Processes

17 pages, 1655 KiB

Open AccessArticle

The Law of Acid Pressure Fracture Propagation in Maokou Formation Carbonate Reservoir in Central Sichuan

by Yu Fan, Hailong Jiang, Zhouyang Wang, Jinsui Li, Xing Yang, Zefei Lv, Xiangfei Zhang and Xueyuan Han

Processes 2025, 13(6), 1634; https://doi.org/10.3390/pr13061634 - 22 May 2025

Abstract

The Dolomite reservoir of the Maokou Formation is rich in gas resources in the central Sichuan Basin. Acid fracturing is an important technical means to improve reservoir permeability and productivity. The interaction mode of the dolomite and limestone acid system will affect the [...] Read more.

The Dolomite reservoir of the Maokou Formation is rich in gas resources in the central Sichuan Basin. Acid fracturing is an important technical means to improve reservoir permeability and productivity. The interaction mode of the dolomite and limestone acid system will affect the effect of reservoir reconstruction. In order to clarify the influence of complex structure on fracture morphology, we explore the fracturing effect of different acid systems. Physical simulation experiments of true triaxial acid fracturing were carried out with two acid systems and downhole full-diameter cores. The experimental results show: (1) After the carbonate rock is subjected to acid fracturing using a “self-generated acid + gel acid” system, the fracture pressure drops significantly by up to 60%. The morphology of the acid-eroded fractures becomes more complex, with an increase in geometric complexity of about 28% compared to a single acid solution system. It is prone to form three-dimensional “spoon” shaped fractures, and the surface of the acid-eroded fractures shows light yellow acid erosion marks. Analysis of the acid erosion marks indicates that the erosion depth on the fracture surface reaches 0.8–1.2 mm, which is deeper than the 0.2 mm erosion depth achieved with a single system. (2) Acid solution is difficult to penetrate randomly distributed calcite veins with a low porosity and permeability structure. When the fracture meets the calcite vein, the penetration rate of acid solution drops sharply to 15–20% of the initial value, resulting in a reduction of about 62% of the acid erosion area in the limestone section behind. And the acid erosion traces in the limestone behind the calcite vein are significantly reduced. The acid erosion cracks are easy to open on the weak surface between dolomite and limestone, causing the fracture to turn. (3) The results of field engineering and experiment are consistent, and injecting authigenic acid first in the process of reservoir reconstruction is helpful to remove pollution. The recovery rate of near-well permeability is more than 85% with pre-generated acid. Reinjection of gelled acid can effectively communicate the natural weak surface and increase the complexity of cracks. The average daily oil production of the completed well was increased from 7.8 m³ to 22.5 m³, and the increase factor reached 2.88. Full article

(This article belongs to the Special Issue Environmentally Friendly Production of Energy from Natural Gas Hydrates)

20 pages, 825 KiB

Open AccessArticle

A Hierarchical Distributed and Local Voltage Control Strategy for Photovoltaic Clusters in Distribution Networks

by Zhiwei Liu, Zhe Wang, Yuzhe Chen, Qirui Ren, Jinli Zhao, Sihai Qiu, Yuxiao Zhao and Hao Zhang

Processes 2025, 13(6), 1633; https://doi.org/10.3390/pr13061633 - 22 May 2025

Abstract

The increasing integration of distributed photovoltaics (PVs) has intensified voltage violations in active distribution networks (ADNs). Traditional centralized voltage regulation approaches face substantial challenges in terms of communication and computation. Distributed control methods can help mitigate these issues through distributed algorithms but struggle [...] Read more.

The increasing integration of distributed photovoltaics (PVs) has intensified voltage violations in active distribution networks (ADNs). Traditional centralized voltage regulation approaches face substantial challenges in terms of communication and computation. Distributed control methods can help mitigate these issues through distributed algorithms but struggle to track real-time fluctuations in PV generation. Local control offers fast voltage adjustments but lacks coordination among different PV units. This paper presents a hierarchical distributed and local voltage control strategy for PV clusters. First, the alternating direction method of multipliers (ADMM) algorithm is adopted to coordinate the reactive power outputs of PV inverters across clusters, providing reference values for local control. Then, in the local control phase, a Q-P control strategy is utilized to address real-time PV fluctuations. The flexibility of the local control strategy is enhanced using the lifted linear decision rule, enabling a rapid response to PV power fluctuations. Finally, the proposed strategy is tested on both the modified IEEE 33-node distribution system and a practical 53-node distribution system to evaluate its performance. The results demonstrate that the proposed method effectively mitigates voltage issues, reducing the average voltage deviation by 53.93% while improving flexibility and adaptability to real-time changes in PV output. Full article

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

17 pages, 953 KiB

Open AccessArticle

A System Designed for Modelling, Monitoring, and Control of Fermentation Processes, Powered by Metaheuristic Algorithms

by Velislava Lyubenova, Dafina Zoteva, Maya Ignatova, Denitsa Kristeva and Olympia Roeva

Processes 2025, 13(6), 1632; https://doi.org/10.3390/pr13061632 - 22 May 2025

Abstract

This paper introduces an Interactive System for Education in Modelling and Control of Biotechnological Processes (InSEMCoBio) for biotechnologists, including students and researchers. The system integrates advanced optimisation algorithms for identifying models of nonlinear systems, along with algorithms for monitoring and controlling biotechnological processes. [...] Read more.

This paper introduces an Interactive System for Education in Modelling and Control of Biotechnological Processes (InSEMCoBio) for biotechnologists, including students and researchers. The system integrates advanced optimisation algorithms for identifying models of nonlinear systems, along with algorithms for monitoring and controlling biotechnological processes. InSEMCoBio was developed in MATLAB R2017a, featuring a user-friendly graphical interface that bridges complex computational tasks. Three metaheuristics for model identification are built in: an evolutionary algorithm, a genetic algorithm, and a hybrid metaheuristic algorithm. The software allows users to execute integrated algorithms and obtain desired results through visualisation (graphical and tabular) of data, without requiring programming knowledge. The functions of the system are demonstrated through the operation of the algorithms on two biotechnological processes. The ability to compare results of different algorithms is shown. The embedded identification algorithms are applicable beyond biotechnology. This provides the developed system with potential for other interesting applications. Full article

(This article belongs to the Section Process Control and Monitoring)

21 pages, 4230 KiB

Open AccessArticle

A Study on the Crack Propagation Behavior of Cement Sheath Interfaces Considering Bond Strength

by Jiwei Wu, Xuegang Wang, Shiyuan Xie, Yanxian Wu, Yilin Li, Zhenhui Shu, Xiaojun Zhang, Wei Lian and Dandan Yuan

Processes 2025, 13(6), 1631; https://doi.org/10.3390/pr13061631 - 22 May 2025

Abstract

Existing studies have not considered the impact of interface bond strength on the ease of crack propagation at the cement sheath interface. Through Brazilian splitting and direct shear tests, the normal and shear bond strengths at interfaces I and II of a cement [...] Read more.

Existing studies have not considered the impact of interface bond strength on the ease of crack propagation at the cement sheath interface. Through Brazilian splitting and direct shear tests, the normal and shear bond strengths at interfaces I and II of a cement sheath were quantified. Based on this, a crack propagation model for the cement sheath interface was established using cohesive zone elements. The propagation characteristics of cracks along the axial and circumferential directions at interfaces I and II of a cement sheath during hydraulic fracturing were analyzed, along with their influencing factors. The results show that, due to the difference in interface bond strength, the crack propagation rate and length at interface I in the axial direction are greater than those at interface II, while the interface II crack is more likely to propagate in the circumferential direction. The elastic modulus of the cement sheath is a key factor affecting the integrity of the cement seal. Both excessively low and high elastic moduli can lead to different forms of failure in the cement sheath. It is recommended to control the elastic modulus of the cement sheath between 7 and 8 GPa. As the internal casing pressure increases, the axial propagation length of cement sheath interface cracks also increases. During fracturing, reducing pump pressure can reduce the axial crack propagation length in the cement sheath, alleviating or preventing the risk of fluid migration between stages and clusters. The findings of this study provide theoretical references and engineering support for the control of cement sheath seal integrity. Full article

(This article belongs to the Section Materials Processes)

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22 pages, 1026 KiB

Open AccessArticle

Wellhead Stability During Development Process of Hydrate Reservoir in the Northern South China Sea: Sensitivity Analysis

by Qingchao Li, Qiang Li, Jingjuan Wu, Kaige He, Yifan Xia, Junyi Liu, Fuling Wang and Yuanfang Cheng

Processes 2025, 13(6), 1630; https://doi.org/10.3390/pr13061630 - 22 May 2025

Abstract

Natural gas hydrates are a promising alternative energy source for oil and gas in the future. However, geomechanical issues, such as wellhead instability, may arise, affecting the safe and efficient development of hydrates. In the present work, a sensitivity analysis was performed on [...] Read more.

Natural gas hydrates are a promising alternative energy source for oil and gas in the future. However, geomechanical issues, such as wellhead instability, may arise, affecting the safe and efficient development of hydrates. In the present work, a sensitivity analysis was performed on sediment subsidence and wellhead instability during the development of marine hydrates using a multi-field coupled model. This is accomplished by adjusting the corresponding parameters based on the basic data of the default case. Meanwhile, the corresponding influencing mechanisms were explored. Finally, design recommendations for operation parameters were proposed based on the research findings regarding wellhead stability. It was found that the wellhead undergoes rapid sinking during a certain period in the early stage of hydrate development, followed by a slower, continued sinking. The sensitivity analysis found that when the depressurization amplitude is small, the wellhead sinking is also minimal. To maintain wellhead stability during the development process, it is recommended that neither the depressurization amplitude or drawdown pressure exceed 3.0 MPa. Although a high heating temperature can increase gas production to some extent, the accompanying excessive hydrate dissociation may compromise the stability of both the formation and wellhead. To balance gas production and wellhead stability, it is recommended that the heating amplitude does not exceed 50 °C. In addition, the permeability influences the distribution of pore pressure, which in turn affects sediment subsidence and wellbore stability. Wellhead stability deteriorates as permeability increases. Therefore, it is crucial to accurately determine the reservoir characteristics (such as permeability) before developing hydrates to avoid wellhead instability. Finally, the investigation results reveal that using different versions of the investigation model can impact the accuracy of the results, and neglecting certain physical fields may lead to an underestimation of the wellhead sinking. Full article

(This article belongs to the Special Issue Environmentally Friendly Production of Energy from Natural Gas Hydrates)

31 pages, 1045 KiB

Open AccessFeature PaperReview

Downstream Processes in a Microalgae Biorefinery: Cascaded Enzymatic Hydrolysis and Pulsed Electric Field as Green Solution

by Gianpiero Pataro, Elham Eslami, Francesco Pignataro and Alessandra Procentese

Processes 2025, 13(6), 1629; https://doi.org/10.3390/pr13061629 - 22 May 2025

Abstract

Microalgae are a promising source of valuable compounds, including proteins, pigments, lipids, vitamins, and ingredients for cosmetics and animal feed. Despite their potential, downstream processing remains a major bottleneck in microalgae biorefineries, particularly in achieving high extraction efficiency with low energy and chemical [...] Read more.

Microalgae are a promising source of valuable compounds, including proteins, pigments, lipids, vitamins, and ingredients for cosmetics and animal feed. Despite their potential, downstream processing remains a major bottleneck in microalgae biorefineries, particularly in achieving high extraction efficiency with low energy and chemical input. While several extraction methods exist, few balance efficiency with selectivity and sustainability. Recently, mild and selective techniques such as Pulsed Electric Field (PEF) and Enzymatic Hydrolysis (EH) have gained attention, both individually and in combination. This review provides the first comprehensive comparative analysis of PEF and EH, emphasizing their mechanisms of action, specific cellular targets, and potential for integration into a cascaded, wet-route biorefinery process. Studies involving PEF, EH, and their sequential application (PEF-EH and EH-PEF) are analyzed, focusing on microalgae species, operational conditions, and extraction yields. The advantages and challenges of each method, including compound selectivity, environmental impact, and economic feasibility, are critically evaluated. The goal is to gain insight into whether the synergistic use of PEF and EH can enhance the recovery of intracellular compounds while improving the overall sustainability and efficiency of microalgae-based bioprocessing. Full article

(This article belongs to the Special Issue Process Intensification towards Sustainable Biorefineries)

21 pages, 7194 KiB

Open AccessArticle

Rain-Resilient Image Restoration for Reliable and Sustainable Visual Monitoring in Industrial Inspection and Quality Control

by Miao Zhang, Shanqin Wang and Shiqun Yin

Processes 2025, 13(6), 1628; https://doi.org/10.3390/pr13061628 - 22 May 2025

Abstract

Reliable visual monitoring is essential for industrial quality control systems under adverse weather conditions. Rain-induced degradation, such as occlusions, texture blurring, and depth distortions, can significantly hinder image clarity and compromise precision in surface defect detection. To address this, we propose a novel [...] Read more.

Reliable visual monitoring is essential for industrial quality control systems under adverse weather conditions. Rain-induced degradation, such as occlusions, texture blurring, and depth distortions, can significantly hinder image clarity and compromise precision in surface defect detection. To address this, we propose a novel image deraining framework, the Degradation-Background Perception Network (DBPNet). DBPNet features a hierarchical encoder–decoder structure and incorporates two core modules: the Frequency Degradation Perception Module (FDPM) and the Depth Background Perception Module (DBPM). FDPM focuses on frequency decomposition to remove high-frequency rain streaks while retaining critical image features using cross-attention mechanisms. DBPM is proposed to integrate robust depth maps, which remain unaffected by rain degradation, as explicit constraints to guide the model in reconstructing clean scenes. Furthermore, we propose the Selective Focus Attention (SFA) module, which enhances interactions between frequency-domain features and background priors, ensuring accurate reconstruction and effective rain removal. Experimental results on five synthetic and real-world benchmark datasets demonstrate that our method outperforms state-of-the-art CNN and transformer-based approaches. This framework contributes to more robust visual input for process control, enabling better fault detection, predictive maintenance, and sustainable system operation. Full article

(This article belongs to the Section Process Control and Monitoring)

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36 pages, 11692 KiB

Open AccessArticle

Integrating Model Predictive Control with Deep Reinforcement Learning for Robust Control of Thermal Processes with Long Time Delays

by Kevin Marlon Soza Mamani and Alvaro Javier Prado Romo

Processes 2025, 13(6), 1627; https://doi.org/10.3390/pr13061627 - 22 May 2025

Abstract

Thermal processes with prolonged and variable delays pose considerable difficulties due to unpredictable system dynamics and external disturbances, often resulting in diminished control effectiveness. This work presents a hybrid control strategy that synthesizes deep reinforcement learning (DRL) strategies with nonlinear model predictive control [...] Read more.

Thermal processes with prolonged and variable delays pose considerable difficulties due to unpredictable system dynamics and external disturbances, often resulting in diminished control effectiveness. This work presents a hybrid control strategy that synthesizes deep reinforcement learning (DRL) strategies with nonlinear model predictive control (NMPC) to improve the robust control performance of a thermal process with a long time delay. In this approach, NMPC cost functions are formulated as learning functions to achieve control objectives in terms of thermal tracking and disturbance rejection, while an actor–critic (AC) reinforcement learning agent dynamically adjusts control actions through an adaptive policy based on the exploration and exploitation of real-time data about the thermal process. Unlike conventional NMPC approaches, the proposed framework removes the need for predefined terminal cost tuning and strict constraint formulations during the control execution at runtime, which are typically required to ensure robust stability. To assess performance, a comparative study was conducted evaluating NMPC against AC-based controllers built upon policy gradient algorithms such as the deep deterministic policy gradient (DDPG) and the twin delayed deep deterministic policy gradient (TD3). The proposed method was experimentally validated using a temperature control laboratory (TCLab) testbed featuring long and varying delays. Results demonstrate that while the NMPC–AC hybrid approach maintains tracking control performance comparable to NMPC, the proposed technique acquires adaptability while tracking and further strengthens robustness in the presence of uncertainties and disturbances under dynamic system conditions. These findings highlight the benefits of integrating DRL with NMPC to enhance reliability in thermal process control and optimize resource efficiency in thermal applications. Full article

(This article belongs to the Section Process Control and Monitoring)

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17 pages, 3823 KiB

Open AccessArticle

Study of Adaptive Frequency Compensated Droop Control for Microgrid Inverters

by Li Fang, Hanzhong Liu and Zhou Fang

Processes 2025, 13(6), 1626; https://doi.org/10.3390/pr13061626 - 22 May 2025

Abstract

In distributed microgrid systems, inverters serve as the core components when distributed generation (DG) modules are integrated into the grid. Traditional inverters typically employ droop control; however, they lack damping and inertia mechanisms. Consequently, fluctuations in the grid frequency and voltage occur when [...] Read more.

In distributed microgrid systems, inverters serve as the core components when distributed generation (DG) modules are integrated into the grid. Traditional inverters typically employ droop control; however, they lack damping and inertia mechanisms. Consequently, fluctuations in the grid frequency and voltage occur when system loads change, leading to a suboptimal power distribution. To address these limitations, this paper introduces an adaptive strategy into conventional droop control. Based on an adaptive algorithm, the real and reactive power are dynamically computed. Through coordinate transformation, decoupled control, and adaptive frequency compensation, the inverter’s output frequency and voltage are effectively regulated. By adjusting the reference current in a dual-loop control scheme, the active and reactive power distribution is optimized. Additionally, an improved adaptive algorithm is developed to compute the inverter’s AC frequency compensation, enabling the self-adaptive adjustment of the PI controller’s output. This facilitates frequency compensation in droop control, ensuring that the inverter’s output current and voltage remain synchronized with the grid phase, thereby enhancing grid stability during connection. Finally, the feasibility of the proposed algorithm is validated through Simulink simulations. Full article

(This article belongs to the Section Energy Systems)

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20 pages, 4339 KiB

Open AccessArticle

Multi-Scale Dynamic Weighted Fusion for Small-Sample Oil Seal Ring Point Cloud Completion with Transformers

by Wencong Yan, Yetong Liu, Liwen Meng, Enyong Xu, Changbo Lin and Yanmei Meng

Processes 2025, 13(6), 1625; https://doi.org/10.3390/pr13061625 - 22 May 2025

Abstract

Oil seals are vital components in industrial production, necessitating high-precision 3D reconstruction for automated geometric measurement and quality inspection. High-quality point cloud completion is integral to this process. However, existing methods heavily rely on large datasets and often yield sub-optimal outcomes—such as distorted [...] Read more.

Oil seals are vital components in industrial production, necessitating high-precision 3D reconstruction for automated geometric measurement and quality inspection. High-quality point cloud completion is integral to this process. However, existing methods heavily rely on large datasets and often yield sub-optimal outcomes—such as distorted geometry and uneven point distributions—under limited sample conditions, constraining their industrial applicability. To address this, we propose a point cloud completion network that integrates a dynamic weighted fusion of multi-scale features with Transformer enhancements. Our approach incorporates three key innovations: a multi-layer perceptron fused with EdgeConv to enhance local feature extraction for small-sample oil seal rings, a dynamic weighted fusion strategy to adaptively optimize global feature integration across varying missing rates of oil seal rings, and a Transformer-enhanced multi-layer perceptron to ensure geometric consistency by linking global and local features. These innovations collectively enable high-quality point cloud completion for small-sample oil seal rings, achieving significant improvements at a 25% missing rate, reducing CD by 46%, EMD by 49%, and MMD by 74% compared to PF-Net. Experiments on the ShapeNet-Part dataset further validate the model’s strong generalizability across diverse categories. Experimental results on the industrial oil seal ring dataset and the small-sample ShapeNet sub-dataset show that our approach exhibits highly competitive performance compared to existing models. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)

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19 pages, 21045 KiB

Open AccessArticle

Performance of Machine Learning Algorithms in Fault Diagnosis for Manufacturing Systems: A Comparative Analysis

by Abner B. Montejano Leija, Elvia Ruiz Beltrán, Jorge L. Orozco Mora and Jorge O. Valdés Valadez

Processes 2025, 13(6), 1624; https://doi.org/10.3390/pr13061624 - 22 May 2025

Abstract

This study presents a comparative analysis of various machine learning algorithms to evaluate their performance in diagnosing faults within automated manufacturing systems. The primary objective is to identify the most effective model for classifying equipment failures based on historical data. Several algorithms were [...] Read more.

This study presents a comparative analysis of various machine learning algorithms to evaluate their performance in diagnosing faults within automated manufacturing systems. The primary objective is to identify the most effective model for classifying equipment failures based on historical data. Several algorithms were selected, including support vector machines (SVM), Decision trees, boosting, random forest, k-nearest neighbors (KNN), stacking, and artificial neural networks. The research began with the collection of a dataset using an Arduino-based system with sensors (temperature, electrical current, differential pressure, vibration, and sound) to monitor the equipment’s operational condition. Faults were intentionally induced in a motor, an electrovalve, and a pneumatic cylinder. The data were then processed in a Python environment, undergoing normalization and dimensionality reduction. The models were evaluated through cross-validation and compared using metrics such as precision, recall, F1-score, and accuracy. Results indicated that all models performed well, with the SVM algorithm showing the best overall performance, with an average fault diagnosis accuracy of 91.62% when trained on the full dataset and 66.83% under extreme class imbalance. In contrast, decision trees demonstrated lower generalization ability. This study provides insights for future fault diagnosis research using machine learning and offers recommendations for implementing such technologies in industrial environments. Full article

(This article belongs to the Special Issue Machine Learning, Control, and Optimization in Manufacturing and Industry 4.0)

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17 pages, 735 KiB

Open AccessArticle

Economic Assessment of Initial Cell Mass Increase in Maize Hydrolysate Fermentation for Ethanol Production

by Lorena Marcele de Faria Leite, Silvio Roberto Andrietta and Telma Teixeira Franco

Processes 2025, 13(6), 1623; https://doi.org/10.3390/pr13061623 - 22 May 2025

Abstract

Increasing the cell mass used as an inoculum is an effective strategy for enhancing productivity in alcoholic fermentation processes. In batch processes without cell recycling, such as those used in maize ethanol production, this objective can be achieved through two main approaches: (i) [...] Read more.

Increasing the cell mass used as an inoculum is an effective strategy for enhancing productivity in alcoholic fermentation processes. In batch processes without cell recycling, such as those used in maize ethanol production, this objective can be achieved through two main approaches: (i) increasing the amount of commercially acquired dry cell mass or (ii) extending the propagation time. In this study, an economic assessment of both approaches was carried out, considering the Brazilian industrial context of maize ethanol production. Fermentation assays demonstrated that specific substrate consumption decreases with increasing initial cell concentration, following a hyperbolic model. This experimental behavior was used to simulate different operational scenarios and estimate productivity gains and economic impacts. The results showed that both strategies increase ethanol production and revenue, although the associated costs vary significantly. Based on this model, productivity and revenue gains were estimated for both approaches. The findings suggest that extending the propagation time is the most economically viable strategy to increase the initial cell concentration, even in scenarios where the plant lacks existing infrastructure and additional equipment investments are required. The analysis also accounted for operational costs associated with increased energy consumption during extended aeration time. Full article

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

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17 pages, 4176 KiB

Open AccessArticle

An Operational Optimization Model for Micro Energy Grids in Photovoltaic-Storage Agricultural Greenhouses Based on Operation Mode Selection

by Peng Li, Mengen Zhao, Hongkai Zhang, Outing Zhang, Naixun Li, Xianyu Yue and Zhongfu Tan

Processes 2025, 13(6), 1622; https://doi.org/10.3390/pr13061622 - 22 May 2025

Abstract

Addressing the urgent need for sustainable energy transitions in rural development while achieving the dual carbon goals, this study focuses on resolving critical challenges in agricultural photovoltaic (PV) applications, including land-use conflicts, compound energy demands (electricity, heating, cooling), and financial constraints among farmers. [...] Read more.

Addressing the urgent need for sustainable energy transitions in rural development while achieving the dual carbon goals, this study focuses on resolving critical challenges in agricultural photovoltaic (PV) applications, including land-use conflicts, compound energy demands (electricity, heating, cooling), and financial constraints among farmers. To tackle these issues, a dual-mode cost–benefit analysis framework was developed, integrating two distinct investment models: self-invested construction (SIC), where farmers independently finance and manage the system, and energy performance contracting (EPC), where third-party investors fund infrastructure through shared energy-saving or revenue agreements. Then, an integrated photovoltaic-storage agricultural greenhouse (PSAG) microgrid optimization model is established, synergizing renewable energy generation, battery storage, and demand-side management while incorporating operational mode selection. The proposed model is validated through a real-world case study of a village agricultural greenhouse in Gannan, China, characterized by typical rural energy profiles and climatic conditions. Simulation results demonstrate that the optimal system configuration requires 27.91 kWh energy storage capacity and 18.67 kW peak output, with annualized post-depreciation costs of 81,083.69 yuan (SIC) and 74,216.22 yuan (EPC). The key findings reveal that energy storage integration reduces operational costs by 8.5% compared to non-storage scenarios, with the EPC model achieving 9.3% greater cost-effectiveness than SIC through shared-investment mechanisms. The findings suggest that incorporating an energy storage system reduces costs for farmers, with the EPC model offering greater cost savings. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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26 pages, 6091 KiB

Open AccessArticle

Performance of Composite Precast Assembled Concrete Utility Tunnels Subjected to Internal Gas Explosions: A Numerical Parametric Study

by Yushu Lin and Baijian Tang

Processes 2025, 13(6), 1621; https://doi.org/10.3390/pr13061621 - 22 May 2025

Abstract

To address the research gap in gas blast resistance of composite precast assembled utility tunnels, this study investigates structural damage evolution and the mechanisms influencing parameters through validated numerical simulations. A three-dimensional numerical model, incorporating the Karagozian & Case (K&C) concrete damage model [...] Read more.

To address the research gap in gas blast resistance of composite precast assembled utility tunnels, this study investigates structural damage evolution and the mechanisms influencing parameters through validated numerical simulations. A three-dimensional numerical model, incorporating the Karagozian & Case (K&C) concrete damage model and tie-break contact algorithm, was developed using LS-DYNA. The first validation against composite precast concrete slab explosion tests confirmed the model’s reliability, with displacement peak errors below 10%. The second validation focuses on the blast resistance test conducted on an underground utility tunnel, revealing an error margin of less than 10%. Results indicate that the utility tunnel exhibits a progressive failure mode of “joint cracking-interface damage-midspan cracking” under explosive loads, with stiffness degradation observed in joint regions at a loading pressure of 700 kPa. Increasing the normal strength of the interface to 5 MPa suppresses 90% of interface delamination, whereas completely neglecting interface strength results in a 9.0% increase in midspan displacement. Concrete strength shows minimal impact (<2.5%) on displacement under high loading conditions (≥0.9 MPa), and increasing the reinforcement ratio from 0.44% to 0.56% reduces displacement of the roof slab by 10.5%. These findings of address the research gap in the gas explosion response of composite precast assembled utility tunnels and could have significant implications for enhancing the disaster resistance of urban underground spaces. Full article

(This article belongs to the Section Materials Processes)

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27 pages, 1721 KiB

Open AccessFeature PaperReview

Biologically Active Components of Milk—Production and Properties of Lactoferrin

by Monika Ostrowska, Bartosz Brzozowski, Andrzej Babuchowski and Marek Adamczak

Processes 2025, 13(6), 1620; https://doi.org/10.3390/pr13061620 - 22 May 2025

Abstract

The aim of this article was to present the biological activity of milk components, particularly lactoferrin (LF), and techniques for its extraction and purification. Dairy products have long been recognized for their significant contributions to human health and nutrition. Recent studies indicate that [...] Read more.

The aim of this article was to present the biological activity of milk components, particularly lactoferrin (LF), and techniques for its extraction and purification. Dairy products have long been recognized for their significant contributions to human health and nutrition. Recent studies indicate that dairy consumption offers various health benefits, particularly concerning bone health, metabolic wellness, and cardiovascular health. LF, abundantly present in milk, exhibits a range of health-promoting properties that are increasingly recognized for their significance in nutrition and disease prevention. The production of LF can be approached through two main avenues: extraction from milk and recombinant expression systems. Both methods present unique advantages and challenges that influence the efficiency of LF production on an industrial scale. Moreover, advances in purification and drying techniques are crucial to enhance the overall efficiency of LF production. Recent studies have focused on methods such as monolithic ion-exchange chromatography and membrane technologies to improve yield and reduce costs of LF extraction. These innovations not only facilitate the extraction but also preserve the structural integrity and the functional properties of LF. The article presents the discussion of the applications of the LF in the dairy industry, indicating its growing importance as a functional ingredient in health products. Full article

(This article belongs to the Special Issue Improvement and Innovation in Engineering and Bioprocesses in Dairy Technology)

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17 pages, 1956 KiB

Open AccessArticle

MPPT Design of Photovoltaic Power Generation System Based on Improved Fibonacci Linear Search Algorithm

by Zhuo Meng, Yutai Su and Xu Yang

Processes 2025, 13(6), 1619; https://doi.org/10.3390/pr13061619 - 22 May 2025

Abstract

At present, photovoltaic power generation is becoming increasingly popular, and one of its major drawbacks is the low photoelectric conversion efficiency of photovoltaic cells. In order to improve power generation efficiency, an equivalent circuit model of photovoltaic cells and a simulation structure of [...] Read more.

At present, photovoltaic power generation is becoming increasingly popular, and one of its major drawbacks is the low photoelectric conversion efficiency of photovoltaic cells. In order to improve power generation efficiency, an equivalent circuit model of photovoltaic cells and a simulation structure of photovoltaic power generation systems have been established. The working principle and MPPT (Maximum Power Point Tracking) control process of the improved Fibonacci linear search algorithm have been analyzed to achieve the goal of improving power generation efficiency. The improved Fibonacci linear search algorithm is applied in the MPPT technology of photovoltaic power generation system, and simulation experiments are conducted. Through analysis and simulation, MPPT can quickly track the maximum power point when the external lighting conditions change and has good tracking effect. Full article

(This article belongs to the Section Energy Systems)

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27 pages, 5524 KiB

Open AccessArticle

Unraveling Adsorption Mechanisms and Potential of Titanium Dioxide for Arsenic and Heavy Metal Removal from Water Sources

by Marko Šolić, Jasmina Nikić, Aleksandra Kulić Mandić, Tamara Apostolović, Malcolm Watson, Marijana Kragulj Isakovski and Snežana Maletić

Processes 2025, 13(6), 1618; https://doi.org/10.3390/pr13061618 - 22 May 2025

Abstract

Arsenic and heavy metal contamination in water presents serious environmental and public health challenges, requiring effective treatment technologies. Titanium dioxide (TiO₂) nanoparticles offer promising adsorption potential due to their high surface area, mesoporosity, and chemical stability. This study investigates the removal [...] Read more.

Arsenic and heavy metal contamination in water presents serious environmental and public health challenges, requiring effective treatment technologies. Titanium dioxide (TiO₂) nanoparticles offer promising adsorption potential due to their high surface area, mesoporosity, and chemical stability. This study investigates the removal of As(V), Cd(II), Cu(II), and Pb(II) by TiO₂ under environmentally relevant conditions (pH 3 and 7), commonly encountered in industrial and natural waters. TiO₂ was characterized using SEM, XRD, FTIR, BET, and pH_pzc analysis, confirming a mesoporous structure with mixed anatase/rutile phases. Adsorption followed Elovich kinetics, with the Langmuir model providing the best fit to the isotherm data. At pH 3, adsorption capacities (q_m) were of the following order: Pb(II) 30.80 mg g⁻¹ > Cd(II) 10.02 mg g⁻¹ > As(V) 8.45 mg g⁻¹ > Cu(II) 2.73 mg g⁻¹; at pH 7, they were as follows: Cd(II) 26.75 mg g⁻¹ > Pb(II) 26.20 mg g⁻¹ > As(V) 8.50 mg g⁻¹ > Cu(II) 5.05 mg g⁻¹. These results highlight a pH-dependent mechanism involving both chemisorption and physisorption. Principal Component Analysis (PCA) revealed that physicochemical properties, particularly electronegativity, significantly influenced removal efficiency. TiO₂ showed high, selective, and pH-responsive adsorption properties, supporting its use in sustainable water treatment. Future work should address nanoparticle recovery, regeneration, and performance under continuous flow conditions. Full article

(This article belongs to the Special Issue Latest Research on Wastewater Treatment and Recycling)

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14 pages, 2313 KiB

Open AccessArticle

Assessment of the Influence of Erosion Wear on the Design Parameters and Useful Life of the C4-70 Family Centrifugal Fan

by Sandra Arla, Leonardo Goyos and Jose Mier

Processes 2025, 13(5), 1617; https://doi.org/10.3390/pr13051617 - 21 May 2025

Abstract

The design and operating regime of centrifugal fans operating with contaminated flows must consider the influence of different geometric parameters and flow dynamics design variables on fan wear. The influence of fan rotation speed and blade angle of attack on the erosion wear [...] Read more.

The design and operating regime of centrifugal fans operating with contaminated flows must consider the influence of different geometric parameters and flow dynamics design variables on fan wear. The influence of fan rotation speed and blade angle of attack on the erosion wear they may experience when moving fluids contaminated with solid particles is especially relevant. A method is proposed for performing experimental tests that emulate centrifugal fans using a slurry bucket installation, at tangential velocities of 2, 4, and 6 m/s and fluid incidence angles of 16, 22, and 28 degrees. An equation for cumulative wear is found, in which the independent variables incidence angle and linear velocity have a linear and quadratic influence, respectively. It can be specified that when the fan operates at revolutions between 814 and 815 rpm, for a tangential speed of 2 m/s and a flow rate of 20.16 m³/h, an accumulated wear of 1.3124 mg/g is recorded, caused by the impact of solid particles transported by the flow that could impact the surface of the blade when the angle is 22°24′. Full article

(This article belongs to the Special Issue Motor Drive Systems: Control Technology, Fault Diagnosis and Fault Tolerance)

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12 pages, 2176 KiB

Open AccessArticle

Edge-Side Cross-Area State Synchronization Method Adapted to Multiple Delay-Sensitive Services

by Yi Zhou, Li Li, Chang Wang and Lin Yang

Processes 2025, 13(5), 1616; https://doi.org/10.3390/pr13051616 - 21 May 2025

Abstract

With the integration of high proportions of new energy and high proportions of power electronic devices, the spatial–temporal correlation scale of emerging distribution business has experienced a sudden increase, and the demand for inter-distribution area collaboration is increasing steadily. Currently, distribution systems heavily [...] Read more.

With the integration of high proportions of new energy and high proportions of power electronic devices, the spatial–temporal correlation scale of emerging distribution business has experienced a sudden increase, and the demand for inter-distribution area collaboration is increasing steadily. Currently, distribution systems heavily rely on cloud master stations to facilitate the state synchronization process across feeders for such business. However, this approach struggles to adapt to the diverse delay-sensitive characteristics anticipated in future large-scale integrations. Therefore, a novel edge-side state synchronization method for cross-feeder operations in distribution systems tailored to diverse delay-sensitive services is proposed. Firstly, the traditional integrated hierarchical vertical network structure is evolved to construct “edge–cloud–edge” and “edge–edge” dual data transmission channels for inter-distribution area nodes. Secondly, considering the unique characteristics of each channel, their expected synchronization delays for differentiated services are calculated. An optimization problem is then formulated with the objective of maximizing the minimum expected synchronization delay redundancy rate. Finally, an iterative variable weighting method is designed to solve this optimization problem. Simulation analysis shows that the proposed algorithm can better adapt to the high-concurrency differentiated inter-distribution area status synchronization demands of diverse time-sensitive businesses, efficiently supporting the flexible, intelligent, and digital transformation of distribution networks. Full article

(This article belongs to the Special Issue Smart Optimization Techniques for Microgrid Management)

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