Processes

15 pages, 2091 KiB

Open AccessArticle

New Evidence of the Effect of Post-Combustion During Roasting/Reduction of Nickel-Bearing Lateritic Ores

by Hugo Javier Angulo Palma, Norman Toro, Jonathan Castillo, Felipe M. Galleguillos Madrid, Iván Salazar, Ingrid Jamett, Eleazar Salinas-Rodríguez and Manuel Saldana

Processes 2025, 13(8), 2602; https://doi.org/10.3390/pr13082602 (registering DOI) - 17 Aug 2025

Abstract

The roasting/reduction process is one of the fundamental stages of Caron technology, ensuring the adequate transformation of lateritic ore, with post-combustion air feed being one of the main variables considered. In this study, new evidence is presented that justifies the negative effect that [...] Read more.

The roasting/reduction process is one of the fundamental stages of Caron technology, ensuring the adequate transformation of lateritic ore, with post-combustion air feed being one of the main variables considered. In this study, new evidence is presented that justifies the negative effect that post-combustion air has on Ni extractions in H-6 of the furnace. With this objective, three air feed levels were evaluated, measuring the hearth temperature from 495 °C without post-combustion to 780 °C for the maximum post-combustion level analyzed. The evaluation was carried out in a conventional multi-hearth furnace at pilot scale using atomic absorption spectrometry, XRD, SEM, and particle size analysis as the main analytical techniques for the characterization of lateritic ore before and after the reduction process. Increasing the temperature of H-6 resulted in a significant decrease in Ni extractions by 1.68% for every 57 °C increase in hearth temperature, which was due to the resulting decrease in the reaction rate constant and the significant increase in the average particle size of the reduced ore. This research emphasizes the importance of controlling post-combustion air inflow as a technological alternative to optimize Ni extraction and reduce delays in the control of diffusive processes. Full article

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

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18 pages, 1148 KiB

Open AccessArticle

A Coordinated Wind–Solar–Storage Planning Method Based on an Improved Bat Algorithm

by Minglei Jiang, Dachi Zhang, Kerui Ma, Zhipeng Zhang, Shengyao Shi, Xin Li, Shunqiang Feng, Wenyang Xing and Hongbo Zou

Processes 2025, 13(8), 2601; https://doi.org/10.3390/pr13082601 (registering DOI) - 17 Aug 2025

Abstract

With the widespread integration of renewable energy sources such as wind and solar power into power systems, their inherent unpredictability and fluctuations present significant challenges to grid stability and security. To address these issues, Battery Energy Storage Systems (BESSs) offer an effective means [...] Read more.

With the widespread integration of renewable energy sources such as wind and solar power into power systems, their inherent unpredictability and fluctuations present significant challenges to grid stability and security. To address these issues, Battery Energy Storage Systems (BESSs) offer an effective means of enhancing renewable energy absorption and improving the overall system efficiency. This study proposes a coordinated planning method based on the improved bat algorithm (IBA) to tackle the challenges associated with integrating renewable energy into distribution networks. A bi-level optimization framework is introduced to coordinate the planning and operation of the distributed generation (DG) and BESS. The upper-level model focuses on selecting optimal sites and determining the capacity of wind turbines, photovoltaic arrays, and storage systems from an economic perspective. The lower-level model optimizes the curtailment of wind and solar energy and minimizes network losses based on the upper-level planning outcomes. Additionally, the lower-level model also coordinates the dispatch between renewable energy generation and storage systems to ensure the reliable operation of the system. To effectively solve this bi-level optimization model, we have improved the conventional bat algorithm. Simulation results show that the improved bat algorithm not only significantly enhances the convergence speed but also improves the voltage stability, with the photovoltaic utilization rate reaching 90.27% and the wind energy utilization rate reaching 92.18%. These results highlight the practical advantages and success of the proposed method in optimizing renewable energy configurations. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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26 pages, 3103 KiB

Open AccessArticle

An Interpretable Model for Cardiac Arrhythmia Classification Using 1D CNN-GRU with Attention Mechanism

by Waleed Ali, Talal A. A. Abdullah, Mohd Soperi Zahid, Adel A. Ahmed and Hakim Abdulrab

Processes 2025, 13(8), 2600; https://doi.org/10.3390/pr13082600 (registering DOI) - 17 Aug 2025

Abstract

Accurate classification of cardiac arrhythmias remains a crucial task in biomedical signal processing. This study proposes a hybrid deep learning approach called 1D CNN-eGRU that integrates one-dimensional convolutional neural network models (1D CNN) and a gated recurrent unit (GRU) architecture with an attention [...] Read more.

Accurate classification of cardiac arrhythmias remains a crucial task in biomedical signal processing. This study proposes a hybrid deep learning approach called 1D CNN-eGRU that integrates one-dimensional convolutional neural network models (1D CNN) and a gated recurrent unit (GRU) architecture with an attention mechanism for the precise classification of cardiac arrhythmias based on ECG Lead II signals. To enhance the classification of cardiac arrhythmias, we also address data imbalances in the MIT-BIH arrhythmia dataset by introducing a hybrid data balancing method that blends resampling and class-weight learning. Additionally, we apply Sig-LIME, a refined variant of LIME tailored for signal datasets, to provide comprehensive insights into model decisions. The suggested hybrid 1D CNN-eGRU approach, a fusion of 1D CNN-GRU along with an attention mechanism, is designed to acquire intricate temporal and spatial dependencies in ECG signals. It aims to distinguish between four distinct arrhythmia classes from the MIT-BIH dataset, addressing a significant challenge in medical diagnostics. Demonstrating strong performance, the proposed hybrid 1D CNN-eGRU model achieves an overall accuracy of 0.99, sensitivity of 0.93, and specificity of 0.99. Per-class evaluation shows precision ranging from 0.80 to 1.00, sensitivity from 0.83 to 0.99, and F1-scores between 0.82 and 0.99 across four arrhythmia types (normal, supraventricular, ventricular, and fusion). The model also attains an AUC of 1.00 on average, with a final test loss of 0.07. These results not only demonstrate the model’s effectiveness in arrhythmia classification but also underscore the added value of interpretability enabled through the use of the Sig-LIME technique. Full article

(This article belongs to the Special Issue Design, Fabrication, Modeling, and Control in Biomedical Systems)

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13 pages, 381 KiB

Open AccessArticle

A Novel Electric Load Prediction Method Based on Minimum-Variance Self-Tuning Approach

by Sijia Liu, Ziyi Yuan, Qi An and Bo Zhao

Processes 2025, 13(8), 2599; https://doi.org/10.3390/pr13082599 (registering DOI) - 17 Aug 2025

Abstract

Time-series forecasting is widely recognized as essential for integrating renewable energy, managing emissions, and optimizing demand across energy and environmental applications. Initially, traditional forecasting methods are hindered by limitations including poor interpretability, limited generalization to diverse scenarios, and substantial computational demands. Consequently, a [...] Read more.

Time-series forecasting is widely recognized as essential for integrating renewable energy, managing emissions, and optimizing demand across energy and environmental applications. Initially, traditional forecasting methods are hindered by limitations including poor interpretability, limited generalization to diverse scenarios, and substantial computational demands. Consequently, a novel minimum-variance self-tuning (MVST) method is proposed, grounded in adaptive control theory, to overcome these challenges. The method utilizes recursive least squares with self-tuning parameter updates, delivering high prediction accuracy, rapid computation, and robust multi-step forecasting without pre-training requirements. Testing is performed on CO₂ emissions (annual), transformer load (15 min), and building electric load (hourly) datasets, comparing MVST against LSTM, ARDL, fixed-PID, XGBoost, and Prophet across varied scales and contexts. Significant improvements are observed, with prediction errors reduced by 3–8 times and computational time decreased by up to 2000 times compared to these methods. Finally, these advancements facilitate real-time power system dispatch, enhance energy planning, and support carbon emission management, demonstrating substantial research and practical value. Full article

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

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19 pages, 1165 KiB

Open AccessArticle

Integrated (Statistical) Analysis of Honey Enriched with Aromatic Herbs: Phenolic Profile, Heavy Metal and NIR Spectroscopy

by Berat Durmishi, Vesna Knights, Tamara Jurina, Smajl Rizani, Gorica Pavlovska, Valbonë Mehmeti, Ana Jurinjak Tušek, Maja Benković, Davor Valinger and Jasenka Gajdoš Kljusurić

Processes 2025, 13(8), 2598; https://doi.org/10.3390/pr13082598 (registering DOI) - 17 Aug 2025

Abstract

Honey is recognized as a nutritionally rich and functional option, often used as a natural sweetener due to its content of glucose, fructose, vitamins, minerals, enzymes and antioxidants. Its antioxidant, antibacterial and anti-inflammatory properties are well known. Recently, interest has grown in functional [...] Read more.

Honey is recognized as a nutritionally rich and functional option, often used as a natural sweetener due to its content of glucose, fructose, vitamins, minerals, enzymes and antioxidants. Its antioxidant, antibacterial and anti-inflammatory properties are well known. Recently, interest has grown in functional honey enriched with bioactive plant components, such as extracts of rosemary, lavender, oregano, and sage, which can enhance phenolic content and antioxidant capacity. However, such enrichment may alter honey’s sensory characteristics and introduce contaminants, including heavy metals, necessitating comprehensive quality assessment. This study aimed to evaluate the chemical and functional quality of honey enriched with aromatic plant extracts from Kosovo, Albania, and North Macedonia, using an integrated approach. The research included the quantification of total phenolic compounds (TPCs), analysis of heavy metal content, and the application of near-infrared (NIR) spectroscopy with two devices (laboratory and portable). The results showed that geographical origin and herbal additions significantly affect TPC and heavy metal concentrations. Honey from Kosovo had the highest TPC, while Albanian honey showed higher concentrations of iron and nickel. Enrichment with oregano and rosemary significantly increased TPC and, levels of heavy metals such as lead and nickel. These findings underscore both the nutritional potential and safety considerations of enriched honey products. Accurate, non-destructive techniques like NIR spectroscopy offer valuable tools for quality control; however, further work is needed to evaluate sensory acceptance and long-term safety of enriched honey. Full article

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

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15 pages, 647 KiB

Open AccessArticle

Research on a Method for Optimizing the Horizontal Section Length of Ultra-Short-Radius Horizontal Wells

by Huijian Wen, Xueying Li, Shengjuan Qian, Xiangzheng Li and Yuhao Zhang

Processes 2025, 13(8), 2597; https://doi.org/10.3390/pr13082597 (registering DOI) - 17 Aug 2025

Abstract

The primary contradiction in mature oilfields during the high water-cut stage is the uneven vertical water drive, which prevents the effective utilization of residual oil in the upper part of thick sand bodies at small scales. To address this issue, ultra-short-radius horizontal wells [...] Read more.

The primary contradiction in mature oilfields during the high water-cut stage is the uneven vertical water drive, which prevents the effective utilization of residual oil in the upper part of thick sand bodies at small scales. To address this issue, ultra-short-radius horizontal wells are employed to establish large-diameter oil flow channels within the reservoir, thereby achieving precise exploitation of this type of residual oil. Optimizing the length of the horizontal section is a critical issue in the development of small-scale residual oil, but conventional methods for optimizing the length of horizontal sections cannot be directly applied to ultra-short-radius horizontal wells (USRHWs). Therefore, utilizing reservoir seepage mechanics theory, the reservoir numerical simulation method was employed to investigate variations in daily and cumulative oil production for different horizontal section lengths. The theoretical upper limit of the optimal horizontal section length for actual injection and production well patterns was determined. Considering the coupled flow characteristics in the bottom water drive reservoir formation and wellbore, as well as the impact of friction losses caused by the relative roughness of the pipe wall under turbulent flow conditions on productivity, a mathematical model was established for the optimal length of the horizontal section of USRHWs, and the technological optimal value was determined. On this basis, fully accounting for the influence of drilling costs and oil prices on the optimization of the horizontal section length, an economic model for optimizing horizontal section length was established, and we comprehensively determine the optimal length of horizontal sections from multiple perspectives, including simulation, technology, and economics. The effectiveness of this method was validated by the processing results of actual reservoir parameters and the production performance after drilling. Full article

(This article belongs to the Section Energy Systems)

10 pages, 2021 KiB

Open AccessArticle

Rapeseed Meal-Derived Three-Dimensional Porous Carbon for High-Performance Lithium–Selenium Batteries

by Yuanjiang Yang, Xiaoyan Shu, Yi Zhang, Leichao Meng, Nengfei Yu and Baizeng Fang

Processes 2025, 13(8), 2596; https://doi.org/10.3390/pr13082596 (registering DOI) - 17 Aug 2025

Abstract

Lithium–selenium batteries (LSeBs) have potential applications in mobile electronic devices and electric vehicles due to their high theoretical volume specific capacity (3253 mAh cm⁻³). However, their cycling performance is poor because of the serve shuttle effect. Porous carbon can restrict the [...] Read more.

Lithium–selenium batteries (LSeBs) have potential applications in mobile electronic devices and electric vehicles due to their high theoretical volume specific capacity (3253 mAh cm⁻³). However, their cycling performance is poor because of the serve shuttle effect. Porous carbon can restrict the shuttle effect. However, past porous carbon is cumbersome, expensive, and unsuitable for large-scale production. In this work, we develop an annealing/etching method to convert biowaste (Rapeseed meal) to a N, S co-doped three-dimensional porous carbon (NSPC) which is then used as the Se host for LSeBs. The Se/NSPC composite delivers a specific capacity of 496.5 mAh g⁻¹ for 200 cycles at 0.2 C, corresponding to a high-capacity retention of 91.8%. Moreover, the Se/NSPC composite maintains a high capacity over 200 mAh g⁻¹ after 1000 cycles at a high current density of 2 C. Our work provides an efficient approach to addressing biowaste issues while simultaneously facilitating the mass production of economical Se hosts for LSeBs. Full article

(This article belongs to the Section Materials Processes)

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37 pages, 1295 KiB

Open AccessReview

Optimal Operation of Combined Cooling, Heating, and Power Systems with High-Penetration Renewables: A State-of-the-Art Review

by Yunshou Mao, Jingheng Yuan and Xianan Jiao

Processes 2025, 13(8), 2595; https://doi.org/10.3390/pr13082595 (registering DOI) - 16 Aug 2025

Abstract

Under the global decarbonization trend, combined cooling, heating, and power (CCHP) systems are critical for improving regional energy efficiency. However, the integration of high-penetration variable renewable energy (RE) sources introduces significant volatility and multi-dimensional uncertainties, challenging conventional operation strategies designed for stable energy [...] Read more.

Under the global decarbonization trend, combined cooling, heating, and power (CCHP) systems are critical for improving regional energy efficiency. However, the integration of high-penetration variable renewable energy (RE) sources introduces significant volatility and multi-dimensional uncertainties, challenging conventional operation strategies designed for stable energy inputs. This review systematically examines recent advances in CCHP optimization under high-RE scenarios, with a focus on flexibility-enabled operation mechanisms and uncertainty-aware optimization strategies. It first analyzes the evolving architecture of variable RE-driven CCHP systems and core challenges arising from RE intermittency, demand volatility, and multi-energy coupling. Subsequently, it categorizes key flexibility resources and clarifies their roles in mitigating uncertainties. The review further elaborates on optimization methodologies tailored to high-RE contexts, along with their comparative analysis and selection criteria. Additionally, it details the formulation of optimization models, model formulation, and solution techniques. Key findings include the following: Generalized energy storage, which integrates physical and virtual storage, increases renewable energy utilization by 12–18% and reduces costs by 45%. Hybrid optimization strategies that combine robust optimization and deep reinforcement learning lower operational costs by 15–20% while strengthening system robustness against renewable energy volatility by 30–40%. Multi-energy synergy and exergy-efficient flexibility resources collectively improve system efficiency by 8–15% and reduce carbon emissions by 12–18%. Overall, this work provides a comprehensive technical pathway for enhancing the efficiency, stability, and low-carbon performance of CCHP systems in high-RE environments, supporting their scalable contribution to global decarbonization efforts. Full article

(This article belongs to the Special Issue Distributed Intelligent Energy Systems)

14 pages, 1964 KiB

Open AccessArticle

Rapid Joule-Heating Synthesis of Efficient Low-Crystallinity Ru-Mo Oxide Catalysts for Alkaline Hydrogen Evolution Reaction

by Tao Shi, Xiaoling Huang, Zhan Zhao, Zizhen Li, Kelei Huang and Xiangchao Meng

Processes 2025, 13(8), 2594; https://doi.org/10.3390/pr13082594 (registering DOI) - 16 Aug 2025

Abstract

Electrocatalytic water splitting has been demonstrated to be a highly efficient and promising technology for green hydrogen production. However, the inefficiency and instability of the cathode hinder its wide application in water electrolysis. Herein, we report a rapid Joule heating method for synthesizing [...] Read more.

Electrocatalytic water splitting has been demonstrated to be a highly efficient and promising technology for green hydrogen production. However, the inefficiency and instability of the cathode hinder its wide application in water electrolysis. Herein, we report a rapid Joule heating method for synthesizing the Ru-Mo oxide catalyst. Comprehensive characterization results confirmed that the as-prepared catalyst featured an internal porous structure with low crystallinity, which weakened the strength of Ru-H bonds through structural and electronic modulation. The enhanced HER performance was attributed to the incorporation of Mo⁴⁺ species, which strengthened Ru-O-Mo interactions. As tested, the optimized catalyst exhibited ultralow overpotentials (25.08 mV and 120.52 mV @ 10 and 100 mA cm⁻², respectively) and excellent stability (100 h @ 100 mA cm⁻²) in a 1 M KOH solution. Meanwhile, the as-prepared catalyst was equipped in an anion exchange membrane (AEM) alkaline water electrolyzer, which could deliver 185 mA cm⁻² at only 2.16 V with 100% Faradaic efficiency. This study provides a feasible strategy for constructing highly efficient low-crystallinity electrocatalysts. Full article

(This article belongs to the Section Environmental and Green Processes)

20 pages, 4551 KiB

Open AccessArticle

Intelligent Optimization of Single-Stand Control in Directional Drilling with Single-Bent-Housing Motors

by Hu Yin, Yihao Long, Qian Li, Tong Zhao and Xianzhu Wu

Processes 2025, 13(8), 2593; https://doi.org/10.3390/pr13082593 (registering DOI) - 16 Aug 2025

Abstract

Borehole trajectory control is a fundamental task for directional well engineers. Now that there are inevitable errors about single-stand control in the field situation, it is difficult to deal with the complex underground problems in real time. In order to improve the efficiency [...] Read more.

Borehole trajectory control is a fundamental task for directional well engineers. Now that there are inevitable errors about single-stand control in the field situation, it is difficult to deal with the complex underground problems in real time. In order to improve the efficiency of directional operation and the accuracy of wellbore trajectory control, this paper presents an improved Sparrow Search algorithm by integrating the multi-strategy model and Constant-Toolface models to calculate the single-stand control scheme for single-bent-housing motors in directional drilling. To evaluate the performance of the algorithm, the Particle Swarm algorithm, the Sparrow Search algorithm, and the improved Sparrow Search algorithm (LCSSA) are used to optimize the process parameters for each drilling, respectively. Numerical tests based on drilling data show that all three algorithms can predict the drilling parameters. In contrast, the LCSSA exhibits the fastest convergence and the smallest error after optimizing single-stand control, attaining an average convergence time of 0.08 s. It accurately back-calculated theoretical model parameters with high accuracy and met engineering requirements when applied to actual drilling data. In field applications, the LCSSA reduces the deviation from the planned trajectory by over 25%, restricting the deviation to within 0.005 m per stand; additionally the total drilling time was reduced by at least 18% compared to previous methods. The integration of the LCSSA with the drilling system significantly enhances drilling operations by optimizing trajectory accuracy and boosting efficiency and serves as an advanced tool for designing process parameters. Full article

(This article belongs to the Section Automation Control Systems)

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23 pages, 4479 KiB

Open AccessArticle

Optimizing Texture and Drying Behavior of Squid (Todarodes pacificus) for Elder-Friendly Applications Using Alkaline Pretreatment and Intermittent Drying: An Experimental and Numerical Study

by Timilehin Martins Oyinloye and Won Byong Yoon

Processes 2025, 13(8), 2592; https://doi.org/10.3390/pr13082592 (registering DOI) - 16 Aug 2025

Abstract

This study addresses the increasing demand for texture-modified seafood products suitable for elderly consumers by focusing on dried squid, a popular protein source. The aim was to optimize the softening and drying procedures to produce a dried squid product with improved chewability and [...] Read more.

This study addresses the increasing demand for texture-modified seafood products suitable for elderly consumers by focusing on dried squid, a popular protein source. The aim was to optimize the softening and drying procedures to produce a dried squid product with improved chewability and quality. Fresh squid was pretreated using sodium bicarbonate or potassium carbonate solutions (0, 0.3, 0.6, and 0.9 mol/kg) and dried at 40 °C using either continuous (CD) or intermittent drying (ID) until the final moisture content reached 18.34 ± 0.44%. Hardness generally increased with higher alkaline concentrations, with the potassium carbonate-treated samples showing better softening effects. Based on standards for elderly-friendly foods targeting chewable hardness (10,000–50,000 N/m²), low water activity (<0.58), and limited color change (ΔE = 14.32), the optimal result was achieved with 0.3 mol/kg potassium carbonate and ID. Among the thin-layer drying models, the Midilli–Kucuk model showed the best fit, with the highest average R² (0.9974), and lowest SSE (0.0481) and RMSE (0.1688), effectively capturing the drying kinetics. Scanning electron microscopy (SEM) revealed smoother surfaces and consistent porosity in samples dried intermittently, indicating less structural degradation. Finite element analysis showed that ID improved internal moisture distribution, reduced surface crusting, and alleviated internal stresses. These results support mild alkaline soaking combined with ID as an effective strategy for enhancing dried squid quality for elderly individuals. Full article

(This article belongs to the Special Issue Feature Papers in the "Food Process Engineering" Section)

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21 pages, 978 KiB

Open AccessArticle

Optimization and Practice of Deep Carbonate Gas Reservoir Acidizing Technology in the Sinian System Formation of Sichuan Basin

by Song Li, Jian Yang, Weihua Chen, Zhouyang Wang, Hongming Fang, Yang Wang and Xiong Zhang

Processes 2025, 13(8), 2591; https://doi.org/10.3390/pr13082591 (registering DOI) - 16 Aug 2025

Abstract

The gas reservoir of the Sinian Dengying Formation (Member 4) in Sichuan Basin exhibits extensive development of inter-clast dissolution pores and vugs within its carbonate reservoirs, characterized by low porosity (average 3.21%) and low permeability (average 2.19 mD). With the progressive development of [...] Read more.

The gas reservoir of the Sinian Dengying Formation (Member 4) in Sichuan Basin exhibits extensive development of inter-clast dissolution pores and vugs within its carbonate reservoirs, characterized by low porosity (average 3.21%) and low permeability (average 2.19 mD). With the progressive development of the Moxi (MX)structure, the existing stimulation techniques require further optimization based on the specific geological characteristics of these reservoirs. Through large-scale true tri-axial physical simulation experiments, this study systematically evaluated the performance of three principal acid systems in reservoir stimulation: (1) Self-generating acid systems, which enhance etching through the thermal decomposition of ester precursors to provide sustained reactive capabilities. (2) Gelled acid systems, characterized by high viscosity and effectiveness in reducing breakdown pressure (18%~35% lower than conventional systems), are ideal for generating complex fracture networks. (3) Diverting acid systems, designed to improve fracture branching density by managing fluid flow heterogeneity. This study emphasizes hybrid acid combinations, particularly self-generating acid prepad coupled with gelled acid systems, to leverage their synergistic advantages. Field trials implementing these optimized systems revealed that conventional guar-based fracturing fluids demonstrated 40% higher breakdown pressures compared to acid systems, rendering hydraulic fracturing unsuitable for MX reservoirs. Comparative analysis confirmed gelled acid’s superiority over diverting acid in tensile strength reduction and fracture network complexity. Field implementations using reservoir-quality-adaptive strategies—gelled acid fracturing for main reservoir sections and integrated self-generating acid prepad + gelled acid systems for marginal zones—demonstrated the technical superiority of the hybrid system under MX reservoir conditions. This optimized protocol enhanced fracture length by 28% and stimulated reservoir volume by 36%, achieving a 36% single-well production increase. The technical framework provides an engineered solution for productivity enhancement in deep carbonate gas reservoirs within the G-M structural domain, with particular efficacy for reservoirs featuring dual low-porosity and low-permeability characteristics. Full article

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

25 pages, 4673 KiB

Open AccessArticle

Dynamic Monitoring and Evaluation of Fracture Stimulation Volume Based on Machine Learning

by Xiaodong He, Weibang Wang, Luyao Wang, Jinliang Xie, Chang Li, Lu Chen, Qinzhuo Liao and Shouceng Tian

Processes 2025, 13(8), 2590; https://doi.org/10.3390/pr13082590 (registering DOI) - 16 Aug 2025

Abstract

Traditional hydraulic-fracturing models are restricted by low computational efficiency, insufficient field data, and complex physical mechanisms, causing evaluation delays and failing to meet practical engineering needs. To address these challenges, this study innovatively develops a dynamic hydraulic-fracturing monitoring method that integrates machine learning [...] Read more.

Traditional hydraulic-fracturing models are restricted by low computational efficiency, insufficient field data, and complex physical mechanisms, causing evaluation delays and failing to meet practical engineering needs. To address these challenges, this study innovatively develops a dynamic hydraulic-fracturing monitoring method that integrates machine learning with numerical simulation. Firstly, this study uses GOHFER 9.5.6 software to generate 12,000 sets of fracture geometry data and constructs a big dataset for hydraulic fracturing. In order to improve the efficiency of the simulation, a macro command is used in combination with a Python 3.11 code to achieve the automation of the simulation process, thereby expanding the data samples for the surrogate model. On this basis, a parameter sensitivity analysis is carried out to identify key input parameters, such as reservoir parameters and fracturing fluid properties, that significantly affect fracture geometry. Next, a neural-network surrogate model is established, which takes fracturing geological parameters and pumping parameters as inputs and fracture geometric parameters as outputs. Data are preprocessed using the min–max normalization method. A neural-network structure with two hidden layers is chosen, and the model is trained with the Adam optimizer to improve its predictive accuracy. The experimental results show that the efficiency of automated numerical simulation for hydraulic fracturing is significantly improved. The surrogate model achieved a prediction accuracy of over 90% and a response time of less than 10 s, representing a substantial efficiency improvement compared to traditional fracturing models. Through these technical approaches, this study not only enhances the effectiveness of fracturing but also provides a new, efficient, and accurate solution for oilfield fracturing operations. Full article

(This article belongs to the Section Energy Systems)

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14 pages, 4297 KiB

Open AccessArticle

Numerical Simulation of Natural Gas Waste Heat Recovery Through Hydrated Salt Particle Desorption in a Full-Size Moving Bed

by Liang Wang, Minghui Li, Yu Men, Yun Jia and Bin Ding

Processes 2025, 13(8), 2589; https://doi.org/10.3390/pr13082589 - 15 Aug 2025

Abstract

To achieve energy conservation, emission reduction, and green low-carbon goals for gas storage facilities, it is crucial to efficiently recover and utilize waste heat during gas injection while maintaining natural gas cooling rates. However, existing sensible and latent heat storage technologies cannot sustain [...] Read more.

To achieve energy conservation, emission reduction, and green low-carbon goals for gas storage facilities, it is crucial to efficiently recover and utilize waste heat during gas injection while maintaining natural gas cooling rates. However, existing sensible and latent heat storage technologies cannot sustain long-term thermal storage or seasonal utilization of waste heat. Thermal chemical energy storage, with its high energy density and low thermal loss during prolonged storage, offers an effective solution for efficient recovery and long-term storage of waste heat in gas storage facilities. This study proposes a novel heat recovery method by combining a moving bed with mixed hydrated salts (CaCl₂·6H₂O and MgSO₄·7H₂O). By constructing both small-scale and full-scale three-dimensional models in Fluent, which couple the desorption and endothermic processes of hydrated salts, the study analyzes the temperature and flow fields within the moving bed during heat exchange, thereby verifying the feasibility of this approach. Furthermore, the effects of key parameters, including the inlet temperatures of hydrated salt particles and natural gas, flow velocity, and mass flow ratio on critical performance indicators such as the outlet temperatures of natural gas and hydrated salts, the overall heat transfer coefficient, the waste heat recovery efficiency, and the mass fraction of hydrated salt desorption are systematically investigated. The results indicate that in the small-scale model (1164 × 312 × 49 mm) the outlet temperatures of natural gas and mixed hydrated salts are 79.8 °C and 49.3 °C, respectively, with a waste heat recovery efficiency of only 33.6%. This low recovery rate is primarily due to the insufficient residence time of high-velocity natural gas (10.5 m·s⁻¹) and hydrated salt particles (2 mm·s⁻¹) in the moving bed, which limits heat exchange efficiency. In contrast, the full-scale moving bed (3000 × 1500 × 90 mm) not only accounts for variations in natural gas inlet temperature during the three-stage compression process but also allows for optimized operational adjustments. These optimizations ensure a natural gas outlet temperature of 41.3 °C, a hydrated salt outlet temperature of 82.5 °C, a significantly improved waste heat recovery efficiency of 94.2%, and a hydrated salt desorption mass fraction of 69.2%. This configuration enhances the safety of the gas injection system while maximizing both natural gas waste heat recovery and the efficient utilization of mixed hydrated salts. These findings provide essential theoretical guidance and data support for the effective recovery and seasonal utilization of waste heat in gas storage reservoirs. Full article

(This article belongs to the Special Issue Multiphase Flow Process and Separation Technology)

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23 pages, 11598 KiB

Open AccessArticle

Characteristics of Load-Bearing Rupture of Rock–Coal Assemblages with Different Height Ratios and Multivariate Energy Spatiotemporal Evolution Laws

by Bo Wang, Guilin Wu, Guorui Feng, Zhuocheng Yu and Yingshi Gu

Processes 2025, 13(8), 2588; https://doi.org/10.3390/pr13082588 - 15 Aug 2025

Abstract

The destabilizing damage of rock structures in coal beds engineering is greatly influenced by the bearing rupture features and energy evolution laws of rock–coal assemblages with varying height ratios. In this study, we used PFC3D to create rock–coal assemblages with rock–coal height ratios [...] Read more.

The destabilizing damage of rock structures in coal beds engineering is greatly influenced by the bearing rupture features and energy evolution laws of rock–coal assemblages with varying height ratios. In this study, we used PFC3D to create rock–coal assemblages with rock–coal height ratios of 2:8, 4:6, 6:4, and 8:2. Uniaxial compression simulation was then performed, revealing the expansion properties and damage crack dispersion pattern at various bearing phases. The dispersion and migration law of cemented strain energy zoning; the size and location of the destructive energy level and its spatiotemporal evolution characteristics; and the impact of height ratio on the load-bearing characteristics, crack extension, and evolution of multiple energies (strain, destructive, and kinetic energies) were all clarified with the aid of a self-developed destructive energy and strain energy capture and tracking Fish program. The findings indicate that the assemblage’s elasticity modulus and compressive strength slightly increase as the height ratio increases, that the assemblage’s cracks begin in the coal body, and that the number of crack bands inside the coal body increases as the height ratio increases. Also, the phenomenon of crack bands penetrating the rock through the interface between the coal and rock becomes increasingly apparent. The total number of cracks, including both tensile and shear cracks, decreases as the height ratio increases. Among these, tensile cracks are consistently more abundant than shear cracks, and the proportion between the two types remains relatively stable regardless of changes in the height ratio. The acoustic emission ringing counts of the assemblage were not synchronized with the development of bearing stress, and the ringing counts started to increase from the yield stage and reached a peak at the damage stage (0.8

σ_{c}

) after the peak of bearing stress. The larger the rock–coal height ratio, the smaller the peak and the earlier the timing of its appearance. The main body of strain energy accumulation was transferred from the coal body to the rock body when the height ratio exceeded 1.5. The peak values of the assemblage’s strain energy, destructive energy, and kinetic energy curves decreased as the height ratio increased, particularly the energy amplitude of the largest destructive energy event. In order to prevent and mitigate engineering disasters during deep mining of coal resources, the research findings could serve as a helpful reference for the destabilizing properties of rock–coal assemblages. Full article

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

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21 pages, 7884 KiB

Open AccessArticle

Multi-Objective Optimization Inverse Analysis for Characterization of Petroleum Geomechanical Properties During Hydraulic Fracturing

by Shike Zhang, Zhongliang Ru, Lihong Zhao, Bangxiang Li, Hongbo Zhao and Xianglong Wang

Processes 2025, 13(8), 2587; https://doi.org/10.3390/pr13082587 - 15 Aug 2025

Abstract

To address the difficulty in the characterization of the geomechanical properties of reservoirs in petroleum engineering using the traditional formula, due to the complexity of the reservoir, this study proposes a framework of inverse analysis to characterize the geomechanical properties of reservoirs formed [...] Read more.

To address the difficulty in the characterization of the geomechanical properties of reservoirs in petroleum engineering using the traditional formula, due to the complexity of the reservoir, this study proposes a framework of inverse analysis to characterize the geomechanical properties of reservoirs formed through hydraulic fracturing by combining the XGBoost, multi-objective particle swarm optimization (MOPSO), and numerical models. XGBoost was used to generate a surrogate model to approximate the physical model, and the numerical model was used to generate a dataset for XGBoost. MOPSO is regarded as an optimal technology to deal with the conflict between multi-objective functions in inverse analysis. On comparing the results between the actual geomechanical properties and those obtained by using traditional inverse analysis, the proposed framework accurately characterizes the geomechanical parameters of reservoirs obtained through hydraulic fracturing. This provides a feasible, scientific, and promising way to characterize reservoir formation in petroleum engineering, as well as a reference for other fields of engineering. Full article

(This article belongs to the Special Issue Recent Developments in Low-Carbon and Efficient Extraction Technologies of Deep Unconventional Reservoirs)

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18 pages, 796 KiB

Open AccessReview

Research Progress and Prospects of Methods for Estimating Methane Reserves in Closed Coal Mines in China

by Ying Han, Chenxiang Wang, Feiyan Zhang and Qingchao Li

Processes 2025, 13(8), 2586; https://doi.org/10.3390/pr13082586 - 15 Aug 2025

Abstract

The accurate estimation of methane reserves in closed coal mines is crucial for supporting clean energy recovery and reducing greenhouse gas emissions. This study addresses the technical challenges associated with complex geological conditions and limited post-closure data in China’s closed mines. Three mainstream [...] Read more.

The accurate estimation of methane reserves in closed coal mines is crucial for supporting clean energy recovery and reducing greenhouse gas emissions. This study addresses the technical challenges associated with complex geological conditions and limited post-closure data in China’s closed mines. Three mainstream estimation methods—the material balance, resource composition, and decline curve—are systematically reviewed and applied to a case study in the Huoxi Coalfield. Results indicate that the material balance method provides upper-bound estimates but is highly sensitive to incomplete historical data, whereas the resource composition method yields more conservative and geologically realistic values. Although the decline curve method is not applied in this case, it offers potential for forecasting when long-term monitoring data are available. A multi-method integration approach, supported by enhanced data archiving and uncertainty assessments, is recommended to improve the accuracy and reliability of methane reserve evaluations in post-mining environments. Full article

(This article belongs to the Special Issue Coalbed Methane Development Process)

1 pages, 113 KiB

Open AccessCorrection

Correction: Cai et al. Acoustic Characterization of Leakage in Buried Natural Gas Pipelines. Processes 2025, 13, 2274

by Yongjun Cai, Xiaolong Gu, Xiahua Zhang, Ke Zhang, Huiye Zhang and Zhiyi Xiong

Processes 2025, 13(8), 2585; https://doi.org/10.3390/pr13082585 - 15 Aug 2025

Abstract

In the original publication [...] Full article

18 pages, 8590 KiB

Open AccessArticle

Tensile and Fracture Properties Evaluation of Additively Manufactured Different Stainless Steels via Small Punch Testing

by Ran Li, Wenshu Wei, Mengyu Wu, Fengcai Liu, Wenbo Li, Yuehua Lai, Rongming Chen, Hao Liu, Jian Ye, Jianfeng Li and Tianze Cao

Processes 2025, 13(8), 2584; https://doi.org/10.3390/pr13082584 - 15 Aug 2025

Abstract

Laser powder bed fusion (LPBF) can fabricate hydraulic components with significant weight reduction, and in this study, small punch tests (SPTs) evaluated the tensile and fracture properties of four stainless steels (30Cr13, 316L, 15-5PH, 17-4PH), alongside metallographic, scanning electron microscope (SEM), and Electron [...] Read more.

Laser powder bed fusion (LPBF) can fabricate hydraulic components with significant weight reduction, and in this study, small punch tests (SPTs) evaluated the tensile and fracture properties of four stainless steels (30Cr13, 316L, 15-5PH, 17-4PH), alongside metallographic, scanning electron microscope (SEM), and Electron Backscatter Diffraction (EBSD) analyses which examined their fracture modes, grain orientation, phase distribution, and grain boundary distribution. The tensile property results showed ductility rankings as 316L > 17-4PH > 15-5PH > 30Cr13, with correlations between R_p0.2 and R_m from SPT and uniaxial tensile tests for all four, while high-magnification SEM fractographs revealed ductile dimples on 15-5PH, 17-4PH, and 316L SPT specimens versus distinct cleavage fracture in 30Cr13. EBSD analysis indicated austenite content order as 316L > 17-4PH > 30Cr13 > 15-5PH, grain size order as 316L > 17-4PH > 15-5PH > 30Cr13, and high-angle grain boundaries ranking as 15-5PH > 30Cr13 > 17-4PH > 316L; additionally, notched SPT specimens inspected per EN 10371 for fracture toughness showed J-integral (J_IC) values in the order 316L > 17-4PH > 15-5PH > 30Cr13, consistent with ductility and grain size results. Full article

(This article belongs to the Special Issue Welding and Additive Manufacturing Processes)

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