Processes

4 pages, 324 KB

Open AccessEditorial

Special Issue on “CFD Applications in Renewable Energy Systems”

by Omar D. Lopez Mejia and Santiago Laín

Processes 2025, 13(10), 3091; https://doi.org/10.3390/pr13103091 (registering DOI) - 26 Sep 2025

The global energy landscape is undergoing a critical transformation driven by the urgent need to mitigate climate change, reduce greenhouse gas (GHG) emissions, and ensure long-term energy security [...] Full article

(This article belongs to the Special Issue CFD Applications in Renewable Energy Systems)

18 pages, 4723 KB

Open AccessArticle

Study on Production System Optimization and Productivity Prediction of Deep Coalbed Methane Wells Considering Thermal–Hydraulic–Mechanical Coupling Effects

by Sukai Wang, Yonglong Li, Wei Liu, Siyu Zhang, Lipeng Zhang, Yan Liang, Xionghui Liu, Quan Gan, Shiqi Liu and Wenkai Wang

Processes 2025, 13(10), 3090; https://doi.org/10.3390/pr13103090 (registering DOI) - 26 Sep 2025

Abstract

Deep coalbed methane (CBM) resources possess significant potential. However, their development is challenged by geological characteristics such as high in situ stress and low permeability. Furthermore, existing production strategies often prove inadequate. In order to achieve long-term stable production of deep coalbed methane [...] Read more.

Deep coalbed methane (CBM) resources possess significant potential. However, their development is challenged by geological characteristics such as high in situ stress and low permeability. Furthermore, existing production strategies often prove inadequate. In order to achieve long-term stable production of deep coalbed methane reservoirs and increase their final recoverable reserves, it is urgent to construct a scientific and reasonable drainage system. This study focuses on the deep CBM reservoir in the Daning-Jixian Block of the Ordos Basin. First, a thermal–hydraulic–mechanical (THM) multi-physics coupling mathematical model was constructed and validated against historical well production data. Then, the model was used to forecast production. Finally, key control measures for enhancing well productivity were identified through production strategy adjustment. The results indicate that controlling the bottom-hole flowing pressure drop rate at 1.5 times the current pressure drop rate accelerates the early-stage pressure drop, enabling gas wells to reach the peak gas production earlier. The optimized pressure drop rates for each stage are as follows: 0.15 MPa/d during the dewatering stage, 0.057 MPa/d during the gas production rise stage, 0.035 MPa/d during the stable production stage, and 0.01 MPa/d during the production decline stage. This strategy increases peak daily gas production by 15.90% and cumulative production by 3.68%. It also avoids excessive pressure drop, which can cause premature production decline during the stable phase. Consequently, the approach maximizes production over the entire life cycle of the well. Mechanistically, the 1.5× flowing pressure drop offers multiple advantages. Firstly, it significantly shortens the dewatering and production ramp-up periods. This acceleration promotes efficient gas desorption, increasing the desorbed gas volume by 1.9%, and enhances diffusion, yielding a 39.2% higher peak diffusion rate, all while preserving reservoir properties. Additionally, this strategy synergistically optimizes the water saturation and temperature fields, which mitigates the water-blocking effect. Furthermore, by enhancing coal matrix shrinkage, it rebounds permeability to 88.9%, thus avoiding stress-induced damage from aggressive extraction. Full article

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

15 pages, 3383 KB

Open AccessArticle

Analysis of Trace Rare Earth Elements in Uranium-Bearing Nuclear Materials

by Ziao Li, Yang Shao, Futao Xin, Chun Li, Jilong Zhang, Xi Li, Min Luo, Diandou Xu and Lingling Ma

Processes 2025, 13(10), 3089; https://doi.org/10.3390/pr13103089 - 26 Sep 2025

Abstract

Rare earth elements (REEs) have significant application value in the quality control of nuclear materials and in traceability research in nuclear forensics. Methods were developed for the determination of REEs in uranium-bearing nuclear materials. The digestion parameters for uranium oxides and uranium ores, [...] Read more.

Rare earth elements (REEs) have significant application value in the quality control of nuclear materials and in traceability research in nuclear forensics. Methods were developed for the determination of REEs in uranium-bearing nuclear materials. The digestion parameters for uranium oxides and uranium ores, such as the digestion acid, digestion temperature, and digestion time, were optimized and reported. The optimized digestion parameters for uranium oxides were 2 mL HNO₃ at 160 °C for 3 h, and those for uranium ores were 7 mL mixed acid (HNO₃–HClO₄–HF = 5:5:3) at 180 °C for 36 h. Two digestion methods were demonstrated to be effective for the quantitative recovery of REEs. The suitable system and specifications for different resin columns were investigated to achieve a high decontamination factor of U (10⁵) by UTEVA resin. The corresponding loading system was 10 mL 4 M HNO₃, and the elution system was 6 mL 4 M HNO₃. Additionally, the analysis of ultra-trace REEs in high-uranium matrices was accomplished using two UTEVA resins. The developed methods were subjected to the Cochran test and the Grubbs test, and the relative standard deviation (RSD) for all REEs was below 6%. In uranium oxide samples with different spiked amounts, the recovery of REEs exceeded 80% in all cases, and the RSDs were all less than 10%. The method’s detection limits were below 10 ppt for all REEs (except for Ce), ensuring the accurate measurement of REEs in uranium-bearing nuclear materials. Full article

(This article belongs to the Section Materials Processes)

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19 pages, 1286 KB

Open AccessFeature PaperReview

Waste to Value: L-Asparaginase Production from Agro-Industrial Residues

by Enzo Corvello, Bruno C. Gambarato, Nathalia V. P. Veríssimo, Thiago Q. J. Rodrigues, Alice D. R. Pesconi, Ana K. F. Carvalho and Heitor B. S. Bento

Processes 2025, 13(10), 3088; https://doi.org/10.3390/pr13103088 - 26 Sep 2025

Abstract

The agro-industrial sector is a key pillar of the global economy, playing a central role in the supply of food, energy, and industrial inputs. However, its production chain generates significant amounts of residues and by-products, which, if not properly managed, may cause considerable [...] Read more.

The agro-industrial sector is a key pillar of the global economy, playing a central role in the supply of food, energy, and industrial inputs. However, its production chain generates significant amounts of residues and by-products, which, if not properly managed, may cause considerable environmental impacts. In this context, the search for alternatives to reuse these materials is essential, particularly when they can be converted into high-value products. One promising application is their use as a nutrient source for microorganisms in high-value biotechnological processes, such as the production of L-Asparaginase, an important enzyme used both in mitigating acrylamide formation in foods and as a biopharmaceutical in Acute Lymphoblastic Leukemia therapy. This approach offers a sustainable and competitive pathway, combining robust, scalable, and economical enzyme production with waste valorization and circular economy benefits. Although interest in developing more sustainable processes is growing, supported by international agreements and strategies for the valorization of agricultural residues, important challenges remain. The variability and impurity of residues pose significant challenges for producing biological products for the pharmaceutical and food industries. In addition, meeting regulatory requirements is essential to ensure product safety and traceability, while achieving high yields is crucial to maintain production viability compared to conventional media. Overcoming these barriers is critical to enable industrial-scale application of this approach. This review provides a residue-centered revision of the most relevant agro-industrial by-products used as substrates for L-asparaginase production, systematically comparing their compositional characteristics, fermentation strategies, and reported yields. Additionally, we present a novel SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis that critically examines the technical, regulatory, and economic challenges of implementing residue-based processes on an industrial scale. Full article

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

22 pages, 8501 KB

Open AccessArticle

Estimation of Chlorophyll and Water Content in Maize Leaves Under Drought Stress Based on VIS/NIR Spectroscopy

by Qi Su, Jingyong Wang, Huarong Ling, Ziting Wang and Jingyao Gai

Processes 2025, 13(10), 3087; https://doi.org/10.3390/pr13103087 - 26 Sep 2025

Abstract

Maize (Zea mays) is a key crop, with its growth impacted by drought stress. Accurate, non-destructive assessment of drought severity is crucial for precision agriculture. VIS/NIR reflectance spectroscopy is widely used for estimating plant parameters and detecting stress. However, the relationship [...] Read more.

Maize (Zea mays) is a key crop, with its growth impacted by drought stress. Accurate, non-destructive assessment of drought severity is crucial for precision agriculture. VIS/NIR reflectance spectroscopy is widely used for estimating plant parameters and detecting stress. However, the relationship between key parameters—such as chlorophyll and water content—and VIS/NIR spectra under drought conditions in maize remains unclear, lacking comprehensive models and validation. This study aims to develop a non-destructive and accurate method for predicting chlorophyll and water content in maize leaves under drought stress using VIS/NIR spectroscopy. Specifically, maize leaf reflectance spectra were collected under varying drought stress conditions, and the effects of different spectral preprocessing methods, dimensionality reduction techniques, and machine learning algorithms were evaluated. An optimal data processing pipeline was systematically established and deployed on an edge computing unit to enable rapid, non-destructive prediction of chlorophyll and water content in maize leaves. The experimental results demonstrated that the combination of stepwise regression (SR) for feature selection and a stacking regression model achieved the best performance for chlorophyll content prediction (

R_{p}^{2}

= 0.8740, RMSE_p = 0.2768). For leaf water content prediction, random forest (RF) feature selection combined with a stacking model yielded the highest accuracy (

R_{p}^{2}

= 0.7626, RMSE_p = 4.12%). This study confirms the effectiveness and potential of integrating VIS/NIR spectroscopy with machine learning algorithms for monitoring drought stress in maize, offering a valuable theoretical foundation and practical reference for non-destructive crop physiological monitoring in precision agriculture. Full article

(This article belongs to the Special Issue Innovative Robotic Process Control in Agriculture: Enhancing Efficiency and Sustainability)

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13 pages, 1662 KB

Open AccessArticle

Loading of Ni²⁺ in Coal by Hydrothermal Treatment to Conduct Catalytic Pyrolysis Under the Context of In Situ Pyrolysis

by Li Xiao, Xiaodan Wu, Youwu Li, Ying Tang, Yue Zhang, Shixin Jiang, Jingyun Cui, Chao Wang and Zhibing Chang

Processes 2025, 13(10), 3086; https://doi.org/10.3390/pr13103086 - 26 Sep 2025

Abstract

Identifying suitable catalyst types and efficient loading methods remains a key research challenge for implementing the in situ catalytic pyrolysis of tar-rich coal. This study investigated a lignite and a gas coal, employing NiCl₂ solution for Ni²⁺ catalyst loading via room-temperature [...] Read more.

Identifying suitable catalyst types and efficient loading methods remains a key research challenge for implementing the in situ catalytic pyrolysis of tar-rich coal. This study investigated a lignite and a gas coal, employing NiCl₂ solution for Ni²⁺ catalyst loading via room-temperature impregnation and hydrothermal treatment on coal particles sized 6–13 mm. The efficiency of Ni²⁺ loading through hydrothermal treatment and the characteristics of pyrolysis product distribution and composition before and after treatment were examined. The results indicated that after NiCl₂ solution impregnation, the Ni²⁺ content in lignite increased from nearly undetectable to over 20 mg/g, whereas in gas coal, it only rose to less than 2 mg/g. Ion exchange is hypothesized to be a primary pathway for Ni²⁺ loading into coal. After hydrothermal treatment at 170 °C, the Ni²⁺ loadings in lignite and gas coal reached 33.6 and 1.45 mg/g, respectively. The loaded Ni²⁺ exhibited distinct catalytic effects on the two coals. For lignite, Ni²⁺ catalyzed the deoxygenation of oxygen-containing compounds and the aromatization of aliphatic hydrocarbons. For gas coal, hydrothermal treatment with NiCl₂ solution at 170 and 220 °C promoted hydrogen transfer reactions, resulting in an increase in tar yield from 10.67% to 11.30% and 11.64%, respectively. Also, the H₂ yield decreased, accompanied by a decrease in aromatic hydrocarbons and an increase in phenolic compounds within the tar. Full article

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

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22 pages, 1349 KB

Open AccessArticle

Bayesian Optimization of LSTM-Driven Cold Chain Warehouse Demand Forecasting Application and Optimization

by Tailin Li, Shiyu Wang, Tenggao Nong, Bote Liu, Fangzheng Hu, Yunsheng Chen and Yiyong Han

Processes 2025, 13(10), 3085; https://doi.org/10.3390/pr13103085 - 26 Sep 2025

Abstract

With the gradual adoption of smart hardware such as the Internet of Things (IoT) in warehousing and logistics, the efficiency bottlenecks and resource wastage inherent in traditional storage management models are now poised for breakthrough through digital and intelligent transformation. This study focuses [...] Read more.

With the gradual adoption of smart hardware such as the Internet of Things (IoT) in warehousing and logistics, the efficiency bottlenecks and resource wastage inherent in traditional storage management models are now poised for breakthrough through digital and intelligent transformation. This study focuses on the cross-border cold chain storage scenario for Malaysia’s Musang King durians. Influenced by the fruit’s extremely short 3–5-day shelf life and the concentrated harvesting period in primary production areas, the issue of delayed dynamic demand response is particularly acute. Utilizing actual sales order data for Mao Shan Wang durians from Beigang Logistics in Guangxi, this study constructs a demand forecasting model integrating Bayesian optimization with bidirectional long short-term memory networks (BO-BiLSTM). This aims to achieve precise forecasting and optimization of cold chain storage inventory. Experimental results demonstrate that the BO-BiLSTM model achieved an R² of 0.6937 on the test set, with the RMSE reduced to 19.1841. This represents significant improvement over the baseline LSTM model (R² = 0.5630, RMSE = 22.9127). The bidirectional Bayesian optimization mechanism effectively enhances model stability. This study provides a solution for forecasting inventory demand of fresh durians in cold chain storage, offering technical support for optimizing the operation of backbone hub cold storage facilities along the New Western Land–Sea Trade Corridor. Full article

(This article belongs to the Special Issue AI-Supported Methods and Process Modeling in Smart Manufacturing)

26 pages, 4038 KB

Open AccessArticle

Eco-Friendly Oxidative–Adsorptive Desulfurization for Real Diesel Fuel Using Green MnO₂ Biowaste-Extracted Calcite in Digital Basket Reactor

by Jasim I. Humadi, Khaleel I. Hamad, Hiba A. Abdulkareem, Maha Nazar Ismael, Aysar T. Jarullah, Mustafa A. Ahmed, Shymaa A. Hameed, Amer T. Nawaf and Iqbal M. Mujtaba

Processes 2025, 13(10), 3084; https://doi.org/10.3390/pr13103084 - 26 Sep 2025

Abstract

Achieving ultra-low-sulfur diesel is a crucial objective in modern fuel refining, driven by increasingly stringent environmental regulations. This study presents the development of a highly efficient oxidative–adsorptive desulfurization process utilizing a nanocatalyst synthesized from biowaste eggshell-extracted calcite. The oxidation reaction was conducted in [...] Read more.

Achieving ultra-low-sulfur diesel is a crucial objective in modern fuel refining, driven by increasingly stringent environmental regulations. This study presents the development of a highly efficient oxidative–adsorptive desulfurization process utilizing a nanocatalyst synthesized from biowaste eggshell-extracted calcite. The oxidation reaction was conducted in a digital basket reactor (DBR), an advanced reactor system designed to enhance mass transfer and catalytic efficiency. To further augment the catalyst’s performance, the calcite was modified with eco-friendly MnO₂, while activated carbon was employed as an adsorbent to effectively capture oxidized sulfur compounds, ensuring compliance with ultra-low-sulfur fuel standards. The synthesized nanocatalyst underwent comprehensive physicochemical characterization using SEM, EDX, BET, and FTIR, confirming its high surface area, structural integrity, and superior catalytic activity. The MnO₂/P–calcite catalyst achieved a sulfur removal efficiency of 96.5% at 90 °C, 80 min, and 600 rpm, demonstrating excellent oxidative–adsorptive performance for real diesel fuel. The integration of this innovative nanocatalyst with the DBR system presents a sustainable, cost-effective, and industrially viable approach for deep desulfurization, offering significant advancements in clean fuel production and environmental sustainability. Full article

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

15 pages, 3802 KB

Open AccessArticle

Experimental Study on the Mechanism of Steam Flooding for Heavy Oil in Pores of Different Sizes

by Dong Zhang, Li Zhang, Yan Wang, Jiyu Zhou, Peng Sun and Kuo Zhan

Processes 2025, 13(10), 3083; https://doi.org/10.3390/pr13103083 - 26 Sep 2025

Abstract

Nowadays, most of the heavy oil fields around the world have entered difficult exploiting stages, with problems regarding high viscosity and poor fluidity. However, there has been little previous research on the accurate identification and distribution of remaining oil with different levels of [...] Read more.

Nowadays, most of the heavy oil fields around the world have entered difficult exploiting stages, with problems regarding high viscosity and poor fluidity. However, there has been little previous research on the accurate identification and distribution of remaining oil with different levels of steam dryness. Therefore, this paper proposes a new nuclear magnetic resonance (NMR) interpretation method, as well as a new samples analysis method for remaining oil in the core. We conducted core displacement experiments using different methods. The nuclear magnetic resonance (NMR) tests and analysis of core thin sections after steam flooding were used to study the effect of different steam dryness levels on the migration and sedimentation mechanisms of heavy oil components. The results showed that the viscosity of crude oil and the permeability of rock cores are both sensitive to steam dryness; therefore, the improvement of steam dryness is beneficial for improving oil recovery. Heavy oil is mainly distributed in the medium pores of 10–50 μm and the small pores of 1–10 μm. However, with the decrease in steam dryness, the dynamic amount of crude oil in both medium and small pores decreases, and the bitumen in crude oil stays in the pores in the form of stars, patches, and envelopes, which leads to a decline in oil displacement efficiency. Thus, our study provides a micro-level understanding of remaining oil which lays the foundation for the further enhancement of oil recovery in heavy oilfields. Full article

(This article belongs to the Section Energy Systems)

23 pages, 4045 KB

Open AccessArticle

Analysis and Optimization of Dynamic Characteristics of Primary Frequency Regulation Under Deep Peak Shaving Conditions for Industrial Steam Extraction Heating Thermal Power Units

by Libin Wen, Jinji Xi, Hong Hu and Zhiyuan Sun

Processes 2025, 13(10), 3082; https://doi.org/10.3390/pr13103082 - 26 Sep 2025

Abstract

This study investigates the primary frequency regulation dynamic characteristics of industrial steam extraction turbine units under deep peak regulation conditions. A high-fidelity integrated dynamic model was established, incorporating the governor system, steam turbine with extraction modules, and interconnected pipeline dynamics. Through comparative simulations [...] Read more.

This study investigates the primary frequency regulation dynamic characteristics of industrial steam extraction turbine units under deep peak regulation conditions. A high-fidelity integrated dynamic model was established, incorporating the governor system, steam turbine with extraction modules, and interconnected pipeline dynamics. Through comparative simulations and experimental validation, the model demonstrates high accuracy in replicating real-unit responses to frequency disturbances. For the power grid system in this study, the frequency disturbance mainly comes from three aspects: first, the power imbalance formed by the random mutation of the load side and the intermittence of new energy power generation; second, transformation of the energy structure directly reduces the available frequency modulation resources; third, the system-equivalent inertia collapse effect caused by the integration of high permeability new energy; the rotational inertia provided by the traditional synchronous unit is significantly reduced. In the cogeneration unit and its control system in Guangxi involved in this article, key findings reveal that increased peak regulation depth (30~50% rated power) exacerbates nonlinear fluctuations. This is due to boiler combustion stability thresholds and steam pressure variations. Key parameters—dead band, power limit, and droop coefficient—have coupled effects on performance. Specifically, too much dead band (>0.10 Hz) reduces sensitivity; likewise, too high a power limit (>4.44%) leads to overshoot and slow recovery. The robustness of parameter configurations is further validated under source-load random-intermittent coupling disturbances, highlighting enhanced anti-interference capability. By constructing a coordinated control model of primary frequency modulation, the regulation strategy of boiler and steam turbine linkage is studied, and the optimization interval of frequency modulation dead zone, adjustment coefficient, and frequency modulation limit parameters are quantified. Based on the sensitivity theory, the dynamic influence mechanism of the key control parameters in the main module is analyzed, and the degree of influence of each parameter on the frequency modulation performance is clarified. This research provides theoretical guidance for optimizing frequency regulation strategies in coal-fired units integrated with renewable energy systems. Full article

(This article belongs to the Section Energy Systems)

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27 pages, 18999 KB

Open AccessArticle

Research on Suppression of Negative Effects of Vibration in In-Wheel Motor-Driven Electric Vehicles Based on DMPC

by Xiangpeng Meng, Yang Rong, Renkai Ding, Wei Liu, Dong Sun and Ruochen Wang

Processes 2025, 13(10), 3081; https://doi.org/10.3390/pr13103081 - 26 Sep 2025

Abstract

In-wheel motor (IWM)-driven electric vehicles (EVs) are susceptible to road excitation, which can induce eccentricity between the stator and rotor of the IWM. This eccentricity leads to unbalanced electromagnetic forces (UEFs) and electromechanical coupling (EMC) effects, severely degrading vehicle dynamic performance. To address [...] Read more.

In-wheel motor (IWM)-driven electric vehicles (EVs) are susceptible to road excitation, which can induce eccentricity between the stator and rotor of the IWM. This eccentricity leads to unbalanced electromagnetic forces (UEFs) and electromechanical coupling (EMC) effects, severely degrading vehicle dynamic performance. To address this issue, this study first established an EMC system model encompassing UEF, IWM drive, and vehicle dynamics. Based on this model, four typical operating conditions—constant speed, acceleration, deceleration, and steering—were designed to thoroughly analyze the influence of EMC effects on vehicle dynamic response characteristics. The analysis results were validated through real-vehicle experiments. The results indicate that the EMC effects caused by motor eccentricity primarily affect the vehicle’s vertical dynamics performance (especially during acceleration and deceleration), leading to increased vertical body acceleration and reduced ride comfort, while having a relatively minor impact on longitudinal and lateral dynamics performance. Additionally, these effects significantly increase the relative eccentricity of the motor under various operating conditions, further degrading motor performance. To mitigate these negative effects, this paper designs an active suspension controller based on distributed model predictive control (DMPC). Simulation and experimental validation demonstrate that the proposed controller effectively improves ride comfort and body posture stability while significantly suppressing the growth of the motor’s relative eccentricity, thereby enhancing motor operational performance. Full article

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

18 pages, 13450 KB

Open AccessFeature PaperArticle

Formation of η-Carbides by Mechanical Alloying of Co₂₅Mo₂₅C₅₀ and Their Performance in Hydrodesulfurization

by Brenda Edith García Caudillo, Ignacio Carvajal-Mariscal, Adriana Isabel Reyes de la Torre, Jesús Noé Rivera Olvera, Vicente Garibay Febles, Leonardo González Reyes and Lucía Graciela Díaz Barriga Arceo

Processes 2025, 13(10), 3080; https://doi.org/10.3390/pr13103080 - 26 Sep 2025

Abstract

Cobalt–molybdenum

η

-carbides are attractive hydrodesulfurization (HDS) catalysts, yet controlling their phase composition and nanostructure remains challenging. Here, a

{Co}_{25} {Mo}_{25} C_{50}

powder was prepared by mechanical alloying in a horizontal mill, with and without superimposed vertical vibration. Phase composition [...] Read more.

Cobalt–molybdenum

η

-carbides are attractive hydrodesulfurization (HDS) catalysts, yet controlling their phase composition and nanostructure remains challenging. Here, a

{Co}_{25} {Mo}_{25} C_{50}

powder was prepared by mechanical alloying in a horizontal mill, with and without superimposed vertical vibration. Phase composition was determined by X-ray diffraction using the reference-intensity-ratio method, and the nanostructure was examined by SEM and HRTEM. Aquathermolysis of a heavy crude was monitored by ATR-FTIR in the window characteristic of S–S and C–S vibrations. Both milling routes produced the

η

-carbides

{Co}_{3} {Mo}_{3}

C and

{Co}_{6} {Mo}_{6}

C, as well as

{Co}_{2} {Mo}_{3}

,

{Co}_{7} {Mo}_{6}

, and

{Co}_{3}

C; vibration-assisted milling increased the

{Co}_{6} {Mo}_{6}

C fraction and generated thin lamellae exhibiting Moiré contrast. In FTIR, the

{Co}_{6} {Mo}_{6}

C-rich powder showed strong attenuation of the disulfide and thioether bands, whereas the

{Co}_{3} {Mo}_{3}

C-rich powder behaved similarly to the water-only baseline under mild conditions (100 °C, 4 h). These results indicate that mechanical alloying with superposed vibration enables control over phase and nanostructure, and that a higher

{Co}_{6} {Mo}_{6}

C fraction correlates with a stronger HDS response under aquathermolysis. The approach offers a scalable route to Co–Mo carbides that are active for desulfurization at 100 °C in water without added

H_{2}

. Full article

(This article belongs to the Special Issue Preparation, Characterization and Application of Heterogeneous Catalysts)

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18 pages, 4993 KB

Open AccessFeature PaperArticle

Stable Non-Competitive DPP-IV Inhibitory Hexapeptide from Parkia timoriana Seeds: A Candidate for Functional Food Development in Type 2 Diabetes

by Sakinah Hilya Abida, Christoper Caesar Yudho Sutopo, Wei-Ting Hung, Nhung Thi Phuong Nong, Tunjung Mahatmanto and Jue-Liang Hsu

Processes 2025, 13(10), 3079; https://doi.org/10.3390/pr13103079 - 26 Sep 2025

Abstract

The tree bean (Parkia timoriana), an underutilized legume valued for its nutritional profile, represents a potential source of bioactive peptides for diabetes management. To our knowledge, this is the first study to identify and characterize DPP-IV inhibitory peptides derived from tree [...] Read more.

The tree bean (Parkia timoriana), an underutilized legume valued for its nutritional profile, represents a potential source of bioactive peptides for diabetes management. To our knowledge, this is the first study to identify and characterize DPP-IV inhibitory peptides derived from tree bean seed protein hydrolysates. The tree bean proteins were digested with trypsin, thermolysin, chymotrypsin, pepsin, and simulated gastrointestinal (SGI) enzymes, among which SGI hydrolysis yielded the highest degree of hydrolysis (14%) and strongest DPP-IV inhibitory activity (IC₅₀ = 1289 ± 58 µg/mL). Guided by DPP-IV inhibitory assays, sequential fractionation using strong cation exchange and RP-HPLC yielded the most potent fraction, H5, with an IC₅₀ of 949 ± 50 µg/mL. After peptide identification and synthesis, APLGPF (AF6) emerged as the most potent inhibitor, with an IC₅₀ of 396 ± 18 µM. Enzyme kinetics revealed a non-competitive inhibition mechanism, corroborated by molecular docking, which indicated binding at an allosteric site of DPP-IV. Furthermore, AF6 remained stable under simulated gastrointestinal digestion and enzymatic exposure, highlighting its resistance to proteolysis. Taken together, these findings highlight P. timoriana as an underexplored source of peptides with DPP-IV inhibitory activity and identify AF6 as a promising lead for developing functional foods or nutraceuticals aimed at type 2 diabetes management. Full article

(This article belongs to the Special Issue Peptides: Advances and Innovations from Discovery to Application)

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25 pages, 5319 KB

Open AccessArticle

Cooperative Planning Model of Multi-Type Charging Stations Considering Comprehensive Satisfaction of EV Users

by Xin Yang, Fan Zhou, Yalin Zhong, Ran Xu, Chunhui Rui, Chengrui Zhao and Yinghao Ma

Processes 2025, 13(10), 3078; https://doi.org/10.3390/pr13103078 - 25 Sep 2025

Abstract

With the rapid advancement of the electric vehicle (EV) industry, the ownership of EVs and their charging power have increased significantly, gradually exerting a greater impact on the power grid. To meet the diverse charging needs of different EV users, the coordinated planning [...] Read more.

With the rapid advancement of the electric vehicle (EV) industry, the ownership of EVs and their charging power have increased significantly, gradually exerting a greater impact on the power grid. To meet the diverse charging needs of different EV users, the coordinated planning of fast- and slow-charging stations can reduce the influence of charging loads on the power grid while fulfilling user demands and increasing the number of EVs that can be served. This paper establishes a collaborative planning model for multi-type charging stations (CSs), considering the comprehensive satisfaction of EV users. Firstly, a comprehensive satisfaction model of multi-type EV users considering their behavioral characteristics is established to characterize the impact of fast- and slow-charging CSs on the satisfaction of different types of users. Secondly, a two-layer cooperative planning model of multi-type CSs considering comprehensive satisfaction of EV users is established to determine the location of CSs and the number of fast- and slow-charging configurations to satisfy the users’ demand for different types of charging piles. Thirdly, a solution algorithm for the two-layer planning model based on the greedy theory algorithm is proposed, which transforms the upper layer charging pile planning model into a charging pile multi-round expansion problem to speed up the model solving. Finally, the validity of the proposed models is verified through case studies, and the results show that the planning scheme obtained can take into account the user’s charging satisfaction while guaranteeing the economy, and at the same time, the scheme has a positive significance in the promotion of new energy consumption, reduction in network loss, and alleviation of traffic congestion. Full article

(This article belongs to the Section Energy Systems)

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18 pages, 3189 KB

Open AccessArticle

Optimizing Hole Cleaning in Horizontal Shale Wells: Integrated Simulation Modeling in Bakken Formation Through Insights from South Pars Gas Field

by Sina Kazemi, Farshid Torabi and Ali Cheperli

Processes 2025, 13(10), 3077; https://doi.org/10.3390/pr13103077 - 25 Sep 2025

Abstract

Horizontal wells in shale formations, such as those in the South Pars gas field (Iran) and the Bakken shale (Canada/USA), are essential for production from ultralow-permeability reservoirs but remain limited by poor hole cleaning, high torque, and unstable fluid transport. This study integrates [...] Read more.

Horizontal wells in shale formations, such as those in the South Pars gas field (Iran) and the Bakken shale (Canada/USA), are essential for production from ultralow-permeability reservoirs but remain limited by poor hole cleaning, high torque, and unstable fluid transport. This study integrates real-time field data from South Pars with Drillbench simulations in the Bakken to develop practical strategies for improving drilling efficiency. A water-based mud system (9–10.2 ppg, 29–35 cP) supplemented with 2 wt.% sulphonated asphalt was applied to mitigate shale hydration, enhance cuttings transport, and preserve near-wellbore injectivity. Field implementation in South Pars demonstrated that adjusting drillstring rotation to 90 RPM and circulation rates to 1100 GPM reduced torque by ~70% (24 to 7 klbf·ft) and increased the rate of penetration (ROP) by ~25% (8 to 10 m/h) across a 230 m interval. Simulations in the Bakken confirmed these improvements, showing consistent torque and pressure trends, with cuttings transport efficiency above 95%. Inducing controlled synchronous whirl further improved sweep efficiency by ~15% and stabilized annular velocities at 0.7 m/s. Overall, these optimizations enhanced drilling efficiency by up to 25%, reduced operational risks, and created better well conditions for field development and EOR applications. The results provide clear, transferable guidelines for designing and drilling shale wells that balance immediate operational gains with long-term reservoir recovery. Full article

(This article belongs to the Special Issue Recent Developments in Enhanced Oil Recovery (EOR) Processes)

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19 pages, 2846 KB

Open AccessArticle

Sensitivity Analysis in Simple Cycles for Hydrogen Liquefaction

by Kevin M. Omori, Ramón Mazon-Cartagena, María J. Fernández-Torres, José A. Caballero, Mauro A. S. S. Ravagnani, Leandro V. Pavão and Caliane B. B. Costa

Processes 2025, 13(10), 3076; https://doi.org/10.3390/pr13103076 - 25 Sep 2025

Abstract

Due to the increase in global energy demand, as well as environmental concerns, hydrogen presents itself as a promising energy source. Liquid hydrogen is more suited for long-distance transportation, but hydrogen liquefaction is an energy-intensive process, and many studies have been published proposing [...] Read more.

Due to the increase in global energy demand, as well as environmental concerns, hydrogen presents itself as a promising energy source. Liquid hydrogen is more suited for long-distance transportation, but hydrogen liquefaction is an energy-intensive process, and many studies have been published proposing more efficient liquefaction cycles. In this study, simple hydrogen liquefaction cycles like Claude, pre-cooled Linde–Hampson (PLH), single mixed refrigerant (SMR), and dual mixed refrigerant (DMR) were assessed regarding the influence of the cycle’s high pressure on energy efficiency, exergy destruction, and its distribution along the equipment. Among the main results, Claude presented the best specific energy consumption (SEC) of 16.47 kWh/kg_LH, followed by DMR with an SEC of 17.30 kWh/kg_LH, SMR with 17.58 kWh/kg_LH, and finally PLH, with an SEC of 45.07 kWh/kg_LH. The exergy efficiency followed the same pattern as the SEC, with Claude having the lowest exergy destruction, followed by DMR and SMR with close exergy destruction, and finally PLH. Nonetheless, although cycles were not optimized in evaluating the effect of increasing the high pressure, which constrains the direct applicability of the result found, especially in the pre-cooled cycles, the analysis provides valuable insights into the sensitivity of cycle performance. The method and its findings provide the basis for further studies, including optimization steps. Full article

(This article belongs to the Section Energy Systems)

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19 pages, 1106 KB

Open AccessArticle

Service Restoration Strategy for Distribution Networks Considering Multi-Source Collaboration and Incomplete Fault Information

by Xunting Wang, Cheng Xie, Lingzhi Xia, Jianlin Li, Han Wang and Lei Sun

Processes 2025, 13(10), 3075; https://doi.org/10.3390/pr13103075 - 25 Sep 2025

Abstract

To address the severe damage and outage risks to distribution networks caused by extreme weather, this paper proposes a coordinated optimization strategy for distribution network repair sequencing and rapid restoration, which considers multi-source collaboration and incomplete fault information. In response to the challenge [...] Read more.

To address the severe damage and outage risks to distribution networks caused by extreme weather, this paper proposes a coordinated optimization strategy for distribution network repair sequencing and rapid restoration, which considers multi-source collaboration and incomplete fault information. In response to the challenge of incomplete fault information after a disaster, a two-layer robust optimization model is constructed. The upper-layer model aims to minimize the completion time of repairs for all faults under the most unfavorable fault scenario to obtain a robust repair time for potential faulty lines, providing a reliable basis for the restoration decisions of the lower-layer model. The lower-layer model’s objective is to maximize the weighted restored load quantity by comprehensively coordinating mobile diesel generators (MDGs), distributed generators (DGs), photovoltaics (PVs), wind turbines (WTs), and energy storage systems (ESSs) to achieve the optimal restoration strategy. The proposed service restoration strategy is validated through simulation on a modified IEEE 33-bus power system, and the results demonstrate that the strategy can efficiently and comprehensively utilize multi-source collaborative resources and improve the resilience of the distribution network. Full article

(This article belongs to the Special Issue Modeling, Optimization, and Control of Distributed Energy Systems)

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18 pages, 4073 KB

Open AccessArticle

Pore Structure and Fractal Characteristics of Kelasu Ultra-Deep Tight Sandstone Gas Reservoirs

by Liandong Tang, Yongbin Zhang, Xingyu Tang, Qihui Zhang, Mingjun Chen, Xuehao Pei, Yili Kang, Yiguo Zhang, Yuting Liu, Bihui Zhou, Jun Li, Pandong Tian and Di Wu

Processes 2025, 13(10), 3074; https://doi.org/10.3390/pr13103074 - 25 Sep 2025

Abstract

Ultra-deep tight sandstone gas reservoirs are key targets for natural gas exploration, yet their pore structures under high temperature, pressure, and stress greatly affect gas occurrence and flow. This study investigates representative reservoirs in the Kelasu structural belt, Tarim Basin. Porosity–permeability were measured [...] Read more.

Ultra-deep tight sandstone gas reservoirs are key targets for natural gas exploration, yet their pore structures under high temperature, pressure, and stress greatly affect gas occurrence and flow. This study investigates representative reservoirs in the Kelasu structural belt, Tarim Basin. Porosity–permeability were measured under in situ conditions, and multi-scale pore structures were analyzed using thin sections, a SEM, mercury intrusion, and nitrogen adsorption. The results show that (1) the median permeability of cores at an ambient temperature and a confining stress of 3 MPa is 13.33–29.63 times that under the in situ temperature and pressure conditions. When the core permeability is lower than 0.1 mD, the stress sensitivity effect is significantly enhanced; (2) nanopores and micron-fractures are well developed yet exhibit poor connectivity. The majority of a core’s porosity is derived from the intergranular pores in clay minerals; (3) the volume of nano-sized pores within the 100 nm diameter range is mainly composed of mesopores, with an average proportion of 73.37%, while the average proportions of macropores and micropores are 22.29% and 4.34%, respectively; (4) full-scale pore sizes show bimodal peaks at 100–1000 nm and >100 μm, which are poorly connected; (5) the pore structure exhibits distinct fractal characteristics. The fractal dimension D_f₁ (2.65 on average) corresponds to the larger pore diameters of the primary intergranular pores, residual intergranular pores, and intragranular dissolution pores. The fractal dimension D_f₂ (2.10 on average) corresponds to the grain margin fractures, micron-fractures and partial throats. The pore types corresponding to the fractal dimensions D_f₃ (2.36 on average) and D_f₄ (2.58 on average) are mainly intercrystalline pores of clay minerals and a small number of intraparticle dissolution pores. These findings clarify the pore structure of ultra-deep tight sandstones and provide insights into their gas occurrence and flow mechanisms. Full article

(This article belongs to the Section Energy Systems)

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23 pages, 3482 KB

Open AccessArticle

Robust Distribution System State Estimation with Physics-Constrained Heterogeneous Graph Embedding and Cross-Modal Attention

by Siyan Liu, Zhuang Tang, Bo Chai and Ziyu Zeng

Processes 2025, 13(10), 3073; https://doi.org/10.3390/pr13103073 - 25 Sep 2025

Abstract

Real-time distribution system state estimation is hampered by limited observability, frequent topology changes, and measurement errors. Neural networks can capture the nonlinear characteristics of power-grid operation through a data-driven approach that possesses important theoretical value and is promising for engineering applications. In that [...] Read more.

Real-time distribution system state estimation is hampered by limited observability, frequent topology changes, and measurement errors. Neural networks can capture the nonlinear characteristics of power-grid operation through a data-driven approach that possesses important theoretical value and is promising for engineering applications. In that context, we develop a deep learning framework that leverages General Attributed Multiplex Heterogeneous Network Embedding to explicitly encode the multiplex, heterogeneous structure of distribution networks and to support inductive learning that adapts to dynamic topology. A cross-modal attention mechanism further models fine-grained interactions between input measurements and node/edge attributes, enabling the capture of nonlinear correlations essential for accurate state estimation. To ensure physical feasibility, soft power-flow residuals are incorporated into training as a physics-constrained regularization, guiding predictions toward consistency with grid operation. Extensive studies on IEEE/CIGRE 14-, 70-, and 179-bus systems show that the proposed method surpasses conventional weighted least squares and representative neural baselines in accuracy, convergence speed, and computational efficiency while exhibiting strong robustness to measurement noise and topological uncertainty. Full article

(This article belongs to the Special Issue Advances in Hydrogen Energy Systems Integration, Modeling and Optimization)

17 pages, 3062 KB

Open AccessArticle

Enhancing AVR System Stability Using Non-Monopolize Optimization for PID and PIDA Controllers

by Ahmed M. Mosaad, Mahmoud A. Attia, Nourhan M. Elbehairy, Mohammed Alruwaili, Amr Yousef and Nabil M. Hamed

Processes 2025, 13(10), 3072; https://doi.org/10.3390/pr13103072 - 25 Sep 2025

Abstract

This work suggests a new use for the Non-Monopolize Optimization (NO) method to improve the dynamic stability and robustness of PID and PIDA controllers in Automatic Voltage Regulator (AVR) systems when there are load disruptions. The NO algorithm is a new search method [...] Read more.

This work suggests a new use for the Non-Monopolize Optimization (NO) method to improve the dynamic stability and robustness of PID and PIDA controllers in Automatic Voltage Regulator (AVR) systems when there are load disruptions. The NO algorithm is a new search method that does not use metaphors and only looks for one answer. It utilizes adaptive dimension modifications to strike a balance between exploration and exploitation. Its addition to AVR control makes parameter tweaking more efficient, without relying on random metaphors or population-based heuristics. MATLAB/Simulink R2025a runs full simulations to check how well the system works in both the time domain (step response, root locus) and the frequency domain (Bode plot). We compare the results to those of well-known optimizers like WOA, TLBO, ARO, GOA, and GA. The suggested NO-based PID and PIDA controllers always show less overshoot, faster rise and settling periods, and higher phase and gain margins, which proves that they are more stable and responsive. A robustness test with a load change of ±50% shows that NO-tuned controllers are even more reliable. The results show that using NO to tune different controllers could be a good choice for real-time AVR controller tuning in modern power systems because it is lightweight and works well. Full article

(This article belongs to the Special Issue AI-Based Modelling and Control of Power Systems)

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21 pages, 4753 KB

Open AccessArticle

Exploring the Green Synthesis Process of 2-Mercaptobenzothiazole for Industrial Production

by Yan Zhang, Qi Zhang, Xiansuo Li, Ruiguo Dong, Xiaolai Zhang and Qinggang Sun

Processes 2025, 13(10), 3071; https://doi.org/10.3390/pr13103071 - 25 Sep 2025

Abstract

This study outlines a high-yield green method for synthesizing MBT using aniline, carbon disulfide and sulfur as raw materials via a one-step reaction combined with high–low-temperature extraction. The process is supported by experimental results and lab-scale tests, and the operating conditions of the [...] Read more.

This study outlines a high-yield green method for synthesizing MBT using aniline, carbon disulfide and sulfur as raw materials via a one-step reaction combined with high–low-temperature extraction. The process is supported by experimental results and lab-scale tests, and the operating conditions of the amplification process are evaluated using Aspen Plus simulation software, supplemented with Gaussian09 calculations. The sensitivity analysis results indicate that the MBT yield reaches its maximum value when the feed mass ratio of S:CS₂:C₆H₇N:C₇H₈ is 6:17:20:90. Additionally, setting the reaction temperature to 240 °C and pressure to 10 MPa improves the MBT synthesis yield from 58% to 82.5%. Optimal condensation and extraction conditions are achieved at −30 °C and 1 atm, followed by a separation step at 40 °C. The simulation results provide valuable guidance for the industrial production of MBT. Full article

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

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19 pages, 2073 KB

Open AccessArticle

Precision Design Method for Superplastic Forming Process Parameters Based on an Improved Back Propagation Neural Network

by Xiaoke Guo, Wanran Yang, Qian Zhang, Junchen Pan, Chengyue Xiong and Le Wu

Processes 2025, 13(10), 3070; https://doi.org/10.3390/pr13103070 - 25 Sep 2025

Abstract

A significant contradiction exists between the demand for standardized processes and the need for precise process parameter design in the rapid design of superplastic forming (SPF). To address this, an SPF process parameter design method integrating a knowledge graph and artificial intelligence is [...] Read more.

A significant contradiction exists between the demand for standardized processes and the need for precise process parameter design in the rapid design of superplastic forming (SPF). To address this, an SPF process parameter design method integrating a knowledge graph and artificial intelligence is proposed. Firstly, based on process data analysis, the entity labels, relationship categories, and attributes are determined. On this basis, the knowledge graph for the SPF process is constructed, comprising the pattern layer and the data layer, which provides structured knowledge support for process generation. Secondly, the process parameter prediction model based on small samples and an improved back propagation (BP) neural network is constructed, with model convergence ensured through an adaptive maximum iteration strategy. Experimental results show that the improved BP model significantly outperforms support vector regression (SVR), random forest (RF), extreme gradient boosting (XGBoost), and standard BP models in prediction accuracy. Compared to the standard BP model, the improved model reduces the mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) by 82.1% (to 0.0005), 46% (to 0.0188), and 57.1% (to 0.0229), respectively. Finally, the effectiveness, feasibility, and superiority of the method in the SPF process parameter design are verified by taking typical hemispherical parts as an example. Full article

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

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19 pages, 2855 KB

Open AccessFeature PaperArticle

Disruption of Early Streptococcus mutans Biofilm Development on Orthodontic Aligner Materials

by Matea Badnjević, Mirna Petković Didović, Ivana Jelovica Badovinac, Sanja Lučić Blagojević, Marko Perčić, Stjepan Špalj and Ivana Gobin

Processes 2025, 13(10), 3069; https://doi.org/10.3390/pr13103069 - 25 Sep 2025

Abstract

(1) Background: This study aimed to determine the optimum parameters for the treatment of Streptococcus mutans biofilm on clear dental aligners. (2) Methods: A 24-h-old S. mutans biofilm was grown on polyurethane (PU) and poly(ethylene terephthalate glycol) (PETG) aligners. These samples were treated [...] Read more.

(1) Background: This study aimed to determine the optimum parameters for the treatment of Streptococcus mutans biofilm on clear dental aligners. (2) Methods: A 24-h-old S. mutans biofilm was grown on polyurethane (PU) and poly(ethylene terephthalate glycol) (PETG) aligners. These samples were treated with three chlorhexidine digluconate (CHX)-based antiseptic solutions, manual brushing, and a combination of both, with varying exposure times. The number of adhered bacteria was determined in both untreated and treated samples after sonication. Materials were analyzed with atomic force and scanning electron microscopy, and surface free energy (SFE) values were determined using three different models. (3) Results: Our findings indicated that control strategies do not depend on the type of material. PU and PETG surfaces exhibited similar SFE values (41–45 mJ/m²). Differences in surface roughness were insufficient to cause significant changes in S. mutans behavior. The highest efficacy of all three tested antiseptics was established for the exposure time of 1 min, with efficacy deteriorating just after 3 min. (4) Conclusions: The efficacy of CHX against S. mutans early biofilm is material-independent and time-dependent. The optimal exposure time of 1 min should be combined with brushing, with a general recommendation of the antiseptic-first approach. Full article

(This article belongs to the Special Issue Microbial Biofilms: Latest Advances and Prospects)

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Graphical abstract

24 pages, 3037 KB

Open AccessReview

Remanufacturing Process Under Uncertainty: Review, Challenges, and Future Directions

by Yaoyao Tu, Xiaoxiao Si, Yimin Wu, Xuehong Shen and Jianqing Chen

Processes 2025, 13(10), 3068; https://doi.org/10.3390/pr13103068 - 25 Sep 2025

Abstract

In the context of the global transition toward carbon neutrality and the circular economy, remanufacturing has emerged as a vital strategy for enhancing resource efficiency and reducing environmental impact. However, the remanufacturing sector faces significant uncertainties—including fluctuations in market demand, variability in the [...] Read more.

In the context of the global transition toward carbon neutrality and the circular economy, remanufacturing has emerged as a vital strategy for enhancing resource efficiency and reducing environmental impact. However, the remanufacturing sector faces significant uncertainties—including fluctuations in market demand, variability in the quality of returned products, and dynamic policy changes. These factors collectively challenge production decision-making and system sustainability. Following the preferred peporting items for systematic reviews and meta-analyses (PRISMA) guidelines, this study conducted a systematic review and bibliometric analysis of 98 core articles published between 2015 and 2024, with a focused examination of three interdisciplinary themes: (1) decision-making and optimization under uncertainty, (2) supply chain coordination and policy mechanisms, and (3) digital transformation and the application of emerging technologies. A novel micro–meso–macro analytical framework is proposed to integrate fragmented findings. The results highlight a paradigm shift from static models to dynamic, real-time decision-making systems, facilitated by digital twins (DTs), blockchain, and intelligent algorithms. Furthermore, the study identifies the synergistic effects of carbon-financial instruments and policy incentives in aligning economic and environmental objectives. This research develops a systematic framework to understand and address uncertainties in remanufacturing, offering policymakers and industry practitioners actionable insights to enhance the resilience, sustainability, and global applicability of remanufacturing systems. Full article

(This article belongs to the Special Issue Multi Dimensional Value Driven Sustainable Manufacturing Processes and Innovation)

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28 pages, 7105 KB

Open AccessArticle

Insights into Foamy Oil Phenomenon in Porous Media: Experimental and Numerical Investigation

by Morteza Sabeti, Farshid Torabi and Ali Cheperli

Processes 2025, 13(10), 3067; https://doi.org/10.3390/pr13103067 - 25 Sep 2025

Abstract

Cyclic Solvent Injection (CSI) is a method for enhanced heavy oil recovery, offering a reduced environmental impact. CSI processes typically involve fluid flow through both wormholes and the surrounding porous media in reservoirs. Therefore, understanding how foamy oil behavior differs between bulk phases [...] Read more.

Cyclic Solvent Injection (CSI) is a method for enhanced heavy oil recovery, offering a reduced environmental impact. CSI processes typically involve fluid flow through both wormholes and the surrounding porous media in reservoirs. Therefore, understanding how foamy oil behavior differs between bulk phases and porous media is crucial for optimizing CSI operations. However, despite CSI’s advantages, limited research has explained why foamy oil, a key mechanism in CSI, displays weaker strength and stability in bulk phases than in porous media. To address this gap, three advanced visual micromodels were employed to monitor bubble behavior from nucleation through collapse under varying porosity with a constant pressure reduction. A sandpack depletion test in a large cylindrical model further validated the non-equilibrium bubble-reaction kinetics observed in the micromodels. Experiments showed that, under equivalent operating conditions, bubble nucleation in porous media required less energy and initiated more rapidly than in a bulk phase. Micromodels with lower porosity demonstrated up to a 2.5-fold increase in foamy oil volume expansion and higher bubble stability. Moreover, oil production in the sandpack declined sharply at pressures below 1800 kPa, indicating the onset of critical gas saturation, and yielded a maximum recovery of 37% of the original oil in place. These findings suggest that maintaining reservoir pressure above critical gas saturation pressure enhances oil recovery performance during CSI operations. Full article

(This article belongs to the Special Issue Recent Developments in Enhanced Oil Recovery (EOR) Processes)

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5 pages, 176 KB

Open AccessEditorial

Special Issue on “Technologies for Climate-Neutral Energy Systems”

by Carlos Herce, Benedetta de Caprariis and Yolanda Lara

Processes 2025, 13(10), 3066; https://doi.org/10.3390/pr13103066 - 25 Sep 2025

Abstract

Climate-neutral economy aims to achieve net-zero greenhouse gas (GHG) emissions in all human activities, thus implying a paradigm shift that must be based on sustainability principles, leading towards an ecological transition integrating economic growth, social well-being, and environmental protection [...] Full article

(This article belongs to the Special Issue Technologies for Climate-Neutral Energy Systems)

35 pages, 15203 KB

Open AccessArticle

Influence of External Store Distribution on the Flutter Characteristics of the Romanian IAR-99 HAWK Aircraft

by Tudor Vladimirescu, Ion Fuiorea, Tudor Vladimirescu, Jr. and Grigore Cican

Processes 2025, 13(10), 3065; https://doi.org/10.3390/pr13103065 - 25 Sep 2025

Abstract

This study presents a flutter answer analysis of the Romanian IAR-99 HAWK advanced trainer aircraft equipped with multiple external store configurations. A high-fidelity finite element model (FEM) of the complete aircraft, including pylons and external stores, was coupled with a Doublet Lattice Method [...] Read more.

This study presents a flutter answer analysis of the Romanian IAR-99 HAWK advanced trainer aircraft equipped with multiple external store configurations. A high-fidelity finite element model (FEM) of the complete aircraft, including pylons and external stores, was coupled with a Doublet Lattice Method (DLM) aerodynamic model. The aeroelastic framework was validated against Ground Vibration Test (GVT) data to ensure structural accuracy. Four representative configurations were assessed: (A) RS-250 drop tanks on inboard pylons and PRN 16 × 57 unguided rocket launchers on outboard pylons; (B) four B-250 bombs; (C) eight B-100 bombs mounted on twin racks; and (D) a hybrid layout with B-100 bombs inboard and PRN 32 × 42 launchers outboard. Results show that spanwise distribution governs aeroelastic stability more strongly than total carried mass. Distributed stores lower wing-bending frequencies and densify the modal spectrum, producing critical pairs and subsonic crossings near M ≈ 0.82 at sea level, whereas compact heavy loads remain subsonic-stable. A launcher-specific modal family around ≈29.8 Hz is also identified in the hybrid layout. The validated FEM–DLM framework captures store-driven mode families (≈4–7 Hz) and provides actionable guidance for payload placement, certification, and modernization of the IAR-99 and similar platforms. Full article

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

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19 pages, 3897 KB

Open AccessArticle

Enhanced Adsorption of Pb(II) and Cd(II) by Activated Carbon Derived from Peach Stones for Efficient Water Decontamination

by Guilherme Medina Cameu, Leandro Almeida, Ana Paula Oliveira, Andrei Igansi, Débora Pez Jaeschke, Nauro Silveira, Jr., Rafael Paes, Daiane Dias, Luiz Antonio de Almeida Pinto and Tito Roberto Sant’Anna Cadaval, Jr.

Processes 2025, 13(10), 3064; https://doi.org/10.3390/pr13103064 - 25 Sep 2025

Abstract

This work employed peach stones as the precursor material for producing activated carbon (AC-PS). AC-PS was impregnated with H₃PO₄ and carbonized using a pyrolysis reactor under a reducing atmosphere. The surface area, average pore size, and total pore volume of [...] Read more.

This work employed peach stones as the precursor material for producing activated carbon (AC-PS). AC-PS was impregnated with H₃PO₄ and carbonized using a pyrolysis reactor under a reducing atmosphere. The surface area, average pore size, and total pore volume of AC-PS were determined using the BET method. Morphological characteristics of AC-PS were observed through scanning electron microscopy (SEM), the surface composition was identified by energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD) analyses were conducted to determine the crystalline structure of carbon. The thermal stability of AC-PS and its interactions with lead and cadmium were analyzed by thermogravimetric analyses (TGA/DTG) and infrared spectra (FTIR), respectively. The Elovich model described the adsorption kinetics of both lead and cadmium, and the Weber and Morris model indicated intraparticle diffusion as the controlling mechanism of the adsorption process. The equilibrium study showed that the Freundlich model was adequate for both ions, with adsorption capacities increasing with temperature, reaching around 150 mg g⁻¹ for lead and 80 mg g⁻¹ for cadmium at 45 °C. Economic analysis indicated costs of $0.25 g⁻¹ and $0.51 g⁻¹ for the removal of lead and cadmium from the contaminated water, respectively. Full article

(This article belongs to the Special Issue Design, Modeling, and Optimization of Adsorption Process for Pollutant Removal)

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21 pages, 1277 KB

Open AccessArticle

Assessing the Effect of Cooling Techniques on Performance Improvement of a Binary Geothermal Power Plant by Using Exergy-Based Analysis

by Ali Şimşek and Aysegul Gungor Celik

Processes 2025, 13(10), 3063; https://doi.org/10.3390/pr13103063 - 25 Sep 2025

Abstract

Geothermal energy is a renewable and sustainable resource, but its efficient utilization is often constrained by operational inefficiencies and inadequate system management, highlighting the need for detailed energy assessments to improve performance and ensure long-term sustainability. This study aims for a comparative assessment [...] Read more.

Geothermal energy is a renewable and sustainable resource, but its efficient utilization is often constrained by operational inefficiencies and inadequate system management, highlighting the need for detailed energy assessments to improve performance and ensure long-term sustainability. This study aims for a comparative assessment of the performance of a binary geothermal power plant (GPP) considering air-cooled and evaporative cooling configurations using exergy analysis, based on real operating data. Exergetic parameters were applied to evaluate both overall system efficiency and the performance of individual components. The effect of geothermal fluid mass flow rate on turbine net power output was investigated. Additionally, a carbon emission analysis was conducted to assess environmental impact. Based on the energy content of the geothermal fluid entering the heat exchanger, the plant’s energy efficiency was calculated to be 7.5% for the air-cooled condenser configuration and 8.5% for the evaporative condenser configuration. On the basis of the heat input to the Rankine cycle, the overall energy efficiencies of the plant were found to be 39.76% and 43% for the air-cooled and evaporative condenser cases, respectively. The findings suggest that the overall exergy efficiency of the plant improves when employing the evaporative cooling system, reaching 53.57% compared to 48.38% for the air-cooled system. In the air-cooled configuration, Condenser I accounted for the highest exergy destruction at 27%, whereas in the evaporative system, Vaporizer II had the largest share at 25%. Furthermore, it was determined that the plant with an evaporative cooling system produced approximately 13% less carbon emissions compared to the air-cooled plant, which represents an advantage in terms of environmental sustainability. Full article

(This article belongs to the Section Energy Systems)

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20 pages, 1896 KB

Open AccessReview

Research Progress on Optimization Method of Magnetic Grinding Process for Inner Surface of Aircraft Engine Bend Pipe

by Chunfang Xiao, Junjie Xiao, Bing Han and Cheng Wen

Processes 2025, 13(10), 3062; https://doi.org/10.3390/pr13103062 - 25 Sep 2025

Abstract

The level of magnetic grinding technology determines the accuracy and efficiency of magnetic grinding on the inner surface of aircraft engine bend pipes. This article analyzes the optimization methods of magnetic grinding process parameters for the inner surface of aircraft engine bent pipes, [...] Read more.

The level of magnetic grinding technology determines the accuracy and efficiency of magnetic grinding on the inner surface of aircraft engine bend pipes. This article analyzes the optimization methods of magnetic grinding process parameters for the inner surface of aircraft engine bent pipes, such as the multiple regression prediction method, the response surface method, and the grey relational analysis method. It is pointed out that the current optimization methods for magnetic grinding technology on the inner surface of aircraft engine bent pipes do not consider the nonlinear characteristics between various grinding process parameters, resulting in defects such as low precision and efficiency of magnetic particle grinding technology. An optimization approach was proposed to accurately predict the optimal magnetic grinding process parameters for the inner surface of aircraft engine bent pipes, establish a nonlinear mapping relationship that reflects the roughness of the inner surface of the bent pipe and the main process parameters, optimize the BP neural network model based on the genetic algorithm, design magnetic grinding experiments on the inner surface of aircraft engine bend pipes, and explore the magnetic grinding process that is beneficial for improving the accuracy and efficiency of magnetic grinding on the inner surface of aircraft engine bend pipes. It can achieve efficient and accurate prediction of magnetic grinding of the inner surface of aircraft engine bend pipes. It provides a basis for the manufacturing and maintenance of high-precision aircraft engine bend pipes. Full article

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

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