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Volume 14
Issue 4
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Processes, Volume 14, Issue 4 (February-2 2026) – 148 articles

Cover Story (view full-size image): Automated Fiber Placement (AFP) is vital for high-performance composite manufacturing, yet process deviations often cause defects like gaps, compromising structural integrity. Predicting these defects directly from sensor data remains challenging. This work leverages the Transformer architecture and its self-attention mechanism to estimate gap width during AFP. A customized positional encoding scheme captures the spatial context inherent to AFP, allowing the model to learn complex relationships between manufacturing variables. Experimental validation demonstrates high prediction accuracy, while SHapley Additive exPlanations (SHAP) analysis reveals how process parameters interact to influence defect formation. The study highlights the potential of interpretable, attention-based virtual metrology in relation to zero-defect composite manufacturing. View this paper
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25 pages, 6110 KB  
Article
Evaluation Methods for Aeration Parameters in Flotation Separation Modelling with Neural Network Applications
by Tatiana Aleksandrova, Bulat Gatiatullin, Valentin Kuznetsov and Shlykov Nikita
Processes 2026, 14(4), 728; https://doi.org/10.3390/pr14040728 - 23 Feb 2026
Viewed by 412
Abstract
This study is dedicated to the application of neural network technologies for determining aeration parameters in order to predict the efficiency of flotation separation. Within the framework of the research, digital technology solutions were actively employed, including a neural network for segmentation at [...] Read more.
This study is dedicated to the application of neural network technologies for determining aeration parameters in order to predict the efficiency of flotation separation. Within the framework of the research, digital technology solutions were actively employed, including a neural network for segmentation at the stage of determining the granulometric characteristics of bubbles and a convolutional neural network module for determining the froth layer height. An analysis was conducted to examine the variation in the statistical parameter d32, which characterizes the bubble size distribution, as a function of flotation time and measurement height. The analysis revealed that the d32 values determined by neural network processing remained within the range of acceptable dispersion and are therefore suitable for subsequent analytical procedures. Furthermore, a comparative evaluation of the obtained size distributions indicated the absence of statistically significant differences between the neural network measurements and manually labelled data with a p-value equal to 0.64. A neural network for object detection was used to record the height of the froth layer during the experiment to obtain a time series, that were subsequently processed with data processing approaches including Savitzky–Golay and Singular Spectra Analysis. Based on the analysis of the sum of the obtained dependences, a criterion is proposed and modeled for evaluating the selectivity of frother by connecting the diameter of bubble in pulp and bubble in froth. Based on the modeling results, it was determined that the optimal range of bubble sizes and froth size ratios for MIBC is constrained to d32 values ranging from 1.058 to 1.089 mm, with the ratio of froth bubble radius to d32 ranging from 1.302 to 2.098, depending on the floatability ratios of the respective fractions. When employing OPF, the values for d32 fall within the interval of 0.868 to 1.113 mm, while the Dₓ parameter ranges from 0.559 to 0.931. Full article
(This article belongs to the Special Issue Mineral Processing Equipments and Cross-Disciplinary Approaches)
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16 pages, 4921 KB  
Article
Preparation and Optimization of Backfill Slurry from Ultrafine Tailings in Tianxing Iron Mine and Its Engineering Application
by Shuai Li, Zilin Guo, Youli Ma, Zhenyu Dan and Tubing Yin
Processes 2026, 14(4), 727; https://doi.org/10.3390/pr14040727 - 23 Feb 2026
Viewed by 300
Abstract
Subsequent backfilling mining methods are critical technologies for the safe exploitation of deep metal mines, while the resource utilization of ultrafine tailings is a core component of green mining practices. This study focuses on the ultrafine tailings from the Tianxing iron mine to [...] Read more.
Subsequent backfilling mining methods are critical technologies for the safe exploitation of deep metal mines, while the resource utilization of ultrafine tailings is a core component of green mining practices. This study focuses on the ultrafine tailings from the Tianxing iron mine to investigate the preparation and optimization of backfill slurry. The goal is to develop a low-cost, high-strength slurry suitable for large-scale preparation and long-distance pipeline transportation. The main findings are as follows: the 6920-type anionic flocculant was identified as the optimal agent, with an optimal dosage of 20 g/t, achieving an underflow concentration of 70.1% under dynamic testing conditions; a novel cementitious material (NCM) exhibited a 28-day uniaxial compressive strength of 3.14 MPa at a low binder-to-tailings ratio of 1:10, outperforming ordinary Portland cement and Slag Micro-powder; and the optimal slurry concentration was determined to be 70%, which provides a favorable balance between mechanical strength and flowability. Furthermore, economic analysis indicates that adopting NCM can reduce annual backfilling costs by approximately 13 million RMB. By establishing an integrated technical framework that includes “property characterization–flocculation optimization–binder selection–rheological regulation,” this study addresses key technical challenges associated with ultrafine tailings backfilling, significantly reduces binder consumption and overall backfilling costs, and provides precise parameter guidance for industrial-scale applications. The proposed approach demonstrates significant practical value for promoting green and sustainable mining development. Full article
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27 pages, 3489 KB  
Article
Research on a Highly Self-Cleaning Cyclone Separation System for Wheat Breeding Plot Combine Harvesting
by Zenghui Gao, Cheng Yang, Nan Xu, Chao Xia, Changjie Han, Shuqi Shang and Dongwei Wang
Processes 2026, 14(4), 726; https://doi.org/10.3390/pr14040726 - 23 Feb 2026
Viewed by 311
Abstract
Domestically developed wheat breeding plot combine harvesters in China currently utilize cyclone separation self-cleaning systems. However, these systems struggle to meet the agronomic requirement of zero wheat grain residue. Seed mixing caused by residual grains can compromise the accuracy of entire breeding field [...] Read more.
Domestically developed wheat breeding plot combine harvesters in China currently utilize cyclone separation self-cleaning systems. However, these systems struggle to meet the agronomic requirement of zero wheat grain residue. Seed mixing caused by residual grains can compromise the accuracy of entire breeding field trials. This study focused on the structural design of a cyclone separation self-cleaning system based on high self-cleaning agronomic requirements. Research was conducted on the key structural and operational parameters of the cyclone separator and the negative-pressure centrifugal fan, preliminarily determining the ranges for critical parameters such as the diameter of the cylindrical section of the separator wall, the dust outlet diameter, and the rotational speed of the negative-pressure centrifugal fan. A test bench for the cyclone separation self-cleaning system of wheat breeding plot combine harvesters was designed and developed. Through single-factor experiments and Box–Behnken design optimization, the effects of key parameters on system performance were investigated. The optimal parameter combination—cylindrical section diameter of 614 mm, dust outlet diameter of 290 mm, and fan speed of 1495 r/min—achieved a self-cleaning rate of 100%, self-cleaning time ≤ 12 s, loss rate of 1.70%, and impurity rate of 0.16%, fully meeting the requirements for high-quality, rapid, and effective separation and self-cleaning operations. Full article
(This article belongs to the Section Separation Processes)
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30 pages, 1890 KB  
Article
Economic Analysis of Nuclear Power Peak Shaving Based on AEL Hydrogen Production
by Jiaoshen Xu, Ge Qin, Chengcheng Zhang, Bo Dong, Dongyuan Li, Jinling Lu and Hui Ren
Processes 2026, 14(4), 725; https://doi.org/10.3390/pr14040725 - 23 Feb 2026
Viewed by 337
Abstract
Under high renewable energy penetration, nuclear power units face significant challenges in peak regulation and market clearing due to constraints on minimum technical output and ramping capability. To address this issue, a long-term power system of Guangdong Province in 2035 is taken as [...] Read more.
Under high renewable energy penetration, nuclear power units face significant challenges in peak regulation and market clearing due to constraints on minimum technical output and ramping capability. To address this issue, a long-term power system of Guangdong Province in 2035 is taken as the study case, and an energy–reserve co-clearing simulation framework based on Security-Constrained Unit Commitment (SCUC) and Security-Constrained Economic Dispatch (SCED) is established to systematically evaluate the clearing performance of nuclear power and the formation mechanism of residual electricity under multiple market scenarios. On this basis, a nuclear power-coupled Alkaline Electrolysis (AEL) hydrogen production pathway is proposed as a peak-shaving utilization option, and an economic assessment model for nuclear-based hydrogen production is developed to quantify the investment performance under different hydrogen production capacities and operating modes. The results indicate that the integration of an AEL hydrogen production system can effectively alleviate the rigidity of nuclear power output. Under the “12-3-48-3” flexible peak-shaving mode, the residual electricity available for hydrogen production increases by approximately 30% compared with a typical peak-shaving strategy. Under scenarios with low electricity prices and green hydrogen prices, when the hydrogen production capacity is configured at 50–100 MW, the investment payback period is approximately six years, and the project exhibits strong economic robustness against variations in the discount rate. These findings demonstrate that nuclear-based hydrogen production is economically feasible in future power systems with high renewable penetration, providing quantitative support for nuclear flexibility enhancement and the coordinated development of low-carbon energy systems. Full article
(This article belongs to the Special Issue Optimal Design, Control and Simulation of Energy Management Systems)
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17 pages, 3811 KB  
Article
Valorization of Farmyard Manure Compost as a Sustainable Amendment for Rehabilitating Degraded Non-Cracking Soils
by Fathia O. Musa, Mubarak A. Abdalla, Khozima M. Yousif, Abbas M. Doka, Khaled D. Alotaibi, Abdemalik M. Abdelmalik, Nasser H. Almeaiweed and Ibrahim A. Abdelfadeel
Processes 2026, 14(4), 724; https://doi.org/10.3390/pr14040724 - 23 Feb 2026
Viewed by 339
Abstract
Degraded non-cracking soils (locally known as Naga’a) are widespread in semi-arid regions of Sudan and are characterized by severe compaction, low organic matter, poor water retention, and limited crop productivity. Sustainable rehabilitation strategies for these soils remain underexplored. This study evaluated the [...] Read more.
Degraded non-cracking soils (locally known as Naga’a) are widespread in semi-arid regions of Sudan and are characterized by severe compaction, low organic matter, poor water retention, and limited crop productivity. Sustainable rehabilitation strategies for these soils remain underexplored. This study evaluated the potential of farmyard manure compost (FYM) as a soil amendment to improve physicochemical properties, soil water retention, and sorghum (Sorghum bicolor L.) performance in degraded Naga’a soil. Aerobic composting of FYM was conducted for two months under controlled moisture and C/N ratio conditions, producing a mature compost with enhanced organic carbon, nitrogen, and water-holding capacity. A pot experiment was conducted using five rates (0, 5, 10, 15, and 20 t ha−1) of the produced compost alongside a mineral NPK treatment, assigned in a randomized complete block design. Compost application significantly (p ≤ 0.05) increased soil organic carbon, total nitrogen, total phosphorus, saturation percentage, and water-holding capacity compared with the control and NPK treatments. The highest compost rate (20 t ha−1) improved soil water-holding capacity by approximately 20% and organic carbon by over 90% relative to the control. Sorghum dry matter production and plant nutrient uptake (N, P, K, and Ca) increased significantly with compost rate, while total seasonal irrigation water requirements declined. Water productivity improved progressively with compost addition, reaching a maximum increase of 60.5% at 20 t ha−1 compared to the control. Overall, FYM proved effective in restoring soil functional properties, enhancing water-use efficiency, and improving sorghum growth. The results highlight the valorization of FYM as a sustainable, low-cost strategy for rehabilitating degraded non-cracking soils in arid and semi-arid environments. Full article
(This article belongs to the Special Issue Processes in Sustainable Waste Management and Environmental Protection)
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30 pages, 4265 KB  
Review
Fish Preservation Techniques: An Overview of Principles, Methods, and Quality Implications
by Omar Nateras-Ramírez, Perla Rosa Fitch-Vargas, María del Rosario Martínez-Macias, Rebeca Sánchez-Cárdenas, Sofía Choza-Farías and Arturo Alfonso Fernandez-Jaramillo
Processes 2026, 14(4), 723; https://doi.org/10.3390/pr14040723 - 23 Feb 2026
Cited by 1 | Viewed by 1526
Abstract
Fresh fish is a highly nutritious and widely consumed product that remains highly perishable due to its chemical composition. Conventional preservation methods, such as chilling and freezing, are effective at inhibiting microbial growth but often compromise nutritional and organoleptic quality. Advanced thermal techniques, [...] Read more.
Fresh fish is a highly nutritious and widely consumed product that remains highly perishable due to its chemical composition. Conventional preservation methods, such as chilling and freezing, are effective at inhibiting microbial growth but often compromise nutritional and organoleptic quality. Advanced thermal techniques, including supercooling and cryogenic storage, can extend shelf life to approximately 180 days but involve high infrastructure costs and potential sensory alterations. In response, non-thermal technologies have emerged as promising alternatives capable of minimizing microbial and enzymatic deterioration while reducing oxidative and sensory damage. These include high-pressure processing, cold plasma, gamma irradiation, advanced packaging systems (e.g., modified atmospheres, edible coatings), and natural antioxidants. However, such methods face limitations such as lipid oxidation, flavor changes, and scalability issues, highlighting the need for integrated preservation strategies. This study addresses a critical gap in the application of synergistic, multi-hurdle approaches that combine non-thermal technologies to enhance shelf life without compromising nutritional or sensory quality. It is essential to propose tailored and scalable solutions specific to fishery products to advance the development of sustainable and effective preservation systems that meet the practical needs of the seafood industry. Full article
(This article belongs to the Special Issue Emerging Thermal and Non-Thermal Technologies for Food Preservation and Sustainable Food Processing)
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21 pages, 2026 KB  
Article
Improvement of Nutritional Value and Bioactivity of Bee Pollen by Co-Fermentation Process of Lactobacillus Screened from Bee Bread and Commercial Compound Probiotics
by Fuyi Li, Xiuling Zhou, Chenying Zhang, Shaobo Yang, Hongzhuan Xuan and Yang Zhang
Processes 2026, 14(4), 722; https://doi.org/10.3390/pr14040722 - 22 Feb 2026
Viewed by 457
Abstract
Bee pollen is a nutrient-dense food; however, its dense cell wall limits the bioavailability and digestive absorption of nutrients. This study established a co-fermentation process that combines Lactobacillus strains isolated from bee bread with commercial probiotics to improve the nutritional profile and functional [...] Read more.
Bee pollen is a nutrient-dense food; however, its dense cell wall limits the bioavailability and digestive absorption of nutrients. This study established a co-fermentation process that combines Lactobacillus strains isolated from bee bread with commercial probiotics to improve the nutritional profile and functional properties of bee pollen. L. acidophilus (LBA1) and L. plantarum (LBP3) were isolated from bee bread and used for single-strain fermentation of bee pollen and its co-fermentation with commercial probiotics. The results indicated that fermentation increased the protein, free amino acid, vitamin C, and flavonoid contents. The co-fermentation product (FHL-99) of LBP3 and the commercial inoculant (99 strains) exhibited the highest cell wall disruption rate (67.57%) in artificial intestinal juice. Ex vivo activity analysis revealed enhanced DPPH, hydroxyl, and ABTS+ radical scavenging capacities of fermented bee pollen. Its inhibitory effects on hyaluronidase activity and protein thermal denaturation were also enhanced. FHL-99 demonstrated optimal performance across multiple indices, achieving a DPPH radical scavenging rate of 77.46% and hyaluronidase inhibition rate of 37.38%. In conclusion, synergistic co-fermentation can disrupt pollen cell walls and enrich bioactive constituents, providing an efficient biotechnological approach for the development of high-quality fermented bee pollen products. Full article
(This article belongs to the Special Issue Lactic Acid Bacteria: Screening, Isolation, Evaluation and Applications)
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13 pages, 5341 KB  
Article
Charge Loss Modeling and Lifetime Prediction in 28 nm HKMG SONOS Memory Using a Temperature-Dependent T-Model
by Xiaojun Yu, Bojia Chen, Shice Wei and David Wei Zhang
Processes 2026, 14(4), 721; https://doi.org/10.3390/pr14040721 - 22 Feb 2026
Viewed by 390
Abstract
The continuous scaling of microelectronic technology nodes has imposed fundamental physical constraints on conventional floating-gate (FG) non-volatile memory, driving the adoption of charge-trapping memory such as Silicon–Oxide–Nitride–Oxide–Silicon (SONOS) technology. SONOS devices offer advantages in scalability, endurance, and compatibility with advanced CMOS processes, yet [...] Read more.
The continuous scaling of microelectronic technology nodes has imposed fundamental physical constraints on conventional floating-gate (FG) non-volatile memory, driving the adoption of charge-trapping memory such as Silicon–Oxide–Nitride–Oxide–Silicon (SONOS) technology. SONOS devices offer advantages in scalability, endurance, and compatibility with advanced CMOS processes, yet their high-temperature reliability remains challenging due to charge loss mechanisms influenced by device structure and material properties. In this work, we systematically evaluate the reliability of two-transistor SONOS memory fabricated using a 28 nm high-K metal gate (HKMG) process. A refined temperature-dependent charge loss model (T-model) is introduced, which, by incorporating a characteristic temperature parameter (T0) that captures the dynamic shift in activation energy, fundamentally departs from the constant-activation energy assumption of the conventional Arrhenius model. This approach more accurately describes charge retention behavior across a wide temperature range. Experimental results demonstrate excellent device performance, including endurance exceeding 104 program/erase cycles at 85 °C and data retention over 10 years at 85 °C. The T-model shows strong agreement with measured data, providing a physically grounded framework for predicting long-term reliability. This study not only validated a novel charge loss model, providing insights for predicting the failure time of SONOS memory, but also demonstrated that HKMG-integrated SONOS memory exhibits high reliability. Full article
(This article belongs to the Section Energy Systems)
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15 pages, 4823 KB  
Article
Data-Driven Machine Learning Modeling for Production Planning in Natural Gas Processing Under Open-Market Conditions: A Case Study of Brazil’s Largest Gas Processing Site
by Tayná E. G. Souza, Thiago S. Feital, Maurício B. de Souza, Jr. and Argimiro R. Secchi
Processes 2026, 14(4), 720; https://doi.org/10.3390/pr14040720 - 22 Feb 2026
Viewed by 412
Abstract
The objective of this work is to propose a simulation strategy for production planning that is compatible with the dynamism of natural gas processing, especially under an open-market arrangement, in which several scheduling simulations must be performed within short time horizons. In such [...] Read more.
The objective of this work is to propose a simulation strategy for production planning that is compatible with the dynamism of natural gas processing, especially under an open-market arrangement, in which several scheduling simulations must be performed within short time horizons. In such contexts, traditional first-principles-based approaches, although accurate, require prohibitive computational times, motivating the need for an alternative simulation strategy. This work thus proposes a data-driven model built with the aid of machine learning and applied in a case study with historical data from the largest gas processing site in Brazil: Cabiúnas Petrobras asset. Main plant flowrates were selected: 18 targets and 44 input candidates—1282 observations from three and a half years of operation. Principal Component Analysis was used for order reduction, keeping the 22 main principal components. A forward neural network (2 hidden layers and 225 neurons per layer) was built from training/test sets randomly selected and optimized hyperparameters—learning rate (0.001533) and batch size (8). Training converged in roughly 200 epochs (Adam optimizer), with early stop triggered by the validation set. A mean absolute error of 0.0017 (test set) and R2 = 0.72 were found, a promising result considering plant complexity and data simplicity. Results showed a particularly good fit for lighter products (sales gas and natural gas liquid), also indicating an opportunity for further work by including inputs related to liquid fractionation. Full article
(This article belongs to the Special Issue Modeling and Optimization for Multi-scale Integration)
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73 pages, 3995 KB  
Review
Generative Artificial Intelligence in Aircraft Design Optimization
by Xiaosong Du
Processes 2026, 14(4), 719; https://doi.org/10.3390/pr14040719 - 22 Feb 2026
Viewed by 1498
Abstract
Aircraft design optimization is essential for improving aircraft performance (such as reduced fuel consumption and lowered noise), which leads to more efficient, sustainable, and affordable aircraft. Conventional aircraft design adopts physics-based simulation models, but iteratively evaluating simulation models is computationally intensive, or even [...] Read more.
Aircraft design optimization is essential for improving aircraft performance (such as reduced fuel consumption and lowered noise), which leads to more efficient, sustainable, and affordable aircraft. Conventional aircraft design adopts physics-based simulation models, but iteratively evaluating simulation models is computationally intensive, or even practically impossible. Meanwhile, artificial intelligence (AI) emerges as a revolutionary game changer in the modern engineering industry, including aircraft design optimization. Generative AI (genAI), one of the groundbreaking AI methods, has been advancing aircraft design optimization from various aspects, including intelligent parameterization, predictive modeling, training facilitation, and constraints handling. However, there is a lack of a review summarizing genAI applications in aircraft design optimization. This paper encapsulates four key genAI methods (namely, variational autoencoder, generative adversarial networks, diffusion, and transformer models), followed by advantages and drawbacks, as well as crucial advancements in aircraft design. This work aims to synthesize existing knowledge, identify research gaps, and guide future research for the genAI and aircraft design optimization communities. Full article
(This article belongs to the Special Issue Machine Learning, Control, and Optimization in Manufacturing and Industry 4.0)
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18 pages, 3008 KB  
Article
Application of AI-Driven Methods in Quenching Distortion Control of Mesh Belt Furnaces
by Xusheng Li, Yixiao Sun, Xiaohu Deng, Jiangang Wang, Yang Ju, Mingzhou Wang, Lingchu Wang, Hao Chen and Dongying Ju
Processes 2026, 14(4), 718; https://doi.org/10.3390/pr14040718 - 22 Feb 2026
Viewed by 375
Abstract
Bearing rings are thin-walled components prone to distortion during quenching. Achieving high-precision distortion control for bearing rings remains a critical challenge in high-precision bearing manufacturing. This paper proposes an AI-driven method for distortion control during bearing ring quenching in mesh-belt furnaces. The primary [...] Read more.
Bearing rings are thin-walled components prone to distortion during quenching. Achieving high-precision distortion control for bearing rings remains a critical challenge in high-precision bearing manufacturing. This paper proposes an AI-driven method for distortion control during bearing ring quenching in mesh-belt furnaces. The primary objective is to identify the optimal reverse motor frequency within the furnace. An experimental database is established using distortion results obtained at different reverse motor frequencies. This database serves as training data for deep learning. Subsequently, a large language model (LLM) employs few-shot learning to optimize and predict the reverse motor frequency influencing bearing quenching distortion. Results demonstrate that the LLM method achieves significantly higher prediction accuracy than traditional machine learning approaches. The optimization outcomes validate the effectiveness of generative AI in optimizing the reverse motor frequency of mesh-belt furnaces and controlling distortion during bearing ring quenching. Full article
(This article belongs to the Special Issue Artificial Intelligence: An Innovative Solution to the Optimization and Discovery of Functional Materials)
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35 pages, 4454 KB  
Article
Lightweight Design of Box-Type Double-Girder Overhead Crane Main Girders Based on a Multi-Strategy Improved Dung Beetle Optimization Algorithm
by Maoya Yang, Young-chul Kim, Feng Zhao, Simeng Liu, Junqiang Sun, Feng Li, Boyin Xu, Ziang Lyu and Seong-nam Jo
Processes 2026, 14(4), 717; https://doi.org/10.3390/pr14040717 - 22 Feb 2026
Viewed by 332
Abstract
The lightweight design of box-type double-girder overhead crane main girders is important for improving load-carrying capacity, reducing energy consumption, and enhancing transportation efficiency. However, the structural optimization of crane main girders involves multiple constraints and strong nonlinearity, which often leads to slow convergence [...] Read more.
The lightweight design of box-type double-girder overhead crane main girders is important for improving load-carrying capacity, reducing energy consumption, and enhancing transportation efficiency. However, the structural optimization of crane main girders involves multiple constraints and strong nonlinearity, which often leads to slow convergence and premature stagnation when using traditional optimization methods. To address these issues, a multi-strategy improved dung beetle optimization algorithm (MSIDBO) is proposed for the lightweight design of overhead crane main girders. First, the search mechanism and inherent limitations of the standard dung beetle optimization (DBO) algorithm are analyzed. Subsequently, several enhancement strategies are introduced, including hybrid chaotic population initialization; reflective boundary handling; adaptive quantum jump updating; adaptive hybrid updating; and a staged control strategy for search intensity. These strategies are designed to enhance population diversity and achieve a better balance between global exploration and local exploitation. The performance of MSIDBO was evaluated on 29 CEC2017 benchmark functions. The results show that MSIDBO generally converges faster on 25 functions and reaches the global optimum on 24 functions among the compared algorithms. Finally, based on mechanical analysis and design specifications of overhead crane main girders, a constrained structural optimization model is established. The lightweight design optimization is carried out, and finite element simulations were conducted using ANSYS Workbench to verify the effectiveness and engineering feasibility of the optimized design. The results show that the proposed MSIDBO algorithm exhibits enhanced stability and convergence performance, achieving a weight reduction of 19.4% in the main girder under the specified design configuration, meeting satisfying strength and safety requirements. Full article
(This article belongs to the Special Issue Revolutionizing Smart Manufacturing: Cutting-Edge Technologies in the Industry's Future)
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13 pages, 5040 KB  
Article
Study on the Fabrication and Dynamic Performance of Polypropylene Fiber Laminates with Built-In Heat Source
by Fuwei Gu, Hu Xiao, Zhiyang Chen, Xinpeng Li and Kang Su
Processes 2026, 14(4), 716; https://doi.org/10.3390/pr14040716 - 21 Feb 2026
Viewed by 304
Abstract
To investigate the dynamic behavior of smart composite structures with embedded heat sources over a wide temperature range, this study employed thermoplastic polypropylene as the matrix, combined with glass/carbon fiber prepregs and Ni80Cr20 alloy heating wires, and fabricated functional laminated specimens with integrated [...] Read more.
To investigate the dynamic behavior of smart composite structures with embedded heat sources over a wide temperature range, this study employed thermoplastic polypropylene as the matrix, combined with glass/carbon fiber prepregs and Ni80Cr20 alloy heating wires, and fabricated functional laminated specimens with integrated heating elements via a prepreg molding process. Using a self-developed variable-temperature cantilever beam vibration testing system, the evolution of natural frequencies and damping ratios from room temperature to 140 °C was systematically examined. Results indicate that temperature-induced thermal softening of the polypropylene matrix reduces the effective bending stiffness of the composites, leading to a decline in natural frequencies across all modes. For example, the first-order natural frequency of the sample decreased from approximately 30.8 Hz at room temperature to about 28.3 Hz at 140 °C, representing a reduction of approximately 8.12%. The second-order reduction reached about 8.99%, and the third-order reduction was approximately 9.65%. Carbon fiber-reinforced specimens exhibited relatively smaller frequency reductions due to the high modulus of the fibers. Concurrently, elevated temperatures enhance molecular chain mobility and interfacial viscoelastic dissipation at the fiber–matrix interface, causing a sharp increase in damping ratios at high temperatures (>100 °C). For instance, the damping ratio of the first-order mode increased significantly from approximately 1.02% at room temperature to about 2.9% at 140 °C. By comparatively analyzing carbon fiber and glass fiber systems, the study elucidated the distinct mechanisms underlying the “fiber-dominated” stiffness retention effect and the “resin/interface-dominated” damping dissipation effect under thermal influence. These findings provide critical experimental data and theoretical references for the active thermal regulation of structural performance in thermoplastic composite structures with integrated heat sources, thereby mitigating damage caused by external disturbances. Full article
(This article belongs to the Section Materials Processes)
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16 pages, 2274 KB  
Article
Mine Ventilation Network Calibration Based on Slack Variables and Sequential Quadratic Programming
by Fengliang Wu, Ruitun Wang, Jun Cao and Jianan Gao
Processes 2026, 14(4), 715; https://doi.org/10.3390/pr14040715 - 21 Feb 2026
Viewed by 287
Abstract
In mine ventilation network calibration, sparse and inconsistent airflow measurements often lead to infeasibility in traditional optimization models. To overcome this challenge, this paper proposes a nonlinear programming calibration model incorporating slack variables. The model treats aerodynamic resistance corrections, airflow adjustments, unknown airflows, [...] Read more.
In mine ventilation network calibration, sparse and inconsistent airflow measurements often lead to infeasibility in traditional optimization models. To overcome this challenge, this paper proposes a nonlinear programming calibration model incorporating slack variables. The model treats aerodynamic resistance corrections, airflow adjustments, unknown airflows, and resistance lower-bound slack variables as decision variables. The objective function is formulated to minimize the weighted sum of squares of resistance corrections, while penalty terms account for airflow adjustments and slack variables. Constraints integrate Kirchhoff’s laws with relaxed inequality constraints for resistance lower bounds. A calibration tool integrated via the ObjectARX interface was developed using C++, utilizing the Sequential Quadratic Programming (SQP) algorithm for the solution. The method was validated via a case study of a network comprising 39 branches and 16 measured airflows, optimized under five distinct initial conditions. Results demonstrate that the inclusion of slack variables mathematically guarantees the existence of feasible solutions. With a resistance correction weight of 10−2 and a penalty coefficient of 105, the model applies only minimal necessary corrections to handle overly tight constraints or data conflicts. The SQP algorithm exhibits superior global convergence, consistently iterating to optimal solutions that satisfy network balance laws regardless of initial values. This approach effectively resolves the infeasibility and data conflict issues inherent in traditional methods, demonstrating significant robustness and practical engineering utility. Full article
(This article belongs to the Section Petroleum and Low-Carbon Energy Process Engineering)
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15 pages, 1363 KB  
Review
Engineering Multifunctional Biochars for Integrated Environmental Systems: Multi-Medium Performance, Challenges, and Research Priorities
by Jelena Beljin, Marijana Kragulj Isakovski and Snežana Maletić
Processes 2026, 14(4), 714; https://doi.org/10.3390/pr14040714 - 21 Feb 2026
Viewed by 344
Abstract
The valorization of agricultural and other waste residues into biochar represents a promising strategy for sustainable waste management and environmental remediation within a circular economy framework. Engineering multifunctional biochars like agricultural waste-derived biochars (AWDBs) exhibit tunable physicochemical properties governed by feedstock characteristics and [...] Read more.
The valorization of agricultural and other waste residues into biochar represents a promising strategy for sustainable waste management and environmental remediation within a circular economy framework. Engineering multifunctional biochars like agricultural waste-derived biochars (AWDBs) exhibit tunable physicochemical properties governed by feedstock characteristics and thermochemical conversion conditions, enabling their application across water, soil, and sediment systems. While extensive research has demonstrated the effectiveness of biochar in isolated environmental compartments, natural systems function as interconnected water–soil–sediment continua, where pollutants, nutrients, and organic matter dynamically interact. This review critically synthesizes recent advances in the production, properties, and environmental applications of biochars, with a particular focus on their multifunctional performance in coupled environmental systems. Mechanistic insights into contaminant sequestration, nutrient cycling, and microbial interactions across media are discussed, alongside evidence of synergistic and antagonistic effects arising from cross-media processes. Despite significant progress, major knowledge gaps persist, including limited integrated multi-medium studies, lack of standardized assessment methodologies, insufficient understanding of long-term biochar stability, and challenges associated with field-scale implementation. Future research directions are proposed to address these limitations through standardized protocols, engineered multifunctional biochars, long-term monitoring, and policy integration. Advancing a system-based perspective is essential to unlock the full potential of agricultural waste-derived biochars for sustainable and scalable environmental remediation. Full article
(This article belongs to the Special Issue Waste Supermaterials: Engineered Multifunctional Composites for Dual Water-Waste Solutions)
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17 pages, 405 KB  
Article
Chemical Profile of Gluten-Free Cookies Enriched with Cracked Brazil Nut (Bertholletia excelsa HBK) as a Source of Functional Compounds
by Elison de Souza Sevalho, Caroline Dutra Lacerda, Lucas de Souza Falcão, Silvana Nascimento e Silva, Ana Luísa Schiessl Fabri, Daniele Güllich Silva, Sergio Duvoisin Junior, Maria Manuela Camino Feltes, Ana C. Correia, António M. Jordão and Patrícia Melchionna Albuquerque
Processes 2026, 14(4), 713; https://doi.org/10.3390/pr14040713 - 21 Feb 2026
Viewed by 436
Abstract
The processing of Brazil nuts generates by-products such as cracked kernels and press cakes that are frequently undervalued despite their rich biochemical composition. This study provides a comparative compositional evaluation of partially defatted Brazil nut flour applied to gluten-free cookies, focusing on sugars, [...] Read more.
The processing of Brazil nuts generates by-products such as cracked kernels and press cakes that are frequently undervalued despite their rich biochemical composition. This study provides a comparative compositional evaluation of partially defatted Brazil nut flour applied to gluten-free cookies, focusing on sugars, tocopherols, selected phenolic acids, and mineral composition. Cookies formulated with Brazil nut flour were compared with control formulations and, descriptively, with commercial gluten-free products. The incorporation of Brazil nut flour resulted in consistent compositional enrichment, including higher levels of γ- and α-tocopherols, gallic and caffeic acids, and essential minerals, alongside a lower sucrose content relative to control cookies. From a food biochemistry perspective, these results indicate an improved nutritionally relevant of the formulated cookies. The findings support the compositional potential of Brazil nut by-products as upcycled ingredients for nutritionally improved gluten-free baked products and provide a foundation for future studies addressing biological activity, bioaccessibility, and functional validation. Full article
(This article belongs to the Special Issue Food Biochemistry and Health: Recent Developments and Perspectives)
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29 pages, 7458 KB  
Article
Characterization of Regulated Electricity Consumption Flexibility Using Variability, Entropy, and Latent Profiling
by Jesús Osorio-Lázaro and Javier Rosero-García
Processes 2026, 14(4), 712; https://doi.org/10.3390/pr14040712 - 21 Feb 2026
Cited by 1 | Viewed by 258
Abstract
Energy flexibility in regulated users is examined as a structural property of demand, assessed through variability and disorder metrics derived from smart metering data. Using the coefficient of variation and normalized entropy, the analysis reveals stable routines during weekdays and greater heterogeneity in [...] Read more.
Energy flexibility in regulated users is examined as a structural property of demand, assessed through variability and disorder metrics derived from smart metering data. Using the coefficient of variation and normalized entropy, the analysis reveals stable routines during weekdays and greater heterogeneity in transitional periods such as evenings and weekends. Non-negative matrix factorization (NMF) is applied to extract latent user pro-files, which are subsequently clustered to uncover representative trajectories of consumption. Groups with bimodal or extended load distributions emerge as the most adaptable, highlighting the role of latent profiling in identifying flexibility potential. Simulations of partial load redistribution demonstrate that, while individual savings remain modest, aggregated benefits and improvements in reliability indicators (SAIDI, SAIFI, ENS) are significant. These findings confirm that flexibility is unevenly distributed across users and time, and that its quantification provides a strategic foundation for differentiated demand response schemes and the design of resilient, user-oriented energy systems. Full article
(This article belongs to the Special Issue Optimization and Analysis of Energy System)
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18 pages, 2414 KB  
Article
Co-Ce PROX Catalysts for Renewable, Climate-Independent, and Emission-Free “On-Board” Energy
by Silviya Zh. Todorova, Bozhidar K. Grahovski, Elena Maria Anghel, Daniela B. Karashanova, Zlatka Geshkova, Hristo Kolev, Diana Filkova, Krasimir Tenchev, Iliyana Hristova and Vesselin Idakiev
Processes 2026, 14(4), 711; https://doi.org/10.3390/pr14040711 - 21 Feb 2026
Viewed by 580
Abstract
Trace amounts of CO in H2-rich gas can poison Pt electrodes in proton-exchange-membrane fuel cells, necessitating selective CO removal. Preferential oxidation of CO (PROX) offers an efficient route to oxidize CO while preserving H2. Although noble-metal-based catalysts are widely [...] Read more.
Trace amounts of CO in H2-rich gas can poison Pt electrodes in proton-exchange-membrane fuel cells, necessitating selective CO removal. Preferential oxidation of CO (PROX) offers an efficient route to oxidize CO while preserving H2. Although noble-metal-based catalysts are widely used, their high cost has driven interest in non-precious alternatives. Co3O4–CeO2 catalysts have emerged as particularly promising due to their high activity and stability. Two series of Co–Ce/SiO2 catalysts were prepared via impregnation: in the first, Ce was introduced and calcined prior to Co deposition; in the second, Co and Ce nitrates were co-deposited from a mixed aqueous solution. The latter method enhances the interaction between Co3O4 and CeO2, increasing the availability of surface oxygen species. Stability tests on the most active sample demonstrated remarkable durability, maintaining near-complete CO conversion over 100 h on dry stream. Full article
(This article belongs to the Special Issue Sustainable Pathways in Chemical Process Engineering: Innovations, Optimization and Circular Strategies)
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26 pages, 1615 KB  
Review
A Systematic Review of Lignocellulosic Fibers Modification Techniques; Enhancing Selective Adsorption–Desorption of Textile Dyes
by Bosco Barnabas Mtweve, Muthumuni Managa, Tlou Nathaniel Moja and Mukuna Patrick Mubiayi
Processes 2026, 14(4), 710; https://doi.org/10.3390/pr14040710 - 20 Feb 2026
Viewed by 504
Abstract
Climate change and water pollution are the global focus to address the mitigation measures that will improve water quality and wastewater management. The recent increase in dye pollution from the manufacturing sector, particularly the textile industries, has increased the demand for advanced wastewater [...] Read more.
Climate change and water pollution are the global focus to address the mitigation measures that will improve water quality and wastewater management. The recent increase in dye pollution from the manufacturing sector, particularly the textile industries, has increased the demand for advanced wastewater treatment technologies that are sustainable and affordable. The abundant, renewable lignocellulosic fiber-based adsorbents have emerged as a promising alternative for removing a wide range of heavy metals and dyes. This review highlights in detail various sources of fibers, their physicochemical compositions, and different modification techniques that improve their selectivity and adsorption capacity, particularly for dye removal. The complementary impacts of the presence of the inherent functional groups, specific surface charges, and the primary adsorption mechanisms that can significantly enhance dye selectivity have been well addressed. While the modified fibers demonstrated the promising removal efficiency of above 90% at the laboratory scale, challenges remain in terms of their adsorption kinetics, regeneration efficiency, and long-term stability for large-scale industrial settings. Hence, future studies should focus on enhancing fiber properties for sustainable industrial applications, high-performance, and multifunctionality through a promising hybrid modification technique that will bridge the gap into large industrial implementation. Full article
(This article belongs to the Special Issue Conversion and Valorization of Biomass)
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24 pages, 1382 KB  
Article
Towards Sustainable Industry 5.0: An LLM-Based Co-Pilot for Energy-Efficient Factory Scheduling
by Kahiomba Sonia Kiangala and Zenghui Wang
Processes 2026, 14(4), 709; https://doi.org/10.3390/pr14040709 - 20 Feb 2026
Viewed by 455
Abstract
Industry 5.0 promotes sustainable, resilient, and human-centric manufacturing. Many factories struggle to produce energy-aware schedules that balance throughput, energy, and time-of-use (TOU) tariffs. Classical methods (heuristics and optimization) help but lack transparency and adaptability, limiting operator-in-the-loop use. Generative AI, particularly Large Language Models [...] Read more.
Industry 5.0 promotes sustainable, resilient, and human-centric manufacturing. Many factories struggle to produce energy-aware schedules that balance throughput, energy, and time-of-use (TOU) tariffs. Classical methods (heuristics and optimization) help but lack transparency and adaptability, limiting operator-in-the-loop use. Generative AI, particularly Large Language Models (LLMs), offers reasoning, adaptation, and interaction, yet integration with production scheduling is nascent. We introduce a hybrid framework that combines classical heuristics with GPT-4 reasoning to create an Industry 5.0-compatible Co-Pilot for energy-aware factory scheduling. The Co-Pilot evaluates and adapts machine operation schedules to avoid peak windows and explains trade-offs in natural language. We evaluate on three datasets (CTU synthetic, Kaggle manufacturing, Zenodo benchmark). Results show the heuristic Co-Pilot consistently reduces peak load share versus classical baselines at similar cost; on Zenodo, GPT-4 saves 4–7% in cost and energy, while its performance is less stable on synthetic data. These findings highlight the promise of LLM-powered scheduling and the value of hybrid human-AI strategies in Industry 5.0. Full article
(This article belongs to the Special Issue Machine Learning for Predictive Modeling and Optimization of Manufacturing Processes)
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17 pages, 5296 KB  
Article
Evaluation of the Lipophilicity of 3,28-Disubstituted Betulin Derivatives with Promising Biological Properties
by Elwira Chrobak, Katarzyna Bober-Majnusz, Marta Świtalska, Joanna Wietrzyk and Ewa Bębenek
Processes 2026, 14(4), 708; https://doi.org/10.3390/pr14040708 - 20 Feb 2026
Viewed by 368
Abstract
The identification of bioactive substances among new chemical compounds is based on analyzing the relationships between structure, physicochemical properties, and potential biological activity. The aim of this study was to characterize the physicochemical properties of a group of 3,28-disubstituted betulin derivatives, which have [...] Read more.
The identification of bioactive substances among new chemical compounds is based on analyzing the relationships between structure, physicochemical properties, and potential biological activity. The aim of this study was to characterize the physicochemical properties of a group of 3,28-disubstituted betulin derivatives, which have demonstrated promising antiproliferative activity in an in vitro study. The experimental lipophilicity parameters of betulin derivatives were obtained by RP-TLC and compared with theoretical values determined using various computational programs. Physicochemical and pharmacokinetic parameters were calculated using the SwissADME and pkCSM programs. The relationships between lipophilicity parameters (RM0 and logPTLC) and the anticancer activity, and physicochemical and pharmacokinetic parameters of the studied triterpenoids were analyzed. Chemometric analysis (cluster analysis, principal component analysis, and the sum of ranking differences analysis) was performed. Significant correlations were demonstrated between RM0 and logPTLC, as well as theoretically determined lipophilicity values, in the tested group of compounds. Propynoyl derivatives 4a, 5a and 6a with high antiproliferative activity against MV4-11, PC-3 and Hs249T cells are characterized by higher lipophilicity than their hydroxyl analogs (compounds 4, 5 and 6). Full article
(This article belongs to the Special Issue Phytochemicals: Extraction, Optimization, Identification, Biological Activities, and Applications in the Food, Nutraceutical, and Pharmaceutical Industries (3rd Edition))
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26 pages, 14423 KB  
Article
A Study of Abrasive Solid Particles Erosion for a Centrifugal Pump Operated as a Pump and as a Turbine Using Computational Fluid Dynamics
by Jamal El Mansour, Patrick Hendrick, Abdelowahed Hajjaji and Fouad Belhora
Processes 2026, 14(4), 707; https://doi.org/10.3390/pr14040707 - 20 Feb 2026
Viewed by 436
Abstract
Impeller blades are one of the main parts of a centrifugal pump that affect the performance of the pump. The presence of solid particles in seawater, transported through a centrifugal pump, causes wear in the blade surface that reduces blade lifetime. In the [...] Read more.
Impeller blades are one of the main parts of a centrifugal pump that affect the performance of the pump. The presence of solid particles in seawater, transported through a centrifugal pump, causes wear in the blade surface that reduces blade lifetime. In the orthogonal direction, this wear is an erosion thickness of the blade. Assuming that these particles have a spherical shape, the erosion rate depends on their velocity, size, impingement angle, and material hardness index. In this work, we investigate the erosion thickness of a low-head centrifugal pump operating in pump and turbine modes, with a particle radius ranging from 4 μm to 50 μm. The numerical simulation used an RNG k–ε turbulence model, assuming a perfect bounce collision between the particle and the rotating solid wall. The study shows that the blade pressure side is impacted by a solid particle concentration higher than the suction side. In pump mode, the erosion thickness on the blade sides increases if the particle radius is above 4 μm and reaches a maximum at 40 μm. In turbine mode, the erosion thickness decreases when the particle radius is greater than 5 μm. The thickness loss is greater in turbine mode than in pump mode. The influence of particle flow rate was investigated. Below a particle radius of 10 μm, particles follow the flow directions and reside for a longer time in the blade channel. Passing from a particle radius of 50 μm to 100 μm, the blade lifetime was decreased by a factor of 11. Full article
(This article belongs to the Special Issue CFD Simulation of Fluid Machinery)
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19 pages, 2885 KB  
Article
Improved Depleting Sand Fracture Model
by Kabir Oyekunle Sanni, Derrick Adjei, Vincent N. B. Amponsah, Bilal A. Ibrahim, Mohammad Nezam Uddin and Fathi Boukadi
Processes 2026, 14(4), 706; https://doi.org/10.3390/pr14040706 - 20 Feb 2026
Viewed by 291
Abstract
An improved depleting sand fracture model was derived in this work using Finite Element Methods, taking into consideration the effect of pore pressure and production on in situ stresses. Sets of governing equations from the commercial finite element simulator COMSOL Multiphysics were used [...] Read more.
An improved depleting sand fracture model was derived in this work using Finite Element Methods, taking into consideration the effect of pore pressure and production on in situ stresses. Sets of governing equations from the commercial finite element simulator COMSOL Multiphysics were used to obtain a model that compares well with the existing fracture model, mainly based on the Mohr–Coulomb failure criterion. The model uniquely couples reservoir depletion-induced stress evolution with fracture initiation and propagation within a unified finite element framework. A constant overburden load was used since its value majorly depends on depth, and the formation is assumed to be fixed at the bottom. The reservoir is assumed to be depleting at a constant rate with no water injection to assist pressure, with an average porosity of 25% and an average permeability of 251 mD at the beginning of production. The reservoir compacted during production, and in turn, porosity and permeability were reduced over the years of observation. Fracturing was observed to be much easier for the depleted reservoir, since horizontal stresses, which might have created friction, are reduced during reservoir production, signifying that for depleted reservoirs, a small fracture pressure is required. Created fractures are observed to propagate in the direction of the maximum horizontal stress and perpendicular to the direction of the minimum horizontal stress. Full article
(This article belongs to the Section Petroleum and Low-Carbon Energy Process Engineering)
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17 pages, 2690 KB  
Article
Analysis of the Shear Stresses in a Filling Line of Parenteral Products: The Role of Peristaltic Pumping
by Camilla Moino, Andrea Albano, Bernadette Scutellà, Gianluca Boccardo and Roberto Pisano
Processes 2026, 14(4), 705; https://doi.org/10.3390/pr14040705 - 20 Feb 2026
Viewed by 346
Abstract
Protein-based parenteral drug products processed in a filling line can be exposed to shear stress of varying magnitude depending on the operation. These shear stresses and other factors, such as interfacial stress, have been the focus of many studies in recent years on [...] Read more.
Protein-based parenteral drug products processed in a filling line can be exposed to shear stress of varying magnitude depending on the operation. These shear stresses and other factors, such as interfacial stress, have been the focus of many studies in recent years on the cause of product degradation. Estimating shear stress in individual operating units represents the first step towards a more in-depth study of the shear-induced product instability. In this frame, the present manuscript shows an innovative workflow to obtain a computational model of a peristaltic pump and evaluate the exposure of the product to shear stresses. This was accomplished through Computational Fluid Dynamics simulations combined with Lagrangian Particle Tracking techniques. In this way, the shear stress history of each individual product particle passing through the peristaltic pump was taken into account. The results provide insight into shear stress dynamics within a peristaltic pump and show potential for future applications. Full article
(This article belongs to the Section Pharmaceutical Processes)
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21 pages, 7111 KB  
Article
Study on Corrosion Mechanisms and Inhibitor Dosing Scheme for Tight Sandstone Gas Wells in the X Block of the Ordos Basin
by Xin Fan, Yang Zhang, Ming Li, Zhilin Tuo, Yibei Wu, Xu Su, Haiyang Wang and Desheng Zhou
Processes 2026, 14(4), 704; https://doi.org/10.3390/pr14040704 - 20 Feb 2026
Viewed by 354
Abstract
With the exploitation of tight sandstone gas, the corrosion problem of wellbores in the X block of the Ordos Basin has become increasingly severe, necessitating the implementation of effective measures to mitigate tubing corrosion and enhance corrosion inhibition efficiency. This study conducted field [...] Read more.
With the exploitation of tight sandstone gas, the corrosion problem of wellbores in the X block of the Ordos Basin has become increasingly severe, necessitating the implementation of effective measures to mitigate tubing corrosion and enhance corrosion inhibition efficiency. This study conducted field corrosion monitoring in conjunction with laboratory experiments, employing weight loss method, scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD) to comprehensively characterize the corrosion of gas wells from both macro and micro perspectives. The results show that the gas wells in the X block of the Ordos Basin are exposed to a complex corrosion environment, where the electrochemical corrosion risk in the aqueous phase and the acidic gas corrosion risk in the gas phase coexist, posing a potential threat to wellbore integrity. Corrosion in X-1 and X-2 wells is mainly attributed to CO2, while corrosion in X-3 well is primarily caused by sulfides. The field application of corrosion inhibitor M exhibited significant corrosion inhibition effects on steels, with the best performance at a dosage of 2000 mg/L. Based on experimental data, a corrosion inhibitor dosage prediction model for the X block gas wells was constructed. By increasing the dosing frequency and reducing the dosing concentration, the optimized dosing scheme can annually save approximately 566.4 L of corrosion inhibitor per well, providing a scientific basis for extending the service life of the gas well tubing. Given the prevalence of CO2- and H2S-induced corrosion in many global reservoirs, these findings provide valuable insights for corrosion management in similar international oil and gas fields, enhancing the broader applicability of the study. Full article
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32 pages, 2790 KB  
Article
Modelling and Optimization of Petrochemical Hybrid Renewable Energy Systems Considering Energy Interchangeability, Uncertainty and Storage for Coupling Energy Supply and Utilization Sides
by Qiaoqiao Tang, Yuehao Qu, Fengrong Qiu, Yong Pan, Junjun Tan, Yang Lei, Yuqiu Chen, Chang He, Qinglin Chen and Bingjian Zhang
Processes 2026, 14(4), 703; https://doi.org/10.3390/pr14040703 - 19 Feb 2026
Viewed by 430
Abstract
Petrochemical hybrid renewable energy systems (PHRESs), integrating renewable and fossil energy sources, have garnered more and more attention for sustainable manufacturing. However, achieving concurrent optimization of energy supply reliability and carbon mitigation in these complex systems remains a critical challenge. This study proposes [...] Read more.
Petrochemical hybrid renewable energy systems (PHRESs), integrating renewable and fossil energy sources, have garnered more and more attention for sustainable manufacturing. However, achieving concurrent optimization of energy supply reliability and carbon mitigation in these complex systems remains a critical challenge. This study proposes an innovative bilateral optimization framework coupling supply-side energy management with demand-side flexibility. On the supply side, a scenario-based two-stage stochastic programming method synergizes with energy storage systems to address renewable energy intermittency, considering a time-of-day tariff from the grid. On the utilization side, heat energy-based and shaft work-based energy interchangeability are introduced and leveraged to enable both qualitative and quantitative flexibility in process unit requirements and thus obtain energy consumption relaxation models for relaxing the design boundaries of PHRESs. These dual strategies are then coupled in a two-stage mixed-integer programming model framework for the optimal design of PHRESs. Applied to a large-scale refinery incorporating carbon taxation and dynamic electricity price, the proposed methodology demonstrates superior performance through five comparative cases. Compared to the Base Case, the Optimal Case using the proposed method can reduce the total annual cost by 14.82%, and stochastic programming reveals over a 40% probability of carbon mitigation in the uncertain space. Full article
(This article belongs to the Section Energy Systems)
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14 pages, 277 KB  
Article
Evaluation of Cadmium and Lead Accumulation in Edible Horse Tissues: A Food Safety Perspective
by Rijad Bogućanin, Dragoljub Jovanović, Nikola Čobanović, Branko Suvajdžić, Mirjana Dimitrijević, Ilija Đekić, Neđeljko Karabasil and Nevena Grković
Processes 2026, 14(4), 702; https://doi.org/10.3390/pr14040702 - 19 Feb 2026
Viewed by 606
Abstract
Horse meat is characterized by high nutritional value, but due to the specific physiology and long lifespan of horses, it represents a significant pathway for the bioaccumulation of toxic elements. The aim of this study was to examine the presence of cadmium (Cd) [...] Read more.
Horse meat is characterized by high nutritional value, but due to the specific physiology and long lifespan of horses, it represents a significant pathway for the bioaccumulation of toxic elements. The aim of this study was to examine the presence of cadmium (Cd) and lead (Pb) in muscle, liver and kidney samples of horses slaughtered in Serbia during 2023 and 2024. The toxic elements were determined by flame atomic absorption spectrometry (FAAS). The mean concentrations of cadmium and lead were 0.19 and 0.51 mg/kg in horse muscle; 2.31 and 0.74 mg/kg in horse liver; and 7.70 and 0.68 mg/kg in horse kidneys. Statistically significant differences in mean concentrations were observed between horse tissues, seasons and different age categories (p < 0.001), but there was no difference between sexes (male and female) (p > 0.05). Cadmium levels were above the maximum permitted limits in 93.2% of liver samples, 97.7% of kidney samples, and 31.1% of muscle samples tested. The data obtained indicate the need for continuous monitoring and strict control of animal traceability, especially those raised near ecological hotspots. Full article
(This article belongs to the Special Issue Novel Technologies for the Processing and Quality Assessment of Meat Production)
21 pages, 2886 KB  
Article
A Spectroradiometric Analysis of Alterations in Spectral Distribution and Their Impact on UV Index Estimation for Solar Resource Assessment
by Francesco Nicoletti, Piero Bevilacqua, Daniela Cirone, Carmen Fabbricatore and Natale Arcuri
Processes 2026, 14(4), 701; https://doi.org/10.3390/pr14040701 - 19 Feb 2026
Viewed by 400
Abstract
The accurate estimation of the instantaneous UV Index (UVI) is critical for public health, yet it is often attempted using broadband pyranometers (measuring Global Horizontal Irradiance GHI) or photometers (measuring Lux). This approach is known to be unreliable, particularly under the complex radiative [...] Read more.
The accurate estimation of the instantaneous UV Index (UVI) is critical for public health, yet it is often attempted using broadband pyranometers (measuring Global Horizontal Irradiance GHI) or photometers (measuring Lux). This approach is known to be unreliable, particularly under the complex radiative conditions induced by clouds. However, the physical mechanisms driving this failure, specifically the changes in the spectral quality of sunlight, are not fully quantified. This study utilizes a high-resolution spectroradiometer and pyranometer at a Mediterranean site (Rende, Italy), analyzing instantaneous UVI, GHI and a set of derived analytical metrics: the Erythemal Efficacy, the UV Spectral Quality Ratio and the Clearness Index. The core metric of the paper is the Erythemal Efficacy, designed to quantify the “spectral quality” or “biological hazard” per unit of total energy. It is defined as the ratio of the instantaneous UV Index to the instantaneous GHI measured by the pyranometer. The analysis confirms a decoupling between instantaneous UVI and broadband GHI, exhibiting a wide, non-functional scatter. The paper shows that this failure is caused by the high variability of the Erythemal Efficacy, which is not a constant. Its variability is shown to be linearly governed by the internal Ultraviolet A to Ultraviolet B (UVA/UVB) spectral ratio. Most critically, the Erythemal Efficacy was found to follow a counter-intuitive trend, increasing significantly as the Clearness Index decreases. The common assumption of clouds as spectrally “grey” attenuators is flawed. Clouds act as selective filters, attenuating the GHI, dominated by Visible to Near-Infrared (VIS/NIR), more severely than the UVI. This increases the relative biological hazard of the light that penetrates thick cloud cover. This study provides a physical explanation for the failure of broadband proxies and demonstrates that instantaneous GHI or Lux-based UVI alerts are fundamentally unreliable, as they fail to capture the critical variability of spectral quality. Full article
(This article belongs to the Special Issue Design and Optimisation of Solar Energy Systems)
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22 pages, 1159 KB  
Review
Investigation of the Control Strategies for Enhancing the Efficiency of Natural Gas Separation and Purification Processes
by Alexander Vitalevich Martirosyan and Daniil Vasilievich Romashin
Processes 2026, 14(4), 700; https://doi.org/10.3390/pr14040700 - 19 Feb 2026
Cited by 2 | Viewed by 609
Abstract
Natural gas separation and purification are critical stages for ensuring product quality, operational safety, and economic efficiency in the energy sector. However, a significant research gap exists: conventional control systems, predominantly based on a proportional-integral-derivative (PID) controller, are often static and lack the [...] Read more.
Natural gas separation and purification are critical stages for ensuring product quality, operational safety, and economic efficiency in the energy sector. However, a significant research gap exists: conventional control systems, predominantly based on a proportional-integral-derivative (PID) controller, are often static and lack the adaptability required to handle fluctuations in raw gas composition and operating conditions. This review aims to systematically analyze modern control strategies to identify the most influential parameters and effective methodologies for enhancing process efficiency. The methods involve a comparative assessment of classical PID control against advanced intelligent approaches, including adaptive control, fuzzy logic, and machine learning (ML) models, based on a synthesis of the recent literature and industrial case studies. The key finding is that data-driven and intelligent methods (e.g., neural networks, adaptive fuzzy controllers) demonstrate superior performance in achieving precise parameter adjustment, improving responsiveness, and optimizing energy consumption compared to traditional static systems. Such an integrated strategy transforms decision-making into a multivariable optimization framework with objectives encompassing minimizing pollutants, lowering energy usage, and enhancing end-product specifications. The present work argues for employing methodologies like systemic analyses and advanced computational techniques—particularly artificial neural networks—to forecast gas stream attributes. Full article
(This article belongs to the Section Process Control and Monitoring)
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21 pages, 5403 KB  
Article
Pollution Source Identification and Parameter Sensitivity Analysis in Urban Drainage Networks Using a Coupled SWMM–Bayesian Framework
by Ronghuan Wang, Xuekai Chen, Xiaobo Liu, Guoxin Lan, Fei Dong and Jiangnan Yang
Processes 2026, 14(4), 699; https://doi.org/10.3390/pr14040699 - 19 Feb 2026
Viewed by 483
Abstract
Addressing the challenge of tracing hidden and transient cross-connections in urban drainage networks, this study develops a SWMM–Bayesian coupled model based on the Py SWMM interface using the Daming Lake area in Jinan as a case study. By employing a Markov Chain Monte [...] Read more.
Addressing the challenge of tracing hidden and transient cross-connections in urban drainage networks, this study develops a SWMM–Bayesian coupled model based on the Py SWMM interface using the Daming Lake area in Jinan as a case study. By employing a Markov Chain Monte Carlo (MCMC) algorithm to drive the interaction between dynamic simulation and statistical inference, the model achieves multidimensional joint posterior estimation of pollution source location (Jx), discharge intensity (M), and discharge timing (T). The results indicate: (1) Model accuracy: The coupled model demonstrates strong source tracing capability, with mean absolute errors below 0.6% in single-parameter inversion. Under multi-parameter joint inversion, the true values of all parameters consistently fall within the 95% confidence intervals. (2) Parameter sensitivity: The influence of MCMC step size on the uncertainty of pollution tracing results is systematically clarified. Discrete source location estimates (Jx) exhibit high robustness to step size variation due to spatial heterogeneity in hydraulic responses, whereas continuous physical parameters (M and T) show strong dependence on the selected step size scale. (3) Practical application: The impact of spatial monitoring network configuration on pollution tracing performance is examined. By deploying a complementary monitoring system integrating trunk and branch pipelines, the inversion accuracy for mass (M) and time (T) parameters is significantly improved by 84.2% and 88.5%, respectively. Overall, the proposed pollution source tracing method for urban drainage networks effectively overcomes the multi-solution challenge in complex network inversion, providing critical technical support for refined urban water environment management. Full article
(This article belongs to the Special Issue Advances in Hydrodynamics, Pollution and Bioavailable Transfers)
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