Processes

25 pages, 3120 KB

Open AccessArticle

Exergetic and Economic Analysis of Three Multi-Product Biorefinery Schemes for the Valorization of Agricultural Wastes: A Case Study of Colombia

by Adrian Yaya-González, Daniela Alvarado-Barrios and Yeimmy Peralta-Ruiz

Processes 2026, 14(4), 586; https://doi.org/10.3390/pr14040586 (registering DOI) - 7 Feb 2026

Abstract

Colombia generates large volumes of lignocellulosic residues from agriculture, forestry, and agro-industrial activities. Much of this material is landfilled, openly burned, or left to decompose. These practices drive greenhouse-gas emissions (methane and CO₂), particulate air pollution, water contamination, and pest proliferation. [...] Read more.

Colombia generates large volumes of lignocellulosic residues from agriculture, forestry, and agro-industrial activities. Much of this material is landfilled, openly burned, or left to decompose. These practices drive greenhouse-gas emissions (methane and CO₂), particulate air pollution, water contamination, and pest proliferation. Therefore, this study focuses on the design, simulation, exergetic and economic analysis of lignocellulosic biorefinery schemes in Colombia using corn stover (CS) as feedstock. This approach thus turns an environmental liability into valuable resources. Mass and energy balances obtained from Aspen Plus V10^® were used to calculate exergy efficiency. Economic indicators were provided by the Aspen Process Economic Analyzer (APEA) V10^® software. The first scenario (SCE01) included xylitol, lignin, carbon dioxide, biogas, and biofertilizer production along with in situ ethanol co-production; for scenario 2 (SCE02), a cogeneration (CHP) stage using biogas and biofertilizer as fuel was added; in scenario 3 (SCE03), the ethanol production of scenarios 1 and 2 was replaced by glutamic acid production. The exergy efficiency results were as follows: SCE01 (60.1%), SCE02 (36.8%), SCE03 (37.5%). The largest exergy losses were found in the CHP system. In terms of economic viability, all scenarios showed favorable economic parameters. SCE03 showed better results with an Internal Rate of Return (IRR) of 28.01% and a Net Present Value (NPV) of USD 985.1 M compared to SCE01 (27.48%; USD 769.1 M) and SCE02 (27.13%; USD 643.1 M). In light of these results, the SCE03 approach represents the most attractive investment opportunity, with the potential to integrate the social and environmental pillars of sustainability by fostering rural economic development and CO₂ capture. Optimization strategies can be readily adopted to enhance the overall efficiency of the proposed model, enabling it to serve as a benchmark for scaling and comparing alternative lignocellulosic waste valorization pathways at a national level. Full article

(This article belongs to the Section Sustainable Processes)

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18 pages, 1405 KB

Open AccessArticle

Techno-Economic and Environmental Assessment of CO₂-Based Energy Storage: A Roadmap for Successful Deployment

by Basem Alamri

Processes 2026, 14(4), 585; https://doi.org/10.3390/pr14040585 (registering DOI) - 7 Feb 2026

Abstract

Over the last decade, the use of energy storage in power grids has grown enormously, primarily to improve system stability and maximize the benefits of renewable energy sources. As many countries set ambitious targets to increase the share of renewable energy in their [...] Read more.

Over the last decade, the use of energy storage in power grids has grown enormously, primarily to improve system stability and maximize the benefits of renewable energy sources. As many countries set ambitious targets to increase the share of renewable energy in their energy mix, the deployment of energy storage in future grids is expected to increase. This is crucial for ensuring stable grid operation. Conventional energy storage technologies are limited by technical, environmental, and economic factors. CO₂-based energy storage is an emerging technology that overcomes these limitations. This paper presents a techno-environmental assessment of CO₂-based energy storage technology. It provides a roadmap for technology deployment to help energy leaders and project managers make informed decisions about this emerging technology. This should facilitate a smoother energy transition and decarbonization of power grids. Full article

(This article belongs to the Special Issue New Trends in Thermal Energy Storage and Its Applications)

11 pages, 439 KB

Open AccessArticle

Valorization of Fatty Acid by Catalytic Sugar Derivatization: Lipase Versus Layered Double Hydroxide

by Alan José Corrêa Manso, Ana Gabriela R. A. Soares, Gabriel F. S. Silva, Mayllon S. Oliveira, Gizele C. F. Sant’Ana, Luiz F. B. Malta, Ivana L. M. Ferreira and Jaqueline D. Senra

Processes 2026, 14(4), 584; https://doi.org/10.3390/pr14040584 (registering DOI) - 7 Feb 2026

Abstract

Sugar fatty acid esters represent promising scaffolds for technological applications. These compounds are low-cost and allow rapid modulation of their properties. In this study, we have shown that lipase obtained from solid fermentation from Aspergillus niger (hydrolytic activity of 8.32 × 10⁶ [...] Read more.

Sugar fatty acid esters represent promising scaffolds for technological applications. These compounds are low-cost and allow rapid modulation of their properties. In this study, we have shown that lipase obtained from solid fermentation from Aspergillus niger (hydrolytic activity of 8.32 × 10⁶ U/g) can promote a selective route towards the 2,5-dissubstituted D-mannitol laurate. Indeed, the lipase hydrolytic activity allowed the yield of 80% in DMF (P.A.) at 55 °C and 6 h. Finally, Mg/Al layered double hydroxides (LDH) were compared towards the selectivity of the expected dissubstituted product. The data obtained through the comparative analysis allows establishing some variables such as solvent (DMF), temperature (55 °C) and solvent dehydration degree for obtaining these molecules for future application studies in supramolecular gelation systems. Full article

(This article belongs to the Special Issue Sustainable Bioprocesses for Valorization of Food Industry Waste: Microbial and Enzymatic Innovations and Reactor Design)

23 pages, 1287 KB

Open AccessArticle

GTO-YOLO11n: YOLOv11n-Based Efficient Target Detection in Ship Remote Sensing Imagery

by Bei Xiao, Peisheng Liu, Xiwang Guo, Bin Hu, Jiankang Ren and Yushuang Jiang

Processes 2026, 14(4), 583; https://doi.org/10.3390/pr14040583 (registering DOI) - 7 Feb 2026

Abstract

Accurate and efficient ship detection in remote sensing imagery is a key enabler of intelligent maritime surveillance operations, supporting real-time decision-making in search and rescue, traffic management, and maritime law enforcement. However, remote ship images pose unique challenges for detection. These include densely [...] Read more.

Accurate and efficient ship detection in remote sensing imagery is a key enabler of intelligent maritime surveillance operations, supporting real-time decision-making in search and rescue, traffic management, and maritime law enforcement. However, remote ship images pose unique challenges for detection. These include densely distributed targets, complex sea-land backgrounds, large aspect ratios, diverse ship geometries, and high color similarity between ships and their surroundings. To address these issues under the computational constraints of unmanned aerial platforms, we propose GTO-YOLO11n, an enhanced YOLOv11n-based detection model tailored for efficient maritime ship sensing. First, we introduce the GatedFDConvBlock, which employs gated convolutional filtering to strengthen feature extraction for small and elongated ships while suppressing background clutter, thereby reducing missed and false detections in dense scenes. Second, we improve the C2PSA module with a dynamic multi-scale attention design, TSSABlock_DMS, to adaptively model cross-scale feature interactions and enhance robustness to complex maritime environments. Third, we replace the original detection head with OBB_ED, a parameter-sharing head that incorporates depthwise separable convolution (DSConv) and an angle prediction branch to lower model complexity while preserving high-quality localization and classification. To verify the performance of the algorithm, we were conducted on the public datasets HRSC2016, HRSC2016-MS, and ShipRSImageNet. The mAP@50 results were 95.2%, 88.3%, and 76.7%, showing improvements of 3.2%, 2.2%, and 2.6% compared to the original YOLOv11n. Full article

(This article belongs to the Special Issue Intelligent Operation, Maintenance, and Scheduling of Industrial Manufacturing Processes)

20 pages, 3230 KB

Open AccessArticle

Modulated Solar Irradiation: Impact on Drying Behavior and Quality Attributes of Chile de Agua (Capsicum annuum L.) Peppers Harvested at Different Maturity Stages

by Diana Paola García-Moreira, Ivan Moreno, Neith Pacheco, Emanuel Herrera-Pool and Erick César López-Vidaña

Processes 2026, 14(4), 582; https://doi.org/10.3390/pr14040582 (registering DOI) - 7 Feb 2026

Abstract

Drying chili peppers is a crucial technique for their preservation, as it extends shelf life while minimizing the degradation of high-value bioactive compounds. This study evaluated the impact of modulated solar irradiation on the drying kinetics and quality retention of “Chile de Agua” [...] Read more.

Drying chili peppers is a crucial technique for their preservation, as it extends shelf life while minimizing the degradation of high-value bioactive compounds. This study evaluated the impact of modulated solar irradiation on the drying kinetics and quality retention of “Chile de Agua” (Capsicum annuum L.) peppers across three maturity stages (unripe, ripe, and overripe). Two cylindrical solar dryers were employed: a conventional solar dryer (CSD) and a novel Solar Dryer with Dynamic Irradiance Control (SDIC) utilizing Polymer Dispersed Liquid Crystal (PDLC) technology. Drying behavior was analyzed through moisture ratio and drying rate, while quality attributes were assessed via color parameters, capsaicinoid content, and flavonoid profiling using UPLC-PDA-ESI-MS. Results demonstrated that the maturity stage significantly influences drying kinetics; unripe fruits exhibited the fastest dehydration rate, reducing drying time by approximately 14% compared to overripe fruits. Regarding quality, the CSD better preserved color (ΔE of 15.29 for ripe chilies). At the same time, the SDIC system significantly favored the retention of bioactive compounds, maintaining higher concentrations of total capsaicinoids (up to 1700 µg/g DW) and flavonoids such as luteolin (15.9 mg/100 g DW) and quercitrin (11.5 mg/100 g DW), especially in ripe fruits. The findings suggest that optimal processing requires selecting the drying method based on the targeted final use: CSD for color preservation in unripe chilies, or SDIC for maximizing bioactive retention in ripe fruits. Full article

(This article belongs to the Special Issue Processes in Agri-Food Technology)

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25 pages, 1336 KB

Open AccessArticle

Construction and Reasoning Method of Knowledge Graph for Aircraft Skin Spraying Process

by Danyang Yu, Chengzhi Su, Huilin Tian, Wenyu Song, Yuxin Yue and Haifeng Bao

Processes 2026, 14(4), 581; https://doi.org/10.3390/pr14040581 (registering DOI) - 7 Feb 2026

Abstract

To address the heavy reliance on experiential knowledge, fragmented multi-source information, and limited intelligence in decision-making for aircraft skin spraying processes, this paper proposes a knowledge reasoning method based on a knowledge graph. The authors construct a knowledge graph that integrates multi-structure ontology [...] Read more.

To address the heavy reliance on experiential knowledge, fragmented multi-source information, and limited intelligence in decision-making for aircraft skin spraying processes, this paper proposes a knowledge reasoning method based on a knowledge graph. The authors construct a knowledge graph that integrates multi-structure ontology and physical rule constraints. This graph systematically organizes and manages multi-dimensional knowledge, including painting object attributes, paint performance indicators, and spraying parameters. On this basis, a three-stage reasoning mechanism with multi-granularity semantic understanding, knowledge enhancement, feature fusion, and multi-constraint intelligent matching (MKM) is designed. The model can perform semantic analysis of the user’s fuzzy query, implicit knowledge completion, and dynamic subgraph matching, so as to give the aircraft skin spraying process plan that meets the constraints of safety, compatibility, and feasibility. The experimental results show that the proposed method is superior to the traditional case-based reasoning method, graph convolutional network method, and knowledge graph embedding method in the key evaluation indices of Hit@1, Hit@3, and MRR in the knowledge reasoning task of aircraft skin spraying process. It also has good robustness and promotion value when data are scarce and parameters are uncertain. This study provides a feasible method of intelligent management and dynamic decision-making in terms of aircraft skin spraying process knowledge, and may be applied to other manufacturing fields. Full article

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

23 pages, 5040 KB

Open AccessArticle

Reactive Power Collaborative Control Strategy and Verification Method for Suppressing Voltage Oscillation in Renewable Energy Clusters

by Yanzhang Liu, Lingzhi Zhu, Minhui Qian and Chen Jia

Processes 2026, 14(3), 580; https://doi.org/10.3390/pr14030580 - 6 Feb 2026

Abstract

The rapid integration of renewable energy into power systems has made voltage oscillations caused by the intermittency of wind and solar power a critical operational challenge. To mitigate these issues, this paper proposes a multi-mode coordinated reactive power control strategy to enhance voltage [...] Read more.

The rapid integration of renewable energy into power systems has made voltage oscillations caused by the intermittency of wind and solar power a critical operational challenge. To mitigate these issues, this paper proposes a multi-mode coordinated reactive power control strategy to enhance voltage stability in renewable energy clusters. The approach integrates two key indicators: voltage sensitivity for steady-state regulation and an improved multi-renewable energy station short circuit ratio (MRSCR) that accounts for dynamic power interactions. Validation is conducted using a hardware-in-the-loop (HIL) platform combining real-time RMS-based simulation with physical controllers. Case studies on an offshore wind cluster demonstrate that the proposed method reduces voltage fluctuation amplitude more effectively than conventional automatic voltage control (AVC), successfully suppressing oscillations. The results confirm that the strategy exhibits stronger adaptability to varying grid conditions and offers a scalable solution for oscillation mitigation in large-scale renewable energy integration. Full article

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

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23 pages, 4367 KB

Open AccessArticle

Tuning Gas Fingering in SAGD/SAGP: Operating Windows for NCG Timing and Concentration

by Hao Peng, Siyuan Huang, Mingxi Ge, Zhongyuan Wang, Qi Jiang, Kuncheng Li, Guanchen Jiang and Ian Gates

Processes 2026, 14(3), 579; https://doi.org/10.3390/pr14030579 - 6 Feb 2026

Abstract

A Steam-and-Gas Push (SAGP) enhances energy efficiency in Steam-Assisted Gravity Drainage (SAGD) but induces gas fingering instabilities that limit the sweep efficiency. This study systematically investigates the impact of in situ-generated and externally injected non-condensable gas (NCG) on fingering using fine-grid numerical simulations [...] Read more.

A Steam-and-Gas Push (SAGP) enhances energy efficiency in Steam-Assisted Gravity Drainage (SAGD) but induces gas fingering instabilities that limit the sweep efficiency. This study systematically investigates the impact of in situ-generated and externally injected non-condensable gas (NCG) on fingering using fine-grid numerical simulations based on the Du-84 heavy oil reservoir. Two novel dimensionless indexes (heat–gas overlap index and Y-index) are introduced to quantitatively diagnose the fingering severity and heat transfer mechanisms. The results indicate that vertical chamber growth is convection-dominated by buoyant gas fingers, while lateral expansion remains conduction-dominated and stable. Reservoir heterogeneity significantly exacerbates fingering. An NCG concentration-dependent mechanism is established: low-dose co-injection (~0.5 mol%) suppresses minor fingering and increases oil production via a thin insulating gas cap. Conversely, excessive NCG (>5 mol%) thickens the gas cap, hindering heat transfer. Based on these mechanisms, a practical NCG operating window is proposed: a mid-stage, low-dose injection maximizes the production benefit (+4.4%), while a late-stage, moderate-dose injection (~5 mol%) enhances the oil–steam ratio (OSR) by 20.5% with minimal production loss (3.8%). This research offers critical guidance for optimizing NCG injections to mitigate fingering and improve recovery in heterogeneous reservoirs. Full article

(This article belongs to the Topic Enhanced Oil Recovery Technologies, 4th Edition)

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33 pages, 3096 KB

Open AccessReview

Valorization of Sustainable Antioxidant Sources and New Perspectives for Utilization

by Simona Gavrilaș

Processes 2026, 14(3), 578; https://doi.org/10.3390/pr14030578 - 6 Feb 2026

Abstract

Sustainable sources of natural antioxidants are increasingly important for circular bioeconomy strategies. Plant-derived waste streams represent an underexploited resource with significant potential for recovery of high-value antioxidant compounds such as carotenoids, polyphenols, and resveratrol. This review assesses potential alternative biomass sources, including nonhazardous [...] Read more.

Sustainable sources of natural antioxidants are increasingly important for circular bioeconomy strategies. Plant-derived waste streams represent an underexploited resource with significant potential for recovery of high-value antioxidant compounds such as carotenoids, polyphenols, and resveratrol. This review assesses potential alternative biomass sources, including nonhazardous wastes from agriculture, forestry, and fishing, as well as those from the manufacture of food products, beverages, and tobacco products. It evaluates their valorization potential using statistical evidence at the European level. EUROSTAT datasets were analyzed using XLSTAT 2025.2.0 through correlation analysis, Principal Component Analysis (PCA), Agglomerative Hierarchical Clustering (AHC), and k-means clustering. Variables included fresh vegetable production, plant waste generation, processed waste volumes, and national research and development expenditures and innovation. Correlation analysis revealed a strong association between total processed waste and research and development investments (r = 0.87), suggesting that technological capacity influences waste valorization. A moderate correlation (r = 0.55) between nonhazardous waste and processed quantities supports the operational feasibility of extracting antioxidants from residual biomass. PCA showed that Factor 1 (50.16% variance) is dominated by waste generation and processing capacity, whereas organic agriculture loads primarily on Factor 2 (21.6%). Cluster analyses grouped European countries by bioresource management efficiency, highlighting substantial heterogeneity in their readiness for valorization. The combined statistical evidence supports the use of plant-based waste streams as viable, sustainable feedstocks for antioxidant recovery. Strengthening processing infrastructure, harmonizing data reporting, and accelerating research and development investments are essential steps for integrating antioxidant extraction into circular bioeconomic processes. Full article

(This article belongs to the Special Issue Advances in Extraction and Characterization of Functional Properties of Bioactive Compounds Obtained from Food Processing)

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23 pages, 1741 KB

Open AccessReview

New Trends in the Valorisation of the Solid Fraction of Digestate for the Production of Value-Added Bioproducts

by Jana Font-Pomarol, Esther Molina-Peñate, Adriana Artola and Antoni Sánchez

Processes 2026, 14(3), 577; https://doi.org/10.3390/pr14030577 - 6 Feb 2026

Abstract

The rapid expansion of anaerobic digestion (AD) as a key technology for producing renewable energy has led to a substantial increase in digestate generation. This has intensified the need for sustainable management strategies that align with circular economy principles. While the solid fraction [...] Read more.

The rapid expansion of anaerobic digestion (AD) as a key technology for producing renewable energy has led to a substantial increase in digestate generation. This has intensified the need for sustainable management strategies that align with circular economy principles. While the solid fraction of digestate (SD) is traditionally applied to land or composted, its heterogeneous composition, regulatory constraints, and handling challenges restrict its wider use. This review aims to clarify the current state of SD treatment and highlight emerging opportunities to convert this underexploited resource into value-added bioproducts. A systematic bibliographic analysis of the past decade was conducted to identify consolidated and emerging SD valorisation technologies, supported by an evaluation of EU-level regulatory frameworks and the role of mechanical solid–liquid separation in enabling downstream valorisation. In addition, a comprehensive comparative table compiling physicochemical characterisation data of SD from various feedstocks and separation methods is presented, emphasising the significant variability in composition and its implications for valorisation pathways. The results show that, while composting and thermochemical routes, particularly pyrolysis, remain predominant, novel approaches such as advanced drying, pelletisation, vermicomposting, insect bioconversion, and fermentation-based pathways (including submerged and solid-state fermentation) are rapidly gaining interest. These emerging technologies enable the production of high-value products such as biochar, pellets, enzymes, microbial biopesticides, protein sources, and fungal biomass. However, their adoption is currently limited by feedstock heterogeneity, process complexity, scalability constraints, and economic considerations. Overall, SD is a versatile feedstock whose valorisation is expanding beyond agricultural applications. However, regulatory harmonisation, quality assurance, and process optimisation are still needed to encourage industrial uptake and to fully integrate SD into circular bioeconomy frameworks. Full article

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

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18 pages, 2255 KB

Open AccessArticle

A High-Throughput, Model-Free Marker Library Approach for Multivariate Adulteration Detection in Vegetable Oils: From Metabolomic Discovery to Regulatory Screening

by Hui Wang, Xiaotu Chang, Yan Zhang, Lu Wang, Lili Hu, Nan Deng, Jijun Qin, Feifei Zhong, Ben Li, Fangyun Xie, Dan Ran, Lei Lv and Peng Zhou

Processes 2026, 14(3), 576; https://doi.org/10.3390/pr14030576 - 6 Feb 2026

Abstract

Adulteration of high-value oils such as olive and camellia oil poses serious challenges to market integrity and consumer safety. This study develops a comprehensive, model-free marker library for high-throughput detection of single and multivariate adulteration across nine vegetable oils (olive, camellia, sesame, rapeseed, [...] Read more.

Adulteration of high-value oils such as olive and camellia oil poses serious challenges to market integrity and consumer safety. This study develops a comprehensive, model-free marker library for high-throughput detection of single and multivariate adulteration across nine vegetable oils (olive, camellia, sesame, rapeseed, flaxseed, soybean, peanut, industrial hemp seed, and sunflower seed oils) using untargeted metabolomics via UHPLC-Q-TOF-MS. We identified 34 characteristic markers, including 9 confirmed by reference standards, such as hydroxytyrosol in olive oil, camelliasaponins in camellia oil, and sesamin in sesame oil, which are uniquely present in specific oils and absent in others. The method enables reliable qualitative screening of adulteration at levels as low as 5% without dependence on chemometric models. Validation using binary and multicomponent blends confirmed its robustness and specificity. In commercial sample analysis, adulteration was detected in 16.0% of olive oils (4/25) and 12.7% of camellia oils (7/55), with results consistent with regulatory findings. This work establishes the first integrated marker library for simultaneous screening of nine vegetable oils, offering a standardized, high-throughput tool for large-scale market surveillance that bridges the gap between discovery-based omics and routine regulatory practice. Full article

(This article belongs to the Special Issue Green Technologies for Food Processing)

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26 pages, 3632 KB

Open AccessArticle

Clinoptilolite-Supported ZnO and TiO₂ Composites for High-Efficiency Adsorption of Methylene Blue

by Esra Altintig and Onur Kabadayi

Processes 2026, 14(3), 575; https://doi.org/10.3390/pr14030575 - 6 Feb 2026

Abstract

This study aims to evaluate the adsorption performance of ZnO- and TiO₂-coated clinoptilolite composites for the removal of methylene blue (MB) from aqueous solutions and to clarify the governing adsorption mechanisms. Batch adsorption experiments were systematically conducted to investigate the effects [...] Read more.

This study aims to evaluate the adsorption performance of ZnO- and TiO₂-coated clinoptilolite composites for the removal of methylene blue (MB) from aqueous solutions and to clarify the governing adsorption mechanisms. Batch adsorption experiments were systematically conducted to investigate the effects of initial pH (3–10), MB concentration (50–200 mg/L), adsorbent dosage (0.05–1.00 g/100 mL), contact time (5–300 min), and temperature (298–313 K). Equilibrium, kinetic, and thermodynamic analyses were employed to comprehensively describe the adsorption behavior. The results demonstrated that MB adsorption onto both composites followed the Langmuir isotherm model, indicating monolayer adsorption on homogeneous surfaces. The maximum adsorption capacities were determined as 56 mg/g for ZnO-coated clinoptilolite and 106 mg/g for TiO₂-coated clinoptilolite, confirming the superior adsorption affinity of the TiO₂-modified composite. Thermodynamic parameters further indicated that the adsorption process was spontaneous, feasible, and thermodynamically favorable within the investigated temperature range. Physicochemical characterization by FTIR, SEM, BET, and XRD confirmed the successful surface modification of clinoptilolite and the enhancement of its structural and textural properties. Overall, the findings suggest that ZnO- and TiO₂-coated clinoptilolite composites are efficient and sustainable adsorbents with strong potential for wastewater treatment applications. Full article

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

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17 pages, 2664 KB

Open AccessArticle

Accurate Hourly Forecasting of Wind Energy in Romania Using Deep Learning Models

by Grigore Cican, Adrian-Nicolae Buturache and Florin Popescu

Processes 2026, 14(3), 574; https://doi.org/10.3390/pr14030574 - 6 Feb 2026

Abstract

Wind energy plays a critical role in the European Union’s decarbonization strategy, including Romania’s growing renewable energy capacity. This study proposes a deep learning-based method for forecasting hourly wind energy production in Romania using feedforward neural networks (FFNNs) and recurrent neural networks (RNNs), [...] Read more.

Wind energy plays a critical role in the European Union’s decarbonization strategy, including Romania’s growing renewable energy capacity. This study proposes a deep learning-based method for forecasting hourly wind energy production in Romania using feedforward neural networks (FFNNs) and recurrent neural networks (RNNs), trained on a dataset spanning from 1 January to 31 December 2023. The dataset includes hourly wind energy output data (mean = 850.6 MW, std = 694.0 MW) and 13 meteorological variables (e.g., average wind speed = 4.7 km/h, temperature = 14.4 °C). A total of 1296 models were trained and evaluated, with the best-performing RNN model achieving a coefficient of determination of R² = 0.9680 and a mean absolute error (MAE) of 81.03 MW. The top three models all exceeded R² = 0.966, demonstrating strong generalization on unseen data. The models were also validated using two external time intervals outside the training/testing sets, confirming robustness. These results show that deep learning models can provide highly accurate, data-driven predictions of wind energy output, supporting grid stability and informed decision-making amid renewable energy variability. Full article

(This article belongs to the Special Issue Innovative Approaches to Modeling, Optimization, Control and Monitoring in Industrial Processes: Second Edition)

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18 pages, 898 KB

Open AccessSystematic Review

A Comparative Systematic Review of Life-Cycle Assessments of Treatment Strategies for Swine Slurry with a Focus on Anaerobic Co-Digestion

by Pedro Esperanço, António Ferreira and José Ferreira

Processes 2026, 14(3), 573; https://doi.org/10.3390/pr14030573 - 6 Feb 2026

Abstract

Intensive swine production contributes significantly to the global protein supply but generates considerable environmental pressure, particularly through greenhouse gas emissions and surplus slurry management. Anaerobic digestion (AD), especially (co-AD), has been widely investigated as a mitigation strategy to enhance renewable energy generation and [...] Read more.

Intensive swine production contributes significantly to the global protein supply but generates considerable environmental pressure, particularly through greenhouse gas emissions and surplus slurry management. Anaerobic digestion (AD), especially (co-AD), has been widely investigated as a mitigation strategy to enhance renewable energy generation and nutrient recovery. This systematic review synthesizes life cycle assessment (LCA) studies published between 2019 and 2025 that evaluated AD systems treating swine slurry, following the PRISMA 2020 guidelines. Across diverse methodological approaches and regional contexts, the literature consistently shows that AD can reduce global warming potential compared with conventional slurry management, with stronger environmental benefits when biogas is efficiently valorized and when swine slurry is co-digested with complementary organic substrat. Co-AD emerges as a key mitigation option by improving biogas yields, process stability, and overall environmental performance while also enabling better utilization of external organic waste. However, the results remain highly sensitive to operational factors such as methane leakage, digestate management, energy efficiency, and substrate selection. This review highlights the methodological inconsistencies among LCA studies and underscores the need for harmonized assessment frameworks and improved emission data. Overall, co-AD represents a promising pathway for enhancing the environmental sustainability of swine production systems when integrated into optimized, context-specific management strategies. Full article

(This article belongs to the Special Issue Advanced Technologies for Bioenergy Recovery from Organic Waste and Wastewater)

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17 pages, 1909 KB

Open AccessArticle

Efficient Mass Flow Prediction Through Adiabatic Capillary Tubes via Neural Networks Based on the Homogeneous Equilibrium Model

by Youyi Li and Liangliang Shao

Processes 2026, 14(3), 572; https://doi.org/10.3390/pr14030572 - 6 Feb 2026

Abstract

Capillary tubes are widely used as essential expansion devices in small refrigeration and air-conditioning systems. Accurate prediction of mass flow rate through adiabatic capillaries is a critical aspect of system design and optimization. While there are currently numerous models capable of predicting mass [...] Read more.

Capillary tubes are widely used as essential expansion devices in small refrigeration and air-conditioning systems. Accurate prediction of mass flow rate through adiabatic capillaries is a critical aspect of system design and optimization. While there are currently numerous models capable of predicting mass flow through capillaries, most rely on experimental data containing uncertainties, resulting in suboptimal generalization performance. Unlike previous ANN models and empirical correlations that rely on experimental data, this study addresses this limitation by introducing neural networks based on the homogeneous equilibrium model (HEM) of adiabatic capillaries. Two neural networks—a traditional multi-layer perceptron (MLP) and a deep residual network (ResNet)—are developed using a dataset generated by the HEM. The models are subsequently validated and compared against established models using experimental data for various refrigerants and operating conditions collected from the open literature. The results demonstrate that both neural networks exhibit exceptional generalization ability. The average deviations on the experimental dataset are 5.2% for the MLP and 4.5% for the ResNet, outperforming existing models. Their performance across different refrigerants is stable, with the ResNet demonstrating superior overall performance. Furthermore, the trained neural networks achieve a computational speed substantially superior to that of the HEM. Full article

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14 pages, 3905 KB

Open AccessArticle

Optimization of the Design and Manufacturing Processes for Metal Additive Manufacturing Through Digital Twin

by Hüseyin Botsalı and Cevat Özarpa

Processes 2026, 14(3), 571; https://doi.org/10.3390/pr14030571 - 6 Feb 2026

Abstract

The aim of this study is to develop a digital twin hierarchy that fully examines the design and manufacturing processes of an automotive component for metal additive manufacturing. Initially, a lighter model was obtained that was more resistant to static, dynamic, and fatigue [...] Read more.

The aim of this study is to develop a digital twin hierarchy that fully examines the design and manufacturing processes of an automotive component for metal additive manufacturing. Initially, a lighter model was obtained that was more resistant to static, dynamic, and fatigue loads under various operating conditions. This step improved product strength and resulted in a 28.5% mass reduction. After the product was validated, the orientation of the part direction and the generation of support structures were performed for the manufacturing process. These processes were implemented with the criterion of minimizing production time. Finally, the manufacturing process was digitally implemented using the selective laser melting method and Ti6Al4V material. The design of the experiment was created using the three most frequently preferred values for each of the three important process parameters. After performing process simulations with thermomechanical analyses, Taguchi and ANOVA were applied to the process parameters. The optimum process parameters for layer thickness, hatch spacing, and scanning speed were found to be 30 µm, 50 µm, and 1200 mm/s, respectively. Full article

(This article belongs to the Special Issue Additive Manufacturing of Materials: Process and Applications)

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14 pages, 1734 KB

Open AccessArticle

Biodecolorization of Textile Azo Dyes and Phytotoxicity Assessment of Metabolites by Bacillus subtilis CKCC

by Chanchao Chem, Sarawut Cheunkar, Prattana Ketbot, Sirilak Baramee, Apinya Singkhala, Rattiya Waeonukul, Patthra Pason, Khanok Ratanakhanokchai and Chakrit Tachaapaikoon

Processes 2026, 14(3), 570; https://doi.org/10.3390/pr14030570 - 6 Feb 2026

Abstract

Synthetic azo dyes are widely used in the textile industry; however, their use often poses environmental challenges. Here, we characterized the compost bacterium Bacillus subtilis strain CKCC for the decolorization of various azo dyes, including Congo Red, Reactive Black 5, Reactive Green 19, [...] Read more.

Synthetic azo dyes are widely used in the textile industry; however, their use often poses environmental challenges. Here, we characterized the compost bacterium Bacillus subtilis strain CKCC for the decolorization of various azo dyes, including Congo Red, Reactive Black 5, Reactive Green 19, Reactive Red 120, and Reactive Blue 4. The application of strain CKCC exhibited high decolorization efficiency by utilizing various extracellular enzymes, including azoreductase and ligninolytic enzymes such as laccase, lignin peroxidase, and manganese peroxidase, which are essential for the decolorization of azo dyes. Fourier transform infrared spectroscopy (FTIR) analysis revealed structural changes during decolorization, consistent with the degradation of key functional groups. This transformation was attributed to the cleavage of azo linkages by azoreductase, with ligninolytic enzymes functioning on phenolic and aromatic moieties. While FTIR confirmed these structural changes, our findings only provided insights at the functional-group level, and the presence or absence of specific decolorized metabolites, such as aromatic amines, requires additional analytical techniques. In this study, the phytotoxic metabolites positively affected the germination and growth of Vigna radiata, confirming that decolorization using strain CKCC significantly reduced the toxic properties of the metabolites produced during dye decolorization. Hence, our isolated strain CKCC offers a potentially effective and environmentally sustainable method for treating azo-dye effluent in the textile industry. Full article

(This article belongs to the Special Issue Environmental Pollution Control and Renewable Energy Conversion Technologies)

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24 pages, 1966 KB

Open AccessArticle

Unveiling Capability Structures for Resilient Supply Chains in Cruise Shipbuilding: A Hybrid DEMATEL-ISM-MICMAC Approach

by Dandan Fan, Guanghua Fu and Yibo Shi

Processes 2026, 14(3), 569; https://doi.org/10.3390/pr14030569 - 6 Feb 2026

Abstract

The cruise shipbuilding industry faces significant disruptions stemming from escalating trade frictions and regional conflicts which threaten its operational and economic sustainability. Enhancing supply chain resilience is thus crucial for sustainable development. This study identifies critical resilience factors and examines their interrelationships within [...] Read more.

The cruise shipbuilding industry faces significant disruptions stemming from escalating trade frictions and regional conflicts which threaten its operational and economic sustainability. Enhancing supply chain resilience is thus crucial for sustainable development. This study identifies critical resilience factors and examines their interrelationships within growth-stage cruise shipbuilding supply chains. Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), Interpretive Structural Modeling (ISM), and Matrix Cross-Reference Multiplication Method (MICMAC) analysis are integrated to explore causal linkages, hierarchical structures, and driver-dependence dynamics. The analysis reveals that customized demand responsiveness, learning organization, specialized industrial clusters, and inter-industry collaboration are fundamental causal drivers. In contrast, knowledge stock, risk culture, and final-assembly orchestration serve as critical mediators. Based on these findings, we propose distinct resource-contingent strategic pathways for managers. This study provides an actionable framework for building resilience, offering critical guidance for securing the sustainable development of the cruise shipbuilding industry amid uncertainty. Full article

(This article belongs to the Section Sustainable Processes)

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17 pages, 3990 KB

Open AccessArticle

Rapid Identification and Traceability of Groundwater Pollution Using Fluorescence Spectroscopy Coupled with Hydrochemistry in a Chemical Industrial Park, Southwest, China

by Guo Liu, Yongchang Zhang, Guoming Liu and Guo Liu

Processes 2026, 14(3), 568; https://doi.org/10.3390/pr14030568 - 6 Feb 2026

Abstract

Groundwater contamination in chemical industrial parks (CIPs) is a significant threat to global water security due to spills, leaks, and discharges, as well as the complexity of concealing a diverse range of industrial pollutants. In this article, we collected 30 groundwater samples from [...] Read more.

Groundwater contamination in chemical industrial parks (CIPs) is a significant threat to global water security due to spills, leaks, and discharges, as well as the complexity of concealing a diverse range of industrial pollutants. In this article, we collected 30 groundwater samples from zones of presumed influence across a CIP, including upstream background, within-park, periphery, and downstream, located in Luxian County, Sichuan, China. We employed excitation–emission matrix (EEM) fluorescence spectroscopy with parallel factor analysis (PARAFAC) coupled with comprehensive hydrochemical analysis to deconvolve the dissolved organic matter (DOM) signature and statistically link its fluorescent components to specific hydrogeochemical processes and anthropogenic sources. Results revealed that industrial activities have transformed the groundwater to Ca-HCO₃·Cl and Ca·Na-HCO₃·Cl types from the hydrochemical facies comprising Ca-HCO₃ and Ca·Mg-HCO₃ types. Hydrogeology and groundwater chemistry depend primarily on weathering and atmospheric precipitation, but industrial effluents and evaporation concentration also significantly affect them. EEM-PARAFAC identified three dominant fluorescent components: fulvic-like (C1), humic-like (C2), and tryptophan-like (C3), with the latter serving as a sensitive indicator of recent anthropogenic inputs. The spatial distribution of these components, particularly the enrichment of C3, is primarily governed by anthropogenic inputs (e.g., sewage leakage), modulated by local hydrological conditions. This work demonstrates the integration of optical spectroscopy with conventional hydrochemistry for source apportionment in complex industrial settings. It provides a mechanistic understanding of pollution propagation and a practical, rapid diagnostic tool for targeted groundwater protection in CIPs. Full article

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