Processes

16 pages, 2447 KiB

Open AccessArticle

Genesis Mechanism and Logging Evaluation Methods for Low-Resistivity Contrast Gas-Bearing Layers in Shallow Gas Reservoirs

by Ruijie Huang, Liang Xiao, Wei Zhang, Ruize Shi, Xiaopeng Liu and Ning Wu

Processes 2025, 13(9), 2695; https://doi.org/10.3390/pr13092695 (registering DOI) - 24 Aug 2025

Abstract

Shallow gas reservoirs exhibit low formation pressure and gas injection levels, leading to low-resistivity contrast between gas-bearing reservoirs and fully water-saturated layers. Gas-bearing formation identification and water saturation estimation face great challenges. To improve the accuracy of shallow gas reservoir identification and logging [...] Read more.

Shallow gas reservoirs exhibit low formation pressure and gas injection levels, leading to low-resistivity contrast between gas-bearing reservoirs and fully water-saturated layers. Gas-bearing formation identification and water saturation estimation face great challenges. To improve the accuracy of shallow gas reservoir identification and logging evaluation, it is essential to analyze the genesis mechanisms underlying the low-resistivity contrast. This study used the HJ Formation, a typical shallow gas reservoir located in the BY Sag of the eastern South China Sea Basin as an example. Combining the results of nuclear magnetic resonance (NMR), full rock mineral analysis and X-ray diffraction of clay minerals in the laboratory, it was determined that the genesis mechanism for the low-resistivity contrast in the gas-bearing reservoir was due to the high irreducible water saturation (S_wi) and the cation-induced supplementary conductivity. Afterwards, we integrated three methods, density–neutron correlation, calculation of the apparent formation water resistivity, and cross-plots of conventional and gas-logging curves, to identify shallow gas reservoirs. In addition, we also established a Waxman–Smits-based model to estimate water saturation. Compared with the typical Archie’s equation, the predicted water saturation curve using the Waxman–Smits-based model was more reasonable. The established methods and models can be used in target shallow gas reservoir evaluations, and it also has reference value for other types of oilfields with similar physical characteristics. Full article

(This article belongs to the Special Issue Geophysical Methods and Processes for Unconventional Reservoir Exploration and Characterization in Petroleum System)

16 pages, 4202 KiB

Open AccessArticle

Erosion Wear Characteristics of V-Shaped Elbow in Blooey Line

by Yanru Guo, Xiaokun Chen, Qiuhong Wang, Tiejun Lin, Wantong Sun and Chenxing Wei

Processes 2025, 13(9), 2694; https://doi.org/10.3390/pr13092694 (registering DOI) - 24 Aug 2025

Abstract

In gas drilling operations, the blooey line is highly susceptible to erosion-induced leakage. This study focuses on the use of field-welded V-shaped elbows in blooey lines, establishing a numerical method for erosion prediction and validating its accuracy through experimental data. The numerical results [...] Read more.

In gas drilling operations, the blooey line is highly susceptible to erosion-induced leakage. This study focuses on the use of field-welded V-shaped elbows in blooey lines, establishing a numerical method for erosion prediction and validating its accuracy through experimental data. The numerical results reveal that, due to the inclined configuration of the V-shaped elbow, particles from the central inlet flow directly impact the outer wall of the outlet pipe opposite the inlet, and then rebound and strike the inner wall. Meanwhile, solid particles near the pipeline wall on both sides of the inclined plane collide with the outer wall and exit in a helical flow pattern along the outlet pipe. The maximum erosion rate (3.6 × 10⁻⁴ kg/(m²·s)) occurs at the intersection of these spiral particle flows. Based on erosion predictions under various operating conditions, an empirical formula was established to correlate the erosion rate with the gas injection rate at a rate of penetration (ROP) of 1 m/h, along with corresponding conversion relationships for different ROPs. The predicted residual thickness of the V-shaped elbow showed a 6.8% relative error compared to field measurements. The proposed method can be programmed to enable real-time monitoring of the residual wall thickness and the remaining service life of the blooey line before leakage occurs, assisting field operators in determining optimal pipeline replacement schedules to ensure operational safety. Full article

(This article belongs to the Section Energy Systems)

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28 pages, 1346 KiB

Open AccessArticle

Energy Management for Integrated Energy System Based on Coordinated Optimization of Electric–Thermal Multi-Energy Retention and Reinforcement Learning

by Yan Cheng, Song Yang, Shumin Sun, Peng Yu and Jiawei Xing

Processes 2025, 13(9), 2693; https://doi.org/10.3390/pr13092693 (registering DOI) - 24 Aug 2025

Abstract

With the large-scale access to a large number of distributed electric and thermal flexible resources and multiple loads on the user side, the energy management of the integrated energy system (IES) has become an effective way for the efficient and low-carbon economic operation [...] Read more.

With the large-scale access to a large number of distributed electric and thermal flexible resources and multiple loads on the user side, the energy management of the integrated energy system (IES) has become an effective way for the efficient and low-carbon economic operation of energy systems. In order to explore a new mode of IES energy management with the participation of energy service providers (ESPs) and user clusters (UCs), this paper puts forward an energy management method for electric–thermal microgrids, considering the optimization of user energy consumption characteristics. Firstly, an energy management framework with multi-agent participation of ESP and user cluster is proposed, and a user energy preference model is established considering the user’s electricity and heat consumption preferences. Secondly, considering the operation benefit of ESP and user cluster, based on the reinforcement learning (RL) framework, an energy management model between ESPs and users is established, and a distributed solution algorithm combining Q-learning and quadratic programming is proposed. Finally, the IESs with different user scales and energy units are taken as the test system, and the optimal energy management strategy of the system, considering the user’s energy preference, is analyzed. The simulation results demonstrate that the energy management model proposed enhances the economic efficiency of IES operations and reduces emissions. In a test system with two UCs, the optimized system achieves a 5.05% reduction in carbon emissions. The RL-based distributed solution algorithm efficiently solves the energy management model for systems with varying UC scales, requiring only 6.55 s for systems with two UCs and 13.26 s for systems with six UCs. Full article

(This article belongs to the Special Issue Power System Optimization for Energy Storage: Methods and Applications)

16 pages, 1327 KiB

Open AccessArticle

Prediction of Carbon Emission Reductions from Electric Vehicles Instead of Fuel Vehicles in Urban Transportation

by Hailong Jiang, Lichun Jia, Dongyu Su and Xiao Li

Processes 2025, 13(9), 2692; https://doi.org/10.3390/pr13092692 (registering DOI) - 24 Aug 2025

Abstract

Advanced transportation, especially electric transportation, plays an increasingly significant role in the reduction of CO₂ emissions in urban traffic. A life-cycle CO₂ emission model in which traditional fossil fuels and electricity are considered is a key method to analyze the potential [...] Read more.

Advanced transportation, especially electric transportation, plays an increasingly significant role in the reduction of CO₂ emissions in urban traffic. A life-cycle CO₂ emission model in which traditional fossil fuels and electricity are considered is a key method to analyze the potential of transportation emission reduction. In this study, the life-cycle CO₂ emissions of gasoline, diesel, natural gas, and electricity generated during the production, transportation, and consumption were modeled and calculated. The influence of coal power generation, coal combustion, seasonal energy consumption, and travel patterns on the CO₂ emissions of electric vehicles was discussed. The analysis results show that the life-cycle CO₂ emissions of automobile fuels in the process of combustion, processing, mining, and transportation are from the largest to the smallest. If the proportion of coal power generation is reduced to 50% by replacing gasoline vehicles with electric vehicles, emissions can be reduced by about 48.2%. At the same time, the scale of traffic in different months and in different periods of time of the day causes seasonal energy consumption fluctuations and regular fuel consumption variations of electric vehicles. The cyclical carbon reduction effect can be amplified if measures such as replacing fuel cars in spring and fall, and during peak hours, are used. Full article

(This article belongs to the Special Issue Recent Advances in Modern Carbon-Negative Technologies for CO₂ Capture)

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30 pages, 4457 KiB

Open AccessArticle

A Comprehensive Study of Machine Learning for Waste-to-Energy Process Modeling and Optimization

by Jianzhao Zhou, Jingyuan Liu, Jingzheng Ren and Chang He

Processes 2025, 13(9), 2691; https://doi.org/10.3390/pr13092691 (registering DOI) - 24 Aug 2025

Abstract

This study presents a comprehensive study integrating machine learning, life cycle assessment (LCA) and heuristic optimization to achieve a low-carbon medical waste (MW)-to fuel process. A detailed process simulation coupled with cradle to gate LCA is employed to generate a dataset covering diverse [...] Read more.

This study presents a comprehensive study integrating machine learning, life cycle assessment (LCA) and heuristic optimization to achieve a low-carbon medical waste (MW)-to fuel process. A detailed process simulation coupled with cradle to gate LCA is employed to generate a dataset covering diverse process operation conditions, embodied carbon of supplying H₂ and the associated carbon emission factor of MW treatment (CEF). Four machine learning techniques, including support vector machine, artificial neural network, Gaussian process regression, and XGBoost, are trained, each achieving test R² close to 0.90 and RMSE of ~0.26. These models are integrated with heuristic algorithms to optimize operating parameters under various green hydrogen mixes (20–80%). Our results show that machine learning models outperform the detailed process model (DPM), achieving a minimum CEF of ~1.3 to ~1.1 kg CO₂-eq/kg MW with higher computational stabilities. Importantly, the optimization times dropped from hours (DPM) to seconds (machine learning models) and the combination of Gaussian process regression and particle swarm optimization is highlighted, with an optimization time under one second. The optimized process holds promise in carbon reduction compared to traditional MW disposal methods. These findings show machine learning can achieve high predictive accuracy while dramatically enhancing optimization speed and stability, providing a scalable framework for extensive scenario analysis during waste-to-energy process design and further real-time optimization application. Full article

(This article belongs to the Special Issue Modeling and Optimization for Multi-scale Integration)

17 pages, 5023 KiB

Open AccessArticle

Bio-Based Flame Retardant for Cotton Fabric Prepared from Eggshell Microparticles, Phytic Acid, and Chitosan: An Eco-Friendly Approach for Dry Use

by Raphael Ferreira dos Santos Baraldi, Eduardo Cividini Neiva, Afonso Henrique da Silva Júnior, Tania Maria Costa, Marcel Jefferson Gonçalves, Catia Lange de Aguiar, Thais Costa Nihues, Rodrigo Schlindwein, Maria Elisa Philippsen Missner and Carlos Rafael Silva de Oliveira

Processes 2025, 13(9), 2690; https://doi.org/10.3390/pr13092690 (registering DOI) - 24 Aug 2025

Abstract

This study investigates the development of a sustainable flame-retardant treatment for cotton fabrics using a hybrid coating composed of chitosan, phytic acid, APTES, and eggshell powder at concentrations of 2% and 4%, applied in one and two cycles. FTIR confirmed the deposition of [...] Read more.

This study investigates the development of a sustainable flame-retardant treatment for cotton fabrics using a hybrid coating composed of chitosan, phytic acid, APTES, and eggshell powder at concentrations of 2% and 4%, applied in one and two cycles. FTIR confirmed the deposition of the organic–inorganic layer through the appearance of characteristic bands. Thermogravimetric analysis (TGA/dTGA) revealed enhanced thermal stability for all treated samples, with increased char yield and a shift in the main cellulose degradation peak. Vertical flammability tests demonstrated that all coated fabrics achieved self-extinguishing behavior within 12 s, meeting NFPA 701 criteria. The 2% eggshell formulation with two applications (S2%-II) exhibited the best balance between flame retardancy and mechanical performance. Tensile tests indicated improved fiber cohesion for treated samples, while SEM micrographs confirmed uniform coating deposition and particle integration. Colorimetric analysis showed that the treatment did not cause a significant change in the natural color of the cotton. Although washing resistance remains a limitation due to the natural origin of the components, the samples remained stable over time without microbial growth or staining, suggesting potential for upholstery and covering fabrics not subjected to domestic washing. The results highlight the feasibility of using agro-industrial waste to create eco-friendly flame-retardant finishes for cotton textiles. Full article

(This article belongs to the Special Issue High-Temperature Behavior of Polymers and Composites)

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11 pages, 1709 KiB

Open AccessArticle

Phosphorus Removal from Piggery Wastewater Using Alginate-like Exopolymers from Activated Sludge

by Amábile Cabral, Grazieli Pereira Da Silva, Matheus Cavali, Nelson Libardi Junior and Rejane Helena Ribeiro da Costa

Processes 2025, 13(9), 2689; https://doi.org/10.3390/pr13092689 (registering DOI) - 24 Aug 2025

Abstract

The growing depletion of global phosphorus reserves underscores the urgent need for sustainable and circular nutrient recovery solutions. Rich in phosphorus, piggery wastewater represents not just a waste stream but a valuable resource. In this study, we explore an innovative approach by recovering [...] Read more.

The growing depletion of global phosphorus reserves underscores the urgent need for sustainable and circular nutrient recovery solutions. Rich in phosphorus, piggery wastewater represents not just a waste stream but a valuable resource. In this study, we explore an innovative approach by recovering alginate-like exopolymers (ALE) from activated sludge (AS) and utilizing them to produce biosorbent hydrogel beads capable of removing phosphorus directly from real piggery wastewater. The ALE extraction process yielded approximately 175

m g {V S}_{A L E} / g {V S}_{s l u d g e}

, highlighting the potential of wastewater biomass as a source of functional biopolymers. Adsorption experiments revealed phosphorus removal efficiencies approaching 80%, with capacities ranging from 0.68 to 1.18 mgP/gVS_ALE. Structural and chemical characterizations confirmed both the successful adsorption of phosphorus and the stability of the biosorbent post-treatment. This work demonstrates a dual benefit: the recovery of critical nutrients and the transformation of wastewater-derived materials into value-added biosolids. By integrating phosphorus capture and biosorbent production, the approach offers a cost-effective and environmentally responsible pathway toward nutrient recycling and wastewater valorization. Full article

(This article belongs to the Special Issue Sustainable Management of Wastewater and Sludge)

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28 pages, 1291 KiB

Open AccessArticle

Development of Nonlinear Six-Degree-of-Freedom Dynamic Modelling and High-Fidelity Flight Simulation of an Autonomous Airship

by Muhammad Wasim, Ahsan Ali and Muhammad Umer Sohail

Processes 2025, 13(9), 2688; https://doi.org/10.3390/pr13092688 (registering DOI) - 24 Aug 2025

Abstract

An airship is a lighter-than-air vehicle that offers static lift without consuming much power. This property makes it a potential candidate for many commercial applications. The target applications include rescue operations, surveillance, communication, a data collection platform for research activities and payload delivery [...] Read more.

An airship is a lighter-than-air vehicle that offers static lift without consuming much power. This property makes it a potential candidate for many commercial applications. The target applications include rescue operations, surveillance, communication, a data collection platform for research activities and payload delivery that requires hovering capabilities, etc. To successfully apply airships in these applications and many others, airship autonomous control development is of paramount importance. To accomplish this goal, the initial step is to model airship dynamics that cover the complete flight envelope accurately. The goal is to develop a flight simulator that can test the advanced autonomous control algorithms. In the proposed work, first, the nonlinear six-degree-of-freedom equations of motion are developed using Newtonian mechanics. These equations are used to develop a flight simulator for the University of Engineering and Technology Taxila (UETT) airship. Airship responses to different control inputs are investigated, and the results are validated with the available data in the literature for other airship projects. Also, the obtained longitudinal and lateral eigenmodes show good agreement with the experimental flight data of the UETT airship. The extensive simulation results favour the dynamic analysis of the airship. Full article

(This article belongs to the Special Issue Innovative Techniques for the Control of Linear and Nonlinear Systems and Processes)

27 pages, 5077 KiB

Open AccessReview

Bayesian Optimization for Chemical Synthesis in the Era of Artificial Intelligence: Advances and Applications

by Runqiu Shen, Guihua Luo and An Su

Processes 2025, 13(9), 2687; https://doi.org/10.3390/pr13092687 (registering DOI) - 23 Aug 2025

Abstract

This review highlights recent advances in the application of Bayesian optimization to chemical synthesis. In the era of artificial intelligence, Bayesian optimization has emerged as a powerful machine learning approach that transforms reaction engineering by enabling efficient and cost-effective optimization of complex reaction [...] Read more.

This review highlights recent advances in the application of Bayesian optimization to chemical synthesis. In the era of artificial intelligence, Bayesian optimization has emerged as a powerful machine learning approach that transforms reaction engineering by enabling efficient and cost-effective optimization of complex reaction systems. We begin with a concise overview of the theoretical foundations of Bayesian optimization, emphasizing key components such as Gaussian process-based surrogate models and acquisition functions that balance exploration and exploitation. Subsequently, we examine its practical applications across various chemical synthesis contexts, including reaction parameter tuning, catalyst screening, molecular design, synthetic route planning, self-optimizing systems, and autonomous laboratories. In addition, we discuss the integration of emerging techniques, such as noise-robust methods, multi-task learning, transfer learning, and multi-fidelity modeling, which enhance the versatility of Bayesian optimization in addressing the challenges and limitations inherent in chemical synthesis. Full article

(This article belongs to the Special Issue Machine Learning Optimization of Chemical Processes)

20 pages, 3177 KiB

Open AccessArticle

Intelligent Fault Detection of Wiring Errors in Electricity Meter for New Power System Based on LightGBM Algorithm

by Xiaoqi Huang, Huizhe Zheng, Chongli Zeng, Chaokai Huang, Jianxi Chen and Xiaoshun Zhang

Processes 2025, 13(9), 2686; https://doi.org/10.3390/pr13092686 (registering DOI) - 23 Aug 2025

Abstract

This study proposes an intelligent method for identifying wiring errors in three-phase three-wire electricity meters using a gradient boosting machine (LightGBM) under complex load conditions, including light load and overcompensation. The work addresses a gap where intelligent fault-detection techniques have rarely been applied [...] Read more.

This study proposes an intelligent method for identifying wiring errors in three-phase three-wire electricity meters using a gradient boosting machine (LightGBM) under complex load conditions, including light load and overcompensation. The work addresses a gap where intelligent fault-detection techniques have rarely been applied to three-phase three-wire wiring errors specifically under these conditions, and contributes a mechanism-informed data generation strategy tied to phase-angle behavior that can cause misidentification. Data generation and model training/evaluation were implemented in Python using LightGBM. The experiments demonstrated faster convergence (a 92.4% reduction in loss by the 50th round) and sub-2-s training time for 300 rounds, with >80% overall accuracy and 100% accuracy in specific normal-wiring scenarios relevant to misidentification risk. Feature-importance analysis identified total reactive power as the most informative input (19.8%) and confirmed the consistency between mechanism and model behavior. These results suggest a practical path to automated and accurate wiring-error detection in modern power systems with significant load variability. Full article

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

58 pages, 1927 KiB

Open AccessReview

Marine Metabolites for the Sustainable and Renewable Production of Key Platform Chemicals

by Maedeh Baharlooeian, Menny M. Benjamin, Shifali Choudhary, Amin Hosseinian, George S. Hanna and Mark T. Hamann

Processes 2025, 13(9), 2685; https://doi.org/10.3390/pr13092685 (registering DOI) - 23 Aug 2025

Abstract

Petrochemicals currently represent the predominant global source of energy and consumer products, including the starting materials used in the platform chemical, plastic polymer, and pharmaceutical industries. However, in recent years, the world’s approaches have shifted towards green chemistry and bio-based chemical production in [...] Read more.

Petrochemicals currently represent the predominant global source of energy and consumer products, including the starting materials used in the platform chemical, plastic polymer, and pharmaceutical industries. However, in recent years, the world’s approaches have shifted towards green chemistry and bio-based chemical production in an effort to reduce CO₂ emissions and mitigate climate change. Over the past few decades, researchers have discovered that marine metabolites, primarily sourced from invertebrates, can be utilized to create sustainable and renewable chemicals. This review highlights the significance of advancing marine microorganism-based biotechnology and biochemistry in developing effective conversion systems to enhance the biological production of key platform chemicals, including those utilized as biomaterials and for energy. A background in marine metabolite biochemistry lays the groundwork for potential strategies to mitigate dependence on petroleum for consumer products. This is followed by a discussion of petroleum product replacement technologies, green chemistry alternatives, and CO₂ mitigation efforts for the production of sustainable and renewable key platform chemicals. Full article

(This article belongs to the Section Pharmaceutical Processes)

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17 pages, 9366 KiB

Open AccessArticle

Sustainable Analytical Process for Direct Determination of Soil Texture and Organic Matter Using NIR Spectroscopy and Multivariate Calibration

by Jocelene Soares, José Guilherme Lenz Abich, Isadora Cristina Marleti da Silva, Roberta Oliveira Santos, Marco Flôres Ferrão, Gilson Augusto Helfer and Adilson Ben da Costa

Processes 2025, 13(9), 2684; https://doi.org/10.3390/pr13092684 (registering DOI) - 23 Aug 2025

Abstract

Rapid, accurate, and sustainable methods for assessing soil properties are essential for environmental management. This study proposes a green analytical approach for the direct determination of soil texture and organic matter using benchtop (1250–2500 nm) and portable (900–1700 nm) near-infrared (NIR) spectrophotometers combined [...] Read more.

Rapid, accurate, and sustainable methods for assessing soil properties are essential for environmental management. This study proposes a green analytical approach for the direct determination of soil texture and organic matter using benchtop (1250–2500 nm) and portable (900–1700 nm) near-infrared (NIR) spectrophotometers combined with multivariate calibration. Partial least squares (PLS1 and PLS2) regression models were developed using regional calibration samples and applied to additional samples from the same area. Both individual (PLS1) and simultaneous (PLS2) predictions of clay, sand, silt, and organic matter contents were evaluated. Synergy interval PLS (siPLS) algorithms were used to optimize variable selection. For clay, RMSEP was 2.1% (benchtop) and 2.0% (portable), with RPD values around 2.0. Simultaneous prediction of sand content yielded better results (RPD = 1.3 benchtop; 0.8 portable). Silt prediction showed low accuracy (RPD < 1.0). Organic matter was best predicted by siPLS1 using the benchtop device (RPD = 1.5), followed by portable PLS2 (RPD = 1.2). Benchtop and portable NIR approaches proved satisfactory for direct determination of soil properties. PLS1 models offered greater specificity, while siPLS enhanced accuracy through variable selection. PLS2 models enabled efficient simultaneous predictions. Both devices meet white analytical chemistry principles, aligning performance with sustainability, thus demonstrating that accurate and environmentally responsible soil analysis can be achieved without compromising analytical efficiency. Full article

(This article belongs to the Topic Green and Sustainable Chemical Processes)

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15 pages, 24745 KiB

Open AccessArticle

The Effect of Jet Deviation on the Stability of Pelton Turbine

by Zhiqiang Yuan, Jitao Liu, Jiayang Pang, Jian Zhang, Yuanyuan Gang, Yinhui Cai, Jianan Li, Haoyu Wang, Kang Xu and Xiaobing Liu

Processes 2025, 13(9), 2683; https://doi.org/10.3390/pr13092683 (registering DOI) - 23 Aug 2025

Abstract

During the installation and operation of Pelton turbines, deviations of the jet centerline from the runner pitch circle can compromise turbine stability and efficiency. Utilizing design data from a Pelton turbine on China’s Dadu River, this study employs the SST k-ω and VOF [...] Read more.

During the installation and operation of Pelton turbines, deviations of the jet centerline from the runner pitch circle can compromise turbine stability and efficiency. Utilizing design data from a Pelton turbine on China’s Dadu River, this study employs the SST k-ω and VOF models to investigate the flow characteristics, pressure pulsations, and force on the bucket surface under varying offset conditions. The results demonstrate that radial offset causes the jet to enter the bucket later when deflected outward and earlier when deflected inward. All forms of offset exert adverse effects on turbine performance, with axial offsets causing more severe impacts than radial ones. The maximum pressure pulsation amplitude reached 24%. Afterwards, the erosion of Pelton turbines with different grain sizes was investigated by erosion modeling. It was found that the erosion of large grain size is more serious than that of small grain size. This research provides valuable theoretical insights and an important guiding role for improving the operational stability of Pelton turbines. Full article

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

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14 pages, 1640 KiB

Open AccessArticle

Low-Temperature Pretreatment (LT-PT) of Food Waste as a Strategy to Enhance Biomethane Production

by Filip Gamoń, Martyna Nowakowska, Kacper Ronowicz, Kacper Rosicki, Małgorzata Szopińska, Hubert Byliński, Aneta Łuczkiewicz and Sylwia Fudala-Książek

Processes 2025, 13(9), 2682; https://doi.org/10.3390/pr13092682 (registering DOI) - 23 Aug 2025

Abstract

Food waste (FW) management remains a critical challenge within the circular economy framework. This study examines low-temperature pretreatment (LT-PT) of food waste and its effects on physicochemical transformations and microbial community dynamics. Artificial food waste (AFW) was subjected to LT-PT at 60 °C [...] Read more.

Food waste (FW) management remains a critical challenge within the circular economy framework. This study examines low-temperature pretreatment (LT-PT) of food waste and its effects on physicochemical transformations and microbial community dynamics. Artificial food waste (AFW) was subjected to LT-PT at 60 °C for 24 h, 48 h, and 72 h to assess changes in organic matter solubilization, nitrogen and phosphorus transformations, microbial composition, and biomethane potential. The results show that LT-PT promotes volatile fatty acid (VFA) accumulation, ammonification, and organic matter solubilization, thereby enhancing substrate biodegradability. The largest VFA increase was observed for acetate, whose concentration increased by approximately 0.55 g/L between 0 h and 72 h of LT-PT. Metagenomic analysis revealed a pronounced shift in microbial communities, with fermentative bacteria (Leuconostocaceae) increasing to 53.08% after 24 h of LT-PT, while Cyanobacteria decreased from 81.31% at 0 h to 19.48% at 48 h. Biochemical methane potential (BMP) tests demonstrated that longer LT-PT durations improved methane yield, with the highest production (1170 NmL CH₄) recorded after 72 h of pretreatment. Kinetic modeling using first-order and modified Gompertz equations confirmed that LT-PT enhances methane production efficiency by accelerating substrate hydrolysis. These findings indicate that LT-PT is a promising strategy for optimizing food waste valorization via anaerobic digestion, supporting sustainable waste management and renewable energy generation. Full article

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

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15 pages, 3750 KiB

Open AccessArticle

Hydroxyl Group-Dependent Effects of Alkanolamine Additives on Rheology, Hydration, and Performance of Early-Strength Cement Slurries

by Yifei Zhao, Ya Shi, Longjiang Wang, Yan Zhuang, Yongfei Li and Gang Chen

Processes 2025, 13(9), 2681; https://doi.org/10.3390/pr13092681 (registering DOI) - 23 Aug 2025

Abstract

Alkanolamine additives play a critical role in enhancing the early process performance of cement slurries, thereby improving construction efficiency and structural durability. This study systematically evaluates the effects of ethanolamine (EA), diethanolamine (DEA), and triethanolamine (TEA) on cement slurry properties, including the thickening [...] Read more.

Alkanolamine additives play a critical role in enhancing the early process performance of cement slurries, thereby improving construction efficiency and structural durability. This study systematically evaluates the effects of ethanolamine (EA), diethanolamine (DEA), and triethanolamine (TEA) on cement slurry properties, including the thickening time, rheology, density, shrinkage, and hydration kinetics. Clear structure–activity relationships are established based on the findings. The experimental analysis demonstrated that increasing the hydroxyl group count in the alkanolamines significantly accelerated cement hydration. At a dosage of 1.0%, the thickening time of the cement slurry was significantly shortened to 125 min (EA), 15 min (DEA), and 12 min (TEA), respectively. Concomitantly, a reduction in fluidity was observed, with flow diameters measuring 15.8 cm (EA), 14.6 cm (DEA), and 14.1 cm (TEA). The rheological analysis revealed that the alkanolamine additives significantly increased the consistency coefficient (K) and decreased the flowability index (n) of the slurry, with TEA exhibiting the most pronounced effect. The density measurements confirmed the enhanced settlement stability, as the density differences diminished to 0.1 g/cm³ at the optimal dosages (0.6% TEA and 0.8% DEA). The hydration degree analysis indicated a hydration rate acceleration of up to 32% relative to plain slurry, attributed to the hydroxyl-facilitated promotion of Ca(OH)₂ formation and C₃S dissolution. The XRD analysis confirmed that the alkanolamines modified the reaction kinetics without inducing phase transformation in the hydration products. Crucially, the hydroxyl group count governed the performance: a higher hydroxyl density intensified Ca²⁺/Al³⁺ complexation, thereby reducing ion mobility and accelerating setting. These findings establish a molecular design framework for alkanolamine-based additives that balances early process performance development with practical workability. The study advances sustainable cement technology by enabling targeted optimization of rheological and mechanical properties in high-demand engineering applications. Full article

(This article belongs to the Special Issue Petroleum Characterization and Bioprocesses: Numerical and Experimental Investigation (2nd Edition))

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39 pages, 7455 KiB

Open AccessReview

A Comparative Review of Large Language Models in Engineering with Emphasis on Chemical Engineering Applications

by Khoo-Teck Leong, Tin Sin Lee, Soo-Tueen Bee, Chi Ma and Yuan-Yuan Zhang

Processes 2025, 13(9), 2680; https://doi.org/10.3390/pr13092680 (registering DOI) - 23 Aug 2025

Abstract

This review provides a comprehensive overview of the evolution and application of artificial intelligence (AI) and large language models (LLMs) in engineering, with a specific focus on chemical engineering. The review traces the historical development of LLMs, from early rule-based systems and statistical [...] Read more.

This review provides a comprehensive overview of the evolution and application of artificial intelligence (AI) and large language models (LLMs) in engineering, with a specific focus on chemical engineering. The review traces the historical development of LLMs, from early rule-based systems and statistical models like N-grams to the transformative introduction of neural networks and transformer architecture. It examines the pivotal role of models like BERT and the GPT series in advancing natural language processing and enabling sophisticated applications across various engineering disciplines. For example, GPT-3 (175B parameters) demonstrates up to 87.7% accuracy in structured information extraction, while GPT-4 introduces multimodal reasoning with estimated token limits exceeding 32k. The review synthesizes recent research on the use of LLMs in software, mechanical, civil, and electrical engineering, highlighting their impact on automation, design, and decision-making. A significant portion is dedicated to the burgeoning applications of LLMs in chemical engineering, including their use as educational tools, process simulation and modelling, reaction optimization, and molecular design. The review delves into specific case studies on distillation column and reactor design, showcasing how LLMs can assist in generating initial parameters and optimizing processes while also underscoring the necessity of validating their outputs against traditional methods. Finally, the review addresses the challenges and future considerations of integrating LLMs into engineering workflows, emphasizing the need for domain-specific adaptations, ethical guidelines, and robust validation frameworks. Full article

(This article belongs to the Special Issue Advances in Process Systems Engineering: Selected Papers from China PSE Annual Meeting)

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20 pages, 2806 KiB

Open AccessReview

Interfacial Solar Evaporation for Treating High-Salinity Wastewater: Chance and Necessity

by Shunjian Ji, Zhihong Zhang, Meijie Zhang, Zexin Yang, Yaguang Fan, Juan Zhang, Yingping Pang and Lin Cui

Processes 2025, 13(9), 2679; https://doi.org/10.3390/pr13092679 - 22 Aug 2025

Abstract

The tension in the relationship between water and energy seriously restricts the harmonious coexistence between man and the ecological environment. The solar-powered interface evaporation technology emerging in recent years has shown good application prospects in high-salt wastewater treatment for achieving the zero-discharge treatment [...] Read more.

The tension in the relationship between water and energy seriously restricts the harmonious coexistence between man and the ecological environment. The solar-powered interface evaporation technology emerging in recent years has shown good application prospects in high-salt wastewater treatment for achieving the zero-discharge treatment of wastewater. In this review, advanced solar-driven interfacial evaporation is primarily focused on its mechanisms, photothermal materials optimization, and the structure of solar evaporators for salt removal. The high wide-spectrum solar absorption rate of photothermal materials determines the total energy that can be utilized in the evaporation system. The light-to-heat conversion capacity of photothermal materials directly affects the efficiency and performance of solar interface evaporators. We highlight the microstructures enabled by the nanophotonic designs of photothermal material-based solar absorbers, which can achieve highly efficient light harvesting across the entire solar irradiance spectral range with weighted solar absorptivity. Finally, based on current research, existing problems, and future development directions for high-salt wastewater evaporation research are proposed. The review provides insights into the effective treatment of high-salt wastewater. Full article

(This article belongs to the Special Issue Clean Combustion and Emission Control Technologies)

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19 pages, 4277 KiB

Open AccessArticle

Cu/Bi-NC Composites Derived from Bimetallic MOFs for Efficient and Stable Capture of Multiform Iodine

by Jie Ren, Aotian Gu, Peng Wang, Chunhui Gong, Kaiwei Chen, Ping Mao, Yan Jiao, Kai Chen and Yi Yang

Processes 2025, 13(9), 2678; https://doi.org/10.3390/pr13092678 - 22 Aug 2025

Abstract

With the popularization of nuclear energy in the field of energy application, the effective removal of radioactive iodine isotopes is crucial for the long-term development of nuclear energy. In this paper, bimetallic MOFs with different Cu/Bi ratios were synthesized by a simple solvothermal [...] Read more.

With the popularization of nuclear energy in the field of energy application, the effective removal of radioactive iodine isotopes is crucial for the long-term development of nuclear energy. In this paper, bimetallic MOFs with different Cu/Bi ratios were synthesized by a simple solvothermal method, and a bimetallic nano-adsorbent Cu_x/Bi_10−x-NC was prepared by one-step calcination. Adsorption experiments show that Cu_x/Bi_10−x-NC exhibits excellent adsorption performance for iodide ions, gaseous iodine, and I₂ in cyclohexane solution, with the maximum adsorption capacities reaching up to 484.08 and 233.11 mg g⁻¹, respectively. Through the characterization of the material system before and after adsorption, this excellent adsorption performance is attributed to the synergistic effect between Cu and Bi, as well as the highly dispersed adsorption active sites derived from the MOF template. Therefore, the prepared Cu_x/Bi_10−x-NC has great potential in the efficient and stable capture of various forms of iodine. Full article

(This article belongs to the Special Issue Metal–Organic Frameworks (MOFs) and Applications in Adsorption)

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24 pages, 17040 KiB

Open AccessArticle

Shear-Induced Degradation and Rheological Behavior of Polymer-Flooding Waste Liquids: Experimental and Numerical Analysis

by Bingyu Sun, Hanxiang Wang, Yanxin Liu, Wei Lv, Yubao Li, Shaohua Ma, Xiaoyu Wang and Han Cao

Processes 2025, 13(9), 2677; https://doi.org/10.3390/pr13092677 - 22 Aug 2025

Abstract

Polymer flooding is an enhanced oil recovery (EOR) technique that improves oil extraction by injecting polymer solutions into reservoirs. However, the disposal and treatment of polymer flooding waste liquids (PFWL) present significant challenges due to their high viscosity, complex molecular structure, and environmental [...] Read more.

Polymer flooding is an enhanced oil recovery (EOR) technique that improves oil extraction by injecting polymer solutions into reservoirs. However, the disposal and treatment of polymer flooding waste liquids (PFWL) present significant challenges due to their high viscosity, complex molecular structure, and environmental impact. This study investigates the shear-induced degradation of polymer solutions, focusing on rheological properties, particle size distribution, and morphological changes under controlled shear conditions. Experimental results show that shear forces significantly reduce the viscosity of polymer solutions, with shear rates of 4285.36 s⁻¹ in the rotating domain and 3505.21 s⁻¹ in the fixed domain. The particle size analysis reveals a significant reduction in average particle size, indicating polymer aggregate breakup. SEM images confirm these morphological changes. Additionally, numerical simulations using a power-law model highlight the correlation between shear rate, wall shear stress, and polymer degradation efficiency. This study suggests that optimizing rotor–stator configurations with high shear forces is essential for efficient polymer degradation, offering insights for designing more effective polymer waste liquid treatment systems in oilfields. Full article

(This article belongs to the Special Issue Advances in Evaluation, Development, Simulation and Utilization of Geo-Energy Resources and Underground Space)

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31 pages, 4235 KiB

Open AccessArticle

Dual-Scale Modelling of the Vacuum Drying Process for Transformer Cellulose-Based Insulation

by Nikola Borovnik, Saša Mudrinić and Nenad Ferdelji

Processes 2025, 13(9), 2676; https://doi.org/10.3390/pr13092676 - 22 Aug 2025

Abstract

The vacuum drying of cellulose-based insulation is an essential step in the transformer manufacturing process, typically consisting of both heat and vacuum application. The moisture inside cellulose insulation during this process is transferred by various transport mechanisms, some of which are affected by [...] Read more.

The vacuum drying of cellulose-based insulation is an essential step in the transformer manufacturing process, typically consisting of both heat and vacuum application. The moisture inside cellulose insulation during this process is transferred by various transport mechanisms, some of which are affected by the insulation’s temperature. Moreover, the conditions within the vacuum chamber are generally transient and highly dynamic, depending on the employed process control strategy, and may include various phenomena, such as gas expansion during pump-down and radiative heat transfer. From a modelling perspective, these factors can significantly impact the drying rate by altering the boundary conditions of heat and mass transport equations. To account for such effects, a model that considers the process at both the scale of cellulose insulation and the scale of the vacuum chamber is presented. A simplified drying system with two-point process control is introduced to simulate multiple cases. The results highlight the sensitivity of drying behaviour to both the model parameters and the selected control strategy. A comparison with existing Fickian diffusion models indicates that the proposed model, when properly calibrated, can reliably reproduce drying dynamics and thus provide a powerful tool for optimizing vacuum drying procedures. Full article

(This article belongs to the Section Materials Processes)

26 pages, 628 KiB

Open AccessArticle

Statistical Optimization in the Fermentation Stage for Organic Ethanol: A Sustainable Approach

by Eliani Sosa-Gómez, Irenia Gallardo Aguilar, Ana Celia de Armas Mártínez and Guillermo Sosa-Gómez

Processes 2025, 13(9), 2675; https://doi.org/10.3390/pr13092675 - 22 Aug 2025

Abstract

The growing demand for organic products is having a transformative effect on the alcoholic beverage industry. This work investigates the possibility of producing organic ethanol only from sugarcane final molasses as a nutrient vector and Saccharomyces cerevisiae in the absence of inorganic nitrogen [...] Read more.

The growing demand for organic products is having a transformative effect on the alcoholic beverage industry. This work investigates the possibility of producing organic ethanol only from sugarcane final molasses as a nutrient vector and Saccharomyces cerevisiae in the absence of inorganic nitrogen or phosphorus compounds. The Plackett–Bürman design included the pseudo-factors (X4–X6) due to the experimental design requirements. These factors represent the possible influence of uncontrolled variables, such as pH or nutrient interactions. Subsequently, a predictive quadratic model using Box–Behnken design with the real variables (sugar concentration, yeast dose, and incubation time) was developed and validated

(R^{2} = 0.977)

with internal validation; given the lack of replications and the sample size, this value should be interpreted with caution and not as generalizable predictive evidence. Further experiments with replications and cross-validation will be required to confirm its predictive capacity. Through statistical optimization, the maximum cell proliferation of

432 \times 10^{6}

cells/mL was achieved under optimal conditions of 8°Brix sugar concentration, 20 g/L dry yeast, and 3 h incubation time. The optimized fermentation process produced 7.8% v/v ethanol with a theoretical fermentation efficiency of 78.52%, an alcohol-to-substrate yield of 62.15%, and a productivity of 1.86 g/L·h, representing significant improvements of 21.9%, 24.6%, 31.0%, and 10.1%, respectively, compared with non-optimized conditions. The fermentation time was reduced from 48 to 42 h while maintaining superior performance. These results demonstrate the technical feasibility of producing organic ethanol using certified organic molasses and no chemical additives. Overall, these findings should be regarded as proof of concept. All experiments were single-run without biological or technical replicates; consequently, the optimization and models are preliminary and require confirmation with replicated experiments and external validation. Full article

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

19 pages, 441 KiB

Open AccessReview

Recent Advances and Applications of Nondestructive Testing in Agricultural Products: A Review

by Mian Li, Honglian Yin, Fei Gu, Yanjun Duan, Wenxu Zhuang, Kang Han and Xiaojun Jin

Processes 2025, 13(9), 2674; https://doi.org/10.3390/pr13092674 - 22 Aug 2025

Abstract

With the rapid development of agricultural intelligence, nondestructive testing (NDT) has shown considerable promise for agricultural product inspection. Compared with traditional methods—which often suffer from subjectivity, low efficiency, and sample damage—NDT offers rapid, accurate, and non-invasive solutions that enable precise inspection without harming [...] Read more.

With the rapid development of agricultural intelligence, nondestructive testing (NDT) has shown considerable promise for agricultural product inspection. Compared with traditional methods—which often suffer from subjectivity, low efficiency, and sample damage—NDT offers rapid, accurate, and non-invasive solutions that enable precise inspection without harming the products. These inherent advantages have promoted the increasing adoption of NDT technologies in agriculture. Meanwhile, rising quality standards for agricultural products have intensified the demand for more efficient and reliable detection methods, accelerating the replacement of conventional techniques by advanced NDT approaches. Nevertheless, selecting the most appropriate NDT method for a given agricultural inspection task remains challenging, due to the wide diversity in product structures, compositions, and inspection requirements. To address this challenge, this paper presents a review of recent advancements and applications of several widely adopted NDT techniques, including computer vision, near-infrared spectroscopy, hyperspectral imaging, computed tomography, and electronic noses, focusing specifically on their application in agricultural product evaluation. Furthermore, the strengths and limitations of each technology are discussed comprehensively, quantitative performance indicators and adoption trends are summarized, and practical recommendations are provided for selecting suitable NDT techniques according to various agricultural inspection tasks. By highlighting both technical progress and persisting challenges, this review provides actionable theoretical and technical guidance, aiming to support researchers and practitioners in advancing the effective and sustainable application of cutting-edge NDT methods in agriculture. Full article

(This article belongs to the Special Issue Artificial Intelligence-Based Analytics for Data-Driven Decision-Making in Industrial Process Engineering)

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15 pages, 3992 KiB

Open AccessArticle

Characteristics of Organisms and Origin of Organic Matter in Permian Shale in Western Hubei Province, South China

by Yuying Zhang, Baojian Shen, Dongjun Feng, Bo Gao, Pengwei Wang, Min Li, Yifei Li and Yang Liu

Processes 2025, 13(9), 2673; https://doi.org/10.3390/pr13092673 - 22 Aug 2025

Viewed by 30

Abstract

Permian shale gas is a kind of energy resource with commercial development potential. The characteristics of its organic source and enrichment have received extensive attention in recent years. This study systematically analyzed the variations in types and assemblages of hydrocarbon-forming organisms across different [...] Read more.

Permian shale gas is a kind of energy resource with commercial development potential. The characteristics of its organic source and enrichment have received extensive attention in recent years. This study systematically analyzed the variations in types and assemblages of hydrocarbon-forming organisms across different stratigraphic layers of Permian shale in western Hubei through scanning electron microscopy (SEM) and microscopic observations. Moreover, the source characteristics and enrichment mechanisms of organic matter in Permian shale were identified. Hydrocarbon generation in Permian shale is primarily attributed to planktonic algae-derived acritarchs, supplemented by higher plants and green algae, based on the observation under the SEM and microscope. The hydrocarbon-forming microorganisms in the Gufeng Formation are predominantly characterized by acritarchs. A notable decrease in acritarch content is observed at the bottom of the Wujiaping Formation, accompanied by a significant increase in higher plant constituents and a slight rise in green algae abundance. Subsequently, from the middle-upper members of the Wujiaping Formation through the Dalong Formation, acritarch concentrations rebound while higher plants and green algae contributions diminish. The organic matter in the studied layer is predominantly generated from planktonic algae (acritarchs and green algae), with subordinate contributions from terrestrial higher plants. During the sedimentary stage of the Gufeng Formation, rising sea levels sustained a deep siliceous shelf environment in the E’xi Trough, where organic matter was primarily sourced from acritarchs, with limited terrigenous input. The regressive phase at the bottom of the Wujiaping Formation resulted in coastal marsh throughout the E’xi Trough, creating a mixed organic matter assemblage of aquatic planktonic algae and enhanced terrestrial higher plant material. As sedimentation progressed into the middle-upper Wujiaping Formation and Dalong Formation, the E’xi Trough evolved into a deep siliceous shelf and platform-margin slope environment. During this stage, organic matter was again predominantly supplied by planktonic algae (mainly acritarchs), with reduced terrestrial organic input. These findings provide valuable theoretical insights for guiding Permian shale gas exploration and development strategies. 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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27 pages, 4358 KiB

Open AccessArticle

Study on the Performance of Copper(II) Sorption Using Natural and Fe(III)-Modified Natural Zeolite–Sorption Parameters Optimization and Mechanism Elucidation

by Marin Ugrina, Ivona Nuić and Jelena Milojković

Processes 2025, 13(9), 2672; https://doi.org/10.3390/pr13092672 - 22 Aug 2025

Abstract

This study evaluates and compares the sorption performance of natural zeolite (NZ) and Fe(III)-modified zeolite (FeZ) in removing Cu(II) ions from aqueous solutions, with the goal of assessing their potential for environmental remediation. NZ was modified with Fe(NO₃)₃, NaOH [...] Read more.

This study evaluates and compares the sorption performance of natural zeolite (NZ) and Fe(III)-modified zeolite (FeZ) in removing Cu(II) ions from aqueous solutions, with the goal of assessing their potential for environmental remediation. NZ was modified with Fe(NO₃)₃, NaOH and NaNO₃ solutions to improve its sorption properties. The modification led to a slight decrease in crystallinity (XRD), increase in pore volume (BET), functional groups (FTIR) and negative surface charge (zeta potential), thereby improving the affinity of FeZ towards Cu(II). Batch sorption experiments were conducted to optimize key parameters including pH, solid/liquid ratio (S/L), contact time, and initial Cu(II) concentration. The pH_o and S/L ratio were identified as key factors significantly influencing Cu(II) sorption on both zeolites, with a particularly pronounced effect observed for FeZ. The optimal conditions determined were pH_o = 3–5 for NZ, pH_o = 3 for FeZ, S/L = 10 g/L and a contact time of 600 min. Experimental results confirmed that FeZ has almost twice the sorption capacity for Cu(II) compared to NZ (0.271 mmol/g vs. 0.156 mmol/g), as further supported by elemental analysis, SEM-EDS and mapping analysis of saturated samples. The sorption of Cu(II) followed a mechanism of physical nature driven by ion exchange, dominated by intraparticle diffusion as the rate-controlling step. Leaching of copper-saturated zeolites according to the standard leaching method, DIN 38414 S4, demonstrated the ability of both zeolites to fully retain Cu(II) within their structure over a wide pH range, 4.01 ≤ pH_o ≤ 10.06. These findings highlight the superior performance of FeZ and its potential as an effective material for the remediation of copper-contaminated environments. Full article

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

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24 pages, 2615 KiB

Open AccessReview

Modulation of Enzymatic Activity by Moderate Electric Fields: Perspectives for Prebiotic Epilactose Production via Cellobiose-2-Epimerase

by Tiago Lima de Albuquerque, Ricardo N. Pereira, Sara C. Silvério and Lígia R. Rodrigues

Processes 2025, 13(9), 2671; https://doi.org/10.3390/pr13092671 - 22 Aug 2025

Viewed by 38

Abstract

Modulating enzymatic activity through physical strategies is increasingly recognized as a powerful approach to optimizing biocatalytic processes in food and biotechnology applications. Cellobiose 2-epimerase (C2E), a key enzyme for synthesizing epilactose, a non-digestible disaccharide with established prebiotic effects, is gaining relevance in functional [...] Read more.

Modulating enzymatic activity through physical strategies is increasingly recognized as a powerful approach to optimizing biocatalytic processes in food and biotechnology applications. Cellobiose 2-epimerase (C2E), a key enzyme for synthesizing epilactose, a non-digestible disaccharide with established prebiotic effects, is gaining relevance in functional foods. Emerging strategies, such as the application of moderate electric fields (MEFs), have attracted attention due to their non-thermal, non-invasive nature and their capacity to influence the structural and functional properties of proteins. This review assesses the potential of MEFs to modulate C2E activity and provides an overview of the physicochemical principles governing MEF–protein interactions and summarizes findings from various enzymatic systems, highlighting changes in activity, stability, and substrate affinity under electric field conditions. Particular attention is given to the mechanistic plausibility and processing implications of applying MEFs to C2E-catalyzed reactions. The integration of biochemical, structural, and engineering perspectives suggests that MEF-assisted modulation could overcome current bottlenecks in epilactose production. This approach may enable the sustainable valorization of lactose-rich byproducts and support the development of non-thermal, clean-label technologies for producing functional ingredients. Full article

(This article belongs to the Special Issue Advances in Organic Food Processing and Probiotic Fermentation)

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27 pages, 4651 KiB

Open AccessArticle

Artificial Neural Network Modeling Enhancing Photocatalytic Performance of Ferroelectric Materials for CO₂ Reduction: Innovations, Applications, and Neural Network Analysis

by Meijuan Tong, Xixiao Li, Guannan Zu, Liangliang Wang and Hong Wu

Processes 2025, 13(9), 2670; https://doi.org/10.3390/pr13092670 - 22 Aug 2025

Viewed by 51

Abstract

Photocatalysis is an emerging technology that harnesses light energy to facilitate chemical reactions. It has garnered considerable attention in the field of catalysis due to its promising applications in environmental remediation and sustainable energy generation. Recently, researchers have been exploring innovative techniques to [...] Read more.

Photocatalysis is an emerging technology that harnesses light energy to facilitate chemical reactions. It has garnered considerable attention in the field of catalysis due to its promising applications in environmental remediation and sustainable energy generation. Recently, researchers have been exploring innovative techniques to improve the surface reactivity of ferroelectric materials for catalytic purposes, leveraging their distinct properties to enhance photocatalytic efficiency. With their switchable polarization and improved charge transport capabilities, ferroelectric materials show promise as effective photocatalysts for various reactions, including carbon dioxide (CO₂) reduction. Through a blend of experimental studies and theoretical modeling, researchers have shown that these materials can effectively convert CO₂ into valuable products, contributing to efforts to reduce greenhouse gas emissions and promote a cleaner environment. An artificial neural network (ANN) was employed to analyze parameter relationships and their impacts in this study, demonstrating its ability to manage training data errors and its applications in fields like speech and image recognition. This research also examined changes in charge separation, light absorption, and surface area related to variations in band gap and polarization, confirming prediction accuracy through linear regression analysis. Full article

(This article belongs to the Special Issue New Technologies and Processing of Photoelectric Functional Materials and Devices)

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15 pages, 6518 KiB

Open AccessArticle

Research on Damage Characteristics of Clean Fracturing Fluid in Deep Coal Seam

by Jinqiao Wu, Anbang Liu, Fengsan Zhang, Yiting Liu, Le Yan, Yenan Jie and Chen Wang

Processes 2025, 13(9), 2669; https://doi.org/10.3390/pr13092669 - 22 Aug 2025

Viewed by 84

Abstract

This study focuses on investigating the damage characteristics and mechanisms of Slickwo clean fracturing fluid to the reservoir by using the deep coal seam in the Yan’an gas field as the research subject. During the experiment, fracturing fluids with varying A content were [...] Read more.

This study focuses on investigating the damage characteristics and mechanisms of Slickwo clean fracturing fluid to the reservoir by using the deep coal seam in the Yan’an gas field as the research subject. During the experiment, fracturing fluids with varying A content were employed to displace coal and rock cores. The impact of these fluids on the permeability and pore structure of coal and rock was analyzed using a combination of nuclear magnetic resonance and high-pressure mercury injection technology. The findings indicate that the permeability damage rates of cores Y-1 and Y-2 post-displacement are 48.4% and 53.6% correspondingly, with the damage worsening as the agent A content increases. NMR data reveals that the fracturing fluid exhibits the highest retention in small pores, followed by medium-sized pores, and the least in large pores. The rise in agent A content enhanced the retention degree in individual pore throats and overall, increasing from 62.24% to 68.74%. The escalation in agent A content results in higher macromolecular residues, causing seepage channel blockages and enhancing the adsorption properties between fracturing fluid and coal rock. This phenomenon leads to inadequate backflow, primarily in smaller apertures. Simultaneously, the interaction between the gel breaker and clay minerals triggers particle migration, blockage, and expansion, consequently diminishing the permeability of coal and rock and inducing specific damages. Full article

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

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