Processes

18 pages, 4672 KB

Open AccessArticle

Experimental Study on Electrolytic Simulation of Production Capacity Interference in Asymmetric Fishbone Wells

by Xu Dang, Shijun Huang, Liang Zhai, Bin Yuan and Mengchen Jiang

Processes 2026, 14(1), 179; https://doi.org/10.3390/pr14010179 - 5 Jan 2026

Viewed by 257

As a type of multilateral wells, fishbone wells have the advantages of expanding oil drainage areas and increasing single well controlled reserves. However, there exists obvious productivity interference between branches of fishbone wells. In order to study the influence of fishbone wellbore structural [...] Read more.

As a type of multilateral wells, fishbone wells have the advantages of expanding oil drainage areas and increasing single well controlled reserves. However, there exists obvious productivity interference between branches of fishbone wells. In order to study the influence of fishbone wellbore structural parameters on productivity interference between branches, the method of water-electricity simulation experiments was adopted in this paper. The concepts of productivity interference coefficient and pressure propagation coefficient were proposed. The dependence of the productivity interference coefficient on wellbore morphological parameters was quantified. Research shows that the productivity interference coefficients of fishbone wells increase with the increase in the number of branches and decrease with the increase in branch length and branch angle. The productivity interference phenomenon between branches is caused by pressure interference. Increasing branch spacing by changing morphological parameters is the key to controlling productivity interference. The research results verify the productivity prediction model of fishbone wells and they also have important guiding significance for reasonable well placement and optimization design. Full article

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26 pages, 2243 KB

Open AccessReview

A Study of the Environmental Challenges En Marche Towards Net-Zero: Case Study of Turkish Steel Industry

by Ateş Batıkan Özdamar, Miray Kaya, Abdulkadir Bektaş, Srijita Bhattacharyya, Mert Şahindoğan, Jean-Pierre Birat and Abhishek Dutta

Processes 2026, 14(1), 178; https://doi.org/10.3390/pr14010178 - 5 Jan 2026

Viewed by 498

Abstract

The Turkish steel industry aims to reduce its sectoral carbon dioxide (CO₂) emissions by 55% by 2030, in line with Türkiye’s Paris Agreement commitments and the European Green Deal (EGD), and consistent with the ambition of the European Union’s economy-wide ‘Fit [...] Read more.

The Turkish steel industry aims to reduce its sectoral carbon dioxide (CO₂) emissions by 55% by 2030, in line with Türkiye’s Paris Agreement commitments and the European Green Deal (EGD), and consistent with the ambition of the European Union’s economy-wide ‘Fit for 55’ emissions-reduction target. Türkiye faces significant challenges in achieving net-zero greenhouse gas (GHG) emissions, particularly as a developing country confronting the impacts of climate change and in the market situation, such as the effects of the ongoing Russia-Ukraine conflict, limited access to affordable raw materials, and rising operational costs. This study serves as a guideline for the Turkish steel sector’s roadmap towards modernization and eventual compliance with net-zero targets. The consideration and integration of new technologies planned for the Turkish steel industry, in both electric arc furnace (EAF) and blast furnace-basic oxygen furnace (BF-BOF) facilities, have been outlined in conjunction with green hydrogen and with Carbon Capture and Storage (CCS) technologies. Four different scenarios were analysed to understand the reduction in CO₂ emissions: (1) In a Business-As-Usual (BAU) scenario without any reduction, (2) 39.9% CO₂ emission reduction with the Moderate scenario, (3) 59.6% reduction with the Advanced scenario, and (4) 82.9% reduction in CO₂ emissions from the Turkish steel sector with the Net-Zero scenario. To quantify the uncertainty in these long-term projections, a Monte Carlo simulation was conducted, generating probabilistic confidence intervals that reinforce the robustness and credibility of the net-zero pathway. The official roadmap for the sector is not available as of today; however, an in-depth discussion with a policy innovation leading to it is the objective of this study. Full article

(This article belongs to the Special Issue Modeling Sustainable Processes—Aiming Towards Decarbonization in the Era of Artificial Intelligence)

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24 pages, 4047 KB

Open AccessFeature PaperArticle

Optimization of an NH₃-H₂O Absorption Cooling System Using an Inverted Multivariate Function with Neural Networks and PSO

by Ulises Cruz-Jacobo, Roberto Agustin Conde-Gutiérrez, Wilfrido Rivera, Darío Colorado and José Camilo Jiménez-García

Processes 2026, 14(1), 177; https://doi.org/10.3390/pr14010177 - 5 Jan 2026

Viewed by 271

Abstract

Absorption systems offer a practical alternative to traditional compression systems, especially when low-grade heat sources are available. Their applications range from vaccine preservation to space conditioning, making performance optimization essential. This study employed a multivariate inverse artificial neural network with multiple parameters (ANNim-mp) [...] Read more.

Absorption systems offer a practical alternative to traditional compression systems, especially when low-grade heat sources are available. Their applications range from vaccine preservation to space conditioning, making performance optimization essential. This study employed a multivariate inverse artificial neural network with multiple parameters (ANNim-mp) to simultaneously enhance the cooling load and coefficient of performance in an experimental single-effect ammonia–water absorption cooling system. Optimization was carried out using particle swarm optimization. The results showed significant performance improvements: up to 100% in cooling load and 97% in COP when optimizing two variables. With four-variable optimization, improvements reached 98.7% and 106.7%, respectively. These results demonstrate the strong potential of the ANNim-mp approach in enhancing the efficiency of absorption cooling systems. Full article

(This article belongs to the Special Issue Application of Absorption Cycles in Renewable Energy)

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22 pages, 1436 KB

Open AccessArticle

Optimal Scheduling of Wind–Solar Power Generation and Coalbed Methane Well Pumping Systems

by Ying Gao, Jun Wang, Jiaojiao Yu, Youwu Li, Yue Zhang, Bin Liu, Xiaoyong Gao and Chaodong Tan

Processes 2026, 14(1), 176; https://doi.org/10.3390/pr14010176 - 5 Jan 2026

Viewed by 250

Abstract

With the integrated development of new energy and oil and gas production, introducing wind–solar–storage microgrids in coalbed methane well screw pump discharge systems enhances the renewable energy proportion while promoting green development. However, the cyclical, volatile, and random characteristics of wind and photovoltaic [...] Read more.

With the integrated development of new energy and oil and gas production, introducing wind–solar–storage microgrids in coalbed methane well screw pump discharge systems enhances the renewable energy proportion while promoting green development. However, the cyclical, volatile, and random characteristics of wind and photovoltaic generation create scheduling challenges, with insufficient green power consumption reducing renewable energy utilization efficiency and increasing grid dependence. This study establishes an operation scheduling optimization model for coalbed methane well screw pump discharge systems under wind–solar–storage microgrids, minimizing daily operation costs with screw pump rotational speed as decision variables. The model incorporates power constraints of generation units and production constraints of screw pumps, solved using particle swarm optimization. Results demonstrate that energy storage batteries effectively smooth wind and photovoltaic fluctuations, enhance regulation capabilities, and improve green power utilization while reducing grid purchases and system operation costs. At different coalbed methane extraction stages, the model optimally adjusts screw pump rotational speed according to renewable generation, ensuring high pump efficiency while minimizing operation costs, enhancing green power consumption capacity, and meeting daily drainage requirements. Full article

(This article belongs to the Special Issue Coalbed Methane Development Process)

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19 pages, 7109 KB

Open AccessArticle

Associated LoRaWAN Sensors for Material Tracking and Localization in Manufacturing

by Peter Peniak, Emília Bubeníková and Alžbeta Kanáliková

Processes 2026, 14(1), 175; https://doi.org/10.3390/pr14010175 - 5 Jan 2026

Viewed by 303

Abstract

Material tracking and localization are key applications of Industry 4.0 in manufacturing process control. Traditional approaches—such as barcode or QR code identification and RTLS-based localization using RF/UWB, 5G or GPS–require a large and complex infrastructure. As an alternative, this paper proposes an IoT-based [...] Read more.

Material tracking and localization are key applications of Industry 4.0 in manufacturing process control. Traditional approaches—such as barcode or QR code identification and RTLS-based localization using RF/UWB, 5G or GPS–require a large and complex infrastructure. As an alternative, this paper proposes an IoT-based solution that combines short-range Bluetooth Low Energy (BLE) communication with LPWAN LoRaWAN networks. Hybrid solutions using LoRaWAN and BLE technologies already exist, but pure localization based on BLE tags can lead to ambiguous asset identification in geometrically dense scenarios. Our paper aims to solve this problem with an alternative concept called Associated LoRaWAN Sensors (ALSs). An ALS enables logical grouping and integration of heterogeneous LoRaWAN sensors, providing their own data or directly scanning BLE tags. Sensor data can be combined and supplemented with new information, data, and events, supported by application logic (use case). Although ALS represents a general concept that could be applicable to various use cases (such as warehouse monitoring, object tracking), our paper will focus mainly on material tracking and validation in manufacturing. For this purpose, we designed a specific ALS model that integrates a classic LoRaWAN BLE sensor with an additional LoRaWAN magnetic contact sensor. The magnetic contact switch can provide validation of exact position, in addition to localization by BLE tag. Experimental validation using BLE tags (Trax 10229) and LoRaWAN sensors (IoTracker3, Milesight WS301) demonstrates the usability of the ALS model in typical industrial scenarios. We also measured RSSI and evaluated the accuracy of tag localization (3 × 25 = 75 tests) for the worst-case scenario: material validation on a machine with a BLE tag distance of ~0.5 m. While the traditional approach showed up to a 20% failure rate, our ALS model avoided the issue of incorrect accuracy. An additional magnetic switch in ALS confirmed that the correct carrier with the associated tag is attached to the machine and eliminated incorrect localization. The results confirm that a hybrid model based on BLE and LoRaWAN scanning can reliably support material localization and validation without the need for dense RTLS infrastructures. Full article

(This article belongs to the Special Issue Innovations in Manufacturing Processes and Systems for Sustainable Practices (2nd Edition))

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18 pages, 9445 KB

Open AccessArticle

Integrated Electrochemical–Electrolytic Conversion of Oilfield-Produced Water into Hydrogen

by Pengjun Fan, Guangping Zha, Chao Zhang, Weikang Han, Fuli Wang, Bin Dong and Wenming Jiang

Processes 2026, 14(1), 173; https://doi.org/10.3390/pr14010173 - 5 Jan 2026

Viewed by 279

Abstract

This study tackles the challenge of treating high-oil (≥90 mg/L) and high-salinity (Cl⁻ ≥ 6900 mg/L) oilfield-produced water for green hydrogen production. An integrated technology combining electrochemical cascade purification (EDCF: electro-demulsification–coagulation–flotation) with alkaline water electrolysis is developed. The EDCF process effectively reduces [...] Read more.

This study tackles the challenge of treating high-oil (≥90 mg/L) and high-salinity (Cl⁻ ≥ 6900 mg/L) oilfield-produced water for green hydrogen production. An integrated technology combining electrochemical cascade purification (EDCF: electro-demulsification–coagulation–flotation) with alkaline water electrolysis is developed. The EDCF process effectively reduces oil, suspended solids, and turbidity to <10 mg/L, <20 mg/L, and <20 NTU, respectively, meeting stringent feedwater criteria for electrolysis. An asymmetric electrolysis strategy employing a nickel felt anode/Raney nickel cathode system achieves a low cell voltage of 1.856 V at 1 A/cm² in 6 M KOH at 85 °C, with 96.58% H₂ purity. Crucially, separate anolyte/catholyte (0.5/6 M KOH) mitigates Cl⁻ corrosion, enabling stable 240 h operation (96.66% ± 0.5% H₂ purity) in a duplex steel electrolyzer. The work establishes comprehensive boundary conditions for scalable hydrogen production from treated produced water. Full article

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

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20 pages, 2180 KB

Open AccessArticle

Distributed Robust Optimization Scheduling for Integrated Energy Systems Based on Data-Driven and Green Certificate-Carbon Trading Mechanisms

by Yinghui Chen, Weiqing Wang, Xiaozhu Li, Sizhe Yan and Ming Zhou

Processes 2026, 14(1), 174; https://doi.org/10.3390/pr14010174 - 4 Jan 2026

Viewed by 433

Abstract

High renewable energy penetration in Integrated Energy Systems (IES) introduces significant challenges related to bilateral source-load uncertainty and low-carbon economic dispatch. To address these issues, this paper proposes a novel scheduling framework that synergizes data-driven scenario generation with multi-objective distributionally robust optimization (DRO). [...] Read more.

High renewable energy penetration in Integrated Energy Systems (IES) introduces significant challenges related to bilateral source-load uncertainty and low-carbon economic dispatch. To address these issues, this paper proposes a novel scheduling framework that synergizes data-driven scenario generation with multi-objective distributionally robust optimization (DRO). Specifically, a deep temporal feature extraction model based on Long Short-Term Memory Autoencoder (LSTM-AE) is integrated with K-Means clustering to generate four typical operation scenarios, effectively capturing complex source-load fluctuations. To further enhance system efficiency and environmental sustainability, a refined Power-to-Gas (P2G) model considering waste heat recovery is developed to realize energy cascading, coupled with a joint market mechanism that integrates Green Certificate Trading (GCT) and tiered carbon pricing. Building on this, a multi-objective DRO model based on Conditional Value at Risk (CVaR) is formulated to optimize the trade-off between operating costs and carbon emissions. Case studies based on California test data demonstrate that the proposed method reduces total operating costs by 9.0% and carbon emissions by 139.9 tons compared to traditional robust optimization (RO). Moreover, the results confirm that the system maintains operational safety even under extreme source-load fluctuation scenarios. Full article

(This article belongs to the Section Energy Systems)

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16 pages, 1049 KB

Open AccessArticle

Modeling the Influence of Ionic Strength on Mineral Solubility in Concentrated Brine Solutions

by H. Al-Sairfi, M. A. Salman, Y. Al-Foudari and M. Ahmed

Processes 2026, 14(1), 172; https://doi.org/10.3390/pr14010172 - 4 Jan 2026

Viewed by 338

Abstract

Mineral extraction from brine solutions is a vital issue for resource recovery in many fields of industry, especially in desalination processes. Usually, the solubility limit is viewed as a key factor that plays a determinant role in the efficiency of a prescribed process. [...] Read more.

Mineral extraction from brine solutions is a vital issue for resource recovery in many fields of industry, especially in desalination processes. Usually, the solubility limit is viewed as a key factor that plays a determinant role in the efficiency of a prescribed process. This paper suggests the investigation of the influence of ionic strength, which is a measure of the total concentration of all dissolved ions, on the solubility limits in brines that are extracted from desalination facilities in Kuwait before discharging them into the Persian Gulf. For this purpose, the solubility of two main minerals (CaSO₄ and Mg(OH)₂) was measured for several values of ionic strength achieved by adjusting the concentration of the brine solutions. Brine samples were characterized and concentrated to achieve ionic strength values that are in the range of 1.1–2.0 mol/L. An adapted supersaturation-equilibration method was applied to determine solubility limits. Results show a non-linear relationship between ionic strength and the solubility limit of the target minerals, with behavior similar to that which could be found in the literature. In the case of CaSO₄, it was found that the solubility exhibits an increase (salting in effect) at low ionic strength, followed by a decrease at higher ionic strength (>1.1 M) (salting-out effect). On the other hand, the solubility of Mg(OH)₂ in Kuwait brine water was shown to decrease as the ionic strength increased. These trends, validated against literature data, are attributed to non-ideal solution behavior and specific ion interactions in the complex brine matrix. The findings of this work provide crucial insights for process design, enabling more precise control over precipitation steps and enhancing the overall yield and economic viability of mineral extraction from complex brine resources. Full article

(This article belongs to the Special Issue Modeling in Mineral and Coal Processing)

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14 pages, 1285 KB

Open AccessArticle

A New Absorption Configuration of Partial Lean Solution Vaporization–Compression for CO₂ Capture

by Dongfang Guo, Zhisheng He, Huanjun Wang, Yang Liu, Ye Li and Jian Chen

Processes 2026, 14(1), 171; https://doi.org/10.3390/pr14010171 - 4 Jan 2026

Viewed by 331

Abstract

The CO₂ capture process in coal-fired power plant flue gas still faces the difficulties of low material performance and high energy and cost consumption. It is necessary to develop new capture solvents and materials, and also new capture process configurations, to achieve [...] Read more.

The CO₂ capture process in coal-fired power plant flue gas still faces the difficulties of low material performance and high energy and cost consumption. It is necessary to develop new capture solvents and materials, and also new capture process configurations, to achieve breakthroughs in capture performance and process technology. In various process configurations for CO₂ absorption, lean solution vaporization and compression (LVC) is a commonly used and effective one for reducing the energy and cost consumption. This work propose a partial lean solution vaporization and compression (PLVC) configuration to decrease energy and cost consumption for CO₂ capture, considering the price difference in heat and electricity with the high prices of compressors. The three heat exchange methods of no heat exchange, separate heat exchange, and merged heat exchange for lean solution after flash evaporation are also proposed with PLVC, which could be used in the range of low (0–25%), middle (25–75%), and high split ratios (75–100%) of lean solution for the lowest total heat consumption of the aqueous AMP + PZ solvent. Therefore, the comprehensive cost of the capture process can be minimized by considering different prices of steam heat, electricity, and compression facility. Full article

(This article belongs to the Section Separation Processes)

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21 pages, 367 KB

Open AccessReview

Review of CO₂ Corrosion Modeling for Carbon Capture, Utilization and Storage (CCUS) Infrastructure

by Kenneth René Simonsen, Mohammad Ostadi, Maciej Zychowski, Simon Pedersen and Mads Valentin Bram

Processes 2026, 14(1), 170; https://doi.org/10.3390/pr14010170 - 4 Jan 2026

Viewed by 589

Abstract

CO₂ corrosion remains a critical challenge for the safe and reliable operation of Carbon Capture, Utilization, and Storage (CCUS) infrastructure. This review summarizes CO₂ corrosion implications from material selection, exposure time, CO₂ phase behavior, flow conditions, and impurities such as [...] Read more.

CO₂ corrosion remains a critical challenge for the safe and reliable operation of Carbon Capture, Utilization, and Storage (CCUS) infrastructure. This review summarizes CO₂ corrosion implications from material selection, exposure time, CO₂ phase behavior, flow conditions, and impurities such as H₂O, O₂, SO_x, NO_x, and H₂S. CO₂ corrosion modeling has, since early works by de Waard in 1975, expanded to a wide range of models and software tools, many of which have already been reviewed and compared. This work provides a historical timeline and a comparative summary of models and software tools to assist in selecting models for CCUS applications. Modeling approaches are classified into empirical, semi-empirical, and mechanistic categories, with their assumptions, strengths, and limitations. CO₂ corrosion modeling has persistent challenges relating to data quality, data quantity, and parameter interactions, which reduce model accuracy, especially for machine learning approaches. The provided perspective emphasizes that machine learning and hybrid modeling approaches for CO₂ corrosion prediction are gaining popularity, and their effectiveness is currently limited by the quality and quantity of available corrosion data. The provided opportunities include recommendations for standardized experimental procedures and hybrid modeling strategies that combine physics-based insights from mechanistic modeling approaches with data-driven machine learning approaches. Full article

(This article belongs to the Section Energy Systems)

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21 pages, 4180 KB

Open AccessArticle

Mine Exogenous Fire Detection Algorithm Based on Improved YOLOv9

by Xinhui Zhan, Rui Yao, Yun Qi, Chenhao Bai, Qiuyang Li and Qingjie Qi

Processes 2026, 14(1), 169; https://doi.org/10.3390/pr14010169 - 4 Jan 2026

Viewed by 308

Abstract

Exogenous fires in underground coal mines are characterized by low illumination, smoke occlusion, heavy dust loading and pseudo fire sources, which jointly degrade image quality and cause missed and false alarms in visual detection. To achieve accurate and real-time early warning under such [...] Read more.

Exogenous fires in underground coal mines are characterized by low illumination, smoke occlusion, heavy dust loading and pseudo fire sources, which jointly degrade image quality and cause missed and false alarms in visual detection. To achieve accurate and real-time early warning under such conditions, this paper proposes a mine exogenous fire detection algorithm based on an improved YOLOv9m, termed PPL-YOLO-F-C. First, a lightweight PP-LCNet backbone is embedded into YOLOv9m to reduce the number of parameters and GFLOPs while maintaining multi-scale feature representation suitable for deployment on resource-constrained edge devices. Second, a Fully Connected Attention (FCAttention) module is introduced to perform fine-grained frequency–channel calibration, enhancing discriminative flame and smoke features and suppressing low-frequency background clutter and non-flame textures. Third, the original upsampling operators in the neck are replaced by the CARAFE content-aware dynamic upsampler to recover blurred flame contours and tenuous smoke edges and to strengthen small-object perception. In addition, an MPDIoU-based bounding-box regression loss is adopted to improve geometric sensitivity and localization accuracy for small fire spots. Experiments on a self-constructed mine fire image dataset comprising 3000 samples show that the proposed PPL-YOLO-F-C model achieves a precision of 97.36%, a recall of 84.91%, mAP@50 of 96.49% and mAP@50:95 of 76.6%, outperforming Faster R-CNN, YOLOv5m, YOLOv7 and YOLOv8m while using fewer parameters and lower computational cost. The results demonstrate that the proposed algorithm provides a robust and efficient solution for real-time exogenous fire detection and edge deployment in complex underground mine environments. Full article

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

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26 pages, 1529 KB

Open AccessFeature PaperArticle

Sustainable Valorization of Tsipouro Liquid Waste via Fermentation for Hericium erinaceus Biomass Production

by Eirini Stini, Ilias Diamantis, Stamatina Kallithraka, Seraphim Papanikolaou and Panagiota Diamantopoulou

Processes 2026, 14(1), 168; https://doi.org/10.3390/pr14010168 - 4 Jan 2026

Viewed by 360

Abstract

This study investigates the potential of tsipouro liquid waste (TLW) as a sustainable substrate for cultivating the edible–medicinal mushroom Hericium erinaceus under static liquid fermentation. TLW naturally contains high glycerol levels and significant quantities of phenolic compounds; therefore, five media (0–50% v/ [...] Read more.

This study investigates the potential of tsipouro liquid waste (TLW) as a sustainable substrate for cultivating the edible–medicinal mushroom Hericium erinaceus under static liquid fermentation. TLW naturally contains high glycerol levels and significant quantities of phenolic compounds; therefore, five media (0–50% v/v TLW) with varying phenolic concentrations and a standard initial glycerol level (~20 g/L) were prepared to simulate TLW-type substrates. Throughout fermentation, physicochemical parameters in the culture medium (pH, electrical conductivity, total sugars, free amino nitrogen, proteins, laccase activity, total phenolics, ethanol, glycerol) and biomass composition (intracellular polysaccharides, proteins, lipids, phenolic compounds, flavonoids, triterpenoids, antioxidant activity) were determined. Results showed that increasing TLW concentration enhanced biomass production and bioactive metabolite accumulation. The highest dry biomass (22.8 g/L) and protein (4.06 g/L) content were obtained in 50% v/v TLW, while maximum polysaccharides (6.8 g/L) occurred in 17% v/v TLW. Fungal growth led to a reduction of up to 74% in total phenolic content, indicating simultaneous bioremediation potential. Fruiting body formation—rare and uncommon in liquid cultures—occurred at the end of fermentation period. Fruiting bodies contained higher protein (24.5% w/w) and total phenolic compounds (13.36 mg GAE/g), whereas mycelium accumulated more polysaccharides (49% w/w). This study demonstrates that TLW can serve as a cost-effective, ecofriendly medium for producing high-value H. erinaceus biomass and bioactive metabolites, supporting circular bioeconomy applications in the alcoholic beverage sector. Full article

(This article belongs to the Special Issue Resource Utilization of Food Industry Byproducts)

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Graphical abstract

36 pages, 14020 KB

Open AccessFeature PaperArticle

Improved Two-Stage Theta* Algorithm for Path Planning with Uncertain Obstacles in Unstructured Rescuing Environments

by Jingrui Zhang, Mengxin Zhou, Houde Liu, Xiaojun Zhu, Bin Lan and Zhenhong Xu

Processes 2026, 14(1), 167; https://doi.org/10.3390/pr14010167 - 4 Jan 2026

Viewed by 424

Abstract

Path planning aims to find a safe and efficient path from a starting point to an end point, and it has been well developed in fields such as robot navigation, autonomous driving, and intelligent decision systems. However, traditional path planning faces challenges in [...] Read more.

Path planning aims to find a safe and efficient path from a starting point to an end point, and it has been well developed in fields such as robot navigation, autonomous driving, and intelligent decision systems. However, traditional path planning faces challenges in an uncertain rescuing environment due to limited sensing range and a lack of accurate obstacle information. In order to address this issue, this paper proposes an improved two-stage Theta* algorithm for handling multi-probability obstacle scenarios in unstructured rescue environments. First, a global probability raster map is constructed by integrating historical maps and expert prediction maps with probability weights quantifying the uncertainty in the spatial and temporal distribution of obstacles. Second, a probability-sensitive heuristic function (PSHF) is designed, and a Sigmoid function is used to map the probability field of obstacles, thereby enabling limited penetration in low-risk areas and enforced avoidance in high-risk areas. Furthermore, a multi-stage line-of-sight detection optimization mechanism is proposed, which combines probability soft threshold penetration and backtracking verification to improve the noise robustness. Finally, a hierarchical planning architecture is constructed to separate global probabilistic guidance from local strict obstacle avoidance, ensuring both the global optimality and local adaptability of the path. Extensive simulation results in mine rescue scenarios demonstrate that the proposed method achieves lower path cost and fewer path nodes compared to traditional A*, Dijkstra, and Theta* algorithms, while significantly reducing local replanning overhead and maintaining stable performance across multiple uncertain environments. Full article

(This article belongs to the Special Issue Intelligent Robotics: Autonomous Control, Perception, and Collaboration)

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20 pages, 1636 KB

Open AccessArticle

Integrated Extraction of Carotenoids, Pectin, and Insoluble-Bound Ferulic Acid from Banana Peel

by Larissa de Sousa da Silva, Elivaldo Nunes Modesto Junior, Henrique Silvano Arruda and Gustavo Araujo Pereira

Processes 2026, 14(1), 166; https://doi.org/10.3390/pr14010166 - 4 Jan 2026

Viewed by 518

Abstract

Banana peel, an abundant by-product rich in bioactive compounds, presents high functional and technological potential. Despite its potential, the industrial use of banana peel is limited by enzymatic browning. Thus, this study proposed an integrated sequential extraction process using Generally Recognized As Safe [...] Read more.

Banana peel, an abundant by-product rich in bioactive compounds, presents high functional and technological potential. Despite its potential, the industrial use of banana peel is limited by enzymatic browning. Thus, this study proposed an integrated sequential extraction process using Generally Recognized As Safe (GRAS) solvents and simple methodologies. With this approach, it was possible to recover high-value compounds, including (all-E)-lutein (338.05 µg/g DW), pectin (3.81 g/100 g DW), and ferulic acid (212.48 µg/g DW). In addition to maximizing recovery of bioactive compounds, the process preserved the residual lignocellulosic fraction, namely cellulose (23.14 g/100 g DW), hemicellulose (19.91 g/100 g DW), and lignin (29.63 g/100 g DW), suitable for further bioprocesses such as bioethanol production. The strategy demonstrated technological and economic feasibility, reducing operational steps, eliminating the use of chemical agents, and promoting full biomass utilization. The results confirm the potential of banana peel as a platform for obtaining natural and sustainable ingredients, aligned with the principles of biorefinery and the circular bioeconomy. 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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20 pages, 4153 KB

Open AccessArticle

A CFD–DEM Study on Non-Spherical Cutting Transport in Extended-Reach Wells Under Rotary Drilling

by Zhaoyu Pang, Yanhan Liu, Bingxuan Li, Mengmeng Zhou, Yi Wu, Yi Sun and Xianzhi Song

Processes 2026, 14(1), 165; https://doi.org/10.3390/pr14010165 - 4 Jan 2026

Viewed by 263

Abstract

To investigate the accumulation and transport behavior of non-spherical particles during rotary drilling in extended-reach horizontal wells, a CFD–DEM numerical simulation study was carried out based on actual field drilling parameters. The effects of flow rate, drillpipe rotation speed, drilling fluid viscosity, and [...] Read more.

To investigate the accumulation and transport behavior of non-spherical particles during rotary drilling in extended-reach horizontal wells, a CFD–DEM numerical simulation study was carried out based on actual field drilling parameters. The effects of flow rate, drillpipe rotation speed, drilling fluid viscosity, and particle shape on cutting transport were systematically analyzed in terms of spatial distribution of particle concentration, microscopic movement velocity of particles, and annular pressure drop. A dimensionless pressure-drop–flow-pattern chart was then constructed to characterize the coupled flow–particle transport behavior. The results indicate that flow rate, rotation speed, viscosity, and cutting shape all markedly affect the transition from a stationary cutting bed to suspended transport. Increasing the flow rate, rotation speed, and viscosity promotes hole cleaning. However, once these parameters exceed a certain threshold, further improvements in cutting removal are accompanied by a sharp increase in annular pressure drop. The final Π–DPD dimensionless chart was developed, which can be used for rotary drilling parameter optimization in extended-reach wells, and Π ≈ (2.5–3.1) × 10⁴ is recommended as the preferred range. Full article

(This article belongs to the Special Issue Advances in Drilling and Excavation Theories and Technologies for Petroleum, Coal Seam and Sustainable Resources)

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23 pages, 6944 KB

Open AccessArticle

Machine Learning and Queuing Algorithm Integration for Real-Time Citrus Size Classification on an Industrial Sorting Machine

by Yahir Hernández-Mier, Marco Aurelio Nuño-Maganda, Said Polanco-Martagón, Ángel Dagoberto Cantú-Castro, Rubén Posada-Gómez and José Hugo Barrón-Zambrano

Processes 2026, 14(1), 164; https://doi.org/10.3390/pr14010164 - 4 Jan 2026

Viewed by 375

Abstract

The classification of lemons by size is a crucial industrial process that ensures specific quality standards. Lemon sorting is typically performed by hand or often using expensive, outdated machines. In this article, we develop Machine Learning and Queuing algorithms, program them on low-cost [...] Read more.

The classification of lemons by size is a crucial industrial process that ensures specific quality standards. Lemon sorting is typically performed by hand or often using expensive, outdated machines. In this article, we develop Machine Learning and Queuing algorithms, program them on low-cost hardware—specifically, a microcontroller and a single-board computer—and integrate them with an existing fruit-sorting machine, which classifies lemons by size. We acquired a dataset of 3127 lemon images in six industry-standardized sizes. We developed algorithms to extract geometric features, including one based on the peduncle location, which is estimated using a pre-trained Faster Objects, More Objects (FOMO) model. We used these features to train and evaluate five machine learning models, with the best-performing model achieving 87.22% accuracy over a set of lemons acquired under controlled conditions. We tested the proposed system in a real industrial environment, proving its feasibility by sorting 1558 lemons and obtaining an accuracy of 78.00%, despite the industrial-standard sizes having considerable overlap. Full article

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

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18 pages, 11731 KB

Open AccessArticle

Ignition and Emission Study of an Ammonia–Coal Co-Firing Flame in a Lab-Scale Dual-Swirl Burner

by Yichong Lou, Ghulam Mohi Ud Din, Zuochao Yu, Yong He, Shixing Wang, Wubin Weng and Zhihua Wang

Processes 2026, 14(1), 163; https://doi.org/10.3390/pr14010163 - 3 Jan 2026

Viewed by 477

Abstract

Ammonia–coal co-firing is emerging as a promising technological pathway to reduce carbon production during coal-fired power generation. However, the coupling effects of the ammonia energy ratio (E_NH3) and equivalence ratio on the ignition mechanism and emission characteristics—particularly under staged injection conditions—remain [...] Read more.

Ammonia–coal co-firing is emerging as a promising technological pathway to reduce carbon production during coal-fired power generation. However, the coupling effects of the ammonia energy ratio (E_NH3) and equivalence ratio on the ignition mechanism and emission characteristics—particularly under staged injection conditions—remain insufficiently understood. This study investigates these characteristics in a laboratory-scale furnace. Spontaneous chemiluminescence imaging and flue gas analysis were employed to decouple the effects of aerodynamic interactions and chemical kinetics. The experimental results reveal that the ammonia injection strategy is the critical factor governing coal ignition performance. Compared to the premixed mode, staged injection—which establishes an independent, high-temperature ammonia flame zone—provides a superior thermal environment and circumvents oxygen competition between the fuels, thereby markedly promoting coal ignition. At an E_NH3 of 50%, the staged configuration reduces the ignition delay time of coal volatiles by a striking 60.93%. Within the staged configuration, increasing either the co-firing ratio or the overall equivalence ratio further enhances coal ignition. Analysis of pollutant emissions elucidates that the formation of NO, N₂O, and NH₃ is intimately linked to the local combustion conditions of ammonia. An excessively lean local equivalence ratio leads to incomplete ammonia combustion, thereby increasing N₂O and NH₃ slip. Full article

(This article belongs to the Special Issue Advanced Combustion and Energy Conversion Technologies for Next-Generation Powertrains)

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18 pages, 557 KB

Open AccessArticle

A Sustainable Aluminium-Based Electro-Fenton Process for Pharmaceutical Wastewater Treatment: Optimization, Kinetics, and Cost–Benefit Analysis

by Yousra Bouhoufani, Nabila Bensacia, Ahmed Kettab, Lotfi Mouni, Rim Riahi and Hakim Lounici

Processes 2026, 14(1), 162; https://doi.org/10.3390/pr14010162 - 3 Jan 2026

Viewed by 492

Abstract

Pharmaceutical contamination poses growing environmental risks, yet industrial adoption of advanced oxidation processes (AOPs) remains limited by high costs and the environmental impacts associated with specialized electrodes. This study demonstrates that unmodified aluminum electrodes achieve pharmaceutical degradation performance comparable to precious metal systems [...] Read more.

Pharmaceutical contamination poses growing environmental risks, yet industrial adoption of advanced oxidation processes (AOPs) remains limited by high costs and the environmental impacts associated with specialized electrodes. This study demonstrates that unmodified aluminum electrodes achieve pharmaceutical degradation performance comparable to precious metal systems at dramatically reduced cost and carbon footprint. An aluminum-based electro-Fenton (EF) system was optimized for amlodipine (AML) removal through systematic evaluation of operational parameters. Under optimized conditions (pH 2.7, 35 mg L⁻¹ FeCl₃, 1.3 mM NaCl, 5 V), the system achieved 97% AML degradation within 15 min, following pseudo-first-order kinetics (

k = 0.15

min⁻¹). The mechanism combines hydroxyl radical oxidation with synergistic electrocoagulation resulting from anodic Al³⁺ release and cathodic Fe²⁺ regeneration. Sustainability assessment revealed exceptional performance: an energy consumption of 0.32 kWh m⁻³, a carbon footprint of 0.53 kg CO₂-eq m⁻³ (60–75% lower than conventional AOPs), and operational costs of $0.71–1.05 m⁻³. Aluminum electrodes cost 100× less than platinum alternatives, with the generated Al(OH)₃ sludge offering valorization potential. This work demonstrates that high-performance electrochemical remediation is achievable using Earth-abundant materials, providing a scalable and cost-effective alternative for pharmaceutical wastewater treatment in resource-constrained settings. Full article

(This article belongs to the Special Issue Advanced Oxidation Processes for Waste Treatment)

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9 pages, 702 KB

Open AccessCommunication

Efficient Method for the Purification of Recombinant Amaranth 11S Globulins with Angiotensin-Converting Enzyme Inhibitory Activity

by Andrea L. Cortés-Noriega, Flor de Fátima Rosas-Cárdenas and Silvia Luna-Suárez

Processes 2026, 14(1), 161; https://doi.org/10.3390/pr14010161 - 3 Jan 2026

Viewed by 358

Abstract

Amaranth 11S globulin is a plant protein that is renowned for its high essential amino acid content and nutritional value. It has undergone modification through the insertion of antihypertensive peptides valine-tyrosine (VY), which act as angiotensin-converting enzyme (ACE) inhibitors. The expression of this [...] Read more.

Amaranth 11S globulin is a plant protein that is renowned for its high essential amino acid content and nutritional value. It has undergone modification through the insertion of antihypertensive peptides valine-tyrosine (VY), which act as angiotensin-converting enzyme (ACE) inhibitors. The expression of this protein was carried out in E. coli. Despite the potential of this protein, an efficient purification method is still required to allow its evaluation and subsequent application. This work proposes a procedure that allows for high purification and yield. After obtaining the purified proteins from the inclusion bodies and purifying them in an insoluble form, it was determined that this process did not affect their bioactivity. Full article

(This article belongs to the Special Issue Processes in 2025)

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21 pages, 1453 KB

Open AccessArticle

Intensified Treatment of Pharmaceutical Effluent Using Combined Ultrasound-Based Advanced Oxidation and Biological Oxidation

by Akshara M. Iyer and Parag R. Gogate

Processes 2026, 14(1), 160; https://doi.org/10.3390/pr14010160 - 3 Jan 2026

Viewed by 369

Abstract

The present work investigates the efficacy of ultrasound (US)-based pretreatment methods for the process intensification of biological oxidation (BO) of real pharmaceutical industrial effluent with a high initial COD of 50,000 mgL⁻¹. US, combined with advanced oxidation processes (AOPs), was used [...] Read more.

The present work investigates the efficacy of ultrasound (US)-based pretreatment methods for the process intensification of biological oxidation (BO) of real pharmaceutical industrial effluent with a high initial COD of 50,000 mgL⁻¹. US, combined with advanced oxidation processes (AOPs), was used to degrade recalcitrant compounds. Conventional BO could only reduce the COD by 3.85% and confirmed the requirement of pretreatment. US, under established optimised conditions of 120 W power, 70% duty cycle, pH 6, and 30 °C temperature, gave a COD reduction of 5.77%. Combining US with oxidants like O₃ (2 L/ min), H₂O₂ (1000 mgL⁻¹), Fenton (1:5 Fe²⁺:H₂O₂), and peroxone (2 L min⁻¹ O₃ with 1000 mgL⁻¹ H₂O₂) as pretreatment gave COD reductions of 30.77%, 17.31%, 19.23%, and 42.31%, respectively. Toxicity assays using the agar well diffusion method revealed that the pretreatment techniques reduced the toxicity of the effluent and did not introduce any toxic secondary metabolites into the system. The optimised treatment time for BO was fixed at 30 h, and the COD reduction obtained for the streams pretreated with US, US + O₃, US + H₂O₂, US + Fenton, and US + peroxone were 14.3%, 88.46%, 57.69%, 61.54%, and 94.23%, respectively. The US combined with peroxone method was the best pretreatment for the effluent in terms of overall COD reduction. This work effectively demonstrates the usefulness of US-based methods to intensify the biological oxidation of real industrial effluent with high organic load. Full article

(This article belongs to the Special Issue Processes in 2025)

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21 pages, 3225 KB

Open AccessArticle

Remediation of Heavy Metals (Arsenic, Cadmium, and Lead) from Wastewater Utilizing Cellulose from Pineapple Leaves

by Aminur Rahman

Processes 2026, 14(1), 159; https://doi.org/10.3390/pr14010159 - 2 Jan 2026

Viewed by 629

Abstract

Heavy metals (arsenic, cadmium, and lead) remain one of the most common and complex environmental problems worldwide. Accordingly, there is a growing need for eco-friendly and affordable materials derived from agricultural waste for the removal of heavy metals from contaminated water. This study [...] Read more.

Heavy metals (arsenic, cadmium, and lead) remain one of the most common and complex environmental problems worldwide. Accordingly, there is a growing need for eco-friendly and affordable materials derived from agricultural waste for the removal of heavy metals from contaminated water. This study aims to demonstrate how biodegradable pineapple leaf cellulose (PLC) can be used effectively in the remediation of heavy metals. The PLC adsorbent was prepared by treating it with ethyl alcohol (EtOH, 99.5%), calcium chloride (CaCl₂), and 0.8 M sodium hydroxide. A scanning electron microscope equipped with energy-dispersive X-ray spectroscopy (SEM-EDS) and Fourier transform infrared spectroscopy (FT-IR) was used to investigate the surface of the adsorbent. Inductively coupled plasma mass spectrometry (ICP-MS) was employed to measure the concentration of metals before and after adsorption. Removal of metal ions (As⁵⁺, Cd²⁺, and Pb²⁺) by PLC was investigated under varying conditions, including pH, contact time, and adsorbent dosage. The analysis of cellulose composite revealed significant potential for adsorption of heavy metals such as As⁵⁺, Cd²⁺, and Pb²⁺. The highest removal efficiency of heavy metal ions was detected at a pH ranging from 3 to 7. The biosorption order of PLC at pH 6 was Pb²⁺ > Cd²⁺ > As⁵⁺ with 99.53% (63.45 mg/g), 98.44% (37.23 mg/g), and 42.40% (16.27 mg/g), respectively. After 120 min, the equilibrium of the adsorption process was reached for As⁵⁺, Cd²⁺, and Pb²⁺. FT-IR characterization discovered an increased abundance of functional groups on the adsorbent. The SEM-EDS analysis confirmed the occurrence of elements on the surface of PLC. The study revealed that the use of PLC is an innovative method for removing heavy metals from aquatic milieus, a potential resource for eco-friendly and affordable wastewater treatment. Full article

(This article belongs to the Special Issue Progress in Biosorption, Membrane Separation, and Advanced Oxidation Processes for Sustainable Water and Wastewater Treatment)

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19 pages, 2176 KB

Open AccessArticle

A Mechanical Error Correction Algorithm for Laser Human Body Scanning System

by Yue Wang, Jun Ren, Yuan Xue, Kaixuan Liu, Fei Ma and Maoya Yang

Processes 2026, 14(1), 158; https://doi.org/10.3390/pr14010158 - 2 Jan 2026

Viewed by 469

Abstract

Human body measurement involves large-scale measurement. The acquisition of three-dimensional spatial coordinate data is a complex process. The errors which are generated in each stage of the process can potentially affect the final measurement data. Therefore, the accuracy of measurement remains one of [...] Read more.

Human body measurement involves large-scale measurement. The acquisition of three-dimensional spatial coordinate data is a complex process. The errors which are generated in each stage of the process can potentially affect the final measurement data. Therefore, the accuracy of measurement remains one of the key technical issues that influence the development of the three-dimensional human body scanner. On the basis of analyzing the parameters to be calibrated of the entire measurement system, calibration methods for the parameters of angle a and angle β were established. After analyzing errors of the laser human body scanning system, a mechanical error correction algorithm for the system was established. Then, a mechanical error correction experiment using a standard cylinder was designed, and the overall effect was analyzed. The correctness of the mechanical error correction algorithm was verified, which made the scanner more accurate. To further verify the accuracy and reliability of the measurement result when the system used human bodies as measured objects, a comparative experiment was designed. The results of the comparative experiment demonstrated that the absolute error of the system for 3D measurement of a large-sized human body is less than 2 mm, and the relative error is less than 1%, which can meet the needs of fields such as clothing design and production. Full article

(This article belongs to the Special Issue Revolutionizing Smart Manufacturing: Cutting-Edge Technologies in the Industry's Future)

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29 pages, 11786 KB

Open AccessArticle

Reservoir Identification from Well-Logging Data Using a Focal Loss-Enhanced Convolutional Neural Network: A Case Study from the Chang 8 Formation, Ordos Basin

by Wenbo Li, Dongtao Li, Zhenkai Zhang, Zenglin Hong and Lingyi Liu

Processes 2026, 14(1), 157; https://doi.org/10.3390/pr14010157 - 2 Jan 2026

Viewed by 472

Abstract

Accurate reservoir identification from well-logging data is crucial for hydrocarbon exploration, yet challenges persist due to a series of factors, including limitations such as low efficiency and subjectivity of manual processing for massive datasets, as well as class imbalance and its impact on [...] Read more.

Accurate reservoir identification from well-logging data is crucial for hydrocarbon exploration, yet challenges persist due to a series of factors, including limitations such as low efficiency and subjectivity of manual processing for massive datasets, as well as class imbalance and its impact on machine learning model training. This study develops an intelligent identification model using a Convolutional Neural Network (CNN) enhanced with Focal Loss, applied to real well-logging data from the Chang 8 Member of the Yanchang Formation in the Jiyuan Oilfield, Ordos Basin. A well-based data partitioning strategy is adopted to ensure the model’s generalization ability to new wells, avoiding the overoptimistic performance associated with random sample splitting. Experimental results demonstrate that the proposed model achieves an Accuracy of 84% and a Recall of 83% for oil-bearing layers. In comparison, the Random Forest model achieves a lower Recall of 56% for oil-bearing layers, and the CNN-LSTM model achieves 77%. The key influential well-logging parameters identified are bulk density (DEN), spontaneous potential (SP), true resistivity (RT), and natural gamma ray (GR). The findings confirm that the Focal Loss-enhanced CNN effectively mitigates class imbalance issues and provides a reliable, automated method for reservoir identification, offering significant practical value for the secondary interpretation of well logs in similar tight sandstone reservoirs. Full article

(This article belongs to the Special Issue Advanced Technologies for the Exploration of Conventional and Unconventional Oil and Gas)

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19 pages, 1786 KB

Open AccessArticle

A Machine Learning-Driven Framework for Real-Time Lithology Identification and Drilling Parameter Optimization

by Qingshan Liu, Dengyue Li, Shuo Liu, Hefeng Liang, Yuchen Zhou, Conghui Zhao, Kun Liu, Gang Hui, Feng Ni, Peng Du and Siwen Wang

Processes 2026, 14(1), 156; https://doi.org/10.3390/pr14010156 - 2 Jan 2026

Viewed by 691

Abstract

Conventional drilling parameter optimization, heavily reliant on lagging lithology data from periodic mud logging, suffers from significant delays between formation change detection and parameter adjustment. This latency often leads to reduced Rate of Penetration (ROP), accelerated tool wear, and increased risk of drilling [...] Read more.

Conventional drilling parameter optimization, heavily reliant on lagging lithology data from periodic mud logging, suffers from significant delays between formation change detection and parameter adjustment. This latency often leads to reduced Rate of Penetration (ROP), accelerated tool wear, and increased risk of drilling complications. To address this, this work introduces a closed-loop machine learning framework for real-time lithology identification and autonomous parameter optimization. Its core is a hybrid deep learning model (1D-CNN-LSTM) that establishes a direct mapping from surface drilling parameters, Weight on Bit (WOB), Rotary Speed (RPM), Torque, ROP, to formation lithology, deliberately excluding dependency on expensive Logging-While-Drilling (LWD) tools to ensure cost-effective and broad applicability. Upon lithology change detection, the system retrieves the historically optimal Mechanical Specific Energy (MSE) value for the identified rock type and solves an inverse MSE model to compute optimal WOB and RPM setpoints within operational constraints. Field validation in a comparative trial demonstrated the framework’s efficacy: the test well achieved a 17.4% increase in ROP, a 37.8% reduction in Non-Productive Time, and an 87.5% decrease in stuck pipe incidents compared to an offset well drilled conventionally. Full article

(This article belongs to the Special Issue Advances in Drilling and Excavation Theories and Technologies for Petroleum, Coal Seam and Sustainable Resources)

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19 pages, 667 KB

Open AccessFeature PaperArticle

Replacing Stumbo’s Tables with Simple and Accurate Mathematical Modelling for Food Thermal Process Calculations

by Dario Friso

Processes 2026, 14(1), 155; https://doi.org/10.3390/pr14010155 - 2 Jan 2026

Viewed by 443

Abstract

The practical use of computational thermo-fluid dynamics (CFD) for food thermal process calculations still appears very premature due to both the high costs and the inhomogeneity and anisotropy of foods. Therefore, the traditional formula method with both Ball and Stumbo’s tables is still [...] Read more.

The practical use of computational thermo-fluid dynamics (CFD) for food thermal process calculations still appears very premature due to both the high costs and the inhomogeneity and anisotropy of foods. Therefore, the traditional formula method with both Ball and Stumbo’s tables is still widely used due to its accuracy and safety. In both cases, the calculations require consulting and interpolating data from the respective tables, making the procedure slow and prone to human errors. The computerization of Ball’s tables to speed up and automate the calculations with a new mathematical approach based on the substitution of the integral exponential function and the initial cooling hyperbola has already been developed. The high accuracy obtained, superior to the direct regression of the table data, suggested adopting it also in the computerization of Stumbo’s tables. However, the latter are 14 times larger than those of Ball due to the extension of the thermo-bacteriological parameter z up to over 100 °C and the variability of the cooling lag factor J_cc. Therefore, the mathematical modelling was modified using an additional function, dependent on z and J_cc. The results obtained with the mathematical modelling showed a mean relative error and the standard deviation with respect to the Stumbo’s tables equal to MRE ± SD = 0.62% ± 1.29%. Further validation was obtained by calculating the thermal process time for different lethalities and thermo-bacteriological parameters with MRE ± SD compared to the Stumbo tables equal to 1.04% ± 0.82%. 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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23 pages, 3748 KB

Open AccessArticle

Optimal Design of Off-Grid Wind–Solar–Hydrogen Integrated Energy System Considering Power and Hydrogen Storage: A General Method

by Lihua Lin, Xiaoyong Gao, Xin Zuo, Zhijun Bu, Jian Li and Chaodong Tan

Processes 2026, 14(1), 154; https://doi.org/10.3390/pr14010154 - 2 Jan 2026

Viewed by 509

Abstract

Existing design methodologies for off-grid wind–solar–hydrogen integrated energy systems (WSH-IES) are typically case-specific and lack portability. This study aims to establish a unified design framework to enhance cross-scenario applicability while retaining case-specific adaptability. The proposed framework employs the superstructure concept, dividing the off-grid [...] Read more.

Existing design methodologies for off-grid wind–solar–hydrogen integrated energy systems (WSH-IES) are typically case-specific and lack portability. This study aims to establish a unified design framework to enhance cross-scenario applicability while retaining case-specific adaptability. The proposed framework employs the superstructure concept, dividing the off-grid WSH-IES into three subsystems: energy production, conversion, and storage subsystems. The framework integrates equipment selection and capacity sizing into a unified optimization process described by a mixed-integer programming model. Additionally, the modular constraint template ensures generalizability across scenarios by linking the local resource protocol to the techno-economic parameters of the equipment, allowing the model to be adapted to various situations. The model was applied to two case studies. Economic analysis indicates that the pure electricity architecture is dominated by energy storage (battery costs account for 96.8%), while the hybrid architecture redistributes expenditures between batteries (67.8%) and electrolyzers (28.4%). It utilizes hydrogen as a complementary medium for long-duration energy storage, achieving cost risk diversification and enhanced resilience. Under current techno-economic conditions, real-time bidirectional electricity–hydrogen conversion offers no economic benefits. This framework quantifies cost drivers and design trade-offs for off-grid WSH-IES, providing an open modeling platform for academic research and planning applications. Full article

(This article belongs to the Special Issue Modeling, Operation and Control in Renewable Energy Systems)

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21 pages, 19614 KB

Open AccessArticle

Hydrothermal–Membrane Valorization of Coffee Pulp for Xylooligosaccharide Production

by James Villar, Iris Paola Roncal Huaman, Delicia L. Bazán, Ruly Teran Hilares and Rita de Cássia Lacerda Brambilla Rodrigues

Processes 2026, 14(1), 153; https://doi.org/10.3390/pr14010153 - 2 Jan 2026

Viewed by 623

Abstract

Wet coffee pulp residues (WCPRs) are typically underutilized, and their accumulation increases alongside coffee production, generating significant environmental impacts. This study proposes a sustainable valorization approach through hydrothermal treatment followed by membrane filtration for the production of xylooligosaccharides (XOSs). Extractive-free WCPR contained 35.4% [...] Read more.

Wet coffee pulp residues (WCPRs) are typically underutilized, and their accumulation increases alongside coffee production, generating significant environmental impacts. This study proposes a sustainable valorization approach through hydrothermal treatment followed by membrane filtration for the production of xylooligosaccharides (XOSs). Extractive-free WCPR contained 35.4% structural carbohydrates (20.4% cellulose and 15.0% hemicellulose) and 27.0% lignin. Hydrothermal treatments (180 °C, 3 °C min⁻¹, 15–60 min) were performed with and without citric acid as an organic catalyst. The acid-assisted treatment (T4) enhanced hemicellulose depolymerization and xylose release (16 g·kg⁻¹ dry biomass), whereas milder, non-acidic conditions (T3) promoted the selective formation and recovery of short-chain XOS, reaching cumulative biomass-normalized yields of up to 14 g·kg⁻¹ of xylobiose (X2) and 9 g·kg⁻¹ of xylotriose (X3). Subsequent membrane processing (UF–DF–NF) enabled progressive purification and enrichment of XOS fractions. Diafiltration was identified as the main step governing XOS enrichment, whereas nanofiltration primarily refined separation by directing monomeric sugars to the permeate rather than substantially increasing XOS yields. Additionally, Multiple Factor Analysis (MFA) integrated process and compositional variables, explaining 79.6% of the total variance. Dimension 1 represented process intensity and xylose transport, while Dimension 2 reflected molecular-weight-driven XOS fractionation. The acid-assisted process (T4) exhibited a distinct multivariate signature, characterized by enhanced carbohydrate mobilization and improved XOS recovery with reduced dependence on dilution. Overall, coupling hydrothermal pretreatment with membrane fractionation proved to be an efficient, and environmentally friendly strategy for coffee by-product valorization, consistent with hemicellulose-first biorefinery models and the principles of the circular bioeconomy. Full article

(This article belongs to the Special Issue Advances in Green Extraction and Separation Processes)

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32 pages, 13923 KB

Open AccessArticle

Design of a Hermetic Centrifugal Pump Impeller Using RSM and Evolutionary Algorithms with Application of SLS Technology

by Viorel Bostan, Andrei Petco, Dmitrii Croitor, Nadejda Proca and Vadim Zubac

Processes 2026, 14(1), 152; https://doi.org/10.3390/pr14010152 - 1 Jan 2026

Viewed by 531

Abstract

This study presents the development and validation of a comprehensive numerical optimisation methodology used to improve the energy efficiency of a pump with normal characteristics: volume flow rate, Q nom = 6.3 m³/h, and head, H = 20 mH₂O. [...] Read more.

This study presents the development and validation of a comprehensive numerical optimisation methodology used to improve the energy efficiency of a pump with normal characteristics: volume flow rate, Q nom = 6.3 m³/h, and head, H = 20 mH₂O. The methodology was implemented in ANSYS Workbench using ANSYS CFX and optiSLang. The optimisation process is based on data from 853 RANS (SST) calculations on a sample generated by the LHC method, varying the parameters of the blades and flow path. Response surfaces (RSM) were constructed using anisotropic and classical kriging, which were optimised using an Evolutionary Algorithm (EA). The optimised geometry was verified numerically by URANS SST and experimentally. For physical validation, the wheel was manufactured using SLS technology from PA-12 Industrial powder, a strength assessment FSI was performed, and the geometry was checked by 3D scanning. 3D scanning showed a high manufacturing accuracy (deviations of 0.1–0.3 mm). The result is a geometry that increases efficiency while maintaining head, which has been confirmed by experimental validation. Full article

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

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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 - 1 Jan 2026

Viewed by 308

Abstract

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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25 pages, 5165 KB

Open AccessArticle

Impact of Sensor Network Resolution on Methane Leak Characterization in Large Indoor Spaces for Green-Fuel Vessel Applications

by Wook Kwon, Dahye Choi, Soungwoo Park and Jinkyu Kim

Processes 2026, 14(1), 150; https://doi.org/10.3390/pr14010150 - 1 Jan 2026

Viewed by 477

Abstract

A quantitative understanding of methane leakage has become essential for safety design as eco-friendly fuel systems expand in modern ship applications. To address this need, controlled methane-release experiments were conducted in a large indoor chamber (30 × 16 × 20 m) to evaluate [...] Read more.

A quantitative understanding of methane leakage has become essential for safety design as eco-friendly fuel systems expand in modern ship applications. To address this need, controlled methane-release experiments were conducted in a large indoor chamber (30 × 16 × 20 m) to evaluate how sensor-network resolution (1 m vs. 0.5 m spacing) influences dispersion measurement and 5% Lower Explosive Limit (LEL)-based risk assessment. Initial tests with a 1 m grid showed that most sensors detected only low concentrations except for near the release nozzle, demonstrating that coarse spatial resolution cannot capture the primary dispersion pathway or transient peaks. This limitation motivated the use of a 0.5 m high-density sensor network, which enabled clear identification of the dispersion centerline, concentration-gradient development, early detection behavior, and the evolution of diluted regions, particularly under buoyancy-driven plume rise. Experimental results were compared with CFD simulations using the RNG k–ε and k–ω GEKO turbulence models. Strong agreement was obtained in peak concentration, concentration-rise rates during the accumulation phase, and LEL-based dispersion distances. These findings confirm the suitability of the selected turbulence models for predicting methane behavior in large enclosed spaces and highlight the sensitivity of model–experiment agreement to measurement resolution. The results provide an experimentally grounded reference for sensor layout design and verification of gas-detection strategies in ship compartments, fuel-gas preparation rooms, and modular supply units. Overall, the study establishes a methodological framework that integrates high-resolution experiments with CFD modeling to support safer design and operation of methane-fueled vessels. Full article

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

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