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Improving Combined Cycle Performance with Pressure Gain Combustion in the Gas Turbine
Waste to Value: L-Asparaginase Production from Agro-Industrial Residues
Model Predictive Control of Common Ground PV Multilevel Inverter with Sliding Mode Observer for Capacitor Voltage Estimation

Journal Description

Processes

Processes is an international, peer-reviewed, open access journal on processes/systems in chemistry, biology, material, energy, environment, food, pharmaceutical, manufacturing, automation control, catalysis, separation, particle and allied engineering fields published semimonthly online by MDPI. The Brazilian Association of Chemical Engineering (ABEQ) is affiliated with Processes and its members receive discounts on the article processing charges. Please visit Society Collaborations for more details.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, Inspec, AGRIS, and other databases.
Journal Rank: CiteScore - Q2 (Chemical Engineering (miscellaneous))
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 14.9 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the second half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 2.8 (2024); 5-Year Impact Factor: 3.1 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2227-9717

Latest Articles

19 pages, 5120 KB

Open AccessArticle

Research on the Multi-Layer Optimal Injection Model of CO₂-Containing Natural Gas with Minimum Wellhead Gas Injection Pressure and Layered Gas Distribution Volume Requirements as Optimization Goals

by Biao Wang, Yingwen Ma, Yuchen Ji, Jifei Yu, Xingquan Zhang, Ruiquan Liao, Wei Luo and Jihan Wang

Processes 2026, 14(1), 151; https://doi.org/10.3390/pr14010151 (registering DOI) - 1 Jan 2026

The separate-layer gas injection technology is a key means to improve the effect of refined gas injection development. Currently, the measurement and adjustment of separate injection wells primarily rely on manual experience and automatic measurement via instrument traversal, resulting in a long duration, [...] Read more.

The separate-layer gas injection technology is a key means to improve the effect of refined gas injection development. Currently, the measurement and adjustment of separate injection wells primarily rely on manual experience and automatic measurement via instrument traversal, resulting in a long duration, low efficiency, and low qualification rate for injection allocation across multi-layer intervals. Given the different CO₂-containing natural gas injection rates across different intervals, this paper establishes a coupled flow model of a separate-layer gas injection wellbore–gas distributor–formation based on the energy and mass conservation equations for wellbore pipe flow, and develops a solution method for determining gas nozzle sizes across multi-layer intervals. Based on the maximum allowable gas nozzle size, an optimization method for multi-layer collaborative allocation of separate injection wells is established, with minimum wellhead injection pressure and layered injection allocation as the optimization objectives, and the opening of gas distributors for each layer as the optimization variable. Taking Well XXX as an example, the optimization process of allocation schemes under different gas allocation requirements is simulated. The research shows that the model and method proposed in this paper have high calculation accuracy, and the formulated allocation schemes have strong adaptability and minor injection allocation errors, providing a scientific decision-making method for formulating refined allocation schemes for separate-layer gas injection wells, with significant theoretical and practical value for promoting the refined development of oilfields. Full article

(This article belongs to the Section Petroleum and Low-Carbon Energy Process Engineering)

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21 pages, 3525 KB

Open AccessArticle

Improved YOLOv9 with Dual Convolution and LSKA Attention for Robust Small Defect Detection in Textiles

by Chang Xuan, Weimin Shi, Lei Sun, Ji Wu, Yongchao Zhang and Jiajia Tu

Processes 2026, 14(1), 149; https://doi.org/10.3390/pr14010149 (registering DOI) - 1 Jan 2026

To mitigate the challenges of false positives and undetected small-scale defects in fabric inspection, this study proposes an advanced fabric defect detection system that leverages an optimized YOLOv9 algorithm. First, redundant computations are reduced by introducing DualConv to replace standard convolution. Second, the [...] Read more.

To mitigate the challenges of false positives and undetected small-scale defects in fabric inspection, this study proposes an advanced fabric defect detection system that leverages an optimized YOLOv9 algorithm. First, redundant computations are reduced by introducing DualConv to replace standard convolution. Second, the LSKA attention mechanism is incorporated to increase the weight of important features, thereby enhancing the accuracy of small target detection and improving the generalization ability. Additionally, the focal modulation network is employed to replace the fast spatial pyramid module, mitigating the loss of detailed information caused by the feature pooling operation. Furthermore, the conventional feature pyramid network is replaced with bidirectional feature pyramid network, which is utilized for efficient feature fusion, thereby enhancing multiscale feature representation and improving detection accuracy. Finally, the bounding box loss function is optimized by introducing the shape-IoU loss function, which facilitates more rapid model convergence and significantly improves detection accuracy. Experiments conducted on a fabric defect dataset demonstrate that the proposed algorithm yields a 6.7% increase in mAP@0.5 and a 14.7% improvement in mAP@0.5–0.95, while simultaneously reducing the model’s total parameters by 17.8% and computational FLOPs by 14.4%, compared with those of the original algorithm. The improved YOLOv9 model significantly enhances the precision and accuracy of defect detection while maintaining inference speed (55.8 FPS) that meets industrial requirements. Full article

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

21 pages, 12253 KB

Open AccessArticle

Enhancing Point Cloud Registration Precision of Conical Shells Through Edge Detection Using PCA and Wavelet Transform

by Yucun Zhang, Geqing Xi and Xianbin Fu

Processes 2026, 14(1), 148; https://doi.org/10.3390/pr14010148 (registering DOI) - 1 Jan 2026

Reliability assessment of conical shells in the chemical industry commonly relies on point cloud registration. Thus, accurate edge detection from 3D laser scan data is crucial for high-precision registration. However, existing edge detection methods often misclassify or omit gradual edge points on conical [...] Read more.

Reliability assessment of conical shells in the chemical industry commonly relies on point cloud registration. Thus, accurate edge detection from 3D laser scan data is crucial for high-precision registration. However, existing edge detection methods often misclassify or omit gradual edge points on conical shell structures, significantly compromising registration accuracy and subsequent integrity assessment. This paper proposes an edge point detection method integrating Principal Component Analysis (PCA) and wavelet transform. First, characteristic curves are constructed by computing the ratio of PCA eigenvalues at all points to generate preliminary candidates for gradual edge points. Subsequently, distance vectors are calculated between the centroid of each characteristic curve and its sampled points. These vectors are then encoded via multi-level wavelet transform to produce mapping vectors that capture curvature variations. Finally, gradual edge points are discriminated effectively using these mapping vectors. Experimental results demonstrate that the proposed method achieves superior edge detection performance on complex conical shell surfaces and significantly enhances the accuracy of point cloud registration. Full article

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

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29 pages, 4094 KB

Open AccessArticle

Hybrid LSTM–DNN Architecture with Low-Discrepancy Hypercube Sampling for Adaptive Forecasting and Data Reliability Control in Metallurgical Information-Control Systems

by Jasur Sevinov, Barnokhon Temerbekova, Gulnora Bekimbetova, Ulugbek Mamanazarov and Bakhodir Bekimbetov

Processes 2026, 14(1), 147; https://doi.org/10.3390/pr14010147 (registering DOI) - 1 Jan 2026

The study focuses on the design of an intelligent information-control system (ICS) for metallurgical production, aimed at robust forecasting of technological parameters and automatic self-adaptation under noise, anomalies, and data drift. The proposed architecture integrates a hybrid LSTM–DNN model with low-discrepancy hypercube sampling [...] Read more.

The study focuses on the design of an intelligent information-control system (ICS) for metallurgical production, aimed at robust forecasting of technological parameters and automatic self-adaptation under noise, anomalies, and data drift. The proposed architecture integrates a hybrid LSTM–DNN model with low-discrepancy hypercube sampling using Sobol and Halton sequences to ensure uniform coverage of operating conditions and the hyperparameter space. The processing pipeline includes preprocessing and temporal synchronization of measurements, a parameter identification module, anomaly detection and correction using an ε-threshold scheme, and a decision-making and control loop. In simulation scenarios modeling the dynamics of temperature, pressure, level, and flow (1 min sampling interval, injected anomalies, and measurement noise), the hybrid model outperformed GRU and CNN architectures: a determination coefficient of R² > 0.92 was achieved for key indicators, MAE and RMSE improved by 7–15%, and the proportion of unreliable measurements after correction decreased to <2% (compared with 8–12% without correction). The experiments also demonstrated accelerated adaptation during regime changes. The scientific novelty lies in combining recurrent memory and deep nonlinear approximation with deterministic experimental design in the hypercube of states and hyperparameters, enabling reproducible self-adaptation of the ICS and increased noise robustness without upgrading the measurement hardware. Modern metallurgical information-control systems operate under non-stationary regimes and limited measurement reliability, which reduces the robustness of conventional forecasting and decision-support approaches. To address this issue, a hybrid LSTM–DNN architecture combined with low-discrepancy hypercube probing and anomaly-aware data correction is proposed. The proposed approach is distinguished by the integration of hybrid neural forecasting, deterministic hypercube-based adaptation, and anomaly-aware data correction within a unified information-control loop for non-stationary industrial processes. 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, 10771 KB

Open AccessArticle

When Analog Electronics Extends Solar Life: Gate-Resistance Retuning for PV Reuse

by Euzeli C. dos Santos, Jr., Yongchun Ni, Fabiano Salvadori and Haitham Kanakri

Processes 2026, 14(1), 146; https://doi.org/10.3390/pr14010146 (registering DOI) - 1 Jan 2026

This paper proposes an analog retuning strategy that strengthens the functional longevity of photovoltaic (PV) systems operating within circular-economy environments. Although PV modules can be relocated from large generation sites to low-demand rural or remote settings, their electrical behavior offers no adjustable quantities [...] Read more.

This paper proposes an analog retuning strategy that strengthens the functional longevity of photovoltaic (PV) systems operating within circular-economy environments. Although PV modules can be relocated from large generation sites to low-demand rural or remote settings, their electrical behavior offers no adjustable quantities capable of extending service duration. In many cases, even after formal disposal or decommissioning, these solar panels still retain a considerable portion of their energy-generation capability and can operate for many additional years before their output becomes negligible, making second-life deployment both technically viable and economically attractive. In contrast, the associated power-electronic converters contain modifiable gate-driver parameters that can be reconfigured to moderate transient phenomena and lessen device stress. The method introduced here adjusts the external gate resistance in conjunction with coordinated switching-frequency adaptation, reducing overshoot, ringing, and steep

d v / d t

slopes while preserving the original switching-loss budget. A unified analytical framework connects stress mitigation, ripple evolution, and projected lifetime enhancement, demonstrating that deliberate analog tuning can substantially increase the endurance of aged semiconductor hardware without compromising suitability for second-life PV applications. Analytical results are supported by experimental validation, including hardware measurements of switching waveforms and energy dissipation under multiple gate-resistance configurations. Full article

(This article belongs to the Special Issue The Role of Analog Electronics: From Energy Systems to Food Production)

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22 pages, 1021 KB

Open AccessArticle

Chia Seed Gel Powder as a Clean-Label Enhancer of Texture, Physicochemical Quality, Antioxidant Activity, and Prebiotic Function in Probiotic Low-Fat Yogurt

by Mahmoud E. A. Hamouda, Ratul Kalita, Abdelfatah K. Ali, Pratibha Chaudhary, Pramith U. Don, Omar A. A. Abdelsater, Anjali Verma and Yaser Elderwy

Processes 2026, 14(1), 145; https://doi.org/10.3390/pr14010145 - 31 Dec 2025

This study evaluated the effect of incorporating chia seed gel powder (CSGP) as a natural, clean-label stabilizer on the physicochemical, functional, microbiological, microstructural, antioxidant, and sensory properties of probiotic low-fat yogurt (PLFY) during 21 days of refrigerated storage. Six formulations were prepared using [...] Read more.

This study evaluated the effect of incorporating chia seed gel powder (CSGP) as a natural, clean-label stabilizer on the physicochemical, functional, microbiological, microstructural, antioxidant, and sensory properties of probiotic low-fat yogurt (PLFY) during 21 days of refrigerated storage. Six formulations were prepared using 0–2.5% CSGP, including Control (0% CSGP), YOG1 (0.5% CSGP), YOG2 (1.0% CSGP), YOG3 (1.5% CSGP), YOG4 (2.0% CSGP), and YOG5 (2.5% CSGP). Results showed that increasing CSGP levels noticeably enhanced the total solids, protein content, viscosity, hardness, and water-holding capacity of the PLFY (p < 0.05), while consistently reducing syneresis. Antioxidant activity also rose with higher CSGP concentrations, with YOG5 exhibiting the greatest DPPH scavenging activity (35.12%). Confocal laser scanning microscopy revealed a denser and more uniform protein network in PLFY fortified with CSGP, consistent with rheological measurements showing increased storage (G′) and loss (G″) moduli. Probiotic viability significantly increased (p < 0.05) in CSGP-added samples, indicating a potential prebiotic effect of CSGP. Sensory results demonstrated that although higher CSGP levels slightly darkened the yogurt color, body, texture, flavor, and total sensory scores improved markedly, with YOG5 gaining the highest total score (81.77). The results demonstrate that CSGP acts as a highly effective, multifunctional ingredient that enhances texture, stability, probiotic viability, and antioxidant capacity, making it a strong clean-label candidate for developing high-quality, functional probiotic low-fat yogurt. Full article

(This article belongs to the Special Issue Processing Foods: Process Optimization and Quality Assessment, 4th Edition)

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28 pages, 816 KB

Open AccessReview

Smart Conferences: A Comprehensive Review of Technologies, Analytics and Future Directions

by Hongqiang Lv, Peijun Ye, Jiaxi Liu, Nan Zhang, Xiaoxiao Yu, Weichao Gong, Yonglin Tian, Wendy Ding, Mengchu Zhou and Fei-Yue Wang

Processes 2026, 14(1), 144; https://doi.org/10.3390/pr14010144 - 31 Dec 2025

Academic conferences have been pivotal in scholarly communications, facilitating the exchange of ideas and fostering collaborations among attendees by using advanced sensing, networking, and control technologies. Traditionally held in physical venues, the landscape of academic conferences has been revolutionised by the advent of [...] Read more.

Academic conferences have been pivotal in scholarly communications, facilitating the exchange of ideas and fostering collaborations among attendees by using advanced sensing, networking, and control technologies. Traditionally held in physical venues, the landscape of academic conferences has been revolutionised by the advent of virtual and hybrid formats as supported by the Internet of Things, Artificial Intelligence, and virtual reality tools. Despite the burgeoning literature on smart conferences, there exists a gap in comprehensive reviews that consolidate the various advancements and methodologies in this domain. This article aims to fill this gap by providing a thorough review of the latest developments in smart conference technologies and practices. It offers a multidimensional analysis, including predictive analytics, smart content delivery, networking improvements, and data-driven assessments. Fundamentally, we frame conference activities as a complex process involving multi-stage planning, real-time dynamic execution, and post-event analysis and refinement. This review specifically highlights how smart technologies are transforming this end-to-end process. Additionally, the concept of parallel intelligence is introduced, exploring its potential to transform future conferences. The significance of this article lies in its holistic perspective, offering valuable insights for enhancing conference planning, attendee engagement, and overall conference experiences. Full article

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

28 pages, 8549 KB

Open AccessArticle

Numerical Study on Lost Circulation Mechanism in Complex Fracture Network Coupled Wellbore and Its Application in Lost-Circulation Zone Diagnosis

by Zhichao Xie, Yili Kang, Chengyuan Xu, Lijun You, Chong Lin and Feifei Zhang

Processes 2026, 14(1), 143; https://doi.org/10.3390/pr14010143 - 31 Dec 2025

Deep and ultra-deep drilling operations commonly encounter fractured and fracture-vuggy formations, where weak wellbore strength and well-developed fracture networks lead to frequent lost circulation, presenting a key challenge to safe and efficient drilling. Existing diagnostic practices mostly rely on drilling fluid loss dynamic [...] Read more.

Deep and ultra-deep drilling operations commonly encounter fractured and fracture-vuggy formations, where weak wellbore strength and well-developed fracture networks lead to frequent lost circulation, presenting a key challenge to safe and efficient drilling. Existing diagnostic practices mostly rely on drilling fluid loss dynamic models of single fractures or simplified discrete fractures to invert fracture geometry, which cannot capture the spatiotemporal evolution of loss in complex fracture networks, resulting in limited inversion accuracy and a lack of quantitative, fracture-network-based loss-dynamics support for bridge-plugging design. In this study, a geologically realistic wellbore–fracture-network coupled loss dynamic model is constructed to overcome the limitations of single- or simplified-fracture descriptions. Within a unified computational fluid dynamics (CFD) framework, solid–liquid two-phase flow and Herschel–Bulkley rheology are incorporated to quantitatively characterise fracture connectivity. This approach reveals how instantaneous and steady losses are controlled by key geometrical factors, thereby providing a computable physical basis for loss-zone inversion and bridge-plugging design. Validation against experiments shows a maximum relative error of 7.26% in pressure and loss rate, indicating that the model can reasonably reproduce actual loss behaviour. Different encounter positions and node types lead to systematic variations in loss intensity and flow partitioning. Compared with a single fracture, a fracture network significantly amplifies loss intensity through branch-induced capacity enhancement, superposition of shortest paths, and shortening of loss paths. In a typical network, the shortest path accounts for only about 20% of the total length, but contributes 40%–55% of the total loss, while extending branch length from 300 mm to 1500 mm reduces the steady loss rate by 40%–60%. Correlation analysis shows that the instantaneous loss rate is mainly controlled by the maximum width and height of fractures connected to the wellbore, whereas the steady loss rate has a correlation coefficient of about 0.7 with minimum width and effective path length, and decreases monotonically with the number of connected fractures under a fixed total width, indicating that the shortest path and bottleneck width are the key geometrical factors governing long-term loss in complex fracture networks. This work refines the understanding of fractured-loss dynamics and proposes the concept of coupling hydraulic deviation codes with deep learning to build a mapping model from mud-logging curves to fracture geometrical parameters, thereby providing support for lost-circulation diagnosis and bridge-plugging optimisation in complex fractured formations. Full article

(This article belongs to the Special Issue Computational Fluid Dynamic (CFD) Simulations in Gas and Petroleum Engineering)

32 pages, 2937 KB

Open AccessArticle

Capacity Optimization of Integrated Energy Systems Considering Carbon-Green Certificate Trading and Electricity Price Fluctuations

by Tiannan Ma, Gang Wu, Hao Luo, Bin Su, Yapeng Dai and Xin Zou

Processes 2026, 14(1), 142; https://doi.org/10.3390/pr14010142 - 31 Dec 2025

In order to study the impacts of the carbon-green certificate trading mechanism and the fluctuation of feed-in tariffs on the low-carbon and economic aspects of the investment and operation of the integrated energy system, and to transform the system carbon emission into a [...] Read more.

In order to study the impacts of the carbon-green certificate trading mechanism and the fluctuation of feed-in tariffs on the low-carbon and economic aspects of the investment and operation of the integrated energy system, and to transform the system carbon emission into a low-carbon economic indicator, a two-layer capacity optimization allocation model is established with the objectives of the investment, operation, and maintenance cost and the operation cost, respectively. For the source-load uncertainty, the scenario reduction theory based on Monte Carlo simulation and Wasserstein distance is used to obtain the per-unit value of wind and photovoltaic output, and the K-means clustering method is used to obtain the typical day of electric-heat-cold multi-energy load. Based on the geometric Brownian motion in finance to simulate the feed-in tariffs under different volatilities, the multidimensional analysis scenarios are constructed according to different combinations of carbon emission reduction policies and tariff volatilities. The model is solved using the non-dominated sorting genetic algorithm (NSGA-II) with mixed integer linear programming (MILP) method. Case study results show that under the optimal scenario considering policy interaction and price volatility (δ = 1.0), the total annual operating cost is reduced by approximately 17.9% (from 2.80 million CNY to 2.30 million CNY) compared to the baseline with no carbon policy. The levelized cost of the energy system reaches 0.2042 CNY/kWh, and carbon-green certificate trading synergies contribute about 70% of the operational cost reduction. The findings demonstrate that carbon reduction policies and electricity price volatility significantly affect system configuration and operational economy, providing a new perspective and decision-making basis for integrated energy system planning. Full article

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

15 pages, 1299 KB

Open AccessFeature PaperArticle

Leachate Analysis of Biodried MSW: Case Study of the CWMC Marišćina

by Anita Ptiček Siročić, Dragana Dogančić, Igor Petrović and Nikola Hrnčić

Processes 2026, 14(1), 141; https://doi.org/10.3390/pr14010141 - 31 Dec 2025

A major factor in worldwide ecological harm is the large quantity of municipal solid waste generated because of rapid industrialization and population growth. Nowadays, there are numerous mechanical, biological, and thermal waste treatment processes that can reduce the amount of landfilled waste. A [...] Read more.

A major factor in worldwide ecological harm is the large quantity of municipal solid waste generated because of rapid industrialization and population growth. Nowadays, there are numerous mechanical, biological, and thermal waste treatment processes that can reduce the amount of landfilled waste. A variety of analytical tests are conducted to evaluate the potential risks that landfills pose to human health and the environment. Among these, laboratory leaching tests are commonly employed to assess the release of specific waste constituents that may become hazardous to the environment. Municipal solid waste (MSW) management poses significant environmental risks due to leachate contamination in bioreactor landfills, where acidic conditions (pH ≈ 5) can mobilize heavy metals. This study evaluates the reliability of leaching tests for biodried reject MSW from CWMC Marišćina, Croatia, by comparing standard EN 12457-1 and EN 12457-2 methods (L/S = 2 and 10 L/kg) with simulations of aerobic degradation using acetic acid (10 g/L) to maintain pH = 5 over 9 days. Waste composition analysis revealed plastics (35%), paper/cardboard (25%), metals (15%), and glass (10%) as dominant fractions. Although the majority of parameters determined through standard leaching tests remain below the maximum permissible limits for non-hazardous waste, simulations under acidic conditions demonstrated substantial increases in eluate concentrations between days 6 and 9: Hg (+1500%), As (+1322%), Pb (+1330%), Ni (+786%), and Cd (+267%), with TDS rising 33%. These results highlight the underestimation of risks by conventional tests, emphasizing the need for pH-dependent methods to predict in situ leachate behavior in MBO-treated waste and support improved EU landfill regulations for enhanced environmental protection. Full article

(This article belongs to the Special Issue Innovations in Solid Waste Treatment and Resource Utilization)

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15 pages, 2983 KB

Open AccessArticle

High-Efficiency Biomass Burner for Forest By-Products

by Artemio García-Flores, Juan Manuel Sandoval-Pineda, Luis Armando Flores-Herrera, Alejandro Zacarías-Santiago, René O. Vargas and Raúl Rivera-Blas

Processes 2026, 14(1), 140; https://doi.org/10.3390/pr14010140 - 31 Dec 2025

This study employs CFD simulations carried on ANSYS Fluent 2022 R1 (ANSYS Inc., Canonsburg, PA, USA), to address the design, development, and thermodynamic analysis of a biomass burner, based on mass and energy balances, combustion efficiency, flame temperature, and thermodynamic properties. The prototype [...] Read more.

This study employs CFD simulations carried on ANSYS Fluent 2022 R1 (ANSYS Inc., Canonsburg, PA, USA), to address the design, development, and thermodynamic analysis of a biomass burner, based on mass and energy balances, combustion efficiency, flame temperature, and thermodynamic properties. The prototype incorporates a flow deflector located before the combustion chamber. This component improves the air-fuel mixture to maximise thermal efficiency and minimise pollutant emissions. The burner is specifically designed to use sawdust as fuel and is intended for industrial applications such as heating or drying processes. The integration of the flow deflector results in uniform, complete combustion, achieving 90% thermal efficiency and an adjustable thermal power output of 0–100 kW. Compared to conventional burners, this design reduces CO emissions by 20% and NO_x emissions by 15%, demonstrating significant environmental improvements. The design methodology is based on mass and energy balance equations to evaluate combustion efficiency as a function of the stoichiometric ratio, along with experimental testing. These experimental tests were conducted using an ECOM (America Ltd., Nashua, NH, USA) gas analyser and anemometer. The internal temperature was monitored with a K-type thermocouple (Omega Engineering Inc., Norwalk, CT, USA). The results confirmed the positive influence of the structural design on thermal performance. The proposed burner aims to maximise heat generation in the combustion chamber, offering an innovative alternative for biomass combustion systems. Full article

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

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46 pages, 5142 KB

Open AccessReview

Optimization of the Effects of Electrodeposition Parameters on the Nickel-Based Composite Coatings’ Tribological Properties

by Yassine Abdesselam, Catalin Tampu, Abderrahim Belloufi, Imane Rezgui, Mourad Abdelkrim, Bogdan Chirita, Eugen Herghelegiu, Carol Schnakovszky and Raluca Tampu

Processes 2026, 14(1), 139; https://doi.org/10.3390/pr14010139 - 31 Dec 2025

Mechanical forces, chemical and electrochemical reactions, and environmental variables can all lead to surface degradation of parts. Composite coatings can be applied to these materials to enhance their surface characteristics. Recently, nickel-based composite coatings have gained greater attention because of their remarkable wear [...] Read more.

Mechanical forces, chemical and electrochemical reactions, and environmental variables can all lead to surface degradation of parts. Composite coatings can be applied to these materials to enhance their surface characteristics. Recently, nickel-based composite coatings have gained greater attention because of their remarkable wear resistance. The efficiency, precision, and affordability of this process make it a popular method. In addition, electroplating nickel-based composites offers a more environmentally friendly alternative to traditional dangerous coatings such as hard chrome. Tribological and wear characteristics are highly dependent on several variables, such as particle parameters, deposition energy, fluid dynamics, and bath composition. Mass loss, coefficient of friction, hardness, and roughness are quantitative properties that provide useful information for coating optimization and selection. Under optimized electrodeposition conditions, the Ni-SiC-graphite coatings achieved a 57% reduction in surface roughness (Ra), a 38% increase in microhardness (HV), and a 25% reduction in wear rate (Ws) compared to pure Ni coatings, demonstrating significant improvements in tribological performance. Overall, the incorporation of SiC nanoparticles was found to consistently improve microhardness while graphite or MoS₂ reduces friction. Differences in wear rate among studies appear to result from variations in current density, particle size, or test conditions. Furthermore, researchers run tribology studies and calculate the volume percentage using a variety of techniques, but they fall short in providing a sufficient description of the interface. This work primarily contributes to identifying gaps in tribological research. With this knowledge and a better understanding of electrodeposition parameters, researchers and engineers can improve the lifespan and performance of coatings by tailoring them to specific applications. Full article

(This article belongs to the Special Issue Product Design and the Optimization of Technologies for Manufacturing Processes)

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37 pages, 2985 KB

Open AccessReview

Multiphysics Modelling and Optimization of Hydrogen-Based Shaft Furnaces: A Review

by Yue Yu, Feng Wang, Xiaodong Hao, Heping Liu, Bin Wang, Jianjun Gao and Yuanhong Qi

Processes 2026, 14(1), 138; https://doi.org/10.3390/pr14010138 - 31 Dec 2025

Hydrogen-based direct reduction (H-DR) represents an environmentally benign and energy-efficient alternative in ironmaking that has significant industrial potential. This study reviews the current status of H-DR shaft furnaces and accompanying hydrogen-rich reforming technologies (steam and autothermal reforming), assessing the three dominant numerical frameworks [...] Read more.

Hydrogen-based direct reduction (H-DR) represents an environmentally benign and energy-efficient alternative in ironmaking that has significant industrial potential. This study reviews the current status of H-DR shaft furnaces and accompanying hydrogen-rich reforming technologies (steam and autothermal reforming), assessing the three dominant numerical frameworks used to analyze these processes: (i) porous medium continuum models, (ii) the Eulerian two-fluid model (TFMs), and (iii) coupled computational fluid dynamics (CFD)–discrete element method (DEM) models. The respective trade-offs in terms of computational cost and model accuracy are critically compared. Recent progress is evaluated from an engineering standpoint in four key areas: optimization of the pellet bed structure and gas distribution, thermal control of the reduction zone, sensitivity analysis of operating parameters, and industrial-scale model validation. Current limitations in predictive accuracy, computational efficiency, and plant-level transferability are identified, and possible mitigation strategies are discussed. Looking forward, high-fidelity multi-physics coupling, advanced mesoscale descriptions, AI-accelerated surrogate models, and rigorous uncertainty quantification can facilitate effective scalable and intelligent application of hydrogen-based shaft furnace simulations. Full article

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

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22 pages, 12500 KB

Open AccessArticle

Shrinkage Characteristics of Bentonite–Sand Mixtures Considering the Influence of Sand Content and Pore Water Chemistry

by Dongyue Pan, Chongxi Zhao, Bowen Hu, Pengyu Ren and Ping Liu

Processes 2026, 14(1), 137; https://doi.org/10.3390/pr14010137 - 31 Dec 2025

The safe disposal of high-level radioactive waste (HLW) is a significant challenge in the nuclear industry. As the buffer backfill material for deep geological disposal engineering barriers, the shrinkage characteristics of bentonite–sand mixtures are critical to the long-term stability of repositories. This study [...] Read more.

The safe disposal of high-level radioactive waste (HLW) is a significant challenge in the nuclear industry. As the buffer backfill material for deep geological disposal engineering barriers, the shrinkage characteristics of bentonite–sand mixtures are critical to the long-term stability of repositories. This study systematically conducted drying shrinkage tests using an improved thin-film technique under varying sand contents R_s (0–50%), salt solution concentrations (0–1.5 mol/L), and ion types (Na⁺, Mg²⁺, Ca²⁺, Cl⁻, SO₄²⁻). The mechanisms of the effects of sand content and salt solutions on the shrinkage behavior of bentonite were revealed based on the results. In addition, the rationality of the MCG-B model in simulating the shrinkage characteristics of mixtures was also discussed. The results show that a sand content of 30% is the minimum sand content for inhibiting the shrinkage behavior of bentonite–sand mixtures observed in this work: below this ratio, bentonite dominates the shrinkage process, and samples are prone to cracking due to uneven matrix suction; above this ratio, quartz sand forms a rigid skeleton that significantly inhibits volume shrinkage and accelerates water evaporation. Salt solutions suppress shrinkage by compressing the thickness of the diffuse double layer and inducing ion crystallization. Higher cation concentrations and valences (Mg²⁺ > Na⁺ > Ca²⁺) enhance the inhibitory effect. Crystalline salts such as Na₂SO₄ cause measurement deviations in water content due to hydration and delay the shrinkage process. However, NaCl solutions effectively inhibit shrinkage with minimal impact on shrinkage time. Fitting results with the MCG-B model (Coefficient of determination > 0.97) demonstrate that the MCG-B model can empirically describe the results of thin-film technique experiment, though the model’s prediction accuracy decreases for the residual shrinkage stage at high sand contents (>40%). This study provides a theoretical basis for optimizing buffer material proportions and curing processes, with significant implications for the long-term safety of HLW repositories. Full article

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

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30 pages, 6798 KB

Open AccessReview

Resource Utilization of Sewage Sludge: Heavy Metal Removal and Phosphorus Recovery for Sustainable Bio/Hydro-Char Production

by Xutong Wang, Huwei Li, Junxia Wang, Fan Yu, Guanyi Chen, Beibei Yan, Guiyue Du and Xiaoqiang Cui

Processes 2026, 14(1), 136; https://doi.org/10.3390/pr14010136 - 31 Dec 2025

Sewage sludge production is increasing rapidly, yet current sludge treatment capacity and technology remain insufficient. The thermochemical process has been widely adopted for sewage sludge disposal; its solid product (bio/hydro-char) shows considerable potential to improve soil quality by enriching soil nutrient contents. However, [...] Read more.

Sewage sludge production is increasing rapidly, yet current sludge treatment capacity and technology remain insufficient. The thermochemical process has been widely adopted for sewage sludge disposal; its solid product (bio/hydro-char) shows considerable potential to improve soil quality by enriching soil nutrient contents. However, limited heavy metals are volatilized during the thermochemical process, and the majority is concentrated in the derived bio/hydro-char. Therefore, it is essential to ensure the environmental safety of sewage sludge-derived bio/hydro-char and avoid heavy metal risk, and thus appropriate heavy metal removal technology is required prior to land application. This review provides an overview of the major sewage sludge treatment approaches focusing on the heavy metal removal and phosphorus recovery, along with emerging challenges and future perspectives for the sustainable utilization of sewage sludge. Notably, the combination of electrokinetic treatment with thermochemical treatments emerges as a promising strategy to simultaneously treat sewage sludge and achieve P reclamation. Full article

(This article belongs to the Special Issue Feature Review Papers in Section "Environmental and Green Processes")

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4 pages, 178 KB

Open AccessEditorial

Special Issue “Bioprocess Engineering: Sustainable Manufacturing for a Green Society”

by Pedro Fernandes and Carla C. C. R. de Carvalho

Processes 2026, 14(1), 135; https://doi.org/10.3390/pr14010135 - 30 Dec 2025

The transition from linear, resource-intensive production to circular, low-carbon manufacturing currently presents a major challenge [...] Full article

(This article belongs to the Special Issue Bioprocess Engineering: Sustainable Manufacturing for a Green Society)

23 pages, 1506 KB

Open AccessFeature PaperArticle

Exergoeconomic Assessment of a Cogeneration Unit Using Biogas

by Ana Lívia Formiga Leite de Lima, Carlos Antônio Cabral dos Santos, Alvaro Antonio Villa Ochoa, Daniel Rodríguez López, Paula Suemy Arruda Michima, José Ângelo Peixoto da Costa and Gustavo de Novaes Pires Leite

Processes 2026, 14(1), 134; https://doi.org/10.3390/pr14010134 - 30 Dec 2025

Biogas, a promising fuel for present and future generations, is produced from the anaerobic digestion of organic waste generated by the condominium itself. Therefore, this work aims to evaluate the exergoeconomic performance of a biogas cogeneration unit designed to meet the electrical and [...] Read more.

Biogas, a promising fuel for present and future generations, is produced from the anaerobic digestion of organic waste generated by the condominium itself. Therefore, this work aims to evaluate the exergoeconomic performance of a biogas cogeneration unit designed to meet the electrical and thermal energy demands of a residential condominium in the city of Teresina, Piauí, Northeast Brazil. The cogeneration unit consists of an internal combustion engine (ICE) coupled to an electric generator (genset) to produce electricity, and a heat exchanger that recovers part of the exhaust-gas heat to heat water. The analysis was conducted based on the concepts of Thermodynamics and Exergoeconomics, using the SPECO (Specific Exergy Costing) methodology to define the exergetic costs of the system. The novelty of the work lies in applying the SPECO exergoeconomic analysis to a small-scale biogas cogeneration unit fueled by residential organic waste. The achieved electricity production was 167.40 kW, and the heat transfer rate at the exchange rate was 51.55 kW. The results revealed that the exergy destroyed in the internal combustion chamber (ICE) was 223.65 kW, whereas that in the heat exchanger was significantly higher at 45.67 kW. The exergy efficiency of the ICE reached 39.19%, and the heat exchanger efficiency was around 9%. In financial terms, the cost of exergy destroyed in the ICEC was USD/h 135, but in the heat exchanger, it was dramatically higher at USD/h 158.40. The cost of producing hot water (product) was considered extremely high (USD/GJ 38.98). The relative difference parameter in the heat exchanger also has a value much higher than expected (10.240). This is because the product’s cost (USD/GJ 38.98) is well above the cost of fuel (USD/GJ 3.468). This study concludes that the cogeneration unit is more justifiable by the savings generated through thermal energy production than by electricity production alone, since the cogeneration system significantly enhances performance, raising both the energetic and exergetic efficiencies to 55% and 48%, respectively, thereby confirming the added value of the simultaneous utilization of heat and power. Full article

(This article belongs to the Special Issue Heat and Mass Transfer Optimization in Efficient Energy Conversion and Thermal Management)

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27 pages, 9281 KB

Open AccessArticle

Safety Behavior Recognition for Substation Operations Based on a Dual-Path Spatiotemporal Network

by Xiaping Zhao, Fuqi Ma, Ge Cao, Shixuan Lv and Qian Liu

Processes 2026, 14(1), 133; https://doi.org/10.3390/pr14010133 - 30 Dec 2025

The integration of large-scale renewable energy sources has increased the complexity of operation and maintenance in modern power systems, causing on-site substation operation and maintenance activities to exhibit stronger continuity and dynamics, and thereby placing higher demands on real-time operational perception and safety [...] Read more.

The integration of large-scale renewable energy sources has increased the complexity of operation and maintenance in modern power systems, causing on-site substation operation and maintenance activities to exhibit stronger continuity and dynamics, and thereby placing higher demands on real-time operational perception and safety judgment. However, existing behavior recognition methods have difficulty accurately identifying operational states in complex scenarios involving continuous actions, partial occlusions, and fine-grained manipulations. To address these challenges, this paper proposes a safety behavior recognition method for substation operations based on a dual-path spatiotemporal network. Personnel localization is achieved using YOLOv8, while behavior classification is performed through the SlowFast framework. In the Slow pathway, an ECA attention mechanism is integrated with residual structures to enhance the representation of sustained operational postures. In the Fast pathway, a multi-path excitation residual network is introduced to fuse temporal, channel, and motion information, improving the multi-scale representation of local action variations. Furthermore, to mitigate the issue of class imbalance in substation operation data, Focal Loss based on binary cross-entropy is incorporated to adaptively down-weight easily classified samples. Experimental results demonstrate that the proposed method achieves a recognition accuracy of 87.77% and an F1-score of 85.56% across multiple operation scenarios. The results further indicate improved recognition stability and adaptability, supporting safe substation operation and maintenance in renewable energy-integrated power systems. Full article

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

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34 pages, 5321 KB

Open AccessReview

A Review on the Applications of Various Zeolites and Molecular Sieve Catalysts for Different Gas Phase Reactions: Present Trends in Research and Future Directions

by Preetha Chandrasekharan Meenu, Bhagatram Meena and Panagiotis G. Smirniotis

Processes 2026, 14(1), 132; https://doi.org/10.3390/pr14010132 - 30 Dec 2025

Zeolites and molecular sieves have demonstrated remarkable potential in adsorption, ion exchange, and separation processes since their industrial revolution in the 1950s. Zeolites and molecular sieves are materials of choice in separation applications because of their well-defined microporous architecture, remarkable shape-selectiveness, and tunable [...] Read more.

Zeolites and molecular sieves have demonstrated remarkable potential in adsorption, ion exchange, and separation processes since their industrial revolution in the 1950s. Zeolites and molecular sieves are materials of choice in separation applications because of their well-defined microporous architecture, remarkable shape-selectiveness, and tunable characteristics. The adsorption process can be evaluated using an isotherm to determine the feasibility of gas mixture separation for practical applications. We will also discuss the basic structure of zeolites and molecular sieves based on different metals, along with their distinctive properties in detail. The purpose of this review is to contextualize the importance of zeolites and molecular sieves in adsorption and separation applications. The review has been divided into groups based on how zeolites as well as molecular sieves are established in the adsorption and separation processes. The fundamental adsorption characteristics, structures, and various separation methods that make zeolites appealing for different uses are covered. By incorporating knowledge of adsorption mechanisms, isotherms, and material changes, this review discusses the most recent developments. To augment zeolite-based materials for certain pollutant removal applications, it offers a strategic framework for future study. In this review, we will comprehensively discuss a range of separation and adsorption applications, including wastewater purification, CO₂ capture from flue gases, and hydrogen storage. Furthermore, the review will explore emerging prospects of zeolites and molecular sieves in innovative fields such as energy storage, oil refining, and environmental remediation. Emphasis will be placed on understanding how their tunable pore structures, surface chemistry, and metal incorporation can enhance performance and broaden their applicability in sustainable and clean energy systems. Full article

(This article belongs to the Special Issue Novel Applications of Zeolites in Adsorption Processes)

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18 pages, 2811 KB

Open AccessArticle

Research and Application of Intensive-Stage Fracturing Technology for Shale Oil in ZN Oilfield

by Lin-Peng Zhang, Bin Li, Yi-Fei Wang, Si-Bo Wang, Peng Zheng and Zong-Rui Wu

Processes 2026, 14(1), 131; https://doi.org/10.3390/pr14010131 - 30 Dec 2025

The ZN Oilfield shale reservoir is characterized by thin sand–shale interbeds, strong lateral and vertical heterogeneity, poor porosity–permeability, low formation pressure coefficient, and low brittleness, which together limit fracture propagation and suppress production after conventional hydraulic fracturing. To overcome these constraints, we propose [...] Read more.

The ZN Oilfield shale reservoir is characterized by thin sand–shale interbeds, strong lateral and vertical heterogeneity, poor porosity–permeability, low formation pressure coefficient, and low brittleness, which together limit fracture propagation and suppress production after conventional hydraulic fracturing. To overcome these constraints, we propose an intensive-stage, closely spaced volumetric fracturing technology that couples energy-replenishment pressurization with differentiated parameter design. Numerical simulations were used to quantify how injected fluid volume affects the post-fracturing formation pressure coefficient and estimated ultimate recovery (EUR), and to determine economically optimal energy-replenishment scales. Guided by a “dual sweet spot” evaluation (geological + engineering), field designs reduced stage spacing from 80–100 m to 30–50 m and cluster spacing from 10–20 m to 6–10 m, and increased proppant and fluid intensities to ~5.0 t/m and 22.0 m³/m, respectively. Field monitoring and production data show average fracture half-length increased to 193 m, and average initial oil production per well rose from 8.8 t/d to 12.9 t/d (≈46% increase). These results demonstrate that the proposed approach effectively enlarges fracture-controlled reservoir volume, enhances formation energy, and substantially improves single-well performance in low-pressure shale oil systems. Full article

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

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