighted aggregation of four sub-indicators: legal compliance (Nactual%), the nitrogen dynamic trend (Tnitr%), removal efficiency (Enitr%), and microbial balance (NP%), each of which captures a critical dimension of the nitrogen removal process. The ONI enables the early detection of stress conditions and facilitates adaptive decision-making by quantifying operational status in terms of regulatory thresholds, biological requirements, and dynamic stability. This approach contributes to a shift toward smart wastewater treatment plants, where virtual sensing, autonomous control, and throttling-aware diagnostics converge to improve process efficiency, reduce operational risk, and promote environmental compliance. Full article
(This article belongs to the Special Issue Novel Recovery Technologies from Wastewater and Waste)
►▼ Show Figures

Figure 1

16 pages, 994 KiB  
Article
Reliability Evaluation of New-Generation Substation Relay Protection Equipment Based on ASFSSA-LSTM-GAN
by Baojiang Tian, Kai Chen, Xingwei Du, Wenyan Duan, Yibo Wang, Jiajia Hu and Hongbo Zou
Processes 2025, 13(7), 2300; https://doi.org/10.3390/pr13072300 - 19 Jul 2025
Viewed by 383
Abstract
In order to improve the reliability evaluation accuracy of a new generation of substation relay protection equipment under small-sample failure rate data, a Generative Adversarial Network (GAN) model based on the Adaptive Spiral Flying Sparrow Search Algorithm (ASFSSA) to optimize the Long Short-Term [...] Read more.
In order to improve the reliability evaluation accuracy of a new generation of substation relay protection equipment under small-sample failure rate data, a Generative Adversarial Network (GAN) model based on the Adaptive Spiral Flying Sparrow Search Algorithm (ASFSSA) to optimize the Long Short-Term Memory (LSTM) network is proposed. Because of the adaptability of LSTM for processing time series, LSTM is embedded into the GAN, and the LSTM optimized by ASFSSA is used as the generator of GAN. The trained model is used to expand the original data samples, and the least squares method is used to estimate the distribution model parameters, to obtain the reliability function of the relay protection equipment, and to predict the operating life of the equipment. The results show that compared with other methods, the correlation coefficient of the expanded data samples is closer to the original data, and the life estimation of the equipment is more accurate. The model can be used as a reference for reliability assessment and acceptance testing of the new generation of substation relay protection equipment. Full article
(This article belongs to the Section Energy Systems)
►▼ Show Figures

Figure 1

16 pages, 2975 KiB  
Article
Control Strategy of Distributed Photovoltaic Storage Charging Pile Under Weak Grid
by Yan Zhang, Shuangting Xu, Yan Lin, Xiaoling Fang, Yang Wang and Jiaqi Duan
Processes 2025, 13(7), 2299; https://doi.org/10.3390/pr13072299 - 19 Jul 2025
Viewed by 343
Abstract
Distributed photovoltaic storage charging piles in remote rural areas can solve the problem of charging difficulties for new energy vehicles in the countryside, but these storage charging piles contain a large number of power electronic devices, and there is a risk of resonance [...] Read more.
Distributed photovoltaic storage charging piles in remote rural areas can solve the problem of charging difficulties for new energy vehicles in the countryside, but these storage charging piles contain a large number of power electronic devices, and there is a risk of resonance in the system under weak grid conditions. Firstly, the topology of a photovoltaic storage charging pile is introduced, including a bidirectional DC/DC converter, unidirectional DC/DC converter, and single-phase grid-connected inverter. Then, the maximum power tracking control strategy based on improved conductance micro-increment is derived for a photovoltaic power generation system, and a constant voltage and constant current charge–discharge control strategy is derived for energy storage equipment. Additionally, a segmented reflective charging control strategy is introduced for charging piles, and the quasi-PR controller is introduced for single-phase grid-connected inverters. In addition, an improved second-order general integrator phase-locked loop (SOGI-PLL) based on feed-forward of the grid current is derived. Finally, a simulation model is built to verify the performance of the solar–storage charging pile and lay the technical groundwork for future integrated control strategies. Full article
(This article belongs to the Section Energy Systems)
►▼ Show Figures

Figure 1

46 pages, 10548 KiB  
Review
A Review of Hybrid LSTM Models in Smart Cities
by Bum-Jun Kim and Il-Woo Nam
Processes 2025, 13(7), 2298; https://doi.org/10.3390/pr13072298 - 18 Jul 2025
Viewed by 927
Abstract
Rapid global urbanization poses complex challenges that demand advanced data-driven forecasting solutions for smart cities. Traditional statistical and standalone Long Short-Term Memory (LSTM) models often struggle to capture non-linear dynamics and long-term dependencies in urban time-series data. This review critically examines hybrid LSTM [...] Read more.
Rapid global urbanization poses complex challenges that demand advanced data-driven forecasting solutions for smart cities. Traditional statistical and standalone Long Short-Term Memory (LSTM) models often struggle to capture non-linear dynamics and long-term dependencies in urban time-series data. This review critically examines hybrid LSTM models that integrate LSTM with complementary algorithms, including CNN, GRU, ARIMA, and SVM. These hybrid architectures aim to enhance prediction accuracy, integrate diverse data sources, and improve computational efficiency. This study systematically reviews principles, trends, and real-world applications, quantitatively evaluating hybrid LSTM models using performance metrics such as mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R2), while identifying key study limitations. The case studies considered include traffic management, environmental monitoring, energy forecasting, public health, infrastructure assessment, and urban waste management. For example, hybrid models have achieved substantial accuracy improvements in traffic congestion forecasting, reducing their mean absolute error by up to 29%. Despite the inherent challenges related to structural complexity, interpretability, and data requirements, ongoing research on attention mechanisms, model compression, and explainable AI has significantly mitigated these limitations. Thus, hybrid LSTM models have emerged as vital analytical tools capable of robust spatiotemporal prediction, effectively supporting sustainable urban development and data-driven decision-making in evolving smart city environments. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition))
►▼ Show Figures

Figure 1

20 pages, 5404 KiB  
Article
Flying Steel Detection in Wire Rod Production Based on Improved You Only Look Once v8
by Yifan Lu, Fei Zhang, Xiaozhan Li, Jian Zhang, Xiong Xiao, Lijun Wang and Xiaofei Xiang
Processes 2025, 13(7), 2297; https://doi.org/10.3390/pr13072297 - 18 Jul 2025
Viewed by 634
Abstract
In the process of high-speed wire rod production, flying steel accidents may occur due to various reasons. Current detection methods relying on sensors like hardware make debugging complex as well as limit real-time and accuracy. These methods are complicated to debug, and the [...] Read more.
In the process of high-speed wire rod production, flying steel accidents may occur due to various reasons. Current detection methods relying on sensors like hardware make debugging complex as well as limit real-time and accuracy. These methods are complicated to debug, and the real-time and accuracy of detection are poor. Therefore, this paper proposes a flying steel detection method based on improved You Only Look Once v8 (YOLOv8), which can realize high-precision flying steel detection based on machine vision through the monitoring video of the production site. Firstly, the Omni-dimensional Dynamic Convolution (ODConv) is added to the backbone network to improve the feature extraction ability of the input image. Then, a lightweight C2f-PCCA_RVB module is proposed to be integrated into the neck network, so as to carry out the lightweight design of the neck network. Finally, the Efficient Multi-Scale Attention (EMA) module is added to the neck network to fuse the context information of different scales and improve the feature extraction ability. The experimental results show that the average accuracy (mAP@0.5) of the flying steel detection method based on the improved YOLOv8 is 99.1%, and the latency is reduced to 2.5 ms, which can realize the real-time accurate detection of the flying steel. Full article
(This article belongs to the Special Issue Advances in Computer Vision and Image Processing for Industrial Processes)
►▼ Show Figures

Figure 1

19 pages, 6228 KiB  
Article
Research on Optimization of Orebody Mining Sequence Under Isolation Layer of Filling Body Based on FLAC3D Software
by Yu Wang and Aibing Jin
Processes 2025, 13(7), 2296; https://doi.org/10.3390/pr13072296 - 18 Jul 2025
Viewed by 316
Abstract
This study investigates the stability risks associated with a substandard-thickness (42 m) backfill isolation layer in the open-underground coordinated mining system of the Yongping Copper Mine’s eastern panel at the −150 m level. A numerical simulation based on FLAC3D 3.00 was conducted to [...] Read more.
This study investigates the stability risks associated with a substandard-thickness (42 m) backfill isolation layer in the open-underground coordinated mining system of the Yongping Copper Mine’s eastern panel at the −150 m level. A numerical simulation based on FLAC3D 3.00 was conducted to evaluate the impacts of four mining sequences (south-to-north, north-to-south, center-to-flank, and flank-to-center) on stress redistribution and displacement evolution. A three-dimensional geomechanical model incorporating lithological parameters was established, with 23 monitoring points tracking stress and displacement dynamics. Results indicate that the mining sequence significantly influences the stability of both the isolation layer and the slope. No abrupt displacement occurred during mining, with incremental isolation layer settlement controlled within 3 mm. Post-mining maximum displacement increased to 10–12 mm. The “north-to-south” sequence emerged as the theoretically optimal solution, reducing cumulative displacements in pillars and stopes by 9.1% and 7.8%, respectively, compared to the suboptimal scheme. However, considering the engineering continuity of the existing “south-to-north” sequence at the −100 m level, maintaining consistent directional mining at the −150 m level is recommended to ensure synergistic disturbance control, ventilation system stability, and operational management coherence. Full article
(This article belongs to the Section Energy Systems)
►▼ Show Figures

Figure 1

23 pages, 2903 KiB  
Article
Casson Fluid Saturated Non-Darcy Mixed Bio-Convective Flow over Inclined Surface with Heat Generation and Convective Effects
by Nayema Islam Nima, Mohammed Abdul Hannan, Jahangir Alam and Rifat Ara Rouf
Processes 2025, 13(7), 2295; https://doi.org/10.3390/pr13072295 - 18 Jul 2025
Viewed by 436
Abstract
This paper explores the complex dynamics of mixed convective flow in a Casson fluid saturated in a non-Darcy porous medium, focusing on the influence of gyrotactic microorganisms, internal heat generation, and multiple convective mechanisms. Casson fluids, known for their non-Newtonian behavior, are relevant [...] Read more.
This paper explores the complex dynamics of mixed convective flow in a Casson fluid saturated in a non-Darcy porous medium, focusing on the influence of gyrotactic microorganisms, internal heat generation, and multiple convective mechanisms. Casson fluids, known for their non-Newtonian behavior, are relevant in various industrial and biological contexts where traditional fluid models are insufficient. This study addresses the limitations of the standard Darcy’s law by examining non-Darcy flow, which accounts for nonlinear inertial effects in porous media. The governing equations, derived from conservation laws, are transformed into a system of no linear ordinary differential equations (ODEs) using similarity transformations. These ODEs are solved numerically using a finite differencing method that incorporates central differencing, tridiagonal matrix manipulation, and iterative procedures to ensure accuracy across various convective regimes. The reliability of this method is confirmed through validation with the MATLAB (R2024b) bvp4c scheme. The investigation analyzes the impact of key parameters (such as the Casson fluid parameter, Darcy number, Biot numbers, and heat generation) on velocity, temperature, and microorganism concentration profiles. This study reveals that the Casson fluid parameter significantly improves the velocity, concentration, and motile microorganism profiles while decreasing the temperature profile. Additionally, the Biot number is shown to considerably increase the concentration and dispersion of motile microorganisms, as well as the heat transfer rate. The findings provide valuable insights into non-Newtonian fluid behavior in porous environments, with applications in bioengineering, environmental remediation, and energy systems, such as bioreactor design and geothermal energy extraction. Full article
(This article belongs to the Collection Modeling, Simulation and Computation on Dynamics of Complex Fluids)
►▼ Show Figures

Figure 1

19 pages, 7491 KiB  
Article
A Model and the Characteristics of Gas Generation of the Longmaxi Shale in the Sichuan Basin
by Xuewen Shi, Yi Li, Yuqiang Jiang, Ye Zhang, Wei Wu, Zhiping Zhang, Zhanlei Wang, Xingping Yin, Yonghong Fu and Yifan Gu
Processes 2025, 13(7), 2294; https://doi.org/10.3390/pr13072294 - 18 Jul 2025
Viewed by 319
Abstract
Currently, the Longmaxi shale in the Sichuan Basin is the most successful stratum of shale gas production in China. However, because Longmaxi shale mostly has high over-maturity, a low-maturity sample cannot be obtained for gas generation thermal simulations, and as a result, a [...] Read more.
Currently, the Longmaxi shale in the Sichuan Basin is the most successful stratum of shale gas production in China. However, because Longmaxi shale mostly has high over-maturity, a low-maturity sample cannot be obtained for gas generation thermal simulations, and as a result, a gas generation model has not yet been established for it. Therefore, models of other shales are usually used to calculate the amount of gas generated from Longmaxi shale, but they may produce inaccurate results. In this study, a Longmaxi shale sample with an equivalent vitrinite reflectance calculated from Raman spectroscopy (EqVRo) of 1.26% was obtained from Well Yucan 1 in the Chengkou area, northeast Sichuan Province. This Longmaxi shale may have the lowest maturity in nature. Pyrolysis simulations based on gold tubes were performed on this sample, and the gas generation line was obtained. The amount of gas generated during the low-maturity stage was compensated by referring to gas generation data obtained from Lower Silurian black shale in western Lithuania. Thus, a gas generation model of the Longmaxi shale was built. The model showed that the gas generation process of Longmaxi shale could be divided into three stages: (1) First, there is the quick generation stage (EqVRo 0.5–3.0%), where hydrocarbon gases were generated quickly and constantly, and the generation rate was steady. A maximum of 458 mL/g TOC was reached at a maturity of 3.0% EqVRo. (2) Second, there is the stable stage (EqVRo 3.0–3.25%), where the amount of generated gas reached a plateau of 453–458 mL/g TOC. (3) Third, there is the rapid descent stage (EqVRo > 3.25%), where the amount of generated gas started to decrease, and it was 393 mL/g TOC at an EqVRo of 3.34%. This model allows us to more accurately calculate the amount of gas generated from the Longmaxi shale in the Sichuan Basin. Full article
(This article belongs to the Special Issue Advances in Theory and Technology of Unconventional Oil and Gas Reservoirs)
►▼ Show Figures

Figure 1

17 pages, 979 KiB  
Article
Pressure-Aware Mamba for High-Accuracy State of Charge Estimation in Lithium-Ion Batteries
by Qiwen Wang, Cuiqin Wei and Yucai He
Processes 2025, 13(7), 2293; https://doi.org/10.3390/pr13072293 - 18 Jul 2025
Viewed by 327
Abstract
Accurate State of Charge (SOC) estimation is challenged by battery aging and complex internal dynamics. This work introduces a novel framework, Mamba-PG, that leverages the Mamba architecture to integrate internal gas pressure—a direct indicator of electrochemical state—for high-accuracy SOC estimation. The core innovation [...] Read more.
Accurate State of Charge (SOC) estimation is challenged by battery aging and complex internal dynamics. This work introduces a novel framework, Mamba-PG, that leverages the Mamba architecture to integrate internal gas pressure—a direct indicator of electrochemical state—for high-accuracy SOC estimation. The core innovation is a specialized pressure-aware gating mechanism designed to adaptively fuse the pressure signal with conventional electrical data. On a public dataset, our model achieved a state-of-the-art Mean Absolute Error (MAE) of 0.386%. Furthermore, we demonstrate that the gating mechanism learns a physically-plausible and interpretable strategy, dynamically adjusting the pressure signal’s influence based on its magnitude and the battery’s aging state. This study validates that the synergy of novel physical signals with efficient, interpretable architectures like Mamba presents a robust path toward next-generation Battery Management Systems. Full article
(This article belongs to the Section Chemical Processes and Systems)
►▼ Show Figures

Figure 1

14 pages, 3096 KiB  
Article
Photoelectrochemical CO2 Reduction Measurements of a BiOI Coating Deposited onto a Non-Conductive Glass Support as a Platform for Environmental Remediation
by J. Manuel Mora-Hernandez and A. Hernández-Ramírez
Processes 2025, 13(7), 2292; https://doi.org/10.3390/pr13072292 - 18 Jul 2025
Viewed by 566
Abstract
Aiming to contribute to environmental remediation strategies, this work proposes a novel fabrication of photoelectrocatalytic electrodes containing a BiOI coating deposited onto non-conductive glass (NCG) for CO2 conversion applications. When BiOI electrodes are not deposited onto fluorine-doped tin oxide (FTO) or indium [...] Read more.
Aiming to contribute to environmental remediation strategies, this work proposes a novel fabrication of photoelectrocatalytic electrodes containing a BiOI coating deposited onto non-conductive glass (NCG) for CO2 conversion applications. When BiOI electrodes are not deposited onto fluorine-doped tin oxide (FTO) or indium tin oxide (ITO) conductive supports, the electrochemical measurements enable the registration of the (photo)electrochemical response for bare BiOI, thereby excluding remnant signals from the conductive supports and reporting an exclusive and proper photoelectrocatalytic BiOI response. A systematic procedure was carried out to improve the physicochemical properties of BiOI through a simple variation in the amount of reagents employed in a solvothermal synthesis, thus increasing the crystallite size and surface area of the resulting material (BiOI-X3-20wt.%). The tailored BiOI coating on a non-conductive support showed activity in performing CO2 photoelectroreduction under UV–Vis irradiation in aqueous media. Finally, the BiOI-X3-20wt.% sample was evaluated for photocatalytic CO2 conversion in gaseous media, producing CO as the primary reaction product. This study confirms that BiOI is a suitable and easily synthesized material, with potential applications for CO2 capture and conversion when employed as a photoactive coating for environmental remediation. Full article
(This article belongs to the Special Issue Advanced Application of Photoelectrocatalysis for Energy Conversion)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-374
Previous Issue
Next Issue
Back to TopTop