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Volume 13
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Processes, Volume 13, Issue 7 (July 2025) – 328 articles

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35 pages, 2895 KiB  
Review
Ventilated Facades for Low-Carbon Buildings: A Review
by Pinar Mert Cuce and Erdem Cuce
Processes 2025, 13(7), 2275; https://doi.org/10.3390/pr13072275 (registering DOI) - 17 Jul 2025
Abstract
The construction sector presently consumes about 40% of global energy and generates 36% of CO2 emissions, making facade retrofits a priority for decarbonising buildings. This review clarifies how ventilated facades (VFs), wall assemblies that interpose a ventilated air cavity between outer cladding [...] Read more.
The construction sector presently consumes about 40% of global energy and generates 36% of CO2 emissions, making facade retrofits a priority for decarbonising buildings. This review clarifies how ventilated facades (VFs), wall assemblies that interpose a ventilated air cavity between outer cladding and the insulated structure, address that challenge. First, the paper categorises VFs by structural configuration, ventilation strategy and functional control into four principal families: double-skin, rainscreen, hybrid/adaptive and active–passive systems, with further extensions such as BIPV, PCM and green-wall integrations that couple energy generation or storage with envelope performance. Heat-transfer analysis shows that the cavity interrupts conductive paths, promotes buoyancy- or wind-driven convection, and curtails radiative exchange. Key design parameters, including cavity depth, vent-area ratio, airflow velocity and surface emissivity, govern this balance, while hybrid ventilation offers the most excellent peak-load mitigation with modest energy input. A synthesis of simulation and field studies indicates that properly detailed VFs reduce envelope cooling loads by 20–55% across diverse climates and cut winter heating demand by 10–20% when vents are seasonally managed or coupled with heat-recovery devices. These thermal benefits translate into steadier interior surface temperatures, lower radiant asymmetry and fewer drafts, thereby expanding the hours occupants remain within comfort bands without mechanical conditioning. Climate-responsive guidance emerges in tropical and arid regions, favouring highly ventilated, low-absorptance cladding; temperate and continental zones gain from adaptive vents, movable insulation or PCM layers; multi-skin adaptive facades promise balanced year-round savings by re-configuring in real time. Overall, the review demonstrates that VFs constitute a versatile, passive-plus platform for low-carbon buildings, simultaneously enhancing energy efficiency, durability and indoor comfort. Future advances in smart controls, bio-based materials and integrated energy-recovery systems are poised to unlock further performance gains and accelerate the sector’s transition to net-zero. Emerging multifunctional materials such as phase-change composites, nanostructured coatings, and perovskite-integrated systems also show promise in enhancing facade adaptability and energy responsiveness. Full article
(This article belongs to the Special Issue Sustainable Development of Energy and Environment in Buildings)
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33 pages, 6828 KiB  
Article
Acoustic Characterization of Leakage in Buried Natural Gas Pipelines
by Yongjun Cai, Xiaolong Gu, Xiahua Zhang, Ke Zhang, Huiye Zhang and Zhiyi Xiong
Processes 2025, 13(7), 2274; https://doi.org/10.3390/pr13072274 (registering DOI) - 17 Jul 2025
Abstract
To address the difficulty of locating small-hole leaks in buried natural gas pipelines, this study conducted a comprehensive theoretical and numerical analysis of the acoustic characteristics associated with such leakage events. A coupled flow–acoustic simulation framework was developed, integrating gas compressibility via the [...] Read more.
To address the difficulty of locating small-hole leaks in buried natural gas pipelines, this study conducted a comprehensive theoretical and numerical analysis of the acoustic characteristics associated with such leakage events. A coupled flow–acoustic simulation framework was developed, integrating gas compressibility via the realizable k-ε and Large Eddy Simulation (LES) turbulence models, the Peng–Robinson equation of state, a broadband noise source model, and the Ffowcs Williams–Hawkings (FW-H) acoustic analogy. The effects of pipeline operating pressure (2–10 MPa), leakage hole diameter (1–6 mm), soil type (sandy, loam, and clay), and leakage orientation on the flow field, acoustic source behavior, and sound field distribution were systematically investigated. The results indicate that the leakage hole size and soil medium exert significant influence on both flow dynamics and acoustic propagation, while the pipeline pressure mainly affects the strength of the acoustic source. The leakage direction was found to have only a minor impact on the overall results. The leakage noise is primarily composed of dipole sources arising from gas–solid interactions and quadrupole sources generated by turbulent flow, with the frequency spectrum concentrated in the low-frequency range of 0–500 Hz. This research elucidates the acoustic characteristics of pipeline leakage under various conditions and provides a theoretical foundation for optimal sensor deployment and accurate localization in buried pipeline leak detection systems. Full article
(This article belongs to the Special Issue Design, Inspection and Repair of Oil and Gas Pipelines)
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18 pages, 4432 KiB  
Article
Radial Temperature Distribution Characteristics of Long-Span Transmission Lines Under Forced Convection Conditions
by Feng Wang, Chuanxing Song, Xinghua Chen and Zhangjun Liu
Processes 2025, 13(7), 2273; https://doi.org/10.3390/pr13072273 - 16 Jul 2025
Abstract
This study proposes an iterative method based on thermal equilibrium equations to calculate the radial temperature distribution of long-span overhead transmission lines under forced convection. This paper takes the ACSR 500/280 conductor as the research object, establishes the three-dimensional finite element model considering [...] Read more.
This study proposes an iterative method based on thermal equilibrium equations to calculate the radial temperature distribution of long-span overhead transmission lines under forced convection. This paper takes the ACSR 500/280 conductor as the research object, establishes the three-dimensional finite element model considering the helix angle of the conductor, and carries out the experimental validation for the LGJ 300/40 conductor under the same conditions. The model captures internal temperature distribution through contour analysis and examines the effects of current, wind speed, and ambient temperature. Unlike traditional models assuming uniform conductor temperature, this method reveals internal thermal gradients and introduces a novel three-stage radial attenuation characterization. The iterative method converges and accurately reflects temperature variations. The results show a non-uniform radial distribution, with a maximum temperature difference of 8 °C and steeper gradients in aluminum than in steel. Increasing current raises temperature nonlinearly, enlarging the radial difference. Higher wind speeds reduce both temperature and radial difference, while rising ambient temperatures increase conductor temperature with a stable radial profile. This work provides valuable insights for the safe operation and optimal design of long-span transmission lines and supports future research on dynamic and environmental coupling effects. Full article
(This article belongs to the Section Energy Systems)
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20 pages, 2869 KiB  
Article
Influence of Polyester and Denim Microfibers on the Treatment and Formation of Aerobic Granules in Sequencing Batch Reactors
by Victoria Okhade Onyedibe, Hassan Waseem, Hussain Aqeel, Steven N. Liss, Kimberley A. Gilbride, Roxana Sühring and Rania Hamza
Processes 2025, 13(7), 2272; https://doi.org/10.3390/pr13072272 - 16 Jul 2025
Abstract
This study examines the effects of polyester and denim microfibers (MFs) on aerobic granular sludge (AGS) over a 42-day period. Treatment performance, granulation, and microbial community changes were assessed at 0, 10, 70, 210, and 1500 MFs/L. Reactors with 70 MFs/L achieved rapid [...] Read more.
This study examines the effects of polyester and denim microfibers (MFs) on aerobic granular sludge (AGS) over a 42-day period. Treatment performance, granulation, and microbial community changes were assessed at 0, 10, 70, 210, and 1500 MFs/L. Reactors with 70 MFs/L achieved rapid granulation and showed improved settling by day 9, while 0 and 10 MFs/L reactors showed delayed granule formation, which was likely due to limited nucleation and weaker shear conditions. Severe clogging and frequent maintenance occurred at 1500 MFs/L. Despite > 98% MF removal in all reactors, treatment performance declined at higher MF loads. Nitrogen removal dropped from 93% to 68%. Phosphate removal slightly increased in reactors with no or low microfiber loads (96–99%), declined in reactors with 70 or 210 MFs/L (92–91%, 89–88%), and dropped significantly in the reactor with1500 MFs/L (86–70%, p < 0.05). COD removal declined with increasing MF load. Paracoccus (denitrifiers) dominated low-MF reactors; Acinetobacter (associated with complex organic degradation) and Nitrospira (nitrite-oxidizing genus) were enriched at 1500 MFs/L. Performance decline likely stemmed from nutrient transport blockage and toxic leachates, highlighting the potential threat of MFs to wastewater treatment and the need for upstream MF control. Full article
(This article belongs to the Special Issue State-of-the-Art Wastewater Treatment Techniques)
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23 pages, 1802 KiB  
Article
Economic Operation Optimization for Electric Heavy-Duty Truck Battery Swapping Stations Considering Time-of-Use Pricing
by Peijun Shi, Guojian Ni, Rifeng Jin, Haibo Wang, Jinsong Wang and Xiaomei Chen
Processes 2025, 13(7), 2271; https://doi.org/10.3390/pr13072271 - 16 Jul 2025
Abstract
Battery-swapping stations (BSSs) are pivotal for supplying energy to electric heavy-duty trucks. However, their operations face challenges in accurate demand forecasting for battery-swapping and fair revenue allocation. This study proposes an optimization strategy for the economic operation of BSSs that optimizes revenue allocation [...] Read more.
Battery-swapping stations (BSSs) are pivotal for supplying energy to electric heavy-duty trucks. However, their operations face challenges in accurate demand forecasting for battery-swapping and fair revenue allocation. This study proposes an optimization strategy for the economic operation of BSSs that optimizes revenue allocation and load balancing to enhance financial viability and grid stability. First, factors including geographical environment, traffic conditions, and truck characteristics are incorporated to simulate swapping behaviors, supporting the construction of an accurate demand-forecasting model. Second, an optimization problem is formulated to maximize the weighted difference between BSS revenue and squared load deviations. An economic operations strategy is proposed based on an adaptive Shapley value. It enables precise evaluation of differentiated member contributions through dynamic adjustment of bias weights in revenue allocation for a strategy that aligns with the interests of multiple stakeholders and market dynamics. Simulation results validate the superior performance of the proposed algorithm in revenue maximization, peak shaving, and valley filling. Full article
(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)
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22 pages, 76473 KiB  
Article
Modeling Renewable Energy Feed-In Dynamics in a German Metropolitan Region
by Sebastian Bottler and Christian Weindl
Processes 2025, 13(7), 2270; https://doi.org/10.3390/pr13072270 - 16 Jul 2025
Abstract
This study presents community-specific modeling approaches for simulating power injection from photovoltaic and wind energy systems in a German metropolitan region. Developed within the EMN_SIM project and based on openly accessible datasets, the methods are broadly transferable across Germany. For PV, a cluster-based [...] Read more.
This study presents community-specific modeling approaches for simulating power injection from photovoltaic and wind energy systems in a German metropolitan region. Developed within the EMN_SIM project and based on openly accessible datasets, the methods are broadly transferable across Germany. For PV, a cluster-based model groups systems by geographic and technical characteristics, using real weather data to reduce computational effort. Validation against measured specific yields shows strong agreement, confirming energetic accuracy. The wind model operates on a per-turbine basis, integrating technical specifications, land use, and high-resolution wind data. Energetic validation indicates good consistency with Bavarian reference values, while power-based comparisons with selected turbines show reasonable correlation, subject to expected limitations in wind data resolution. The resulting high-resolution generation profiles reveal spatial and temporal patterns valuable for grid planning and targeted policy design. While further validation with additional measurement data could enhance model precision, the current results already offer a robust foundation for urban energy system analyses and future grid integration studies. Full article
(This article belongs to the Special Issue Recent Advances in Energy and Dynamical Systems)
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21 pages, 3914 KiB  
Article
Simulation and Experimental Analysis of Shelf Temperature Effects on the Primary Drying Stage of Cordyceps militaris Freeze-Drying
by Phuc Nguyen Van and An Nguyen Nguyen
Processes 2025, 13(7), 2269; https://doi.org/10.3390/pr13072269 (registering DOI) - 16 Jul 2025
Abstract
This study employs advanced numerical simulation to investigate the influence of shelf temperature on the freeze-drying kinetics and product quality of Cordyceps militaris. Emphasis is placed on the glass transition and structural collapse mechanisms during the primary drying stage. A detailed computational [...] Read more.
This study employs advanced numerical simulation to investigate the influence of shelf temperature on the freeze-drying kinetics and product quality of Cordyceps militaris. Emphasis is placed on the glass transition and structural collapse mechanisms during the primary drying stage. A detailed computational model was developed to predict temperature profiles, glass transition temperature, collapse temperature, and moisture distribution under varying process conditions. Simulation results indicate that maintaining the shelf temperature below 10 °C minimizes the risk of structural collapse and volume shrinkage while improving drying efficiency and product stability. Based on the model, an optimal freeze-drying protocol is proposed: shelf heating at 0 °C, condenser plate at −32 °C, and chamber pressure at 35 Pa. Experimental validation confirmed the feasibility of this regime, yielding a shrinkage of 9.52%, a color difference (ΔE) of 4.86, water activity of 0.364 ± 0.018, and a rehydration ratio of 55.14 ± 0.789%. Key bioactive compounds, including adenosine and cordycepin, were well preserved. These findings underscore the critical role of simulation in process design and optimization, contributing to the development of efficient and high-quality freeze-dried functional food products. Full article
(This article belongs to the Special Issue Simulation, Modeling, and Decision-Making Processes in Manufacturing Systems and Industrial Engineering, 2nd Edition)
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21 pages, 10513 KiB  
Article
The Influence of Secondary Air Guide Vanes on the Flow Field and Performance of a Turbine Air Classifier
by Fulong Wang, Ziwei Zhao, Jiaxiang Peng and Ying Fang
Processes 2025, 13(7), 2268; https://doi.org/10.3390/pr13072268 - 16 Jul 2025
Abstract
To address the issue where the axial negative velocity on the cylinder wall of the traditional bottom-inlet rotor classifier causes fine particles to be mixed into coarse powder, reducing classification efficiency, this study proposes adding guide vanes to the rotor classifier. By improving [...] Read more.
To address the issue where the axial negative velocity on the cylinder wall of the traditional bottom-inlet rotor classifier causes fine particles to be mixed into coarse powder, reducing classification efficiency, this study proposes adding guide vanes to the rotor classifier. By improving the stability of the secondary elutriation flow field, we enhance the secondary classification of coarse particles. Airflow simulations based on ANSYS Fluent show that the guide vanes can significantly strengthen the intensity of the secondary elutriation zone, increase the tangential velocity in the classification zone, and reduce the particle concentration in the secondary air volute. The key results are as follows: when the installation angle is 30°, the classification accuracy reaches its peak with K = 0.71, and the cut size D50 = 48.9 μm. This research provides a theoretical basis for optimizing the structural design of classifiers. Full article
(This article belongs to the Section Separation Processes)
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20 pages, 9542 KiB  
Article
Effect of Electron Beam Irradiation on Microbiological Safety and Quality of Chilled Poultry Meat from Kazakhstan
by Raushangul Uazhanova, Ulbala Tungyshbayeva, Sungkar Nurdaulet, Almas Zhanbolat, Yus Aniza Yusof, Shakhsanam Seksenbay, Igor Danko and Zamzagul Moldakhmetova
Processes 2025, 13(7), 2267; https://doi.org/10.3390/pr13072267 - 16 Jul 2025
Abstract
Ensuring the safety and extending the shelf life of chilled poultry meat is vital in modern poultry meat production, particularly given the recent increase in demand in this area. Chilled meat has a short shelf life, so producers have limited time to sell [...] Read more.
Ensuring the safety and extending the shelf life of chilled poultry meat is vital in modern poultry meat production, particularly given the recent increase in demand in this area. Chilled meat has a short shelf life, so producers have limited time to sell their products and must rely on various methods of extending shelf life. Compared with other non-thermal methods, electron beam irradiation is a new non-thermal meat preservation technique with low cost, avoidance of contamination, and antibacterial effects. In this study, we investigate the effect of electron beam irradiation on the microbiological and physicochemical quality of chilled poultry meat produced in Kazakhstan to assess its suitability for use in local food processing systems. The samples were electron-beam-treated at doses of 2, 4, 6, and 8 kGy and stored in a refrigerator. Microbiological and physicochemical property evaluations were carried out for a period of 14 days. Our results demonstrated a significant decrease in total aerobic and facultative anaerobic microorganisms, and no detectable levels of Salmonella spp. and Listeria monocytogenes in the irradiated samples. The pH measurements remained stable at low doses; in comparison, higher doses resulted in a slight decrease. Moisture, protein, fat, and ash content were also evaluated and showed minimal changes as functions of irradiation dose. Our results indicate that electron beam irradiation, particularly at a dose of 2–4 kGy, effectively improves microbiological safety and extends the shelf life of chilled poultry meat up to 5–6 days, making it a promising solution for the modern poultry meat industry. Full article
(This article belongs to the Section Food Process Engineering)
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17 pages, 3660 KiB  
Article
Production Decline Rate Prediction for Offshore High Water-Cut Reservoirs by Integrating Moth–Flame Optimization with Extreme Gradient Boosting Tree
by Zupeng Ding, Chuan Lu, Long Chen, Qinwan Chong, Yintao Dong, Wenlong Xia and Fankun Meng
Processes 2025, 13(7), 2266; https://doi.org/10.3390/pr13072266 - 16 Jul 2025
Abstract
The prediction of production decline rate in the development of offshore high water-cut reservoirs predominantly relies on the traditional Arps decline curves. However, the solution process is complex, and the interpretation efficiency is low, making it difficult to meet the demand for rapid [...] Read more.
The prediction of production decline rate in the development of offshore high water-cut reservoirs predominantly relies on the traditional Arps decline curves. However, the solution process is complex, and the interpretation efficiency is low, making it difficult to meet the demand for rapid prediction of production decline rates. To address this, this paper first identifies the key influencing factors of production decline rate through comprehensive feature engineering. Subsequently, it proposes a novel prediction method for the production decline rate in offshore high water-cut reservoirs by integrating Moth–Flame Optimization with Extreme Gradient Boosting Tree (MFO-XGBoost). This method utilizes seven dynamic and static influencing factors, namely vertical thickness, perforated thickness, shale content, permeability, crude oil viscosity, formation flow coefficient, and well deviation angle, to predict the production decline rate. The forecasting outcomes of the MFO-XGBoost method are then compared with those of standard RF, standard DT, the standalone XGBoost model, and the calculated results from the exponential decline model. Additionally, the forecasting capability of the MFO-XGBoost method is benchmarked against Particle Swarm Optimization–XGBoost (PSO-XGBoost) and Bayesian Optimization–XGBoost methods for predicting the production decline rate in offshore high water-cut reservoirs. The findings from the experiments show that the MFO-XGBoost method can achieve accurate prediction of the production decline rate in offshore high water-cut reservoirs, with a coefficient of determination (R2) reaching 0.9128, thereby providing a basis for strategies to mitigate the production decline rate. Full article
(This article belongs to the Section Energy Systems)
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22 pages, 323 KiB  
Article
The First- and Second-Order Features Adjoint Sensitivity Analysis Methodologies for Fredholm-Type Neural Integro-Differential Equations: An Illustrative Application to a Heat Transfer Model—Part II
by Dan Gabriel Cacuci
Processes 2025, 13(7), 2265; https://doi.org/10.3390/pr13072265 - 16 Jul 2025
Abstract
This work illustrates the application of the “First-Order Features Adjoint Sensitivity Analysis Methodology for Neural Integro-Differential Equations of Fredholm-Type” (1st-FASAM-NIDE-F) and the “Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Integro-Differential Equations of Fredholm-Type” (2nd-FASAM-NIDE-F) to a paradigm heat transfer model. This physically [...] Read more.
This work illustrates the application of the “First-Order Features Adjoint Sensitivity Analysis Methodology for Neural Integro-Differential Equations of Fredholm-Type” (1st-FASAM-NIDE-F) and the “Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Integro-Differential Equations of Fredholm-Type” (2nd-FASAM-NIDE-F) to a paradigm heat transfer model. This physically based heat transfer model has been deliberately constructed so that it can be represented either by a neural integro-differential equation of a Fredholm type (NIDE-F) or by a conventional second-order “neural ordinary differential equation (NODE)” while admitting exact closed-form solutions/expressions for all quantities of interest, including state functions and first-order and second-order sensitivities. This heat transfer model enables a detailed comparison of the 1st- and 2nd-FASAM-NIDE-F versus the recently developed 1st- and 2nd-FASAM-NODE methodologies, highlighting the considerations underlying the optimal choice for cases where the neural net of interest is amenable to using either of these methodologies for its sensitivity analysis. It is shown that the 1st-FASAM-NIDE-F methodology enables the most efficient computation of exactly determined first-order sensitivities of the decoder response with respect to the optimized NIDE-F parameters, requiring a single “large-scale” computation for solving the 1st-Level Adjoint Sensitivity System (1st-LASS), regardless of the number of weights/parameters underlying the NIDE-F decoder, hidden layers, and encoder. The 2nd-FASAM-NIDE-F methodology enables the computation, with unparalleled efficiency, of the second-order sensitivities of decoder responses with respect to the optimized/trained weights. Full article
(This article belongs to the Section Energy Systems)
16 pages, 5026 KiB  
Article
Insulation Ability and Morphological Effect of ZrO2 Spacer Layer in Carbon-Based Multiporous Layered Electrode Perovskite Solar Cells
by Takaya Shioki, Naonari Izumoto, Fumitaka Iwakura, Ryuki Tsuji and Seigo Ito
Processes 2025, 13(7), 2264; https://doi.org/10.3390/pr13072264 - 16 Jul 2025
Abstract
Fully printable carbon-based multiporous layered electrode perovskite solar cells (MPLE−PSCs) are close to being commercialized due to their excellent stability, their ability to easily be scaled up, and their amenability to mass production via non-vacuum fabrication processes. To improve their efficiency, it is [...] Read more.
Fully printable carbon-based multiporous layered electrode perovskite solar cells (MPLE−PSCs) are close to being commercialized due to their excellent stability, their ability to easily be scaled up, and their amenability to mass production via non-vacuum fabrication processes. To improve their efficiency, it is important that detailed studies of the morphologies of mesoporous electrodes be carried out. In this study, we prepared five types of ZrO2 spacer layers for MPLE−PSCs, and the morphology of ZrO2 and device performance were evaluated using a scanning electron microscope, nitrogen adsorption/desorption measurements, electrode resistance measurements, UV-visible light reflectance measurements, and current density–voltage measurements. The results reveal that the adequate specific surface area and pore size distribution of mesoporous ZrO2 provided high insulation ability when used as spacers between electrodes and light absorbance, resulting in a 10.92% photoelectric conversion efficiency with a 23.22 mA cm−2 short-circuit current density. This information can serve as a guideline for designing morphologies useful for producing high-efficiency devices. Full article
(This article belongs to the Special Issue Sustainability of Perovskite Solar Cells)
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14 pages, 2239 KiB  
Article
Automatic Delineation of Resistivity Contrasts in Magnetotelluric Models Using Machine Learning
by Ever Herrera Ríos, Mateo Marulanda, Hernán Arboleda, Greg Soule, Erika Lucuara, David Jaramillo, Agustín Cardona, Esteban A. Taborda, Farid B. Cortés and Camilo A. Franco
Processes 2025, 13(7), 2263; https://doi.org/10.3390/pr13072263 - 16 Jul 2025
Abstract
The precise identification of hydrocarbon-rich zones is crucial for optimizing exploration and production processes in the oil industry. Magnetotelluric (MT) surveys play a fundamental role in mapping subsurface geological structures. This study presents a novel methodology for automatically delineating resistivity contrasts in MT [...] Read more.
The precise identification of hydrocarbon-rich zones is crucial for optimizing exploration and production processes in the oil industry. Magnetotelluric (MT) surveys play a fundamental role in mapping subsurface geological structures. This study presents a novel methodology for automatically delineating resistivity contrasts in MT models by employing advanced machine learning and computer vision techniques. This approach commences with data augmentation to enhance the diversity and volume of resistivity data. Subsequently, a bilateral filter was applied to reduce noise while preserving edge details within the resistivity images. To further improve image contrast and highlight significant resistivity variations, contrast-limited adaptive histogram equalization (CLAHE) was employed. Finally, k-means clustering was utilized to segment the resistivity data into distinct groups based on resistivity values, enabling the identification of color features in different centroids. This facilitated the detection of regions with significant resistivity contrasts in the reservoir. From the clustered images, color masks were generated to visually differentiate the groups and calculate the area and proportion of each group within the pictures. Key features extracted from resistivity profiles were used to train unsupervised learning models capable of generalizing across different geological settings. The proposed methodology improves the accuracy of detecting zones with oil potential and offers scalable applicability to different datasets with minimal retraining, applicable to different subsurface environments. Ultimately, this study seeks to improve the efficiency of petroleum exploration by providing a high-precision automated framework with segmentation and contrast delineation for resistivity analysis, integrating advanced image processing and machine learning techniques. During initial analyses using only k-means, the resulting optimal value of the silhouette coefficient K was 2. After using bilateral filtering together with contrast-limited adaptive histogram equalization (CLAHE) and validation by an expert, the results were more representative, and six clusters were identified. Ultimately, this study seeks to improve the efficiency of petroleum exploration by providing a high-precision automated framework with segmentation and contrast delineation for resistivity analysis, integrating advanced image processing and machine learning techniques. Full article
(This article belongs to the Section Energy Systems)
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15 pages, 1633 KiB  
Article
Sustainable Bamboo-Based Packaging and Passive Modified Atmosphere: A Strategy to Preserve Strawberry Quality During Cold Storage
by Giuseppina Adiletta, Marisa Di Matteo, Giuseppe De Filippis, Antonio Di Grazia, Paolo Ciambelli and Milena Petriccione
Processes 2025, 13(7), 2262; https://doi.org/10.3390/pr13072262 - 15 Jul 2025
Viewed by 100
Abstract
This study investigates the potential of bamboo-based sustainable packaging in combination with passive modified atmosphere (MA) and cold storage to enhance the shelf life of strawberries while preserving their physico-chemical properties, bioactive compounds, and antioxidant enzyme activity. The study monitored key parameters such [...] Read more.
This study investigates the potential of bamboo-based sustainable packaging in combination with passive modified atmosphere (MA) and cold storage to enhance the shelf life of strawberries while preserving their physico-chemical properties, bioactive compounds, and antioxidant enzyme activity. The study monitored key parameters such as fruit weight loss, firmness, color, and the content of bioactive compounds as well as phenolics and flavonoids. Additionally, antioxidant enzyme activity, including catalase, ascorbate peroxidase, and superoxide dismutase, was assessed to evaluate oxidative stress during 9 days at 4 °C. The results show that strawberries packaged with bamboo materials in a passive MA retained their physico-chemical traits, exhibiting slower changes in firmness, color, and bioactive compound content compared to those in unpackaged samples. Furthermore, the antioxidant enzyme activity remained significantly higher, suggesting a lower oxidative stress in packaged fruit. This combination of bamboo-based packaging with passive MA is a valid, effective, and sustainable approach to prolonging the qualitative traits of strawberries during cold storage, offering both environmental and nutritional benefits. Full article
(This article belongs to the Special Issue Processing Foods: Process Optimization and Quality Assessment, 3rd Edition)
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18 pages, 3899 KiB  
Article
Multi-Agent-Based Estimation and Control of Energy Consumption in Residential Buildings
by Otilia Elena Dragomir and Florin Dragomir
Processes 2025, 13(7), 2261; https://doi.org/10.3390/pr13072261 - 15 Jul 2025
Viewed by 55
Abstract
Despite notable advancements in smart home technologies, residential energy management continues to face critical challenges. These include the complex integration of intermittent renewable energy sources, issues related to data latency, interoperability, and standardization across diverse systems, the inflexibility of centralized control architectures in [...] Read more.
Despite notable advancements in smart home technologies, residential energy management continues to face critical challenges. These include the complex integration of intermittent renewable energy sources, issues related to data latency, interoperability, and standardization across diverse systems, the inflexibility of centralized control architectures in dynamic environments, and the difficulty of accurately modeling and influencing occupant behavior. To address these challenges, this study proposes an intelligent multi-agent system designed to accurately estimate and control energy consumption in residential buildings, with the overarching objective of optimizing energy usage while maintaining occupant comfort and satisfaction. The methodological approach employed is a hybrid framework, integrating multi-agent system architecture with system dynamics modeling and agent-based modeling. This integration enables decentralized and intelligent control while simultaneously simulating physical processes such as heat exchange, insulation performance, and energy consumption, alongside behavioral interactions and real-time adaptive responses. The system is tested under varying conditions, including changes in building insulation quality and external temperature profiles, to assess its capability for accurate control and estimation of energy use. The proposed tool offers significant added value by supporting real-time responsiveness, behavioral adaptability, and decentralized coordination. It serves as a risk-free simulation platform to test energy-saving strategies, evaluate cost-effective insulation configurations, and fine-tune thermostat settings without incurring additional cost or real-world disruption. The high fidelity and predictive accuracy of the system have important implications for policymakers, building designers, and homeowners, offering a practical foundation for informed decision making and the promotion of sustainable residential energy practices. Full article
(This article belongs to the Special Issue Sustainable Development of Energy and Environment in Buildings)
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15 pages, 1695 KiB  
Article
Multiscale Modeling of Rayleigh–Taylor Instability in Stratified Fluids Using High-Order Hybrid Schemes
by Xiao Wen, Xiutao Chen, Feng Wang and Chen Feng
Processes 2025, 13(7), 2260; https://doi.org/10.3390/pr13072260 - 15 Jul 2025
Viewed by 119
Abstract
Inertial confinement fusion (ICF) stands as one of the approaches to achieve controlled thermonuclear fusion, capable of supplying humans with abundant, economical, and safe energy. In this study, the high-order hybrid compact–WENO scheme is employed to simulate Rayleigh–Taylor instability (RTI) phenomena, one of [...] Read more.
Inertial confinement fusion (ICF) stands as one of the approaches to achieve controlled thermonuclear fusion, capable of supplying humans with abundant, economical, and safe energy. In this study, the high-order hybrid compact–WENO scheme is employed to simulate Rayleigh–Taylor instability (RTI) phenomena, one of the challenges hindering the realization of ICF, and to investigate the interaction of RTI phenomena in a multi-layer fluid system. To ensure a more reasonable comparison, the corresponding initial and boundary conditions for three-layer and four-layer fluids are derived based on the same Atwood number. Numerical results show that with the development of RTI phenomena, the interaction between interfaces can be gradually observed. The number of fluid layers exhibits an inhibitory effect on the development of RTI phenomena. When a pair of spike and bubble at two adjacent interfaces reach the same height, the evolution of the spike–bubble gap changes significantly. Full article
(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)
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27 pages, 871 KiB  
Review
Addressing Challenges in Large-Scale Bioprocess Simulations: A Circular Economy Approach Using SuperPro Designer
by Juan Silvestre Aranda-Barradas, Claudia Guerrero-Barajas and Alberto Ordaz
Processes 2025, 13(7), 2259; https://doi.org/10.3390/pr13072259 - 15 Jul 2025
Viewed by 85
Abstract
Bioprocess simulation is a powerful tool for leveraging circular economy principles in the analysis of large-scale bioprocesses, enhancing decision-making for efficient and sustainable production. By simulating different process scenarios, researchers and engineers can evaluate the techno-economic feasibility of different approaches. This approach enables [...] Read more.
Bioprocess simulation is a powerful tool for leveraging circular economy principles in the analysis of large-scale bioprocesses, enhancing decision-making for efficient and sustainable production. By simulating different process scenarios, researchers and engineers can evaluate the techno-economic feasibility of different approaches. This approach enables the identification of cost-effective and sustainable solutions, optimizing resource use and minimizing waste, thereby enhancing the overall efficiency and viability of bioprocesses within a circular economy framework. In this review, we provide an overview of circular economy concepts and trends before discussing design methodologies and challenges in large-scale bioprocesses. The analysis highlights the application and advantages of using process simulators like SuperPro Designer v.14 in bioprocess development. Process design methodologies have evolved to use specialized software that integrates chemical and biochemical processes, physical properties, and economic and environmental considerations. By embracing circular economy principles, these methodologies evaluate projects that transform waste into valuable products, aiming to reduce pollution and resources use, thereby shifting from a linear to a circular economy. In process engineering, exciting perspectives are emerging, particularly in large-scale bioprocess simulations, which are expected to contribute to the improvement of bioprocess technology and computer applications. Full article
(This article belongs to the Special Issue Trends in Biochemical Processing Techniques)
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29 pages, 372 KiB  
Article
The First- and Second-Order Features Adjoint Sensitivity Analysis Methodologies for Fredholm-Type Neural Integro-Differential Equations: I. Mathematical Framework
by Dan Gabriel Cacuci
Processes 2025, 13(7), 2258; https://doi.org/10.3390/pr13072258 - 15 Jul 2025
Viewed by 73
Abstract
This work presents the “First-Order Features Adjoint Sensitivity Analysis Methodology for Neural Integro-Differential Equations of Fredholm-Type” (1st-FASAM-NIDE-F) and the “Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Integro-Differential Equations of Fredholm-Type” (2nd-FASAM-NIDE-F). It is shown that the 1st-FASAM-NIDE-F methodology enables the most efficient [...] Read more.
This work presents the “First-Order Features Adjoint Sensitivity Analysis Methodology for Neural Integro-Differential Equations of Fredholm-Type” (1st-FASAM-NIDE-F) and the “Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Integro-Differential Equations of Fredholm-Type” (2nd-FASAM-NIDE-F). It is shown that the 1st-FASAM-NIDE-F methodology enables the most efficient computation of exactly-determined first-order sensitivities of decoder response with respect to the optimized NIDE-F parameters, requiring a single “large-scale” computation for solving the 1st-Level Adjoint Sensitivity System (1st-LASS), regardless of the number of weights/parameters underlying the NIDE-F decoder, hidden layers, and encoder. The 2nd-FASAM-NIDE-F methodology enables the computation, with unparalleled efficiency, of the second-order sensitivities of decoder responses with respect to the optimized/trained weights, requiring only as many large-scale computations for solving the 2nd-Level Adjoint Sensitivity System (2nd-LASS) as there are non-zero feature functions of parameters. The application of both the 1st-FASAM-NIDE-F and the 2nd-FASAM-NIDE-F methodologies is illustrated in an accompanying work (Part II) by considering a paradigm heat transfer model. Full article
(This article belongs to the Section Energy Systems)
20 pages, 2909 KiB  
Article
Solar Photo-Fenton: An Effective Method for MCPA Degradation
by Alicia Martin-Montero, Argyro Maria Zapanti, Gema Pliego, Jose A. Casas and Alicia L. Garcia-Costa
Processes 2025, 13(7), 2257; https://doi.org/10.3390/pr13072257 - 15 Jul 2025
Viewed by 140
Abstract
The extensive use of herbicide 2-methyl-4-chlorophenoxyacetic acid (MCPA), coupled with its limited biodegradability, has led to its ubiquitous presence in aquatic environments. This work investigates the removal of MCPA (100 mg/L) in the aqueous phase via solar photo-Fenton. The process was carried out [...] Read more.
The extensive use of herbicide 2-methyl-4-chlorophenoxyacetic acid (MCPA), coupled with its limited biodegradability, has led to its ubiquitous presence in aquatic environments. This work investigates the removal of MCPA (100 mg/L) in the aqueous phase via solar photo-Fenton. The process was carried out in a 700 mL reactor using a Xe lamp that simulates solar radiation (λ: 250–700 nm). A parametric study was conducted to assess the influence of dissolved O2 on the reaction medium, Fe2+ dosage, H2O2 concentration and pH0. The results indicate that dissolved O2 boosts pollutant mineralization, even working at sub-stoichiometric H2O2 concentrations. Under optimal reaction conditions ([Fe2+]: 7.5 mg/L, [H2O2]0: 322 mg/L (stoichiometric dose), pH0: 3.5), the MCPA reached almost complete mineralization (XTOC: 98.40%) in 180 min. Phytotoxicity and ecotoxicity assessments of treated effluents revealed that even working at sub-stoichiometric H2O2 dosages, toxicity decreases with the solar photo-Fenton treatment. Finally, the solar photo-Fenton process was evaluated in relevant matrices (river water and WWTP secondary effluent) and a realistic pollutant concentration (100 µg/L). In all cases, the pollutant degradation was ≥70% in 60 min, demonstrating the potential of this technology as a tertiary treatment. Full article
(This article belongs to the Special Issue Recent Advances in Wastewater Treatment and Water Reuse)
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27 pages, 2260 KiB  
Article
Machine Learning for Industrial Optimization and Predictive Control: A Patent-Based Perspective with a Focus on Taiwan’s High-Tech Manufacturing
by Chien-Chih Wang and Chun-Hua Chien
Processes 2025, 13(7), 2256; https://doi.org/10.3390/pr13072256 - 15 Jul 2025
Viewed by 144
Abstract
The global trend toward Industry 4.0 has intensified the demand for intelligent, adaptive, and energy-efficient manufacturing systems. Machine learning (ML) has emerged as a crucial enabler of this transformation, particularly in high-mix, high-precision environments. This review examines the integration of machine learning techniques, [...] Read more.
The global trend toward Industry 4.0 has intensified the demand for intelligent, adaptive, and energy-efficient manufacturing systems. Machine learning (ML) has emerged as a crucial enabler of this transformation, particularly in high-mix, high-precision environments. This review examines the integration of machine learning techniques, such as convolutional neural networks (CNNs), reinforcement learning (RL), and federated learning (FL), within Taiwan’s advanced manufacturing sectors, including semiconductor fabrication, smart assembly, and industrial energy optimization. The present study draws on patent data and industrial case studies from leading firms, such as TSMC, Foxconn, and Delta Electronics, to trace the evolution from classical optimization to hybrid, data-driven frameworks. A critical analysis of key challenges is provided, including data heterogeneity, limited model interpretability, and integration with legacy systems. A comprehensive framework is proposed to address these issues, incorporating data-centric learning, explainable artificial intelligence (XAI), and cyber–physical architectures. These components align with industrial standards, including the Reference Architecture Model Industrie 4.0 (RAMI 4.0) and the Industrial Internet Reference Architecture (IIRA). The paper concludes by outlining prospective research directions, with a focus on cross-factory learning, causal inference, and scalable industrial AI deployment. This work provides an in-depth examination of the potential of machine learning to transform manufacturing into a more transparent, resilient, and responsive ecosystem. Additionally, this review highlights Taiwan’s distinctive position in the global high-tech manufacturing landscape and provides an in-depth analysis of patent trends from 2015 to 2025. Notably, this study adopts a patent-centered perspective to capture practical innovation trends and technological maturity specific to Taiwan’s globally competitive high-tech sector. Full article
(This article belongs to the Special Issue Machine Learning for Industrial Optimization and Predictive Control)
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23 pages, 6769 KiB  
Article
Prediction of Mud Weight Window Based on Geological Sequence Matching and a Physics-Driven Machine Learning Model for Pre-Drilling
by Yuxin Chen, Ting Sun, Jin Yang, Xianjun Chen, Laiao Ren, Zhiliang Wen, Shu Jia, Wencheng Wang, Shuqun Wang and Mingxuan Zhang
Processes 2025, 13(7), 2255; https://doi.org/10.3390/pr13072255 - 15 Jul 2025
Viewed by 136
Abstract
Accurate pre-drilling mud weight window (MWW) prediction is crucial for drilling fluid design and wellbore stability in complex geological formations. Traditional physics-based approaches suffer from subjective parameter selection and inadequate handling of multi-mechanism over-pressured formations, while machine learning methods lack physical constraints and [...] Read more.
Accurate pre-drilling mud weight window (MWW) prediction is crucial for drilling fluid design and wellbore stability in complex geological formations. Traditional physics-based approaches suffer from subjective parameter selection and inadequate handling of multi-mechanism over-pressured formations, while machine learning methods lack physical constraints and interpretability. This study develops a novel physics-guided deep learning framework integrating rock mechanics theory with deep neural networks for enhanced MWW prediction. The framework incorporates three key components: first, a physics-driven layer synthesizing intermediate variables from rock physics calculations to embed domain knowledge while preserving interpretability; second, a geological sequence-matching algorithm enabling precise stratigraphic correlation between offset and target wells, compensating for lateral geological heterogeneity; third, a long short-term memory network capturing sequential drilling characteristics and geological structure continuity. Case study results from 12 wells in northwestern China demonstrate significant improvements over traditional methods: collapse pressure prediction error reduced by 40.96%, pore pressure error decreased by 30.43%, and fracture pressure error diminished by 39.02%. The proposed method successfully captures meter-scale pressure variations undetectable by conventional approaches, providing critical technical support for wellbore design optimization, drilling fluid formulation, and operational safety enhancement in challenging geological environments. Full article
(This article belongs to the Special Issue Applications of Intelligent Models in the Petroleum Industry)
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18 pages, 1945 KiB  
Article
Research on an Active Distribution Network Planning Strategy Considering Diversified Flexible Resource Allocation
by Minglei Jiang, Youqing Xu, Dachi Zhang, Yuanqi Liu, Qiushi Du, Xiaofeng Gao, Shiwei Qi and Hongbo Zou
Processes 2025, 13(7), 2254; https://doi.org/10.3390/pr13072254 - 15 Jul 2025
Viewed by 122
Abstract
When planning distributed intelligent power distribution networks, it is necessary to take into account the interests of various distributed generation (DG) operators and power supply enterprises, thereby diversifying and complicating planning models. Additionally, the integration of a high proportion of distributed resources has [...] Read more.
When planning distributed intelligent power distribution networks, it is necessary to take into account the interests of various distributed generation (DG) operators and power supply enterprises, thereby diversifying and complicating planning models. Additionally, the integration of a high proportion of distributed resources has triggered a transformation in the power flow pattern of active distribution networks, shifting from the traditional unidirectional flow mode to a bidirectional interactive mode. The intermittent and fluctuating operation modes of distributed photovoltaic and wind power generation have also increased the difficulty of distribution network planning. To address the aforementioned challenges, this paper proposes an active distribution network planning strategy that considers the allocation of diverse flexible resources, exploring scheduling flexibility from both the power supply side and the load side. Firstly, a bi-level optimization model integrating planning and operation is constructed, where the upper-level model determines the optimal capacity of investment and construction equipment, and the lower-level model formulates an economic dispatching scheme. Through iterative solving of the upper and lower levels, the final planning strategy is determined. Meanwhile, to reduce the complexity of problem-solving, this paper employs an improved PSO-CS hybrid algorithm for iterative optimization. Finally, the effectiveness and feasibility of the proposed algorithm are demonstrated through validation using an improved IEEE-33-bus test system. Compared with conventional algorithms, the convergence speed of the method proposed in this paper can be improved by up to 21.4%, and the total investment cost can be reduced by up to 3.26%. Full article
(This article belongs to the Special Issue Applications of Smart Microgrids in Renewable Energy Development)
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26 pages, 4282 KiB  
Article
Optimizing Perforated Duct Systems for Energy-Efficient Ventilation in Semi-Closed Greenhouses Through Process Regulation
by Chuanqing Wang, Jianlu Fu, Qiusheng Zhang, Baoyong Sheng, Fen He, Guanshan Zhang, Xiaoming Ding and Nan Cao
Processes 2025, 13(7), 2253; https://doi.org/10.3390/pr13072253 - 15 Jul 2025
Viewed by 122
Abstract
Traditional perforated duct designs fail to resolve the energy consumption-uniformity conflict in semi-closed greenhouses. To address this, we develop a CFD-RSM-NSGA-II framework that simultaneously minimizes velocity non-uniformity (CV-v), pressure loss (ΔP), and temperature variation (CV-t). Key parameters—hole diameter (6–10 mm), spacing (30–70 mm), [...] Read more.
Traditional perforated duct designs fail to resolve the energy consumption-uniformity conflict in semi-closed greenhouses. To address this, we develop a CFD-RSM-NSGA-II framework that simultaneously minimizes velocity non-uniformity (CV-v), pressure loss (ΔP), and temperature variation (CV-t). Key parameters—hole diameter (6–10 mm), spacing (30–70 mm), and inlet velocity (4–8 m/s)—are co-optimized. Model validation showed that the mean relative errors were 8.6% for velocity, 2.3% for temperature, and pressure deviations below 5 Pa, with the response surface model achieving an R2 of 0.9831 (p < 0.0001). Larger hole diameters improved CV-v, while wider spacings led to a decrease in uniformity. Pressure loss followed an opposite trend. Temperature variation was mostly affected by inlet velocity. Sensitivity analysis revealed that hole diameter was the most influential factor, followed by spacing and velocity, with a significant interaction between diameter and spacing. Using entropy-weighted TOPSIS coupled with NSGA-II, the optimization identified an optimal configuration (hole diameter = 9.0 mm, spacing = 65 mm, velocity = 7.0 m/s). This solution achieved a 58.8% reduction in CV-v, a 10.8% decrease in ΔP, and a 5.2% improvement in CV-t, while stabilizing inlet static pressure at 72.8 Pa. Critically, it reduced power consumption by 17.4%—directly lowering operational costs for farmers. The “larger diameter, wider spacing” strategy resolves energy-uniformity conflicts, demonstrating how integrated multi-objective process control enables efficient greenhouse ventilation. Full article
(This article belongs to the Section Process Control and Monitoring)
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21 pages, 4916 KiB  
Article
Fracture Competitive Propagation and Fluid Dynamic Diversion During Horizontal Well Staged Hydraulic Fracturing
by Yujie Yan, Yanling Wang, Hui Li, Qianren Wang and Bo Wang
Processes 2025, 13(7), 2252; https://doi.org/10.3390/pr13072252 - 15 Jul 2025
Viewed by 144
Abstract
This study addresses the challenge of non-uniform fracture propagation in multi-cluster staged fracturing of horizontal wells by proposing a three-dimensional dynamic simulation method for temporary plugging fracturing, grounded in a fully coupled fluid–solid damage theory framework. A Tubing-CZM (cohesive zone model) coupling model [...] Read more.
This study addresses the challenge of non-uniform fracture propagation in multi-cluster staged fracturing of horizontal wells by proposing a three-dimensional dynamic simulation method for temporary plugging fracturing, grounded in a fully coupled fluid–solid damage theory framework. A Tubing-CZM (cohesive zone model) coupling model was developed to enable real-time interaction computation of flow distribution and fracture propagation. Focusing on the Xinjiang X Block reservoir, this research systematically investigates the influence mechanisms of reservoir properties, engineering parameters (fracture spacing, number of perforation clusters, perforation friction), and temporary plugging parameters on fracture propagation morphology and fluid allocation. Our key findings include the following. (1) Increasing fracture spacing from 10 m to 20 m enhances intermediate fracture length by 38.2% and improves fracture width uniformity by 21.5%; (2) temporary plugging reduces the fluid intake heterogeneity coefficient by 76% and increases stimulated reservoir volume (SRV) by 32%; (3) high perforation friction (7.5 MPa) significantly optimizes fracture uniformity compared to low-friction (2.5 MPa) scenarios, balancing flow allocation ratios between edge and central fractures. The proposed dynamic flow diversion control criteria and quantified temporary plugging design standards provide critical theoretical foundations and operational guidelines for optimizing unconventional reservoir fracturing. Full article
(This article belongs to the Special Issue Complex Fluid Dynamics Modeling and Simulation, 2nd Edition)
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32 pages, 955 KiB  
Review
A Review of the Application of Fuzzy Logic in Bioenergy Technology
by Sibabalwe Zenani, KeChrist Obileke, Odilo Ndiweni and Patrick Mukumba
Processes 2025, 13(7), 2251; https://doi.org/10.3390/pr13072251 - 15 Jul 2025
Viewed by 225
Abstract
Although fuzzy logic is regarded as an old modelling technique, its application in recent studies cannot be overemphasised. Therefore, the study aims to provide recent developments and ideas based on the scholarly contribution from the literature on how uncertainty can be reduced and [...] Read more.
Although fuzzy logic is regarded as an old modelling technique, its application in recent studies cannot be overemphasised. Therefore, the study aims to provide recent developments and ideas based on the scholarly contribution from the literature on how uncertainty can be reduced and to enhance decision-making through fuzzy logic in relation to bioenergy technologies. This is necessary to address the potential of uncertainty, inherently subjective information, and handling imprecise data, as well as identifying sustainable determinants in bioenergy technologies. Fuzzy logic application is an essential modelling technique in this regard. In this paper, a review focusing on the comprehensive and detailed applications of fuzzy logic models in bioenergy technologies is presented. From the review, it is found that the integration and combination of a fuzzy logic model plus other modelling techniques provides a better performance and is known to be effective and efficient. The review demonstrates how fuzzy logic can help to manage complicated variables, thereby ultimately promoting more effective and sustainable bioenergy solutions. Hence, for maximum attention on the review, it is suitable for stakeholders, planners, and decision makers in bioenergy research and industry. Full article
(This article belongs to the Section Energy Systems)
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24 pages, 1485 KiB  
Review
Sustainable and Advanced Strategies for Bioremediation of Highly Contaminated Wastewater
by Marija Vuković Domanovac, Mirela Volf, Monika Šabić Runjavec and Ivana Terzić
Processes 2025, 13(7), 2250; https://doi.org/10.3390/pr13072250 - 15 Jul 2025
Viewed by 165
Abstract
The risk of contamination of the vital resource of water continues to increase and represents an urgent problem for modern society. Globalisation, industrialisation and technological progress have led to the need to treat more and more wastewater streams before they can be released [...] Read more.
The risk of contamination of the vital resource of water continues to increase and represents an urgent problem for modern society. Globalisation, industrialisation and technological progress have led to the need to treat more and more wastewater streams before they can be released into the environment. A high chemical and biochemical oxygen demand as well as the sum of dissolved and suspended organic and inorganic components are the main characteristics of highly contaminated wastewater. Research into environmentally friendly and sustainable technologies is becoming increasingly important in wastewater treatment. Bioremediation utilises the ability to restore the biogenic elements of the environment and is an environmentally friendly method for removing contaminants from the surrounding ecosystem. Forming microbial consortia that exhibit both excellent biosorption properties and a high resistance to toxic conditions is crucial for the biodegradation of complicated systems, such as highly contaminated wastewater. The development of systematic biological molecular tools can further improve the bioremediation process. By integrating innovative technologies with the already existing natural microbial capacity, it is possible to further improve the sustainability of biological treatments of wastewater streams while preserving the natural environment. Full article
(This article belongs to the Special Issue Processes Development for Wastewater Treatment)
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27 pages, 2263 KiB  
Review
Sustainable Remediation: Advances in Red Mud-Based Synergistic Fabrication Techniques and Mechanistic Insights for Enhanced Heavy Metal(Loid)s Sorption in Wastewater
by Feng Li, Renjian Deng, Baolin Hou, Lingyu Peng, Bozhi Ren, Xiangxing Kong, Bo Zhang and Andrew Hursthouse
Processes 2025, 13(7), 2249; https://doi.org/10.3390/pr13072249 - 14 Jul 2025
Viewed by 120
Abstract
Rapid growth in the alumina industry generates vast amounts of highly alkaline red mud (RM), posing significant environmental risks. However, RM shows great promise as a resource for environmental remediation, particularly through its conversion into effective adsorbents. This research reviews recent advancements in [...] Read more.
Rapid growth in the alumina industry generates vast amounts of highly alkaline red mud (RM), posing significant environmental risks. However, RM shows great promise as a resource for environmental remediation, particularly through its conversion into effective adsorbents. This research reviews recent advancements in developing RM-based adsorbents for sustainable wastewater treatment, especially targeting heavy metal(loid)s (HMs). We examine key modification mechanisms to enhance RM’s properties, summarize synthesis methods for various RM- based adsorbents, and evaluate their performance in removing HMs from water, guiding the design of subsequent new materials. Crucially, this review highlights studies on adsorbent reusability, HM leaching, and economic feasibility to address economic and safety concerns. Finally, we discuss adsorption mechanisms and prospects for these materials. Full article
(This article belongs to the Special Issue Sediment Contamination and Metal Removal from Wastewater)
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18 pages, 4231 KiB  
Article
Effect Mechanism of Phosphorus-Containing Flame Retardants with Different Phosphorus Valence States on the Safety and Electrochemical Performance of Lithium-Ion Batteries
by Peng Xi, Fengling Sun, Xiaoyu Tang, Xiaoping Fan, Guangpei Cong, Ziyang Lu and Qiming Zhuo
Processes 2025, 13(7), 2248; https://doi.org/10.3390/pr13072248 - 14 Jul 2025
Viewed by 159
Abstract
With the widespread application of lithium-ion batteries (LIBs), safety performance has become a critical factor limiting the commercialization of large-scale, high-capacity LIBs. The main reason for the safety problem is that the electrolytes of LIBs are extremely flammable. Adding flame retardants to conventional [...] Read more.
With the widespread application of lithium-ion batteries (LIBs), safety performance has become a critical factor limiting the commercialization of large-scale, high-capacity LIBs. The main reason for the safety problem is that the electrolytes of LIBs are extremely flammable. Adding flame retardants to conventional electrolytes is an effective method to improve battery safety. In this paper, trimethyl phosphate (TMP) and trimethyl phosphite (TMPi) were used as research objects, and the flame-retardant test and differential scanning calorimetry (DSC) of the electrolytes configured by them were first carried out. The self-extinguishing time of the electrolyte with 5% TMP and TMPi is significantly reduced, achieving a flame-retardant effect. Secondly, the electrochemical performance of LiFePO4|Li half-cells after adding different volume ratios of TMP and TMPi was studied. Compared with TMPi5, the peak potential difference between the oxidation peak and the reduction peak of the LiFePO4|Li half-cell with TMP5 added is reduced, the battery polarization is reduced, the discharge specific capacity after 300 cycles is large, the capacity retention rate is as high as 99.6%, the discharge specific capacity is larger at different current rates, and the electrode resistance is smaller. TMPi5 causes the discharge-specific capacity to attenuate, which is more obvious at high current rates. LiFePO4|Li half-cells with 5% volume ratio of flame retardant have the best electrochemical performance. Finally, the influence mechanism of the phosphorus valence state on battery safety and electrochemical performance was compared and studied. After 300 cycles, the surface of the LiFePO4 electrode with 5% TMP added had a smoother and more uniform CEI film and higher phosphorus (P) and fluorine (F) content, which was beneficial to the improvement of electrochemical performance. The cross-section of the LiFePO4 electrode showed slight collapse and cracks, which slowed down the attenuation of battery capacity. Full article
(This article belongs to the Section Chemical Processes and Systems)
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16 pages, 3659 KiB  
Article
Investigation of DC Breakdown Properties of Low GWP Gas R404a and Its Mixtures with N2/CO2 as an Alternative to SF6
by Hassan Riaz, Muhammad Zaheer Saleem and Muhammad Faheem
Processes 2025, 13(7), 2247; https://doi.org/10.3390/pr13072247 - 14 Jul 2025
Viewed by 96
Abstract
Sulfur hexafluoride (SF6), an extraordinary gas insulation medium, must be replaced by environmentally friendly gas in electric equipment because of its high global warming potential (GWP). In this research work, the DC breakdown properties of R404a gas and its mixtures with [...] Read more.
Sulfur hexafluoride (SF6), an extraordinary gas insulation medium, must be replaced by environmentally friendly gas in electric equipment because of its high global warming potential (GWP). In this research work, the DC breakdown properties of R404a gas and its mixtures with N2 and CO2 are studied under a sphere–sphere electrode configuration and uniform field conditions. The GWP of R404a is 16% of SF6 and its liquefaction temperature is also in the suitable range for practical applications. Nitrogen and carbon dioxide are mixed with R404a to reduce its boiling point and GWP. Other important parameters such as the self-recoverability, liquefaction temperature, GWP, and synergistic effect of R404a/CO2 and R404a/N2 were also studied to complement the insulation performance and the results are comparable to other gas mixtures. As a result, it was found that both the mixtures containing 80% R404a and 20% N2 or 20% CO2 possess a breakdown strength of 0.83 times that of SF6. Mixtures containing an 80% concentration of R404a possess a GWP equal to only 15% of SF6. These properties make gaseous mixtures containing 80% R404a and 20% N2 or CO2 a suitable alternative to SF6 in medium-voltage gas-insulated equipment. Full article
(This article belongs to the Special Issue Experimental and Numerical Study of Flame Propagation of Biofuels/Oxidizers/Inert Mixtures (Volume II))
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20 pages, 7127 KiB  
Article
Comparative Study on Full-Scale Pore Structure Characterization and Gas Adsorption Capacity of Shale and Coal Reservoirs
by Mukun Ouyang, Bo Wang, Xinan Yu, Wei Tang, Maonan Yu, Chunli You, Jianghai Yang, Tao Wang and Ze Deng
Processes 2025, 13(7), 2246; https://doi.org/10.3390/pr13072246 - 14 Jul 2025
Viewed by 115
Abstract
Shale and coal in the transitional marine–continental facies of the Ordos Basin serve as unconventional natural gas reservoirs, with their pore structures controlling gas adsorption characteristics and occurrence states. To quantitatively characterize the pore structure features and differences between these two reservoirs, this [...] Read more.
Shale and coal in the transitional marine–continental facies of the Ordos Basin serve as unconventional natural gas reservoirs, with their pore structures controlling gas adsorption characteristics and occurrence states. To quantitatively characterize the pore structure features and differences between these two reservoirs, this study takes the Shanxi Formation shale and coal in the Daning–Jixian area on the eastern margin of the Ordos Basin as examples. Field-emission scanning electron microscopy (FE-SEM), high-pressure mercury intrusion, low-temperature N2 adsorption, and low-pressure CO2 adsorption experiments were employed to analyze and compare the full-scale pore structures of the shale and coal reservoirs. Combined with methane isothermal adsorption experiments, the gas adsorption capacity and its differences in these reservoirs were investigated. The results indicate that the average total organic carbon (TOC) content of shale is 2.66%, with well-developed organic pores, inorganic pores, and microfractures. Organic pores are the most common, typically occurring densely and in clusters. The average TOC content of coal is 74.22%, with organic gas pores being the dominant pore type, significantly larger in diameter than those in transitional marine–continental facies shale and marine shale. In coal, micropores contribute the most to pore volume, while mesopores and macropores contribute less. In shale, mesopores dominate, followed by micropores, with macropores being underdeveloped. Both coal and shale exhibit a high SSA primarily contributed by micropores, with organic matter serving as the material basis for micropore development. The methane adsorption capacity of coal is 8–29 times higher than that of shale. Coal contains abundant organic micropores, providing a large SSA and numerous adsorption sites for methane, facilitating gas adsorption and storage. This study comprehensively reveals the similarities and differences in pore structures between transitional marine–continental facies shale and coal reservoirs in the Ordos Basin at the microscale, providing a scientific basis for the precise evaluation and development of unconventional oil and gas resources. Full article
(This article belongs to the Topic Reservoir Characteristics and Evolution Mechanisms of the Shale)
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