Processes

28 pages, 1891 KiB

Open AccessReview

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 (registering DOI) - 14 Jul 2025

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)

18 pages, 4231 KiB

Open AccessArticle

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 (registering DOI) - 14 Jul 2025

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, 1889 KiB

Open AccessArticle

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 (registering DOI) - 14 Jul 2025

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))

20 pages, 2588 KiB

Open AccessArticle

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 (registering DOI) - 14 Jul 2025

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)

18 pages, 5711 KiB

Open AccessFeature PaperArticle

Oxidative Degradation of Anthocyanins in Red Wine: Kinetic Characterization Under Accelerated Aging Conditions

by Khulood Fahad Saud Alabbosh, Violeta Jevtovic, Jelena Mitić, Zoran Pržić, Vesna Stankov Jovanović, Reem Ali Alyami, Maha Raghyan Alshammari, Badriah Alshammari and Milan Mitić

Processes 2025, 13(7), 2245; https://doi.org/10.3390/pr13072245 (registering DOI) - 14 Jul 2025

Abstract

The oxidative degradation of anthocyanins in red wine was investigated under controlled conditions using hydroxyl radicals generated in the presence of Cu (II) as a catalyst. A full factorial experimental design with 23 replicates was used to evaluate the effects of hydrogen peroxide [...] Read more.

The oxidative degradation of anthocyanins in red wine was investigated under controlled conditions using hydroxyl radicals generated in the presence of Cu (II) as a catalyst. A full factorial experimental design with 23 replicates was used to evaluate the effects of hydrogen peroxide concentration, catalyst dosage, and reaction temperature on anthocyanin degradation over a fixed time. Statistical analysis (ANOVA and multiple regression) showed that all three variables and the main interactions significantly affected anthocyanin loss, with temperature identified as the most influential factor. The combined effects were described by a first-order polynomial model. The activation energies for degradation ranged from 56.62 kJ/mol (cyanidin-3-O-glucoside) to 40.58 kJ/mol (peonidin-3-O-glucoside acetate). Increasing the temperature from 30 °C to 40 °C accelerated the degradation kinetics, almost doubled the rate constants and shortened the half-life of the pigments. At 40 °C, the half-lives ranged from 62.3 min to 154.0 min, depending on the anthocyanin structure. These results contribute to a deeper understanding of the stability of anthocyanins in red wine under oxidative stress and provide insights into the chemical behavior of derived pigments. The results are of practical importance for both oenology and viticulture and support efforts to improve the color stability of wine and extend the shelf life of grape-based products. Full article

(This article belongs to the Special Issue Processes in Agri-Food Technology)

23 pages, 6300 KiB

Open AccessArticle

Deciphering the Time-Dependent Deformation and Failure Mechanism of the Large Underground Powerhouse in Baihetan Hydropower Station

by Wenjie Zu, Jian Tao and Jun Wang

Processes 2025, 13(7), 2244; https://doi.org/10.3390/pr13072244 (registering DOI) - 14 Jul 2025

Abstract

During the excavation of the underground cavern at the Baihetan hydropower station, significant time-dependent deformation of the surrounding rock was observed, posing a serious challenge to the long-term stability control of the caverns. In this study, numerical models of the layered excavation for [...] Read more.

During the excavation of the underground cavern at the Baihetan hydropower station, significant time-dependent deformation of the surrounding rock was observed, posing a serious challenge to the long-term stability control of the caverns. In this study, numerical models of the layered excavation for typical monitoring sections in the main and auxiliary powerhouses on both banks of the Baihetan hydropower station were established using a viscoplastic damage model. The time-dependent deformation responses of the surrounding rock during the entire underground cavern excavation process were successfully simulated, and the deformation and failure mechanisms of the surrounding rock during layered excavation were analyzed in combination with field monitoring data. The results demonstrate that the maximum stress trajectories at the right-bank powerhouse under higher stress conditions exceeded those at the left-bank powerhouse by 6 MPa after the powerhouse excavation. A larger stress difference caused stress trajectories to move closer to the rock strength surface, therefore making creep failure more likely to occur in the right bank. Targeted reinforcement in high-disturbance zones of the right-bank powerhouse reduced the damage progression rate at borehole openings from 0.295 per month to 0.0015 per month, effectively suppressing abrupt deformations caused by cumulative damage. These findings provide a basis for optimizing the excavation design of deep underground caverns. Full article

(This article belongs to the Section AI-Enabled Process Engineering)

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22 pages, 5644 KiB

Open AccessArticle

Analysis of the Impact of the Drying Process and the Effects of Corn Race on the Physicochemical Characteristics, Fingerprint, and Cognitive-Sensory Characteristics of Mexican Consumers of Artisanal Tostadas

by Oliver Salas-Valdez, Emmanuel de Jesús Ramírez-Rivera, Adán Cabal-Prieto, Jesús Rodríguez-Miranda, José Manuel Juárez-Barrientos, Gregorio Hernández-Salinas, José Andrés Herrera-Corredor, Jesús Sebastián Rodríguez-Girón, Humberto Marín-Vega, Susana Isabel Castillo-Martínez, Jasiel Valdivia-Sánchez, Fernando Uribe-Cuauhtzihua and Víctor Hugo Montané-Jiménez

Processes 2025, 13(7), 2243; https://doi.org/10.3390/pr13072243 - 14 Jul 2025

Abstract

The objective of this study was to analyze the impact of solar and hybrid dryers on the physicochemical characteristics, fingerprints, and cognitive-sensory perceptions of Mexican consumers of traditional tostadas made with corn of different races. Corn tostadas from different native races were evaluated [...] Read more.

The objective of this study was to analyze the impact of solar and hybrid dryers on the physicochemical characteristics, fingerprints, and cognitive-sensory perceptions of Mexican consumers of traditional tostadas made with corn of different races. Corn tostadas from different native races were evaluated with solar and hybrid (solar-photovoltaic solar panels) dehydration methods. Proximal chemical quantification, instrumental analysis (color, texture), fingerprint by Fourier transform infrared spectroscopy (FTIR), and sensory-cognitive profile (emotions and memories) and its relationship with the level of pleasure were carried out. The data were evaluated using analysis of variance models, Cochran Q, and an external preference map (PREFMAP). The results showed that the drying method and corn race significantly (p < 0.05) affected only moisture content, lipids, carbohydrates, and water activity. Instrumental color was influenced by the corn race effect, and the dehydration type influenced the fracturability effect. FTIR fingerprinting results revealed that hybrid samples exhibited higher intensities, particularly associated with higher lime concentrations, indicating a greater exposure of glycosidic or protein structures. Race and dehydration type effects impacted the intensity of sensory attributes, emotions, and memories. PREFMAP vector model results revealed that consumers preferred tostadas from the Solar-Chiquito, Hybrid-Pepitilla, Hybrid-Cónico, and Hybrid-Chiquito races for their higher protein content, moisture, high fracturability, crunchiness, porousness, sweetness, doughy flavor, corn flavor, and burnt flavor, while images of these tostadas evoked positive emotions (tame, adventurous, free). In contrast, the Solar-Pepitilla tostada had a lower preference because it was perceived as sour and lime-flavored, and its tostada images evoked more negative emotions and memories (worried, accident, hurt, pain, wild) and fewer positive cognitive aspects (joyful, warm, rainy weather, summer, and interested). However, the tostadas of the Solar-Cónico race were the ones that were most rejected due to their high hardness and yellow to blue tones and for evoking negative emotions (nostalgic and bored). Full article

(This article belongs to the Special Issue Applications of Ultrasound and Other Technologies in Food Processing)

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22 pages, 3812 KiB

Open AccessArticle

Optimal Collaborative Scheduling Strategy of Mobile Energy Storage System and Electric Vehicles Considering SpatioTemporal Characteristics

by Liming Sun and Tao Yu

Processes 2025, 13(7), 2242; https://doi.org/10.3390/pr13072242 - 14 Jul 2025

Abstract

The widespread adoption of electric vehicles introduces significant challenges to power grid stability due to uncoordinated large-scale charging and discharging behaviors. By addressing these challenges, mobile energy storage systems emerge as a flexible resource. To maximize the synergistic potential of jointly scheduling electric [...] Read more.

The widespread adoption of electric vehicles introduces significant challenges to power grid stability due to uncoordinated large-scale charging and discharging behaviors. By addressing these challenges, mobile energy storage systems emerge as a flexible resource. To maximize the synergistic potential of jointly scheduling electric vehicles and mobile energy storage systems, this study develops a collaborative scheduling model incorporating the prediction of geographically and chronologically varying distributions of electric vehicles. Non-dominated sorting genetic algorithm-III is then applied to solve this model. Validation through case studies, conducted on the IEEE-69 bus system and an actual urban road network in southern China, demonstrates the model’s efficacy. Case studies reveal that compared to the initial disordered state, the optimized strategy yields a 122.6% increase in profits of the electric vehicle charging station operator, a 44.7% reduction in costs to the electric vehicle user, and a 62.5% decrease in voltage deviation. Furthermore, non-dominated sorting genetic algorithm-III exhibits superior comprehensive performance in multi-objective optimization when benchmarked against two alternative algorithms. Full article

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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21 pages, 12877 KiB

Open AccessArticle

Calibration of DEM Parameters for Multi-Component Chinese Cuisine

by Haiyun Song, Huangzhen Lyu, Yongjun Zheng, Lina Zhang, Yakai He, Mengqiang Zhang, Jun Du, Mengfan Han, Huabin Jian and Zhilong Du

Processes 2025, 13(7), 2241; https://doi.org/10.3390/pr13072241 - 14 Jul 2025

Abstract

With the industrialization and standardization of Chinese cuisine, accurate discrete element simulation parameters are essential for analyzing the flow and conveying behavior of dishes. This study focused on standardized Kung Pao Chicken and employed the Hertz–Mindlin (JKR) model to develop a discrete element [...] Read more.

With the industrialization and standardization of Chinese cuisine, accurate discrete element simulation parameters are essential for analyzing the flow and conveying behavior of dishes. This study focused on standardized Kung Pao Chicken and employed the Hertz–Mindlin (JKR) model to develop a discrete element model suitable for cohesive, multi-component Chinese cuisine. The triaxial dimensions of diced chicken, peanuts, and scallions were measured to construct the model. Physical experiments were conducted to obtain basic parameters. The main parameters of the constitutive model were determined using a stepwise regression fitting method. For inter-material contact parameters that are difficult to measure directly, key model parameters were calibrated by fitting simulated repose angle results to experimental measurements. The calibrated parameters enabled high simulation accuracy, with repose angle errors below 0.05%, confirming the model’s reliability. This study provides a theoretical foundation for the simulation and design of automated conveying systems tailored to Chinese cuisine. Full article

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

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23 pages, 7174 KiB

Open AccessFeature PaperArticle

Enhancing Wastewater Treatment Through Python ANN-Guided Optimization of Photocatalysis with Boron-Doped ZnO Synthesized via Mechanochemical Route

by Vladan Nedelkovski, Milan Radovanović, Dragana Medić, Sonja Stanković, Iosif Hulka, Dejan Tanikić and Milan Antonijević

Processes 2025, 13(7), 2240; https://doi.org/10.3390/pr13072240 - 14 Jul 2025

Abstract

This study explores the enhanced photocatalytic performance of boron-doped zinc oxide (ZnO) nanoparticles synthesized via a scalable mechanochemical route. Utilizing X-ray diffraction (XRD) and scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS), the structural and morphological properties of these nanoparticles were assessed. Specifically, nanoparticles [...] Read more.

This study explores the enhanced photocatalytic performance of boron-doped zinc oxide (ZnO) nanoparticles synthesized via a scalable mechanochemical route. Utilizing X-ray diffraction (XRD) and scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS), the structural and morphological properties of these nanoparticles were assessed. Specifically, nanoparticles with 1 wt%, 2.5 wt%, and 5 wt% boron doping were analyzed after calcination at temperatures of 500 °C, 600 °C, and 700 °C. The obtained results indicate that 1 wt% B-ZnO nanoparticles calcined at 700 °C show superior photocatalytic efficiency of 99.94% methyl orange degradation under UVA light—a significant improvement over undoped ZnO. Furthermore, the study introduces a predictive model using the artificial neural network (ANN) technique, developed in Python, which effectively forecasts photocatalytic performance based on experimental conditions with R² = 0.9810. This could further enhance wastewater treatment processes, such as heterogeneous photocatalysis, through ANN-guided optimization. Full article

(This article belongs to the Special Issue Metal Oxides and Their Composites for Photocatalytic Degradation)

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26 pages, 3806 KiB

Open AccessArticle

A Novel Approach for Voltage Stability Assessment and Optimal Siting and Sizing of DGs in Radial Power Distribution Networks

by Salah Mokred, Yifei Wang, Mohammed Alruwaili and Moustafa Ahmed Ibrahim

Processes 2025, 13(7), 2239; https://doi.org/10.3390/pr13072239 - 14 Jul 2025

Abstract

The increasing integration of renewable energy sources and the rising demand for electricity has intensified concerns over voltage stability in radial distribution systems. These networks are particularly susceptible to voltage collapse under heavy loading conditions, posing serious system reliability and efficiency risks. Integrating [...] Read more.

The increasing integration of renewable energy sources and the rising demand for electricity has intensified concerns over voltage stability in radial distribution systems. These networks are particularly susceptible to voltage collapse under heavy loading conditions, posing serious system reliability and efficiency risks. Integrating distributed generation (DG) has emerged as a strategic solution to strengthen voltage profiles and reduce power losses. To address this challenge, this study proposes a novel distribution voltage stability index (NDVSI) for accurately assessing voltage stability and guiding optimal DG placement and sizing. The NDVSI provides a reliable tool to identify weak buses and their neighboring nodes that critically impact stability. By targeting these locations, the method ensures DG units are installed where they offer maximum improvement in voltage support and minimum power losses. The approach is implemented using MATLAB R2019a (MathWorks Inc., Natick, MA, USA) and validated on three benchmark radial distribution systems, including IEEE 12-bus, 33-bus, and 69-bus systems, demonstrating its scalability and effectiveness across different grid complexities. Comparative analysis with existing voltage stability indices confirms the superiority of NDVSI in both diagnostic precision and practical application. The proposed approach offers a technically sound and economically viable tool for enhancing the reliability, stability, and performance of modern distribution networks. Full article

(This article belongs to the Section Energy Systems)

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4 pages, 130 KiB

Open AccessEditorial

Remediation Strategies for Soil and Water

by Junxia Wang and Xiaoqiang Cui

Processes 2025, 13(7), 2238; https://doi.org/10.3390/pr13072238 - 14 Jul 2025

Abstract

With the rapid development of industry worldwide, soil and water pollution has increased in recent decades [...] Full article

(This article belongs to the Special Issue Remediation Strategies for Soil and Water)

20 pages, 1753 KiB

Open AccessArticle

Hybrid Cloud-Based Information and Control System Using LSTM-DNN Neural Networks for Optimization of Metallurgical Production

by Kuldashbay Avazov, Jasur Sevinov, Barnokhon Temerbekova, Gulnora Bekimbetova, Ulugbek Mamanazarov, Akmalbek Abdusalomov and Young Im Cho

Processes 2025, 13(7), 2237; https://doi.org/10.3390/pr13072237 (registering DOI) - 13 Jul 2025

Abstract

A methodology for detecting systematic errors in sets of equally accurate, uncorrelated, aggregate measurements is proposed and applied within the automatic real-time dispatch control system of a copper concentrator plant (CCP) to refine the technical and economic performance indicators (EPIs) computed by the [...] Read more.

A methodology for detecting systematic errors in sets of equally accurate, uncorrelated, aggregate measurements is proposed and applied within the automatic real-time dispatch control system of a copper concentrator plant (CCP) to refine the technical and economic performance indicators (EPIs) computed by the system. This work addresses and solves the problem of selecting and obtaining reliable measurement data by exploiting the redundant measurements of process streams together with the balance equations linking those streams. This study formulates an approach for integrating cloud technologies, machine learning methods, and forecasting into information control systems (ICSs) via predictive analytics to optimize CCP production processes. A method for combining the hybrid cloud infrastructure with an LSTM-DNN neural network model has been developed, yielding a marked improvement in TEP for copper concentration operations. The forecasting accuracy for the key process parameters rose from 75% to 95%. Predictive control reduced energy consumption by 10% through more efficient resource use, while the copper losses to tailings fell by 15–20% thanks to optimized reagent dosing and the stabilization of the flotation process. Equipment failure prediction cut the amount of unplanned downtime by 30%. As a result, the control system became adaptive, automatically correcting the parameters in real time and lessening the reliance on operator decisions. The architectural model of an ICS for metallurgical production based on the hybrid cloud and the LSTM-DNN model was devised to enhance forecasting accuracy and optimize the EPIs of the CCP. The proposed model was experimentally evaluated against alternative neural network architectures (DNN, GRU, Transformer, and Hybrid_NN_TD_AIST). The results demonstrated the superiority of the LSTM-DNN in forecasting accuracy (92.4%), noise robustness (0.89), and a minimal root-mean-square error (RMSE = 0.079). The model shows a strong capability to handle multidimensional, non-stationary time series and to perform adaptive measurement correction in real time. Full article

(This article belongs to the Section AI-Enabled Process Engineering)

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18 pages, 1756 KiB

Open AccessArticle

Ultra-Short-Term Wind Power Prediction Based on Fused Features and an Improved CNN

by Hui Li, Siyao Li, Hua Li and Liang Bai

Processes 2025, 13(7), 2236; https://doi.org/10.3390/pr13072236 - 13 Jul 2025

Abstract

It is difficult for a single feature in wind power data to fully reflect the multifactor coupling relationship with wind power, while the forecast model hyperparameters rely on empirical settings, which affects the prediction accuracy. In order to effectively predict the continuous power [...] Read more.

It is difficult for a single feature in wind power data to fully reflect the multifactor coupling relationship with wind power, while the forecast model hyperparameters rely on empirical settings, which affects the prediction accuracy. In order to effectively predict the continuous power in the future time period, an ultra-short-term prediction model of wind power based on fused features and an improved convolutional neural network (CNN) is proposed. Firstly, the historical power data are decomposed using dynamic modal decomposition (DMD) to extract their modal features. Then, considering the influence of meteorological factors on power prediction, the historical meteorological data in the sample data are extracted using kernel principal component analysis (KPCA). Finally, the decomposed power modal and the extracted meteorological components are reconstructed into multivariate time-series features; the snow ablation optimisation algorithm (SAO) is used to optimise the convolutional neural network (CNN) for wind power prediction. The results show that the root-mean-square error of the prediction result is 31.9% lower than that of the undecomposed one after using DMD decomposition; for the prediction of the power of two different wind farms, the root-mean-square error of the improved CNN model is reduced by 39.8% and 30.6%, respectively, compared with that of the original model, which shows that the proposed model has a better prediction performance. Full article

(This article belongs to the Section Energy Systems)

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21 pages, 632 KiB

Open AccessArticle

An Electricity Market Pricing Method with the Optimality Limitation of Power System Dispatch Instructions

by Zhiheng Li, Anbang Xie, Junhui Liu, Yihan Zhang, Yao Lu, Wenjing Zu, Yi Wang and Xiaobing Zhang

Processes 2025, 13(7), 2235; https://doi.org/10.3390/pr13072235 (registering DOI) - 13 Jul 2025

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Abstract

The electricity market can optimize the resource allocation in power systems by calculating the market clearing problem. However, in the market clearing process, various market operation requirements must be considered. These requirements might cause the obtained power system dispatch instructions to deviate from [...] Read more.

The electricity market can optimize the resource allocation in power systems by calculating the market clearing problem. However, in the market clearing process, various market operation requirements must be considered. These requirements might cause the obtained power system dispatch instructions to deviate from the optimal solutions of original market clearing problems, thereby compromising the economic properties of locational marginal price (LMP). To mitigate the adverse effects of such optimality limitations, this paper proposes a pricing method for improving economic properties under the optimality limitation of power system dispatch instructions. Firstly, the underlying mechanism through which optimality limitations lead to economic property distortions in the electricity market is analyzed. Secondly, an analytical framework is developed to characterize economic properties under optimality limitations. Subsequently, an optimization-based electricity market pricing model is formulated, where price serves as the decision variable and economic properties, such as competitive equilibrium, are incorporated as optimization objectives. Case studies show that the proposed electricity market pricing method effectively mitigates the economic property distortions induced by optimality limitations and can be adapted to satisfy different economic properties based on market preferences. Full article

(This article belongs to the Special Issue Evaluation, Decision–Making and Market Simulation of a New Type of Power System)

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17 pages, 2819 KiB

Open AccessArticle

High-Strain-Rate Deformation Behavior and Damage Mechanisms of Ti/Al Interpenetrating Phase Composites

by Zhou Li, Zhongli Zhang, Jiahao Tian, Junhao Li, Shiqi Xia, Libo Zhou and Long Yu

Processes 2025, 13(7), 2234; https://doi.org/10.3390/pr13072234 - 12 Jul 2025

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Abstract

Interpenetrating phase composites (IPCs) have demonstrated tremendous potential across various fields, particularly those based on triply periodic minimal surface (TPMS) structures, whose uniquely interwoven lattice architectures have attracted widespread attention. However, current research on the dynamic mechanical properties of such IPC remains limited, [...] Read more.

Interpenetrating phase composites (IPCs) have demonstrated tremendous potential across various fields, particularly those based on triply periodic minimal surface (TPMS) structures, whose uniquely interwoven lattice architectures have attracted widespread attention. However, current research on the dynamic mechanical properties of such IPC remains limited, and their impact resistance and damage mechanisms are yet to be thoroughly understood. In this study, a novel design of two volume fractions of IPCs based on the TPMS IWP configuration is developed using Python-based parametric modeling, with the Ti6Al4V alloy TPMS scaffolds fabricated via selective laser melting (SLM) and the AlSi12 reinforcing phase through infiltration casting. The influence of Ti alloy volume fraction and strain rate on the dynamic mechanical behavior of the Ti/Al IPC is systematically investigated using a split Hopkinson pressure bar (SHPB) experimental setup. Microscopic characterization validates the effectiveness and reliability of the proposed IPC fabrication method. Results show that the increasing Ti alloy volume fraction significantly affects the dynamic mechanical properties of the IPC, and IPCs with different Ti alloy volume fractions exhibit contrasting mechanical behaviors under increasing strain rates, attributed to the dominance of different constituent phases. This study enhances the understanding of the dynamic behavior of TPMS-based IPCs and offers a promising route for the development of high-performance energy-absorbing materials. Full article

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

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26 pages, 9003 KiB

Open AccessArticle

A Pilot-Scale Gasifier Freeboard Equipped with Catalytic Filter Candles for Particulate Abatement and Tar Conversion: 3D-CFD Simulations and Experimental Tests

by Alessandra Tacconi, Pier Ugo Foscolo, Sergio Rapagnà, Andrea Di Carlo and Alessandro Antonio Papa

Processes 2025, 13(7), 2233; https://doi.org/10.3390/pr13072233 - 12 Jul 2025

Viewed by 24

Abstract

This work deals with the catalytic steam reforming of raw syngas to increase the efficiency of coupling gasification with downstream processes (such as fuel cells and catalytic chemical syntheses) by producing high-temperature, ready-to-use syngas without cooling it for cleaning and conditioning. Such a [...] Read more.

This work deals with the catalytic steam reforming of raw syngas to increase the efficiency of coupling gasification with downstream processes (such as fuel cells and catalytic chemical syntheses) by producing high-temperature, ready-to-use syngas without cooling it for cleaning and conditioning. Such a combination is considered a key point for the future exploitation of syngas produced by steam gasification of biogenic solid fuel. The design and construction of an integrated gasification and gas conditioning system were proposed approximately 20 years ago; however, they still require further in-depth study for practical applications. A 3D model of the freeboard of a pilot-scale, fluidized bed gasification plant equipped with catalytic ceramic candles was used to investigate the optimal operating conditions for in situ syngas upgrading. The global kinetic parameters for methane and tar reforming reactions were determined experimentally. A fluidized bed gasification reactor (~5 kWth) equipped with a 45 cm long segment of a fully commercial filter candle in its freeboard was used for a series of tests at different temperatures. Using a computational fluid dynamics (CFD) description, the relevant parameters for apparent kinetic equations were obtained in the frame of a first-order reaction model to describe the steam reforming of key tar species. As a further step, a CFD model of the freeboard of a 100 kWth gasification plant, equipped with six catalytic ceramic candles, was developed in ANSYS FLUENT^®. The composition of the syngas input into the gasifier freeboard was obtained from experimental results based on the pilot-scale plant. Simulations showed tar catalytic conversions of 80% for toluene and 41% for naphthalene, still insufficient compared to the threshold limits required for operating solid oxide fuel cells (SOFCs). An overly low freeboard temperature level was identified as the bottleneck for enhancing gas catalytic conversions, so further simulations were performed by injecting an auxiliary stream of O₂/steam (50/50 wt.%) through a series of nozzles at different heights. The best simulation results were obtained when the O₂/steam stream was fed entirely at the bottom of the freeboard, achieving temperatures high enough to achieve a tar content below the safe operating conditions for SOFCs, with minimal loss of hydrogen content or LHV in the fuel gas. Full article

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

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22 pages, 6239 KiB

Open AccessArticle

Synthesis of Fe–Cu Alloys via Ball Milling for Electrode Fabrication Used in Electrochemical Nitrate Removal from Wastewater

by Hannanatullgharah Hayeedah, Aparporn Sakulkalavek, Bhanupol Klongratog, Nuttakrit Somdock, Pisan Srirach, Pichet Limsuwan and Kittisakchai Naemchanthara

Processes 2025, 13(7), 2232; https://doi.org/10.3390/pr13072232 - 12 Jul 2025

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Abstract

Fe and Cu powders were mixed at a 50:50 ratio. Then, Fe-Cu alloys were prepared using the ball milling technique with different milling times of 6, 12, 18, 24, 30, 36, and 42 h. The crystalline structure was analyzed using X-ray diffraction (XRD), [...] Read more.

Fe and Cu powders were mixed at a 50:50 ratio. Then, Fe-Cu alloys were prepared using the ball milling technique with different milling times of 6, 12, 18, 24, 30, 36, and 42 h. The crystalline structure was analyzed using X-ray diffraction (XRD), and it was found that the optimum milling time was 30 h. The homogeneity of the Fe and Cu elements in the Fe–Cu alloys was analyzed using the scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM–EDX) mapping technique. Additionally, the crystal orientation of the Fe–Cu alloys was investigated using transmission electron microscopy (TEM). To fabricate the cathode for nitrate removal via electrolysis, an Fe–Cu alloy milled for 30 h was deposited onto a copper substrate using mechanical milling, then annealed at 800 °C. A pulsed DC electrolysis method was developed to test the nitrate removal efficiency of the Fe–Cu-coated cathode. The anode used was an Al sheet. The synthesized wastewater was prepared from KNO₃. Nitrate removal experiments from the synthesized wastewater were performed for durations of 0–4 h. The results show that the nitrate removal efficiency at 4 h was 96.90% compared to 74.40% with the Cu cathode. Full article

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

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20 pages, 31306 KiB

Open AccessArticle

Cavitation Performance Analysis in the Runner Region of a Bulb Turbine

by Feng Zhou, Qifei Li, Lu Xin, Xiangyu Chen, Shiang Zhang and Yuqian Qiao

Processes 2025, 13(7), 2231; https://doi.org/10.3390/pr13072231 - 12 Jul 2025

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Abstract

As a core component in renewable energy systems for grid regulation, hydropower units are increasingly exposed to flow conditions that elevate the risk of cavitation and erosion, posing significant challenges to the safe operation of flow-passage components. In this study, model testing and [...] Read more.

As a core component in renewable energy systems for grid regulation, hydropower units are increasingly exposed to flow conditions that elevate the risk of cavitation and erosion, posing significant challenges to the safe operation of flow-passage components. In this study, model testing and computational fluid dynamics (CFD) simulations are employed to investigate the hydraulic performance and cavitation behavior of a bulb turbine operating under rated head conditions and varying cavitation numbers. The analysis focuses on how changes in cavitation intensity affect flow characteristics and efficiency within the runner region. The results show that as the cavitation number approaches its critical value, the generation, growth, and collapse of vapor cavities increasingly disturb the main flow, causing a marked drop in blade hydraulic performance and overall turbine efficiency. Cavitation predominantly occurs on the blade’s suction side near the trailing edge rim and in the clearance zone near the hub, with bubble coverage expanding as the cavitation number decreases. A periodic inverse correlation between surface pressure and the cavitation area is observed, reflecting the strongly unsteady nature of cavitating flows. Furthermore, lower cavitation numbers lead to intensified pressure pulsations, aggravating flow unsteadiness and raising the risk of vibration. Full article

(This article belongs to the Special Issue Advanced Design, Analysis and Optimization Techniques of Fluid Machinery Systems for Renewable Energy Storage)

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22 pages, 1620 KiB

Open AccessArticle

Stochastic Distributionally Robust Optimization Scheduling of High-Proportion New Energy Distribution Network Considering Detailed Modeling of Energy Storage

by Bin Lin, Yan Huang, Dingwen Yu, Chenjie Fu and Changming Chen

Processes 2025, 13(7), 2230; https://doi.org/10.3390/pr13072230 - 12 Jul 2025

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Abstract

In the context of building a new type of power system, the optimal operation of high-proportion new-energy distribution networks (HNEDNs) is a current hot topic. In this paper, a stochastic distribution robust optimization method for HNEDNs that considers energy-storage refinement modeling is proposed. [...] Read more.

In the context of building a new type of power system, the optimal operation of high-proportion new-energy distribution networks (HNEDNs) is a current hot topic. In this paper, a stochastic distribution robust optimization method for HNEDNs that considers energy-storage refinement modeling is proposed. First, an energy-storage lifetime loss model based on the rainfall-counting method is constructed, and then an optimal operation model of an HNEDN considering energy storage refinement modeling is constructed, aiming to minimize the total operation cost while taking into account the energy cost and the penalty cost of abandoning wind and solar power. Then, a source-load uncertainty model of HNEDN is constructed based on the Wasserstein distance and conditional value at risk (CvaR) theory, and the HNEDN optimization model is reconstructed based on the stochastic distribution robust optimization method; based on this, the multiple linearization technique is introduced to approximate the reconstructed model, which aims to both reduce the difficulty in solving the model and ensure the quality of the solution. Finally, the modified IEEE 33-bus power distribution system is used as an example for case analysis, and the simulation results show that the method presented in this paper, through reducing the loss of life in the battery storage device, can reduce the average daily energy storage depreciation cost compared to an HNEDN optimization method that does not take the energy storage life loss into account; this, in turn, reduces the total operating cost of the system. In addition, the stochastic distribution robust optimization method used in this paper can adaptively adjust the economy and robustness of the HNEDN operation strategy according to the confidence level and the available historical sample data on new energy-output prediction errors to obtain the optimal HNEDN operation strategy when compared with other uncertainty treatment methods. Full article

(This article belongs to the Special Issue Operation and Control of New Power System with Large-Scale Renewable Energy Integration)

19 pages, 3865 KiB

Open AccessFeature PaperArticle

The Voltage Regulation of Boost Converters via a Hybrid DQN-PI Control Strategy Under Large-Signal Disturbances

by Pengqiang Nie, Yanxia Wu, Zhenlin Wang, Song Xu, Seiji Hashimoto and Takahiro Kawaguchi

Processes 2025, 13(7), 2229; https://doi.org/10.3390/pr13072229 - 12 Jul 2025

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Abstract

The DC-DC boost converter plays a crucial role in interfacing low-voltage sources with high-voltage DC buses in DC microgrid systems. To enhance the dynamic response and robustness of the system under large-signal disturbances and time-varying system parameters, this paper proposes a hybrid control [...] Read more.

The DC-DC boost converter plays a crucial role in interfacing low-voltage sources with high-voltage DC buses in DC microgrid systems. To enhance the dynamic response and robustness of the system under large-signal disturbances and time-varying system parameters, this paper proposes a hybrid control strategy that integrates proportional–integral (PI) control with a deep Q-network (DQN). The proposed framework leverages the advantages of PI control in terms of steady-state regulation and a fast transient response, while also exploiting the capabilities of the DQN agent to learn optimal control policies in dynamic and uncertain environments. To validate the effectiveness and robustness of the proposed hybrid control framework, a detailed boost converter model was developed in the MATLAB 2024/Simulink environment. The simulation results demonstrate that the proposed framework exhibits a significantly faster transient response and enhanced robustness against nonlinear disturbances compared to the conventional PI and fuzzy controllers. Moreover, by incorporating PI-based fine-tuning in the steady-state phase, the framework effectively compensates for the control precision limitations caused by the discrete action space of the DQN algorithm, thereby achieving high-accuracy voltage regulation without relying on an explicit system model. Full article

(This article belongs to the Special Issue Challenges and Advances of Process Control Systems)

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16 pages, 2747 KiB

Open AccessArticle

Fractal Dimension and Classification Evaluation of Microfractured Tight Reservoirs in Yongjin Oilfield

by Chunguang Li, Dongqi Wang, Daiyin Yin and Yang Sun

Processes 2025, 13(7), 2228; https://doi.org/10.3390/pr13072228 - 12 Jul 2025

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Abstract

The microfractured tight reservoirs in Yongjin Oilfield have low permeability and a complex pore structure. The development of microfractures in reservoirs is crucial for their impact on productivity. To understand the impact of pore structure and microfracture development on permeability and productivity, research [...] Read more.

The microfractured tight reservoirs in Yongjin Oilfield have low permeability and a complex pore structure. The development of microfractures in reservoirs is crucial for their impact on productivity. To understand the impact of pore structure and microfracture development on permeability and productivity, research on the fractal dimension and classification evaluation of microfractured tight reservoirs is proposed. Micropore and microfracture parameter characteristics are determined via CT scanning and mercury intrusion experiments. Based on the fractal theory and box counting dimension methods, the fractal dimension of pores and fractures in microfractured tight reservoirs are calculated, which can be used as an evaluation index. Then, a comprehensive quantitative evaluation method (REI) is conducted on the microfractured tight reservoir of Yongjin Oilfield to determine the classification boundary of evaluation indicators and reservoir classification results. The research results show that the microfractured tight reservoirs in Yongjin Oilfield can be classified into three types based on their development effect from good to poor. The comprehensive evaluation index (REI) of type I reservoirs is greater than 0.7, and the fractal dimension of pores and fractures is less than 2.4. The comprehensive evaluation index (REI) of type II reservoirs ranges from 0.4 to 0.7, and the fractal dimension of pores and fractures ranges from 2.4 to 2.6. The comprehensive evaluation index (REI) of type III reservoirs is less than 0.4, and the fractal dimension of pores and fractures is greater than 2.6. The classification results are consistent with the dynamic data, and this achievement can provide a scientific basis for rapid reservoir evaluation and development strategy formulation. Full article

(This article belongs to the Section Energy Systems)

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12 pages, 537 KiB

Open AccessFeature PaperReview

An Overview of Electrochemical Advanced Oxidation Processes for Pesticide Removal

by Maiara A. P. Frigulio, Alexandre S. Valério and Juliane C. Forti

Processes 2025, 13(7), 2227; https://doi.org/10.3390/pr13072227 - 11 Jul 2025

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Abstract

This article provides an overview of the use of electrochemical advanced oxidation processes (EAOPs) applied to the treatment of water contaminated by pesticides. Given the global increase in the use of pesticides and the ineffectiveness of conventional treatment methods, EAOPs emerge as promising [...] Read more.

This article provides an overview of the use of electrochemical advanced oxidation processes (EAOPs) applied to the treatment of water contaminated by pesticides. Given the global increase in the use of pesticides and the ineffectiveness of conventional treatment methods, EAOPs emerge as promising alternatives. They stand out for their efficiency in the degradation of organic compounds, minimal reliance on additional chemical reagents, and minimal generation of waste. The main methods addressed include anodic oxidation, photoelectro-oxidation, electro-Fenton and photoelectro-Fenton, which use hydroxyl radicals, a potent non-selective oxidant, to mineralize pollutants. A total of 165 studies were reviewed, with emphasis on the contributions of countries such as China, Spain, Brazil, and India. Factors such as electrode type, presence of catalysts, pH, and current density influence the effectiveness of treatments. Combined processes, especially those integrating UV light and renewable sources, have proven to be more efficient. Despite challenges related to electrode cost and durability, recent advances highlight the sustainability and scalability of EAOPs for the treatment of agricultural and industrial effluents contaminated with pesticides. Full article

(This article belongs to the Special Issue Green Separation and Purification Processes)

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14 pages, 3279 KiB

Open AccessArticle

Kinematic Analysis of the Jaw Crusher Drive Mechanism: A Different Mathematical Approach

by Emilian Mosnegutu, Narcis Barsan, Dana Chitimus, Vlad Ciubotariu, Luminita Bibire, Diana Mirilă, Marcin Jasiński, Nicoleta Sporea and Ivona Camelia Petre

Processes 2025, 13(7), 2226; https://doi.org/10.3390/pr13072226 - 11 Jul 2025

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Abstract

This paper presents a detailed kinematic analysis of a double-toggle jaw crusher used for the primary crushing of hard and bulky materials. The study introduces an innovative mathematical modeling method for the motion of the mechanism’s components, eliminating the need for traditional decomposition [...] Read more.

This paper presents a detailed kinematic analysis of a double-toggle jaw crusher used for the primary crushing of hard and bulky materials. The study introduces an innovative mathematical modeling method for the motion of the mechanism’s components, eliminating the need for traditional decomposition into structural groups. General equations are developed to determine the positions, linear velocities, and angular displacements of the moving elements, providing a solid foundation for equipment design and study. The generated mathematical model was validated using real-world dimensions of an SMD-117-type jaw crusher and by comparison with simulation results obtained from Mathcad, Linkage, Roberts Animator, and GIM software. The results demonstrated a high degree of agreement between the calculated and simulated trajectories and linear velocities. The analysis of angular displacements and linear velocities confirmed the cyclic nature of the mechanism’s motion, characterized by sinusoidal variations and low oscillations, which are relevant for assessing variable loads. Through its rigorous approach and multi-source validation, the research makes a significant contribution to the development of more efficient, durable, and adaptable jaw crushers for modern industrial requirements. Full article

(This article belongs to the Special Issue Modelling and Optimizing Process in Industry 4.0)

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14 pages, 3306 KiB

Open AccessArticle

Optimization of Saponin Extract from Red Sage (Salvia miltiorrhiza) Roots Using Response Surface Methods and Its Antioxidant and Anticancer Activities

by Hoang Chau Le, Hai Dang Le, Thi Dung Tran, Loan Thi Thanh Nguyen and Hang T. T. Nguyen

Processes 2025, 13(7), 2225; https://doi.org/10.3390/pr13072225 - 11 Jul 2025

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Abstract

Red sage (Salvia miltiorrhiza Bunge) is a perennial herb containing various bioactive compounds that promote human health. In this study, single-factor experiments were first conducted, followed by the optimization of extraction conditions to maximize the saponin content from red sage root extracts. [...] Read more.

Red sage (Salvia miltiorrhiza Bunge) is a perennial herb containing various bioactive compounds that promote human health. In this study, single-factor experiments were first conducted, followed by the optimization of extraction conditions to maximize the saponin content from red sage root extracts. In the single-factor experiments, the highest saponin content (47.5 ± 0.88 mg/g) was obtained using 80% ethanol, a solvent-to-material ratio of 40:1 (mL/g), an extraction period of 3 h, and an extraction temperature of 60 °C. Response Surface Methodology (RSM) was performed to optimize the extraction parameters with a material-to-solvent ratio of 41.31:1 (mL/g), an extraction temperature of 58.08 °C, and an extraction time of 3.16 h. Under these optimized conditions, the experimental saponin content reached 47.71 ± 0.15 mg/g. Additionally, crude extract of red sage exhibited antioxidant activity against 2,2-diphenyl-1-picrylhydrazyl (DPPH) radicals with an IC₅₀ value of 16.24 µg/mL. This extract also demonstrated anticancer against 61.79 ± 3.57% HepG2 cancer cells at a concentration of 100 µg/mL. Full article

(This article belongs to the Section Separation Processes)

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24 pages, 4757 KiB

Open AccessArticle

Effect of Port-Injecting Isopropanol on Diesel Engine Performance and Emissions by Changing EGR Ratio and Charge Temperature

by Horng-Wen Wu, Po-Hsien He and Ting-Wei Yeh

Processes 2025, 13(7), 2224; https://doi.org/10.3390/pr13072224 (registering DOI) - 11 Jul 2025

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Abstract

Researchers have tended to blend isopropanol (IPA) with other fuels in diesel engines to reduce emissions and improve performance. However, low-reactivity controlled compression ignition via port injection at a low cetane number results in a well-mixed charge of low-reactivity fuel, air, and recirculated [...] Read more.

Researchers have tended to blend isopropanol (IPA) with other fuels in diesel engines to reduce emissions and improve performance. However, low-reactivity controlled compression ignition via port injection at a low cetane number results in a well-mixed charge of low-reactivity fuel, air, and recirculated exhaust gas (EGR). This study’s novel approach combines critical elements, such as the mass fraction of port-injected IPA, EGR ratio, and charge temperature, to improve combustion characteristics and lessen emissions from a diesel engine. The results demonstrated that the injection of IPA and the installation of EGR at the inlet reduced NO_x, smoke, and PM_2.5. On the contrary, HC and CO increased with the port-injection of IPA and EGR. Preheating air at the inlet can suppress the emissions of HC and CO. Under 1500 rpm and 60% load, when compared to diesel at the same EGR ratio and charge temperature, the maximum smoke decrease rate (26%) and PM_2.5 decrease rate (21%) occur at 35% IPA, 45 °C, and 10% EGR, while the maximum NO_x decrease rate (24%) occurs at 35% IPA, 60 °C, and 20% EGR. These findings support the novelty of the research. Conversely, it modestly increased CO and HC emissions. However, port-injecting IPA increased thermal efficiency by up to 24% at 60 °C, 1500 rpm, and 60% load with EGR. Full article

(This article belongs to the Special Issue Combustion Processes and Emissions Assessment of Internal Combustion Engines)

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25 pages, 5700 KiB

Open AccessArticle

Transfer Learning-Based LRCNN for Lithium Battery State of Health Estimation with Small Samples

by Yuchao Xiong, Tiangang Lv, Liya Gao, Jingtian Hu, Zhe Zhang and Haoming Liu

Processes 2025, 13(7), 2223; https://doi.org/10.3390/pr13072223 - 11 Jul 2025

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Abstract

Traditional data-driven approaches to lithium battery state of health (SOH) estimation face the challenges of difficult feature extraction, insufficient prediction accuracy and weak generalization. To address these issues, this study proposes a novel prediction framework with transfer learning-based linear regression (LR) and a [...] Read more.

Traditional data-driven approaches to lithium battery state of health (SOH) estimation face the challenges of difficult feature extraction, insufficient prediction accuracy and weak generalization. To address these issues, this study proposes a novel prediction framework with transfer learning-based linear regression (LR) and a convolutional neural network (CNN) under limited data. In this framework, first, variable inertia weight-based improved particle swarm optimization for variational mode decomposition (VIW-PSO-VMD) is proposed to mitigate the volatility of the “capacity resurgence point” and extract its time-series features. Then, the T-Pearson correlation analysis is introduced to comprehensively analyze the correlations between multivariate features and lithium battery SOH data and accurately extract strongly correlated features to learn the common features of lithium batteries. On this basis, a combination model is proposed, applying LR to extract the trend features and combining them with the multivariate strongly correlated features via a CNN. Transfer learning based on temporal feature analysis is used to improve the cross-domain learning capabilities of the model. We conduct case studies on a NASA dataset and the University of Maryland dataset. The results show that the proposed method is effective in improving the lithium battery SOH estimation accuracy under limited data. Full article

(This article belongs to the Special Issue Transfer Learning Methods in Equipment Reliability Management)

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20 pages, 2883 KiB

Open AccessArticle

Health Risk Assessment and Accumulation of Potentially Toxic Elements in Capsella bursa-pastoris (L.) Medik

by Ivana Mikavica, Dragana Ranđelović, Miloš Ilić, Marija Simić, Jelena Petrović, Marija Koprivica and Jelena Mutić

Processes 2025, 13(7), 2222; https://doi.org/10.3390/pr13072222 (registering DOI) - 11 Jul 2025

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Abstract

Capsella bursa-pastoris (L.) Medik (C. bursa-pastoris) is an underexplored medicinal herb and bioindicator of potentially toxic elements (PTEs). Its broad traditional utilization combined with its high capacity for PTE accumulation may endanger human health. Herein, we investigated the concentrations and mobility [...] Read more.

Capsella bursa-pastoris (L.) Medik (C. bursa-pastoris) is an underexplored medicinal herb and bioindicator of potentially toxic elements (PTEs). Its broad traditional utilization combined with its high capacity for PTE accumulation may endanger human health. Herein, we investigated the concentrations and mobility of PTEs (Ba, Co, Cr, Cu, Fe, Mn, Ni, Sr, and Zn) in the urban soil–C. bursa-pastoris system and comprehensively assessed potential health risks associated with exposure to contaminated soils, plant and herbal extracts. Cu, Zn, Sr, and Mn were the most abundant in soils and predominantly phytoavailable. The calculated values of the geo-accumulation index (I_geo) indicated moderate to heavy Cu, Zn, and Sr contamination in the soil. C. bursa-pastoris demonstrated two strategies for PTEs—the exclusion of Ba, Cr, Mn, and Sr, and the accumulation of Cu, Ni, Co, and Fe. Principal Component Analysis (PCA) classified samples from four cities based on the PTE levels in soils, plants, and herbal extracts. Although plant tissues contained elevated levels of PTEs, the estimated daily intake (EDI), target hazard quotient (THQ), and lifetime carcinogenic risk (LCR) demonstrated no significant health risks from consuming C. bursa-pastoris and its extracts. The obtained results indicated the higher sensitivity of children to the hazardous effects of PTEs compared to adults. Extensive risk assessments of polluted soils and inhabiting plants are crucial in PTE monitoring. This study underscored its importance and delivered new insights into the contamination of medicinal herbs, aiming to contribute to implementing safety policies in public health protection. Full article

(This article belongs to the Special Issue Chemometrics in Food Quality Control: New Challenges and Cutting-Edge Approaches)

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18 pages, 3913 KiB

Open AccessArticle

A Fracture Extraction Method for Full-Diameter Core CT Images Based on Semantic Segmentation

by Ruiqi Huang, Dexin Qiao, Gang Hui, Xi Liu, Qianxiao Su, Wenjie Wang, Jianzhong Bi and Yili Ren

Processes 2025, 13(7), 2221; https://doi.org/10.3390/pr13072221 - 11 Jul 2025

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Abstract

Fractures play a critical role in the storage and migration of hydrocarbons within subsurface reservoirs, and their characteristics can be effectively studied through core sample analysis. This study proposes an automated fracture extraction method for full-diameter core Computed Tomography (CT) images based on [...] Read more.

Fractures play a critical role in the storage and migration of hydrocarbons within subsurface reservoirs, and their characteristics can be effectively studied through core sample analysis. This study proposes an automated fracture extraction method for full-diameter core Computed Tomography (CT) images based on a deep learning framework. A semantic segmentation network called SCTNet is employed to perform high-precision semantic segmentation, while a sliding window strategy is introduced to address the challenges associated with large-scale image processing during training and inference. The proposed method achieves a mean Intersection over Union (mIoU) of 72.14% and a pixel-level segmentation accuracy of 97% on the test dataset, outperforming traditional thresholding techniques and several state-of-the-art deep learning models. Besides fracture detection, the method enables quantitative characterization of fracture-related parameters, including fracture proportion, dip angle, strike, and aperture. Experimental results indicate that the proposed approach provides a reliable and efficient solution for the interpretation of large-volume CT data. Compared to manual evaluation, the method significantly accelerates the analysis process—reducing time from hours to minutes—and demonstrates strong potential to enhance intelligent workflows for geological core fracture analysis. Full article

(This article belongs to the Topic Exploitation and Underground Storage of Oil and Gas)

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31 pages, 5892 KiB

Open AccessFeature PaperArticle

RANS Simulation of Turbulent Flames Under Different Operating Conditions Using Artificial Neural Networks for Accelerating Chemistry Modeling

by Tobias Reiter, Jonas Volgger, Manuel Früh, Christoph Hochenauer and Rene Prieler

Processes 2025, 13(7), 2220; https://doi.org/10.3390/pr13072220 - 11 Jul 2025

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Abstract

Combustion modeling using computational fluid dynamics (CFD) offers detailed insights into the flame structure and thermo-chemical processes. Furthermore, it has been extensively used in the past to optimize industrial furnaces. Despite the increasing computational power, the prediction of the reaction kinetics in flames [...] Read more.

Combustion modeling using computational fluid dynamics (CFD) offers detailed insights into the flame structure and thermo-chemical processes. Furthermore, it has been extensively used in the past to optimize industrial furnaces. Despite the increasing computational power, the prediction of the reaction kinetics in flames is still related to high calculation times, which is a major drawback for large-scale combustion systems. To speed-up the simulation, artificial neural networks (ANNs) were applied in this study to calculate the chemical source terms in the flame instead of using a chemistry solver. Since one ANN may lack accuracy for the entire input feature space (temperature, species concentrations), the space is sub-divided into four regions/ANNs. The ANNs were tested for different fuel mixtures, degrees of turbulence, and air-fuel/oxy-fuel combustion. It was found that the shape of the flame and its position were well predicted in all cases with regard to the temperature and CO. However, at low temperature levels (<800 K), in some cases, the ANNs under-predicted the source terms. Additionally, in oxy-fuel combustion, the temperature was too high. Nevertheless, an overall high accuracy and a speed-up factor for all simulations of 12 was observed, which makes the approach suitable for large-scale furnaces. Full article

(This article belongs to the Special Issue Data-Driven Modeling and Applications for Flow, Heat Transfer, and Combustion)

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