Processes

36 pages, 6431 KB

Open AccessArticle

Synthesis of Poly(lactide)/Poly(ε-caprolactone) Systems Functionalized with Titanium Dioxide–Silicon Dioxide for Photocatalytic Applications

by Gamaliel Alvarado-Molina, Pamela Nair Silva-Holguin, Nahum A. Medellín-Castillo, Manuel Sánchez Polo, Ericka Berenice Herrera-Ríos, Claudia Alejandra Hernández-Escobar, Mónica Elvira Mendoza-Duarte, Armando Erasto Zaragoza-Contreras and Simón Yobanny Reyes-López

Processes 2026, 14(8), 1324; https://doi.org/10.3390/pr14081324 - 21 Apr 2026

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Abstract

Biodegradable poly(lactide)/poly(ε-caprolactone) (PLA/PCL) systems functionalized with TiO₂-SiO₂ were synthesized via in situ ring-opening polymerization of a eutectic L-lactide/ε-caprolactone system. This work introduces a TiO₂-SiO₂ composite with a dual function, acting as a catalytic initiator that governs polymerization [...] Read more.

Biodegradable poly(lactide)/poly(ε-caprolactone) (PLA/PCL) systems functionalized with TiO₂-SiO₂ were synthesized via in situ ring-opening polymerization of a eutectic L-lactide/ε-caprolactone system. This work introduces a TiO₂-SiO₂ composite with a dual function, acting as a catalytic initiator that governs polymerization and microstructure, while simultaneously serving as a reinforcing and photocatalytic phase. The system exhibits high polymerization efficiency, reaching conversions up to 99% with low filler loadings (0.1–1.0 wt%). Structural analyses confirm polymer formation and reveal modifications in ester groups associated with coordination-driven mechanisms. Notably, the presence of TiO₂-SiO₂ promotes increased PLA tacticity, directly influencing mechanical performance. The resulting materials show enhanced tensile strength (~250,000 Pa) and Young’s modulus (1.5–2.0 MPa) compared to conventional systems. In addition, excellent photocatalytic activity was achieved, with up to 99.7% degradation of methyl orange. These findings demonstrate a synergistic strategy to simultaneously control polymer structure and functionality, positioning PLA/PCL–TiO₂-SiO₂ systems as promising multifunctional materials for environmental applications. Full article

(This article belongs to the Section Catalysis Enhanced Processes)

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19 pages, 3783 KB

Open AccessArticle

Coupled Thermo–Hydro–Mechanical Analysis of Leak-off-Induced Fracture Width Evolution and Lost Circulation in Depleted Reservoirs

by Zengwei Chen, Yanbin Zang, Yi Wang, Yan Zhang, Mengjiang Wang, Shusen Wang, Lianke Cui and Chunbo Zhu

Processes 2026, 14(8), 1323; https://doi.org/10.3390/pr14081323 - 21 Apr 2026

Viewed by 252

Abstract

This study develops a fully coupled thermo–hydro–mechanical (THM) finite-element model to investigate fracture-induced fluid loss in depleted formations. To address the issue of assuming a homogeneous, unfractured medium, this approach incorporates the effects of pre-existing or induced fractures. By integrating thermoelastic stresses, fluid [...] Read more.

This study develops a fully coupled thermo–hydro–mechanical (THM) finite-element model to investigate fracture-induced fluid loss in depleted formations. To address the issue of assuming a homogeneous, unfractured medium, this approach incorporates the effects of pre-existing or induced fractures. By integrating thermoelastic stresses, fluid flow, and transient heat transfer, the model provides a more accurate simulation of coupled interactions, enabling a deeper understanding of stress evolution and fracture aperture behavior under temperature variations. The results show that pressure depletion reduces horizontal principal stresses in an approximately linear manner, with the minimum horizontal stress being more sensitive. The influence of wellbore pressure is concentrated in the near-wellbore region (r/rw < 2), where it increases circumferential stress at low azimuths and exhibits an almost linear positive correlation with fracture aperture. Fracture length has a negligible effect on pore-pressure variations (≤0.19 MPa) but increases circumferential stress at high azimuths and enlarges the aperture near the wellbore. Temperature effects, through thermoelastic stresses, dominate local stress redistribution, with the 90° azimuth showing the strongest sensitivity. Higher injection temperatures increase circumferential and radial stresses while decreasing near-wellbore aperture, whereas lower temperatures produce the opposite response. Although temperature differences cause only minor changes in pore pressure and far-field stresses, they exert first-order control on near-wellbore width evolution and the likelihood of lost circulation. These findings indicate that coordinated optimization of wellbore pressure, fracture dimensions, and injection temperature under depletion conditions is important for mitigating fracture-induced fluid loss and improving drilling safety and efficiency. Full article

(This article belongs to the Special Issue Hydraulic Fracturing Experiment, Simulation, and Optimization)

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25 pages, 7674 KB

Open AccessArticle

Numerical Simulation of Gas–Liquid Gravity Displacement in Vertical Fractures Under Downhole High-Temperature and High-Pressure Conditions

by Shiwei Xie, Gao Li, Bin Jia, Yang Zheng, Xiaobo Shu, Mubai Duan and Hongtao Li

Processes 2026, 14(8), 1322; https://doi.org/10.3390/pr14081322 - 21 Apr 2026

Viewed by 278

Abstract

Gas–liquid gravity displacement poses a significant risk to drilling safety. However, the underlying mechanisms governing this process under downhole high-temperature and high-pressure (HTHP) conditions in deep and ultra-deep wells remain poorly understood. In this study, a numerical simulation method based on the Volume [...] Read more.

Gas–liquid gravity displacement poses a significant risk to drilling safety. However, the underlying mechanisms governing this process under downhole high-temperature and high-pressure (HTHP) conditions in deep and ultra-deep wells remain poorly understood. In this study, a numerical simulation method based on the Volume of Fluid (VOF) model was developed to investigate gas–liquid gravity displacement behavior under downhole HTHP conditions. The model was validated against 200 data points from visual laboratory experiments, showing excellent agreement with a relative error below 8.58%. Using this validated model, we then conducted 330 numerical simulations to systematically investigate the characteristics of gravity displacement under downhole HTHP conditions. Compared with surface low-pressure conditions, gravity displacement under downhole HTHP is markedly different, characterized by a narrower displacement window, lower gas influx (e.g., 99.5% reduction at −1500 Pa vs. surface conditions) and loss rates, and a smoother gas–liquid interface. As fracture width decreases, both gas influx and drilling fluid loss rates decline nonlinearly, and the displacement window contracts significantly. A critical fracture width for the onset of gravity displacement was identified, ranging from 0.3 to 0.5 mm depending on downhole conditions such as equivalent depth, drilling fluid density, and viscosity. Furthermore, increasing drilling fluid density expands the displacement window and increases the drilling fluid loss rate, whereas higher viscosity reduces both gas influx and drilling fluid loss rates. In contrast, fracture roughness exhibits minimal influence on gravity displacement. These findings provide practical criteria for optimizing well control strategies, thereby reducing drilling risks and improving operational safety. These findings advance the fundamental understanding of gravity displacement and contribute to a theoretical basis for improving drilling safety in deep fractured gas reservoirs. Full article

(This article belongs to the Special Issue Advancements in Oil Reservoir Simulation and Multiphase Flow)

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23 pages, 2472 KB

Open AccessReview

Biomass Pyrolysis: Recent Advances in Characterisation and Energy Utilisation

by Hamid Reza Nasriani and Maryam Nasiri Ghiri

Processes 2026, 14(8), 1321; https://doi.org/10.3390/pr14081321 - 21 Apr 2026

Viewed by 545

Abstract

Biomass pyrolysis has emerged as a flexible platform for converting low-value residues into higher-value energy carriers (bio-oil, biochar and gas) and carbon-rich materials, with realistic potential for negative emissions when biochar is deployed in long-lived sinks. Over the last decade, three developments have [...] Read more.

Biomass pyrolysis has emerged as a flexible platform for converting low-value residues into higher-value energy carriers (bio-oil, biochar and gas) and carbon-rich materials, with realistic potential for negative emissions when biochar is deployed in long-lived sinks. Over the last decade, three developments have driven the field forward: first, a finer mechanistic understanding of devolatilization and secondary reactions; second, major improvements in analytical techniques for characterising feedstocks and products; and third, more rigorous techno-economic and life-cycle assessments that place pyrolysis in a broader energy-system context. Recent experimental work on forestry and agro-industrial residues has clarified how biomass composition, ash chemistry and operating conditions jointly govern product yields, energy content and stability. Parallel advances in GC×GC–MS, high-resolution mass spectrometry, NMR and thermogravimetric methods have shifted the discussion from bulk “bio-oil” and “char” to families of molecules and well-defined structural domains, which can be deliberately targeted by reactor and catalyst design. Data-driven models, ranging from support vector machines applied to TGA curves to ANFIS and random forests for yield prediction, are now accurate enough to support process screening and multi-objective optimisation. At the system level, commercial fast pyrolysis biorefineries report overall useful energy efficiencies on the order of 80–86%, while slow pyrolysis configurations centred on biochar can be economically viable when carbon storage and co-products are appropriately valued. Thermodynamic analyses confirm that indirect gasification via fast-pyrolysis oil sacrifices some energy and exergy efficiency relative to direct solid-biomass gasification but may offer logistical and integration advantages. This review synthesises recent work on (i) feedstock and process characterisation; (ii) state-of-the-art analytical methods for bio-oil, biochar and gas; (iii) modelling and machine-learning tools; and (iv) energy-system deployment of pyrolysis products. Throughout, the emphasis is on how characterisation and modelling inform concrete design choices and on the trade-offs that arise when pyrolysis is considered as part of a wider decarbonisation portfolio. By integrating laboratory-scale characterisation with system-level modelling, this review aligns biomass pyrolysis with several United Nations Sustainable Development Goals (SDGs). The optimisation of thermochemical conversion pathways for forestry and agro-industrial residues directly supports SDG 7 (Affordable and Clean Energy) by enhancing the efficiency of bio-oil and syngas production. Furthermore, the deployment of biochar as a stable carbon sink for negative emissions and soil amendment addresses SDG 13 (Climate Action) and SDG 15 (Life on Land). By converting low-value waste streams into high-value energy carriers and chemicals within a circular bioeconomy framework, the research further contributes to SDG 12 (Responsible Consumption and Production) and SDG 9 (Industry, Innovation and Infrastructure). Full article

(This article belongs to the Special Issue Biomass Pyrolysis Characterization and Energy Utilization)

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26 pages, 26117 KB

Open AccessArticle

Study on Corrosion in Wet Gas Pipelines Under the Influence of Gas Composition and Geometric Configuration

by Xuesong Huang, Jianhua Gong, Yanhui Ren, Defei Du, Linling Wang, Xueyuan Long, Hang Yang and Qian Huang

Processes 2026, 14(8), 1320; https://doi.org/10.3390/pr14081320 - 21 Apr 2026

Viewed by 222

Abstract

In response to corrosion challenges encountered during the gathering and transportation of wet natural gas, this study systematically investigates the corrosion behavior of L245NCS steel in environments containing O₂, H₂S, CO₂ and simulated oilfield-produced water. The research employs [...] Read more.

In response to corrosion challenges encountered during the gathering and transportation of wet natural gas, this study systematically investigates the corrosion behavior of L245NCS steel in environments containing O₂, H₂S, CO₂ and simulated oilfield-produced water. The research employs a combined approach involving high-pressure autoclave experiments and transparent flow loop simulations. Autoclave tests reproduce gas phase, liquid phase, and gas–liquid interface conditions under a controlled O₂-H₂S-CO₂ mixture, while a visual flow loop equipped with elbows and undulating sections is used to examine liquid accumulation behavior and flow characteristics under dynamic, real-world operating conditions. Results indicate that corrosion is most severe at the gas–liquid interface. H₂S is identified as the primary corrosive agent, exerting a stronger influence than CO₂ or O₂. Liquid accumulation is the main factor leading to non-uniform corrosion distribution, and its formation is influenced by water content, pressure, temperature difference, and pipeline shutdown and restart operations. Critical areas such as low-lying sections, downhill bottoms, and the beginning of uphill sections exhibit localized corrosion rates up to 61.4% higher than areas without liquid accumulation. This integrated methodology bridges mechanistic understanding with engineering practice, providing a basis for corrosion risk assessment, optimal monitoring point placement, and integrity management of wet gas pipelines. Full article

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

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17 pages, 1893 KB

Open AccessArticle

Analysis of the Potential for Thermochemical Utilization of Post-Production Maize Waste Through the Production of Coal Substitutes in the Pyrolysis Process

by Piotr Piersa, Szymon Szufa, Katarzyna Piersa, Olgierd Spławski and Paweł Kazimierski

Processes 2026, 14(8), 1319; https://doi.org/10.3390/pr14081319 - 21 Apr 2026

Viewed by 326

Abstract

The dynamic growth of global maize production results in the generation of large amounts of residues originating from both cultivation and processing, creating a need to develop efficient and sustainable management pathways. The aim of this study was to evaluate the feasibility of [...] Read more.

The dynamic growth of global maize production results in the generation of large amounts of residues originating from both cultivation and processing, creating a need to develop efficient and sustainable management pathways. The aim of this study was to evaluate the feasibility of utilizing selected maize-derived residues (straw, cobs, technical maize, and post-fermentation DDGS) for the production of densified solid fuels based on biochar obtained through pyrolysis at 500 °C. The study included analyses of the mineral composition of biomass and biochar, determination of biochar yield, ash content, and higher heating value (HHV). The biochar yield ranged from 30.19% to 42.49%, with the highest values obtained for DDGS (dried distillers grains with solubles). The pyrolysis process led to an increase in HHV to 25.3–32.14 MJ/kg. These values are comparable to the calorific values of hard coal. The results indicate that biochar derived from maize residues may represent a promising feedstock for the production of solid fuels with increased energy density, while the ashes generated during their combustion show potential for agricultural applications. Full article

(This article belongs to the Special Issue Biomass Pyrolysis Characterization and Energy Utilization)

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29 pages, 2301 KB

Open AccessArticle

A Rough Set-Based Decision Process for Evaluating and Promoting Green Community Sustainability

by Chun-Che Huang, Wen-Yau Liang, Yo-Der Huang, Tzu-Liang (Bill) Tseng and Chi-Wen Hsiao

Processes 2026, 14(8), 1318; https://doi.org/10.3390/pr14081318 - 21 Apr 2026

Viewed by 227

Abstract

Green communities play a critical role in advancing sustainable development; however, evaluating their performance and identifying appropriate improvement strategies remain challenging due to uncertain, incomplete, and multidimensional information. This study formalizes three key processes essential to green community governance—sustainability evaluation, attribute reduction, and [...] Read more.

Green communities play a critical role in advancing sustainable development; however, evaluating their performance and identifying appropriate improvement strategies remain challenging due to uncertain, incomplete, and multidimensional information. This study formalizes three key processes essential to green community governance—sustainability evaluation, attribute reduction, and decision-rule generation—and proposes a rough set-based decision framework that integrates quantitative indicators, expert knowledge, and rule-based reasoning. Using empirical assessment data from Nantou County, the framework identifies the most influential determinants of community performance, including accessibility-related facilities, remote-area status, and socioeconomic conditions. The results reveal clear drivers of sustainable community performance. Remote villages lacking community hubs face structural barriers to participation. Communities without facilities supporting vulnerable groups tend to stall at the registration stage, while bronze-level villages require equity-focused engagement despite possessing stronger resource endowments. Notably, silver-level performance is consistently associated with moderate income levels and moderate income disparity, underscoring socioeconomic balance—rather than economic extremes—as a key precondition for stable sustainability advancement. Together, these findings provide interpretable, evidence-based guidance for policymakers and community managers to identify performance gaps and allocate resources more effectively. Full article

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

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20 pages, 6015 KB

Open AccessArticle

Build-Up Rate Prediction for Point-the-Bit Rotary Steerable System Based on 3D Dynamic Finite Element Method

by Zheng Tian, Yufa He, Yu Chen, Junjie He and Yanwei Sun

Processes 2026, 14(8), 1317; https://doi.org/10.3390/pr14081317 - 21 Apr 2026

Viewed by 317

Abstract

Point-the-bit rotary steerable systems (RSSs) achieve trajectory build-up through the coupled action of internal steering offset, bit attitude change, bottom hole assembly (BHA) flexure, and nonlinear wellbore interaction. Unlike conventional rigid or quasi-static BUR models, this study developed a 3D dynamic finite element [...] Read more.

Point-the-bit rotary steerable systems (RSSs) achieve trajectory build-up through the coupled action of internal steering offset, bit attitude change, bottom hole assembly (BHA) flexure, and nonlinear wellbore interaction. Unlike conventional rigid or quasi-static BUR models, this study developed a 3D dynamic finite element model for point-the-bit RSS. The drill string was discretized using Euler–Bernoulli beam elements, with an equivalent “hinge-deflection angle” constraint introduced at the steering unit. Relative angle loading was imposed using the penalty function method, with nonlinear boundary conditions (bit–formation interaction and borehole friction) coupled into the model. Based on the established model, the effects of deflection angle, weight on bit (WOB), and rotary speed were systematically quantified. The results show that when the deflection angle increases from 0.5° to 1.5°, the average BUR rises from 1.452°/30 m to 4.251°/30 m; when the WOB increases from 60 kN to 100 kN, the average BUR increases from 2.281°/30 m to 2.814°/30 m. Within the range of 50–90 r/min, rotary speed has a limited effect on the average BUR, but it can alter the characteristics of transient fluctuations. This approach provides a robust theoretical basis for BUR evaluation, parameter optimization, and control strategy design for rotary steerable tools. Full article

(This article belongs to the Special Issue Oil and Gas Drilling Processes: Control and Optimization, 2nd Edition)

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2 pages, 153 KB

Open AccessCorrection

Correction: Monteiro dos Santos et al. Co-Cultivation between the Microalga Tetradesmus obliquus and Filamentous Fungus Cunninghamella echinulata Improves Tertiary Treatment of Cheese Whey Effluent in Semicontinuous Mode. Processes 2024, 12, 1573

by Leandro Monteiro dos Santos, Joyce Camila Barbosa da Silva, Carlos Eduardo de Farias Silva, Brígida Maria Villar da Gama, Josimayra Almeida Medeiros, Giorgos Markou, Renata Maria Rosas Garcia Almeida and Ana Karla de Souza Abud

Processes 2026, 14(8), 1316; https://doi.org/10.3390/pr14081316 - 21 Apr 2026

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Abstract

The authors require two adjustments in the original manuscript [...] Full article

21 pages, 10485 KB

Open AccessArticle

Collaborative Optimization Between Efficient Thermal Dissipation and Microstructure of Ceramic Matrix Composite Component Under Non-Uniform Thermal Loads

by Yanchao Chu, Zecan Tu, Junkui Mao, Chao Yang, Weilong Wu and Keke Zhu

Processes 2026, 14(8), 1315; https://doi.org/10.3390/pr14081315 - 21 Apr 2026

Viewed by 357

Abstract

This paper presents a collaborative optimization design methodology aimed at improving heat dissipation efficiency through the modulation of microstructural variations. The approach addresses the thermal protection requirements of high-temperature components, such as ceramic matrix composite turbine blades, which are subjected to complex and [...] Read more.

This paper presents a collaborative optimization design methodology aimed at improving heat dissipation efficiency through the modulation of microstructural variations. The approach addresses the thermal protection requirements of high-temperature components, such as ceramic matrix composite turbine blades, which are subjected to complex and elevated thermal loads. Through the integration of numerical simulation and experimental validation, a bidirectional mapping model linking carbon nanotube (CNT) content with the macroscopic anisotropic thermal conductivity of the material was developed. Furthermore, a thermal conduction analysis and optimization framework for Ceramic Matrix Composite (CMC) high-temperature components under non-uniform thermal loads was established. This study expands the adjustable range of the material’s thermal conductivity by allowing flexible modulation of carbon nanotube content. The results demonstrate that this methodology effectively enhances the heat dissipation capacity of CMC materials in extreme thermal environments: the maximum surface temperature of the optimized flat plate is reduced by 8.96%, the peak temperature gradient is lowered by 46.64%, and the maximum thermal stress is decreased by 38.17%. This research provides new insights into the comprehensive integration of thermal dissipation requirements for CMC hot components. Full article

(This article belongs to the Special Issue Thermal Properties of Composite Materials)

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25 pages, 1381 KB

Open AccessReview

A Review of Thermochemical, Physical, and Chemical Conversion Pathways of Coconut and Açaí Residues: Technological Progress and Readiness Assessment

by Luis J. Cruz-Reina, Fabian Velásquez, John Espitia, Edwin Villagrán and Jader Rodríguez

Processes 2026, 14(8), 1314; https://doi.org/10.3390/pr14081314 - 21 Apr 2026

Viewed by 572

Abstract

The growing demand for sustainable energy sources has intensified research on the valorization of biomass residues as feedstocks for energy production. This scoping review provides a comprehensive analysis of recent technological approaches for converting coconut and açaí residues into energy carriers and bioenergy [...] Read more.

The growing demand for sustainable energy sources has intensified research on the valorization of biomass residues as feedstocks for energy production. This scoping review provides a comprehensive analysis of recent technological approaches for converting coconut and açaí residues into energy carriers and bioenergy products. A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. In addition to synthesizing the existing literature, this study evaluates the technology readiness level (TRL) of the reported conversion pathways based on the experimental evidence provided in the reviewed studies. The literature search was conducted using Scopus, Web of Science, and ScienceDirect, focusing on peer-reviewed publications between 2015 and 2025 that reported experimental or pilot-scale research on thermochemical, chemical, and physical conversion processes for coconut and açaí residues. The TRL assessment indicates that most technologies remain at laboratory validation stages, with only a limited number reaching pilot or prototype demonstration levels. Nevertheless, several pathways—particularly thermochemical and densification processes—show promising potential for decentralized bioenergy applications. These findings are especially relevant for regions where coconut and açaí value chains generate significant volumes of agricultural residues. Their valorization could support decentralized energy systems, improve residue management, and contribute to sustainable bioeconomy strategies. Overall, this review identifies the main technological advances, limitations, and research gaps associated with the energy conversion of coconut and açaí residues, providing insights for future technological development and deployment. Full article

(This article belongs to the Special Issue Biomass Pyrolysis Characterization and Energy Utilization)

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1 pages, 113 KB

Open AccessRetraction

RETRACTED: Cao et al. Energy-Efficient Distributed Welding Shop Scheduling Based on Multi-Objective Seagull Algorithm. Processes 2025, 13, 197

by Wengang Cao, Runkang Peng, Cuiruikai Li and Meimei Li

Processes 2026, 14(8), 1313; https://doi.org/10.3390/pr14081313 - 21 Apr 2026

Viewed by 249

Abstract

The journal retracts the article titled “Energy-Efficient Distributed Welding Shop Scheduling Based on Multi-Objective Seagull Algorithm” [...] Full article

16 pages, 3556 KB

Open AccessFeature PaperArticle

Degradation Pathways and Energy Efficiency on Non-Thermal Plasma for Sulfonamide Antibiotics Removal: A Comparative Study

by Hee-Jun Kim, Donggwan Lee, Sanghoon Han, Jae-Cheol Lee and Hyun-Woo Kim

Processes 2026, 14(8), 1312; https://doi.org/10.3390/pr14081312 - 20 Apr 2026

Viewed by 469

Abstract

The non-thermal plasma (NTP) process is a promising advanced oxidation process (AOP) for removing non-biodegradable organics from wastewater, owing to the efficient formation of reactive chemicals. Despite its effective oxidizing capability, the decomposition mechanism of organic pollutants is not well understood. This study [...] Read more.

The non-thermal plasma (NTP) process is a promising advanced oxidation process (AOP) for removing non-biodegradable organics from wastewater, owing to the efficient formation of reactive chemicals. Despite its effective oxidizing capability, the decomposition mechanism of organic pollutants is not well understood. This study evaluates NTP for two representative sulfonamides (SMZ and STZ) and reports on (i) time-resolved removal to the method detection limit, (ii) transformation mapping using LC-ESI/MS/MS, which confirmed previously proposed hydroxylation and bond-cleavage pathways and further identified additional hydroxylated intermediates formed on the thiazole and benzene rings under NTP conditions, and (iii) energy evaluation through energy per order (EEO) within a single, reproducible operating window. The EEO values for SMZ and STZ degradation via NTP were calculated at 22.4 and 7.5 kWh/m³/order, respectively. These values are up to 37- and 118-fold lower than those reported for comparable AOPs, quantitatively confirming that the proposed NTP process achieves superior energy efficiency for sulfonamide degradation. Degradation is primarily attributed to reactive oxygen species (ROS) generated by plasma, which initiate the breakdown of the antibiotic structure. Overall, this study demonstrates that NTP is a highly effective AOP for driving the rapid primary degradation and intermediate structural transformation of recalcitrant sulfonamide antibiotics. Full article

(This article belongs to the Special Issue Applications of Non-Thermal and Thermal Technologies in Food Processing, 2nd Edition)

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29 pages, 8671 KB

Open AccessArticle

Data-Driven Multi-Mode Time–Cost Trade-Off Optimization for Construction Project Scheduling Using LightGBM

by Shike Jia, Cuinan Luo, Ruchen Wang, Qiangwen Zong, Yunfeng Wang, Fei Chen, Weiquan Guan and Yong Liao

Processes 2026, 14(8), 1311; https://doi.org/10.3390/pr14081311 - 20 Apr 2026

Viewed by 362

Abstract

Large infrastructure projects frequently experience schedule slippage and cost escalation; however, time–cost planning still relies on expert-assigned activity parameters that fail to reflect the variability induced by construction modes, resource supply, and on-site conditions. This study focuses on activity-level multi-mode time–cost trade-off planning [...] Read more.

Large infrastructure projects frequently experience schedule slippage and cost escalation; however, time–cost planning still relies on expert-assigned activity parameters that fail to reflect the variability induced by construction modes, resource supply, and on-site conditions. This study focuses on activity-level multi-mode time–cost trade-off planning and its dynamic correction during project execution. The proposed methodology is intended for project-level short-term operational scheduling and rolling re-scheduling within a finite project execution horizon, rather than long-term strategic or portfolio-level scheduling. A predict–optimize–update framework is proposed, where light gradient boosting machine (LightGBM) is employed to predict the duration and direct cost of activity–mode pairs using unified features extracted from BIM/IFC records, schedule-resource ledgers, and cost-settlement data, covering engineering quantities, mode and resource decisions, and contextual factors. These predicted parameters are then fed into a time-indexed bi-objective mixed-integer linear program (MILP), which minimizes both project makespan and total cost (including indirect cost) to generate an interpretable Pareto frontier via a weighted-sum approach. Meanwhile, real-time monitoring updates refresh the predictors and re-solve the remaining project network to ensure dynamic adaptability. Validated on a desensitized proprietary enterprise multi-source dataset comprising 25 completed infrastructure projects and 5258 activity–mode samples, the proposed method achieves a mean absolute error (MAE) of 2.7 days and a coefficient of determination (R²) of 0.89 for duration prediction, as well as an MAE of 7.4 × 10⁴ CNY and an R² of 0.91 for direct-cost prediction. The generated Pareto set exhibits a diminishing return trend: as the project duration is relaxed from 101 to 146 days, the total cost decreases from 45.10 to 40.27 million CNY. A weather-triggered update case demonstrates that the completion forecast is revised from 133 to 128 days, with the total cost reduced from 53.05 to 52.75 million CNY. This framework enables explainable schedule–cost co-control, thereby effectively aiding decision-making for the planning and control of large infrastructure projects. Full article

(This article belongs to the Special Issue Artificial Intelligence-Based Analytics for Data-Driven Decision-Making in Industrial Process Engineering)

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28 pages, 3003 KB

Open AccessArticle

Short-Term Wind Power Non-Crossing Quantile Forecasting Based on Two-Stage Multi-Similarity Segment Matching

by Dengxin Ai, Li Zhang, Junbang Lv, Song Liu, Zhigang Huang and Lei Yan

Processes 2026, 14(8), 1310; https://doi.org/10.3390/pr14081310 - 20 Apr 2026

Viewed by 332

Abstract

Accurate wind power forecasting is essential for the stability of modern power systems. However, current probabilistic forecasting frameworks often encounter a fundamental conflict between the computational efficiency required for high-dimensional meteorological pattern matching and the physical consistency of the resulting probability distributions. Existing [...] Read more.

Accurate wind power forecasting is essential for the stability of modern power systems. However, current probabilistic forecasting frameworks often encounter a fundamental conflict between the computational efficiency required for high-dimensional meteorological pattern matching and the physical consistency of the resulting probability distributions. Existing methods frequently fail to maintain the logical monotonicity of quantiles or overlook the fine-grained temporal correlations in massive historical datasets. To address these critical gaps, this research develops a comprehensive framework that synergizes a hierarchical similarity filtering mechanism with a structurally constrained non-crossing quantile regression model. First, the target sample is partitioned into several weather segments, and a new two-stage high-similarity weather pattern matching method is developed to screen multiple sets of historical samples that are highly similar to the target weather pattern. Second, a deep learning model for probabilistic wind power quantile forecasting is proposed, which incorporates historical data augmentation. The model utilizes an attention mechanism to extract the correlation between the target and historical segments, while an improved non-crossing quantile regression model is adopted to ensure the validity of the output quantiles. Finally, the effectiveness of the proposed method is validated through case studies using real-world data from an actual wind farm. Full article

(This article belongs to the Special Issue Applications of Smart Microgrids in Renewable Energy Development)

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32 pages, 2688 KB

Open AccessArticle

Research on an Anti-Speculation Revenue Allocation Mechanism in Multi-Virtual Power Plants

by Mengxue Zhang, Qiang Zhou, Youchao Zhang, Jing Ji and Yiming Qiu

Processes 2026, 14(8), 1309; https://doi.org/10.3390/pr14081309 - 20 Apr 2026

Viewed by 371

Abstract

In the joint operation of multiple virtual power plants, after day-ahead optimal dispatch is completed, some participants may engage in speculative behaviors such as misreporting profit contribution data to obtain greater benefits during profit distribution, thereby undermining fairness. To address this issue, this [...] Read more.

In the joint operation of multiple virtual power plants, after day-ahead optimal dispatch is completed, some participants may engage in speculative behaviors such as misreporting profit contribution data to obtain greater benefits during profit distribution, thereby undermining fairness. To address this issue, this paper constructs a profit distribution model designed to prevent speculation. An improved Nash bargaining equilibrium algorithm based on a third-party trading intermediary is proposed to curb speculative actions. Furthermore, a dual-layer monitoring mechanism centered on profit deviation is established, which can effectively identify both single-day speculative behaviors and long-term systematic speculative trends, thereby triggering verification procedures. This forms a closed-loop management mechanism for speculation prevention—“detection, monitoring, analysis, verification”—ensuring fair profit distribution among participants within virtual power plants. Case study results demonstrate that the proposed method achieves an average deviation of only 2.32% compared to the profit distribution outcome under non-speculative conditions. In contrast, commonly used methods such as the Shapley value method, nucleolus method, and Nash–Harsanyi bargaining solution exhibit an average deviation as high as 18.44%. The research presented in this paper enables the detection of speculative behaviors among participants and facilitates verification, significantly enhancing the fairness and rationality of profit distribution. Full article

(This article belongs to the Section Energy Systems)

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20 pages, 1351 KB

Open AccessArticle

Modeling the Gradual Evaporation of the Aqueous Phase from Highly Stable Water–Hydrocarbon Emulsions in a Batch Reactor for Thermomechanical Dehydration: A Comparison of Average and Extreme Vapor Formation Rates

by Aliya Gabdelfayazovna Safiulina and Ismagil Shakirovich Khusnutdinov

Processes 2026, 14(8), 1308; https://doi.org/10.3390/pr14081308 - 20 Apr 2026

Viewed by 389

Abstract

In various sectors of the petrochemical and metallurgical industries, significant volumes of waste in the form of highly stable water–hydrocarbon emulsions are generated and stored. The presence of an aqueous phase limits their further use. To utilize this waste and obtain valuable commercial [...] Read more.

In various sectors of the petrochemical and metallurgical industries, significant volumes of waste in the form of highly stable water–hydrocarbon emulsions are generated and stored. The presence of an aqueous phase limits their further use. To utilize this waste and obtain valuable commercial products, a thermomechanical dewatering method based on the evaporation of the aqueous phase under turbulent emulsion flow conditions has been proposed and tested. However, the dynamics of aqueous phase evaporation and vapor phase formation within this method remain poorly understood. This understanding is crucial, as it directly influences the optimal selection of necessary auxiliary equipment. To address this gap, the dynamics of vapor formation during the boiling off of the aqueous phase from highly stable water–hydrocarbon emulsions in a batch thermomechanical dewatering reactor were simulated. To identify general patterns, the gradual evaporation process was calculated as a set of multiple single-effect evaporation steps with a two-degree increment. Initially, modeling results showed that to obtain a commercial product with a water content of less than 1%, temperatures must be maintained at up to 150 °C. This finding was in complete agreement with experimental data, thereby confirming the accuracy of the calculations. Subsequently, extreme vaporization rates were identified, which significantly (1.7–9 times) exceeded the average vapor formation rates in a batch reactor. Maximum vapor formation rates were observed in the temperature range of 100–120 °C. Furthermore, increasing the feedstock water content above 10% was found to significantly prolong the processing time and elevate the maximum vapor formation rate. The patterns presented in this article facilitate the optimization of operating modes for commercial thermomechanical dewatering units, enable the informed selection of necessary auxiliary equipment, and help maintain both the safety and efficiency of the industrial process. Full article

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15 pages, 2287 KB

Open AccessArticle

Flow Mechanism of Grouting Slurry in Rough Fracture Based on CFD-DEM Coupling Method

by Yuanyuan Hou, Chenxi Miao, Desheng Zhu, Zhenhua Li, Feng Du, Wenqiang Wang, Xufan Yang and Zhengzheng Cao

Processes 2026, 14(8), 1307; https://doi.org/10.3390/pr14081307 - 20 Apr 2026

Cited by 1 | Viewed by 448

Abstract

The flow field regulation and medium migration characteristics during aggregate slurry grouting in rough fractures are directly related to the grouting repair engineering in various geotechnical projects. The selected three grouting velocities (0.5, 0.55, 0.6 m/s) are within the typical range of 0.3–0.8 [...] Read more.

The flow field regulation and medium migration characteristics during aggregate slurry grouting in rough fractures are directly related to the grouting repair engineering in various geotechnical projects. The selected three grouting velocities (0.5, 0.55, 0.6 m/s) are within the typical range of 0.3–0.8 m/s for high-pressure jet grouting in geothermal reservoirs. This study uses the Hurst exponent method to construct a 3D rough fracture model and simulates cement slurry flow and aggregate migration based on Fluent–EDEM two-way coupling, analyzing flow field characteristics and their impact on aggregate migration. Results show that differences in flow field pressure and viscosity affect rough fracture flow field distribution and aggregate migration, leading to segmented non-uniform velocity—higher in the ascending section (Up-leg) and Down-leg (Down-leg) and stable in the gentle section (Flat-leg) of the rough fracture—coupled with wall morphology. Particle motion is controlled by the flow field, consistent with the pattern shown in velocity contours, verifying that geometry, pressure and shear characteristics collectively govern fluid and particle movement. Pressure contours show that the pressure distribution in rough fractures is coupled with wall morphology: high pressure occurs at abrupt sections, while pressure is stable in Flat-leg. Viscosity contours indicate that the proportion of high-viscosity regions at abrupt sections is lower than that in Flat-leg. This provides theoretical support for optimizing aggregate slurry migration, improving flow field uniformity, reducing grout waste, and enhancing the construction quality and efficiency of underground engineering Full article

(This article belongs to the Section Materials Processes)

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18 pages, 3503 KB

Open AccessArticle

Fracture Propagation Laws in Lamina-Developed Shale Based on the Discrete Element Method

by Mingjing Lu, Xuelin Zheng, Dongying Wang, Kang Wang, Feng Yang and Zilin Zhang

Processes 2026, 14(8), 1306; https://doi.org/10.3390/pr14081306 - 20 Apr 2026

Viewed by 389

Abstract

Shale oil in continental faulted basins of eastern China, represented by Jiyang Depression, has achieved breakthroughs in productivity. However, challenges such as deep burial, high formation pressure, and poor crude oil mobility pose significant obstacles to achieving high and stable production. Hydraulic fracturing [...] Read more.

Shale oil in continental faulted basins of eastern China, represented by Jiyang Depression, has achieved breakthroughs in productivity. However, challenges such as deep burial, high formation pressure, and poor crude oil mobility pose significant obstacles to achieving high and stable production. Hydraulic fracturing is required to form complex fracture networks for stimulation. Factors such as the lamellar structure of shale, geomechanical conditions, and fracturing operation parameters affect fracture propagation. Therefore, this study establishes a numerical model of fracture propagation in lamina-developed shale using the discrete element software PFC2D 6.0, conducts simulation analysis of fracture propagation laws under in situ stress conditions, and characterizes the influence of lamellar structure and construction technology on fracture complexity. The results show that, for lamina-developed shale, the initiation pressure decreases with increasing injection rate; as the difference between the two horizontal principal stresses increases, hydraulic fractures gradually tend to propagate toward the direction of the maximum principal stress; under high injection pressure, a complex network of short fractures is formed, while, under low injection pressure, the length of the main fracture is prompted to increase. High density (9–10 strips/100 mm) enhances lamina penetration, favoring extension toward maximum horizontal principal stress; low density (4–5 strips/100 mm) strengthens lamina guidance, with fractures propagating along laminae near the injection hole. This research clarifies the mechanisms of fracture initiation and propagation in laminated shale, providing theoretical and technical support for optimizing hydraulic fracturing designs. Full article

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

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30 pages, 2384 KB

Open AccessReview

Applications of Deep Learning to Metal Surface Defect Detection: Status and Challenges

by Yizhe Wang, Mengchu Zhou, Chenyang Zhang and Khaled Sedraoui

Processes 2026, 14(8), 1305; https://doi.org/10.3390/pr14081305 - 19 Apr 2026

Viewed by 632

Abstract

The detection technology for metal surface defects plays a crucial role in improving metal product quality and production efficiency in various manufacturing and 3-D printing factories. Metal defect detection faces scale variation and irregular shapes, which limit the adaptability of general object detection [...] Read more.

The detection technology for metal surface defects plays a crucial role in improving metal product quality and production efficiency in various manufacturing and 3-D printing factories. Metal defect detection faces scale variation and irregular shapes, which limit the adaptability of general object detection models in industrial scenarios. Deep learning-based methods are widely used for metal surface defect detection due to their strong adaptability and high automation. Yet, their existing studies pay limited attention to adaptability, evaluation, and recommendations across different detection methods for metal surface defects. This work mainly discusses YOLO, R-CNN, and transformers, as well as FPN, and analyzes their applications in metal surface defect detection according to their respective characteristics, to provide guidance for future research. YOLO has advantages in real-time industrial online detection, while R-CNN and transformer models show potential advantages in handling complex defect cases. Additionally, this work summarizes commonly used datasets and evaluation metrics for metal surface defect detection and analyzes the benchmark performance of different types of detection methods. It also discusses future research directions, including the current status and improvement paths of different models in terms of accuracy, real-time performance, and adaptability. Future models should focus on balancing accuracy and real-time performance, exploring new hybrid architectures, and improving adaptability to different metal surface defects to support further development in this field. Full article

(This article belongs to the Topic Intelligent Maintenance and Health Management in Smart Manufacturing)

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22 pages, 1755 KB

Open AccessArticle

Process Engineering Evaluation of Plant-Based Corrosion Inhibitors: Case Study of Citrus limon and Eucalyptus globulus

by Sadjia Bertouche, Souhila Kadem, Sabrina Koribeche, Khalida Allaoui, Fatima Zahra Aougabi, Lilia Farah, Nour El Houda Laoufi, Dounia Lezar, Nassila Sabba and Seif El Islam Lebouachera

Processes 2026, 14(8), 1304; https://doi.org/10.3390/pr14081304 - 19 Apr 2026

Viewed by 587

Abstract

Corrosion continues to be a major concern in industrial systems, causing material degradation and raising maintenance costs. In recent years, plant-derived corrosion inhibitors have gained interest as environmentally friendly alternatives to conventional chemical treatments. In this work, ethanolic extracts from the leaves of [...] Read more.

Corrosion continues to be a major concern in industrial systems, causing material degradation and raising maintenance costs. In recent years, plant-derived corrosion inhibitors have gained interest as environmentally friendly alternatives to conventional chemical treatments. In this work, ethanolic extracts from the leaves of Citrus limon (L.) Osbeck and Eucalyptus globulus Labill. were evaluated as green corrosion inhibitors for C45 carbon steel in 1 M HCl solution. The extracts were prepared by continuous Soxhlet extraction and characterized through antioxidant activity measurements using the 2,2-diphenyl-1-picrylhydrazyl DPPH radical scavenging method, gravimetric (weight loss) tests, and electrochemical techniques including potentiodynamic polarization. In addition, the extraction parameters were optimized using a face-centered central composite design (CCD) within a response surface methodology (RSM) framework, and the resulting models were analyzed by analysis of variance (ANOVA). The effects of inhibitor concentration and temperature on corrosion inhibition performance were systematically examined. The antioxidant assay indicated that E. globulus extract reached a scavenging activity above 95% at 1000 mg/L, while C. limon extract showed moderate activity around 71%. Gravimetric tests revealed that both extracts reduced the corrosion rate, with optimal inhibition efficiencies of approximately 67% for C. limon (at 0.3 g/100 mL) and 82% for E. globulus (at 1.0 g/100 mL). Beyond these optimal concentrations, a decline in performance was observed, suggesting surface saturation. The statistical optimization showed that the C. limon response model was solvent-driven (R² = 92.05%), whereas the E. globulus model was curvature-driven (R² = 95.45%), with contrasting response surface topographies. Electrochemical measurements confirmed that both extracts acted as mixed-type inhibitors, shifting the corrosion potential toward less negative values and reducing the corrosion current density. Overall, E. globulus extract demonstrated superior performance across all methods, and both extracts represent promising candidates for sustainable corrosion protection in acidic industrial environments. Full article

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25 pages, 11903 KB

Open AccessArticle

Study on the Evolution Law of Oil–Water Fronts in Horizontal Wells of Offshore Edge-Water Drive Reservoirs

by Haitao Li, Lijiaxin Chen, Nan Zhang, Wanqi Dong, Zhongyu Lei and Fengjun Xie

Processes 2026, 14(8), 1303; https://doi.org/10.3390/pr14081303 - 19 Apr 2026

Viewed by 252

Abstract

To address the problems of rapid water cut increases and severe interlayer interference in offshore composite rhythmic edge-water reservoirs, this paper aims to reveal the three-dimensional spatiotemporal evolution laws of water-flooding fronts under complex heterogeneous conditions. A systematic study was carried out using [...] Read more.

To address the problems of rapid water cut increases and severe interlayer interference in offshore composite rhythmic edge-water reservoirs, this paper aims to reveal the three-dimensional spatiotemporal evolution laws of water-flooding fronts under complex heterogeneous conditions. A systematic study was carried out using a combination of three-dimensional large-scale physical simulation, mathematical derivation, and orthogonal numerical simulation. The results indicate that under composite rhythmic conditions, the dynamic interplay between the interlayer permeability differential and gravity segregation exacerbates bottom-water channeling, while a bottom low-permeability zone and a large formation dip angle effectively inhibit water underride. Crude oil viscosity and liquid production rate are the core factors affecting the recovery factor. Furthermore, the constructed water breakthrough time prediction model, which considers additional gravity potential energy, demonstrates a stable calculation error within 4.6%. The study confirms that promoting the longitudinally balanced advancement of multilayer oil–water fronts is the key to improving macroscopic sweep efficiency, and the optimized balanced sweep mode improves the ultimate recovery factor by up to 8.57% compared to the extreme channeling mode, providing scientific guidance for water control well selection and the optimization of liquid production schedules in offshore edge-water reservoirs. Full article

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

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23 pages, 53556 KB

Open AccessArticle

Investigation of Liquid Spreading Processes Enhanced by Textured Structures on Hydrophilic Surfaces

by Long Chen and Yefei Liu

Processes 2026, 14(8), 1302; https://doi.org/10.3390/pr14081302 - 19 Apr 2026

Viewed by 338

Abstract

The liquid spreading on structured packings plays an essential role in affecting gas–liquid mass transfer in separation columns, yet the synergistic mechanism of surface wettability and textured geometries remains insufficiently understood. This study integrates experimental and computational methods to systematically investigate the liquid [...] Read more.

The liquid spreading on structured packings plays an essential role in affecting gas–liquid mass transfer in separation columns, yet the synergistic mechanism of surface wettability and textured geometries remains insufficiently understood. This study integrates experimental and computational methods to systematically investigate the liquid spreading characteristics on textured surfaces. The synergistic combination of hydrophilic modification and surface textures markedly enhances liquid spreading performance. Compared with the hydrophilic plane surface, the spherical cap texture increases the interface area and wetted area by 25.2% and 49.6%, respectively, while the pyramid-shaped texture leads to improvements of 24.5% and 48.9%, respectively. Based on Weber number analysis, it is identified that the competition between inertial force and surface tension governs the evolution of liquid spreading regimes. In addition, the results suggest that variations in liquid viscosity and density may further influence spreading behavior by modifying the balance among inertial, viscous, and surface tension forces. The geometric parameters of spherical cap textures are systematically examined, and it is revealed that a spherical cap with a non-uniform staggered configuration (Mode III) enables the efficient liquid spreading. A new non-uniform spherical cap texture is designed to enhance liquid spreading, which enhances spreading performance compared with the original plate, increasing the interface area by 27.3% and the wetted area by 47.4%. Although the liquid film thickness increases slightly, the wetted area ratio is significantly improved, indicating enhanced effective surface coverage. Both simulations and experiments confirm that the new textured structure further enhances liquid spreading performance on the textured surface. This research unveils a strategy to improve liquid spreading through tailored surface textures, opening up new possibilities for the design of efficient packings. Full article

(This article belongs to the Section Separation Processes)

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17 pages, 7177 KB

Open AccessArticle

An Approach to Acclimation Mechanisms of the Extremotolerant Cyanobacterium Chroococcidiopsis sp. to Increasing Red-Light Irradiances

by María Robles, Verónica Beltrán, Inés Garbayo, Jacek Wierzchos and Carlos Vílchez

Processes 2026, 14(8), 1301; https://doi.org/10.3390/pr14081301 - 18 Apr 2026

Viewed by 426

Abstract

Chroococcidiopsis sp. was isolated from the endolithic habitat of the Atacama Desert (northern Chile), one of the most challenging-to-life polyextreme environments on Earth. The photosynthetic machinery of microorganisms inhabiting this environment is supposed to be highly adapted to cope with the intense solar [...] Read more.

Chroococcidiopsis sp. was isolated from the endolithic habitat of the Atacama Desert (northern Chile), one of the most challenging-to-life polyextreme environments on Earth. The photosynthetic machinery of microorganisms inhabiting this environment is supposed to be highly adapted to cope with the intense solar radiation of the area. Thus, PAR-red light ranging from 100 to 900 µmol photon·m⁻²·s⁻¹ has been investigated as a strategy to enhance culture productivity and stimulate the synthesis of bioactive molecules in Chroococcidiopsis sp. A control culture was maintained under white light at 100 µmol photon·m⁻²·s⁻¹. The results revealed that red light was utilized more efficiently than white light of similar irradiance, and its modulation enhanced both growth and photosynthetic activity of the cyanobacterium. Furthermore, Chroococcidiopsis sp. appeared to activate mechanisms to mitigate photooxidative stress produced by excess light energy. Specifically, increasing light irradiance induced photoacclimation responses, characterized by a decrease in chlorophyll content and a concomitant increase in carotenoid accumulation, likely aimed at reducing photon flux transduced to photosynthesis. Additionally, scytonemin synthesis was enhanced under high irradiances, contributing to dissipating excess light energy. Overall, this study demonstrates that modulation of red-light irradiance effectively improves the growth of Chroococcidiopsis sp. while promoting the accumulation of antioxidant compounds—primarily carotenoids and, to a lesser extent, scytonemin. Full article

(This article belongs to the Special Issue Advances in Bioenergy and Sustainable Biomaterial Production Processes)

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17 pages, 2683 KB

Open AccessFeature PaperArticle

Development of an Original Method for Analyzing Hydrotreated Vegetable Oil Composition by Gas Chromatography

by Maria Oprea, Rodica Niculescu, Mihaela Nastase, Adrian Clenci, Gabriel Vasilievici, Andreea Luiza Mirt and Ana Maria Apolozan

Processes 2026, 14(8), 1300; https://doi.org/10.3390/pr14081300 - 18 Apr 2026

Viewed by 547

Abstract

The development of modern society has intensified fossil fuel consumption, resulting in the depletion of oil resources and rising greenhouse gas emissions. In this context, the promotion of renewable alternatives in the transport sector has become essential, with Hydrotreated Vegetable Oil (HVO) emerging [...] Read more.

The development of modern society has intensified fossil fuel consumption, resulting in the depletion of oil resources and rising greenhouse gas emissions. In this context, the promotion of renewable alternatives in the transport sector has become essential, with Hydrotreated Vegetable Oil (HVO) emerging as a promising transitional fuel due to its compatibility with conventional diesel engines. To ensure proper engine operation and performance, the physical properties and chemical structure of HVO must be accurately characterized. Gas chromatography is commonly used for this purpose. While dedicated gas chromatography methods for HVO are available on specialized equipment, this study proposes a chromatographic method applicable to conventional gas chromatograph systems equipped with a flame ionization detector, enabling the analysis of HVO using commonly available laboratory equipment. The method was developed using commercially available HVO and pure n-alkanes (C₅–C₁₈) as reference compounds for component identification. The proposed approach enabled the estimation of carbon and hydrogen atom numbers in the analyzed fuel fractions and the determination of the stoichiometric air. The calculated values show good agreement with the literature data, confirming the reliability and applicability of the proposed boiling-point-based chromatographic method. Full article

(This article belongs to the Special Issue Advanced Natural Product Technology: Extraction, Separation, and Analysis)

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25 pages, 3125 KB

Open AccessArticle

Machine Learning-Based Optimization for Predicting Physical Properties of Mound–Shoal Complexes

by Peiran Hao, Gongyang Chen, Yi Ning, Chuan He and Lijun Wan

Processes 2026, 14(8), 1299; https://doi.org/10.3390/pr14081299 - 18 Apr 2026

Viewed by 369

Abstract

Carbonate mound–shoal complexes, despite their complex pore structures and pronounced heterogeneity, represent one of the most productive reservoir units within carbonate formations. Accurately predicting key physical properties—such as porosity, permeability, and flow zone index—from well log data remains a significant challenge for conventional [...] Read more.

Carbonate mound–shoal complexes, despite their complex pore structures and pronounced heterogeneity, represent one of the most productive reservoir units within carbonate formations. Accurately predicting key physical properties—such as porosity, permeability, and flow zone index—from well log data remains a significant challenge for conventional empirical methods. This study investigates the application of machine learning algorithms for optimizing the prediction of reservoir properties in hill-and-plain carbonate bodies. Six machine learning approaches—Support Vector Machines (SVM), Backpropagation Neural Networks (BPNN), Long Short-Term Memory Networks (LSTM), K-Nearest Neighbors (KNN), Random Forests (RF), and Gaussian Process Regression (GPR)—are systematically evaluated and compared. The analysis employed flow zone indices, geological data, and well log curves to classify porosity–permeability types. Seven logging parameters were used as input features: spectral gamma ray (SGR), uranium-free gamma ray (CGR), photoelectric absorption cross-section index (PE), bulk density (RHOB), acoustic travel time (DT), neutron porosity (NPHI), and true resistivity (RT). These features were paired with measured physical property values to train and validate the predictive models. Results demonstrate distinct algorithmic advantages for specific properties. The RF model achieved superior performance in permeability prediction, yielding an R² of 0.6824, whereas the GPR model provided the highest accuracy for porosity estimation, with an R² of 0.7342 and an Accuracy Index (ACI) of 0.9699. Despite these improvements, machine learning models still face limitations in accurately characterizing low-permeability zones within highly heterogeneous hill–terrace reservoirs. To address this challenge, the study integrates geological prior knowledge into the machine learning framework and applies cross-validation techniques to optimize model parameters, thereby providing a practical and robust approach for detailed assessment of mound–hoal carbonate reservoirs. Full article

(This article belongs to the Topic Petroleum and Gas Engineering, 2nd edition)

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17 pages, 2306 KB

Open AccessArticle

Comparison of Aspen Plus and Machine Learning for Syngas Composition Prediction in Biomass Gasification

by Nuno M. O. Dias and Fernando G. Martins

Processes 2026, 14(8), 1298; https://doi.org/10.3390/pr14081298 - 18 Apr 2026

Viewed by 554

Abstract

Accurate prediction of syngas composition is essential for process design, optimization, and scale-up, yet it remains challenging due to interactions among operating conditions, biomass properties, and chemical reactions. This study used a database of 450 experimental observations spanning a wide range of biomass [...] Read more.

Accurate prediction of syngas composition is essential for process design, optimization, and scale-up, yet it remains challenging due to interactions among operating conditions, biomass properties, and chemical reactions. This study used a database of 450 experimental observations spanning a wide range of biomass feedstocks and operating conditions to compare the predictive performance of Aspen Plus simulations and Machine Learning models in estimating the concentrations of CO, CO₂, H₂, and CH₄ in syngas. Aspen Plus was used to simulate the 4 stages of the biomass gasification process under different operating conditions, with special focus on the three reactor modules (RPlug, RGibbs, and REquil) modeling the last two stages. In parallel, Machine Learning models using four regression algorithms (XGBoost, Support Vector Machines, Random Forest and Artificial Neural Networks), with different preprocessing and data-splitting strategies, were evaluated for predicting syngas composition. The best Machine Learning models achieved R² values of 0.753 (CO), 0.866 (CO₂), 0.879 (H₂) and 0.734 (CH₄) on the test set. These results outperformed the Aspen Plus approach and highlight the potential of Machine Learning models as complementary or alternative tools for modelling biomass gasification. Shapley Additive Explanation analysis identified the most influential input variables, revealing key roles for the steam-to-biomass ratio and the equivalence ratio in predicting syngas composition. This study demonstrates that existing Aspen Plus simulation models require further development to improve performance metrics across a wide range of biomass feedstocks and operating conditions. Full article

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

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28 pages, 3195 KB

Open AccessArticle

Valorization of Pumpkin Peels as Agro-Food Processing Waste for Sustainable Biochar and Hydrochar Production: Environmental Assessment and Structural Characterization

by Mürüvet H. Uysal, Monika Sharma, Sema H. Y. Çoban, Ahsen A. Uludağ, Hüseyin Altundağ, Grazyna S. Martynkova, Tuğrul Çetinkaya, Aliye S. E. Yay and Ali O. Kurt

Processes 2026, 14(8), 1297; https://doi.org/10.3390/pr14081297 - 18 Apr 2026

Viewed by 531

Abstract

The valorization of agricultural wastes such as pumpkin peel generated from the food processing industry through thermochemical conversion offers sustainable solutions for both waste management and carbon cycling. This study aims to evaluate the physicochemical properties and environmental impacts of charcoals produced from [...] Read more.

The valorization of agricultural wastes such as pumpkin peel generated from the food processing industry through thermochemical conversion offers sustainable solutions for both waste management and carbon cycling. This study aims to evaluate the physicochemical properties and environmental impacts of charcoals produced from pumpkin peel waste (PPW), without the use of chemicals or pre-washing. In this context, pumpkin peel hydrochar (PPH) was produced by hydrothermal carbonization (HTC) and pumpkin peel biochar (PPB) by pyrolysis. The systems were modeled according to a pilot-scale scenario based on the processing of 100 kg of PPW, and the functional unit was defined as the processing of this amount. The properties of the products were determined by various physicochemical characterization techniques, and environmental impacts were analyzed using Life Cycle Assessment (LCA). The results showed that PPH has a higher specific surface area (16.35 m² g⁻¹) than PPB (9.80 m² g⁻¹), as well as a higher carbon content (76.18% for PPH and 66.07% for PPB). Furthermore, the environmental impact of PPH (16.42 kg CO₂-equivalent/FU) is lower than that of PPB (32.33 kg CO₂-equivalent/FU). Based on the obtained physicochemical properties, the potential of both materials as soil conditioners has been evaluated. The lower environmental impact values suggest that PPH may be a more advantageous alternative in terms of sustainability. However, this evaluation is not based on direct soil application experiments, and further applied studies are needed to confirm this potential. Full article

(This article belongs to the Section Sustainable Processes)

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24 pages, 3018 KB

Open AccessArticle

Research on Reliability Evaluation Method of Distribution Network Considering the Temporal Characteristics of Distributed Power Sources

by Xiaofeng Dong, Zhichao Yang, Qiong Zhu, Junting Li, Binqian Zhou and Junpeng Zhu

Processes 2026, 14(8), 1296; https://doi.org/10.3390/pr14081296 - 18 Apr 2026

Viewed by 279

Abstract

Large-scale integration of photovoltaics (PV) introduces complex source-load temporal volatility and grid-connection/off-grid transitions. Traditional static reliability assessments fail to capture these dynamics, resulting in “considerable deviations” in system indices. This paper proposes a reliability evaluation framework that couples temporal source-load trajectories with a [...] Read more.

Large-scale integration of photovoltaics (PV) introduces complex source-load temporal volatility and grid-connection/off-grid transitions. Traditional static reliability assessments fail to capture these dynamics, resulting in “considerable deviations” in system indices. This paper proposes a reliability evaluation framework that couples temporal source-load trajectories with a multi-stage fault recovery process. Unlike traditional methods that rely on a single static snapshot, the proposed model evaluates the system state across a continuous 5-h restoration window. The novelty lies in the unique integration of a Dynamic Time Warping (DTW)–Kmedoids method to preserve temporal phase-shifts and a multi-stage Mixed-Integer Linear Programming (MILP) model to simulate PV grid-connection transitions throughout this window. By capturing the intra-outage evolution of sources and loads, the framework fundamentally corrects the “considerable deviations” of static assessments. Case studies demonstrate high precision with an error of less than 0.71% and a 20-fold speedup. Crucially, the framework corrects the 22.31% risk underestimation bias inherent in static models by tracking real-time source-load evolution. This confirms that temporal coordination performance is the primary determinant of the reliability ceiling in active distribution networks. The findings reveal that the precise alignment of intermittent generation and fluctuating demand defines the actual operational safety margin, providing a superior quantitative foundation for grid resilience enhancement. Full article

(This article belongs to the Section Energy Systems)

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13 pages, 17271 KB

Open AccessArticle

Analysis and Color Studies of Some Symmetrically Structured Disazo-Stilbene Dyes Based on Non-Genotoxic 4,4′-Diaminostilbene-2,2′-Disulfonic Acid

by Maria Elena Radulescu-Grad, Sorina Boran, Giannin Mosoarca, Sabina Nitu and Simona Popa

Processes 2026, 14(8), 1295; https://doi.org/10.3390/pr14081295 - 18 Apr 2026

Viewed by 229

Abstract

This study presents a detailed colorimetric evaluation using the CIEL*a*b* system for a novel series of symmetrically structured disazo-stilbene dyes. The synthesis utilized the non-genotoxic 4,4′-diaminostilbene-2,2′-disulfonic acid as the diazotizing component, with the coupling components being N-substituted acetoacetanilide derivatives. The purity of the [...] Read more.

This study presents a detailed colorimetric evaluation using the CIEL*a*b* system for a novel series of symmetrically structured disazo-stilbene dyes. The synthesis utilized the non-genotoxic 4,4′-diaminostilbene-2,2′-disulfonic acid as the diazotizing component, with the coupling components being N-substituted acetoacetanilide derivatives. The purity of the obtained dyes was confirmed by HPLC analysis. The color analysis was initially conducted on the dyes in solid state (powder) to investigate potential structure–color correlations. Subsequently, these parameters were applied to analyze the performance of the dyes incorporated into acrylic resin films. Titanium dioxide (P.W.6; C.I. 77891) served as the white standard, along with mixtures of dyes in different concentrations that were applied to a cellulosic substrate. The results characterize these compounds as eco-friendly dyes possessing high tinctorial strength and a significant metamerism effect. Full article

(This article belongs to the Special Issue Biochemical Processes for Sustainability, 2nd Edition)

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