Processes

18 pages, 1053 KB

Open AccessReview

Sorghum Grain: From a Simple Cereal to Food Applications and Health Benefits

by Doina-Georgeta Andronoiu and Oana-Viorela Nistor

Processes 2025, 13(12), 3958; https://doi.org/10.3390/pr13123958 (registering DOI) - 7 Dec 2025

In the present context of climate changes, multipurpose and stress-resistant crops tend to be widely grown in areas with severe environmental conditions, such as drought and saline-alkali land. Due to its effective adaptation to high-temperature dry conditions, Sorghum bicolor (L.) Moench is a [...] Read more.

In the present context of climate changes, multipurpose and stress-resistant crops tend to be widely grown in areas with severe environmental conditions, such as drought and saline-alkali land. Due to its effective adaptation to high-temperature dry conditions, Sorghum bicolor (L.) Moench is a highly resistant and versatile crop. Sorghum is cultivated as a grain, sweet stem, forage material, and broomcorn, and is a source of fuel, alcoholic and non-alcoholic beverages, and building materials. Sorghum could be part of an integrated circular economy due to its special manufacturing possibilities. Despite having plenty of beneficial properties, sorghum is not very popular all over the world. Thus, the main purpose of our study is to reveal its benefits and various manufacturing possibilities. Currently known more for being used as animal feed and for biofuel production, once popularized, sorghum could become an important vector of food security. The present study reviews the latest data, highlighting the potential of sorghum to develop new food products, noting the functional and health properties of sorghum in foods and the processing possibilities of sorghum-based products. Full article

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

18 pages, 993 KB

Open AccessFeature PaperArticle

Taguchi-Based Optimization of Ultrasound-Assisted Valorization of Coffee Silver Skin for Increasing Phenolic Content: Antioxidant Activity, Physical Properties, and Energy Consumption Assessment

by Yu-Xuan Chen and Mohsen Gavahian

Processes 2025, 13(12), 3957; https://doi.org/10.3390/pr13123957 (registering DOI) - 7 Dec 2025

Abstract

Coffee is among the trendy beverages; however, roasting coffee beans generates by-products such as coffee silver skin (CS). Although CS is rich in phenolic compounds with potential health benefits, there are limited reports on applying advanced optimization approaches combined with emerging food processing [...] Read more.

Coffee is among the trendy beverages; however, roasting coffee beans generates by-products such as coffee silver skin (CS). Although CS is rich in phenolic compounds with potential health benefits, there are limited reports on applying advanced optimization approaches combined with emerging food processing technologies to the sustainable valorization of CS. This study aims to optimize the ultrasound extraction process of Taiwanese CS using the L₉ (3⁴) orthogonal Taguchi method, aiming at maximizing total phenolic content (TPC) while assessing total and specific energy consumption (TEC and SEC). The antioxidant activity of the extracts was also evaluated in relation to processing variables. Besides, variations in the extracts’ CIE color values and total soluble solids were assessed. Independent parameters were extraction time, sonication amplitude, ethanol concentration, and temperature. According to the results obtained, optimized conditions (1 min, 50% amplitude, 80% ethanol, and 25 °C) yielded a TPC value of 7.45 mg GAE/g CS, with the lowest SEC of 0.0067 kWh/mg GAE/g. Antioxidant analysis showed a range of 0.634–1.283 mg/mL for 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging ability, indicating Taguchi’s effectiveness in improving antioxidant activity. In addition, process optimization reduced TEC and SEC by 23.8 and 41.8%, respectively, contributing to green extraction and sustainable food production in line with sustainable development goals. It was found that selecting optimal parameters is crucial for maximizing the extraction of phenolics, enhancing antioxidant properties, while minimizing energy use. Future chemistry-focused studies can explore the interactions between individual phenolic compounds in CS extracts to further support the optimization of multi-parameter process design. Full article

(This article belongs to the Special Issue Processing Foods: Process Optimization and Quality Assessment, 3rd Edition)

► Show Figures

Figure 1

15 pages, 707 KB

Open AccessArticle

Physicochemical, Nutritional, and Organoleptic Properties of Low-Calorie Orange Jam Containing Date Paste as a Sugar Substitute

by El-Sayed A. Abd El-Hady, Eman H. Faqih, Zeinab A. Shabib, Mohamed G. E. Gadallah and Nada A. Alzunaidy

Processes 2025, 13(12), 3956; https://doi.org/10.3390/pr13123956 (registering DOI) - 7 Dec 2025

Abstract

Considering the growing global interest in healthy diets and reducing sugar consumption, date paste has emerged as a natural sweetener and valuable raw material in food processing. This study promotes sustainability by evaluating date paste as a sugar substitute in orange jam production. [...] Read more.

Considering the growing global interest in healthy diets and reducing sugar consumption, date paste has emerged as a natural sweetener and valuable raw material in food processing. This study promotes sustainability by evaluating date paste as a sugar substitute in orange jam production. Sugar was replaced at levels of 0, 20, 40, 50, and 80%, and the resulting products were assessed for chemical composition, physicochemical properties, and sensory characteristics. The results showed that substituting sugar with date paste significantly increased ash, protein, carotenoids, and mineral contents (particularly potassium and iron), while reducing moisture and calorie levels. Both total soluble solids and viscosity values increased as the substitution level increased. Sensory evaluation indicated that samples with 20% and 40% date paste replacement achieved the highest acceptance scores, whereas the 80% replacement level resulted in lower preference. Overall, replacing sugar with date paste enhanced the orange jam’s nutritional and physicochemical properties, with optimal sensory quality observed at moderate substitution levels. Therefore, it is recommended to use date paste as a healthy, natural sweetener (at 20–40% substitution levels) to improve nutritional value and support environmental sustainability and the local economy. Full article

(This article belongs to the Section Food Process Engineering)

► Show Figures

Figure 1

24 pages, 3653 KB

Open AccessArticle

Surface Properties of Heat-Treated Clinoptilolites and Their Relationship to Lead Removal from Aqueous Solutions

by Adolfo Quiroz, Mohamed Abatal, Francisco Anguebes-Franseschi and Claudia Aguilar Ucán

Processes 2025, 13(12), 3955; https://doi.org/10.3390/pr13123955 (registering DOI) - 7 Dec 2025

Abstract

This study investigated the effects of heat treatment on the surface properties, structure, and Pb(II) sorption capacity of natural clinoptilolite zeolite (Nat-CLI). For this purpose, 10 g samples of Nat-CLI were heated separately at 300, 400, and 500 °C for two hours. The [...] Read more.

This study investigated the effects of heat treatment on the surface properties, structure, and Pb(II) sorption capacity of natural clinoptilolite zeolite (Nat-CLI). For this purpose, 10 g samples of Nat-CLI were heated separately at 300, 400, and 500 °C for two hours. The resulting samples were labeled CLI-300, CLI-400, and CLI-500, respectively. The samples were characterized using thermogravimetric analysis (TGA), scanning electron microscopy (SEM) coupled with energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Brunauer–Emmett–Teller (BET) analysis, and point of zero-charge (pH_pzc) measurements. XRD results confirmed that the crystal structure of Nat-CLI remained unchanged after heat treatment, a finding supported by FT-IR and TGA analyses. BET analysis revealed that the heating temperature altered both the specific surface area (S_BET) and the mean pore diameter. The values were as follows: S_BET = 16.5, 14.1, 14.4, and 14.9 m²/g and mean pore diameter = 38.1, 36.0, 48.6, and 36.0 Å for Nat-CLI, CLI-300, CLI-400, and CLI-500, respectively. The results of the kinetic study showed that the pseudo-second order model best agreed with the experimental data for all adsorbents. The maximum sorption capacities of Pb(II) were 28.26, 32.96, 34.69, and 33.85 mg/g for Nat-CLI and CLI-300, CLI-400, and CLI-500, respectively. These results suggest that the sample treated at 400 °C (CLI-400) achieved the highest sorption capacity due to its larger mean pore diameter. Full article

(This article belongs to the Special Issue Innovation of Heavy Metal Adsorption Process)

► Show Figures

Figure 1

22 pages, 12956 KB

Open AccessArticle

Synthesis by Sol-Gel and Coprecipitation of Zn_1−xFe_xO Nanoparticles for the Adsorption of Congo Red Dye

by Carla Yamila Potiliski, Gustavo Raúl Kramer, Florencia Alejandra Bruera, Pedro Darío Zapata and Alicia Esther Ares

Processes 2025, 13(12), 3954; https://doi.org/10.3390/pr13123954 (registering DOI) - 7 Dec 2025

Abstract

The influence of synthesis method on the properties of Zn_1−xFe_xO nanoparticles with different Fe doping levels (x = 0, 0.01, 0.03, and 0.05) for Congo Red (CR) adsorption was investigated. Nanoparticles were prepared by sol–gel and coprecipitation and characterized [...] Read more.

The influence of synthesis method on the properties of Zn_1−xFe_xO nanoparticles with different Fe doping levels (x = 0, 0.01, 0.03, and 0.05) for Congo Red (CR) adsorption was investigated. Nanoparticles were prepared by sol–gel and coprecipitation and characterized by XRD, SEM-EDS, FTIR, and BET analyses. Sol–gel synthesis produced smaller particles (~13 nm) than coprecipitation (~35 nm), and both the method and calcination temperature strongly affected crystallite size. Sol–gel nanoparticles showed significantly higher adsorption efficiency (~90%) due to their larger BET surface area, greater BJH pore volume, and smaller particle size, which increased the number of accessible active sites. In contrast, coprecipitation nanoparticles exhibited a much lower adsorption capacity (~24%). Fe incorporation further enhanced performance by introducing lattice distortions and oxygen vacancies, as evidenced by XRD peak broadening and increased lattice strain. SEM images displayed particle growth and compaction after adsorption, particularly in doped samples. Temperature-dependent experiments indicated that undoped ZnO lost efficiency at 60 °C due to weak physical interactions, whereas Fe-doped nanoparticles maintained high adsorption, due to improved stability of the adsorbent-adsorbate bond. The combination of Fe doping and sol–gel synthesis significantly improved the properties of ZnO, yielding highly efficient adsorbents suitable for environmental remediation. Full article

(This article belongs to the Section Materials Processes)

► Show Figures

Figure 1

14 pages, 2460 KB

Open AccessFeature PaperArticle

Generating Synthetic Data from Real-Time Simulators for Deep Learning Modeling of Machining

by Giambattista Gruosso and Enrico Spateri

Processes 2025, 13(12), 3953; https://doi.org/10.3390/pr13123953 (registering DOI) - 7 Dec 2025

Abstract

Manufacturers of cutting and machining machines face increasing pressure to optimize performance and sustainability while complying with evolving regulations. Traditional machine learning approaches are often limited by biased and repetitive datasets collected during real operations. This article presents a real-time simulation framework for [...] Read more.

Manufacturers of cutting and machining machines face increasing pressure to optimize performance and sustainability while complying with evolving regulations. Traditional machine learning approaches are often limited by biased and repetitive datasets collected during real operations. This article presents a real-time simulation framework for generating large synthetic datasets to train predictive machining models. A mechanistic model with probabilistic parameters is validated on experimental data and integrated into the simulator, enabling neural networks to predict process metrics such as vibrations, cutting forces, and product quality prior to machining. The framework further supports large-scale optimal control by testing setpoint control strategies for virtual prototyping. This approach allows manufacturers to enhance efficiency, reduce waste, and improve product quality while minimizing operational risks. Full article

(This article belongs to the Special Issue Machine Learning, Control, and Optimization in Manufacturing and Industry 4.0)

► Show Figures

Figure 1

6 pages, 305 KB

Open AccessEditorial

Editorial for the Special Issue “Processes in 2023”

by Alina Pyka-Pająk

Processes 2025, 13(12), 3952; https://doi.org/10.3390/pr13123952 (registering DOI) - 6 Dec 2025

Abstract

Scientific research is essential because it is a fundamental way to learn about the world, develop knowledge, and solve problems faced by individuals and society [...] Full article

(This article belongs to the Special Issue Processes in 2023)

15 pages, 1756 KB

Open AccessArticle

Well Group Scheduling Strategy for Photovoltaic Utilization Based on Improved Particle Swarm Optimization Algorithm

by Guangfeng Qi, Chenghan Zhu, Yingqiang Yan, Jiehua Feng, Dongya Zhao and Fei Li

Processes 2025, 13(12), 3951; https://doi.org/10.3390/pr13123951 (registering DOI) - 6 Dec 2025

Abstract

Photovoltaic (PV) generation, a vital component of renewable energy, is key to supporting energy supply and reducing reliance on traditional energy sources. Given the substantial energy consumption of oilfield well groups, increasing the proportion of PV energy is imperative. Furthermore, as oilfields enter [...] Read more.

Photovoltaic (PV) generation, a vital component of renewable energy, is key to supporting energy supply and reducing reliance on traditional energy sources. Given the substantial energy consumption of oilfield well groups, increasing the proportion of PV energy is imperative. Furthermore, as oilfields enter mid-to-late production stages, wells experience reduced oil production with increased energy consumption, necessitating intermittent pumping schedules. This paper addresses the optimized scheduling of pumping unit well groups within a photovoltaic-grid microgrid. The article aims to minimize the difference between the well group system’s total energy consumption and the PV power generation. A nonlinear mixed-integer programming (NMIP) model is constructed, incorporating a PV power forecasting model, a well group energy consumption model, and relevant constraints. An improved Particle Swarm Optimization (PSO) algorithm, integrating a hybrid coding scheme and multiple improvement strategies, is proposed to efficiently solve the NMIP model. The resulting optimal intermittent pumping schedule maximizes on-site PV power consumption, effectively mitigating PV energy wastage and potential grid stability issues associated with direct grid integration. The effectiveness of the proposed optimization algorithm is validated through numerical simulation case studies. Full article

(This article belongs to the Special Issue AI-Driven Optimization in Intelligent Process Control for Power and Energy Systems)

► Show Figures

Figure 1

13 pages, 2149 KB

Open AccessArticle

Process Characterization and Performance Qualification of MCSGP

by Ralf Eisenhuth and Thomas Müller-Späth

Processes 2025, 13(12), 3950; https://doi.org/10.3390/pr13123950 (registering DOI) - 6 Dec 2025

Abstract

MCSGP (Multicolumn Countercurrent Solvent Gradient Purification) with AutoPeak control is increasingly used for production of synthetic peptides and oligonucleotides at scale, requiring guidance on how to perform regulatory-compliant Process Validation. This work, for the first time, presents a Process Characterization and Process Performance [...] Read more.

MCSGP (Multicolumn Countercurrent Solvent Gradient Purification) with AutoPeak control is increasingly used for production of synthetic peptides and oligonucleotides at scale, requiring guidance on how to perform regulatory-compliant Process Validation. This work, for the first time, presents a Process Characterization and Process Performance Qualification approach to support regulatory filings of therapeutics produced using MCSGP, based on the relevant Process Validation guidelines. The approach was demonstrated for the purification of synthetic Bivalirudin. During Process Characterization, MCSGP process parameter criticality was investigated, and the gradient slope was classified as a critical process parameter to be controlled within tighter limits. As a further outcome of Process Characterization, a supervision strategy was developed and verified in four Process Performance Qualification MCSGP runs. The strategy was backed by AutoPeak, a UV-based Process Analytical Technology. The Process Validation/Process Performance Qualification (PPQ) runs not only confirmed the selected control and supervision strategy but also the advantages of MCSGP/AutoPeak as a continuous manufacturing technology, including the fully automatic operation and the reduction in in-process control sampling and Process Mass Intensity (PMI). In the presented case, the PMI was reduced from around 5200 to 1400 kg/kg, the number of in-process controls (IPCs) was reduced from 81 IPCs (60 cm i.D. column batch) per kg to 3.2 IPCs per kg (2 × 30 cm i.D. column MCSGP), while yield (gross-to-gross) increased from 57% to 62%, comparing MCSGP/AutoPeak to a process with extensive side-cut recycling. Full article

(This article belongs to the Special Issue New Frontiers in Chromatographic Separation Technology)

► Show Figures

Figure 1

18 pages, 2718 KB

Open AccessArticle

Cross-Line Transfer of Initial Juice Sugar Content Prediction Model for Sugarcane Milling

by Yujie Qin, Tao Yang, Yanqing Yin, Jiankang Zhong, Jieming Wen, Yanmei Meng, Jiang Ding and Qingshan Duan

Processes 2025, 13(12), 3949; https://doi.org/10.3390/pr13123949 (registering DOI) - 6 Dec 2025

Abstract

Some sugar factories lack sufficient data collection in their milling production lines, making it challenging to construct data-driven models for predicting initial juice sugar content. This paper proposes an adversarial semi-supervised pre-training and fine-tuning modeling method. The model is first trained on data-rich [...] Read more.

Some sugar factories lack sufficient data collection in their milling production lines, making it challenging to construct data-driven models for predicting initial juice sugar content. This paper proposes an adversarial semi-supervised pre-training and fine-tuning modeling method. The model is first trained on data-rich source production lines (190,000 samples, 500 labels, 1 min sampling for process variables, 3 times/day for sugar content) and then fine-tuned using limited data from the target production line (10,000 samples, 100 labels), effectively utilizing both labeled and unlabeled data to enhance the model’s generalization ability. Ablation experiments were conducted using data from two sugarcane milling production lines. The experimental results validate the effectiveness of each component of the proposed method and its prediction accuracy, achieving a reduction in MSE by 0.067 (23.0%) and MAE by 0.066 (15.7%) compared to the standard pre-trained model. This strategy not only optimizes the prediction of initial juice sugar content for production lines with insufficient data collection but also has the potential to improve the efficiency and quality of sugarcane milling industrial production. Full article

(This article belongs to the Section Food Process Engineering)

► Show Figures

Figure 1

15 pages, 1110 KB

Open AccessArticle

An NDIR System with a Synergistic CNN-SVM Model for Discriminating CH₄ in Complex Alkane Mixtures

by Zhaoliang Zhang, Juxiang Zhu and Fei Pan

Processes 2025, 13(12), 3948; https://doi.org/10.3390/pr13123948 (registering DOI) - 6 Dec 2025

Abstract

The selective identification of CH₄ in alkane gas mixtures remains challenging due to overlapping infrared absorption spectra among alkane species. This study introduces a novel algorithmic filter paradigm that fundamentally shifts from hardware-based to software-defined selectivity in Nondispersive Infrared (NDIR) sensing. Instead [...] Read more.

The selective identification of CH₄ in alkane gas mixtures remains challenging due to overlapping infrared absorption spectra among alkane species. This study introduces a novel algorithmic filter paradigm that fundamentally shifts from hardware-based to software-defined selectivity in Nondispersive Infrared (NDIR) sensing. Instead of relying on costly, fixed-wavelength optical filters, we employ a simplified four-source NDIR platform that deliberately captures composite spectral signals from mixed gases. A CNN-SVM hybrid model then serves as the algorithmic filter: the Convolutional Neural Network extracts discriminative features from overlapping spectra, while the Support Vector Machine performs robust classification. This integrated system achieved 89% accuracy in CH₄ identification within complex alkane mixtures. By replacing expensive optical components with intelligent algorithms, this work demonstrates a cost-effective, flexible, and scalable approach. Full article

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

20 pages, 7630 KB

Open AccessArticle

Multi-Time-Scale Source–Storage–Load Coordination Scheduling Strategy for Pumped Storage with Characteristic Distribution

by Bo Yi, Sheliang Wang, Pin Zhang, Yan Liang, Bo Ming, Yi Guo and Qiang Huang

Processes 2025, 13(12), 3947; https://doi.org/10.3390/pr13123947 (registering DOI) - 6 Dec 2025

Abstract

To address the pressing challenges of low new energy utilization, high power system operating costs, and compromised power supply reliability in regional grids, we propose a multi-time-scale source–storage–load coordinated scheduling strategy that explicitly accounts for the characteristic distribution of grid-connected energy storage stations, [...] Read more.

To address the pressing challenges of low new energy utilization, high power system operating costs, and compromised power supply reliability in regional grids, we propose a multi-time-scale source–storage–load coordinated scheduling strategy that explicitly accounts for the characteristic distribution of grid-connected energy storage stations, including their state-of-charge constraints, round-trip efficiency profiles, and location-specific operational dynamics. A day-ahead scheduling framework is developed by integrating the multi-time-scale behavioral patterns of diverse load-side demand response resources with the dynamic operational characteristics of energy storage stations. By embedding intra-day rolling optimization and real-time corrective adjustments, we mitigate prediction errors and adapt to unforeseen system disturbances, ensuring enhanced operational accuracy. The objective function minimizes a weighted sum of system operation costs encompassing generation, transmission, and auxiliary services; wind power curtailment penalties for unused renewables; and load shedding penalties from unmet demand, balancing economic efficiency with supply quality. A mixed-integer programming model formalizes these tradeoffs, solved via MATLAB 2020b coupled CPLEX to guarantee optimality. Simulation results demonstrate that the strategy significantly cuts wind power curtailment, reduces system costs, and elevates new energy consumption—outperforming conventional single-time-scale methods in harmonizing renewable integration with grid reliability. This work offers a practical solution for enhancing grid flexibility in high-renewable penetration scenarios. Full article

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

► Show Figures

Figure 1

16 pages, 1736 KB

Open AccessArticle

A First Process-Oriented Characterization of Eriolobus trilobatus (Labill. ex Poiret) Bark from Turkey: Chemical, Morphological and Energy Properties

by Umut Șen, Cengiz Yücedağ, Büşra Balcı, Şefik Arıcı, Günnur Koçar, Beyza Şat, Catarina Viegas, Margarida Gonçalves, Isabel Miranda and Helena Pereira

Processes 2025, 13(12), 3946; https://doi.org/10.3390/pr13123946 (registering DOI) - 6 Dec 2025

Abstract

For the first time, Eriolobus trilobatus bark from Turkey has been characterized in terms of its chemical, extractive, fuel, and ash characteristics using SEM–EDS, wet chemical analysis, phenolic analysis, FT-IR, TGA, XRF, XRD, BET surface area measurement, proximate analysis, and ash fusion temperature [...] Read more.

For the first time, Eriolobus trilobatus bark from Turkey has been characterized in terms of its chemical, extractive, fuel, and ash characteristics using SEM–EDS, wet chemical analysis, phenolic analysis, FT-IR, TGA, XRF, XRD, BET surface area measurement, proximate analysis, and ash fusion temperature (AFT) determination. The results showed that the bark contains 13% ash, dominated by calcium oxalate, and 15% extractives, largely composed of polar phenolic compounds with moderate radical-scavenging potential. Thermal decomposition of bark proceeds in four distinct stages, associated with the sequential degradation of extractives/hemicelluloses, cellulose, lignin/suberin, and inorganic fractions. The higher calorific value of 14.9 MJ/kg indicates moderate fuel quality compared with conventional woody biomass. Ash is mesoporous with a CaO-rich structure highly suitable for catalytic applications in biodiesel production and biomass gasification. Ash fusion analysis revealed a high flow temperature (1452 °C), indicating a very low slagging risk during thermochemical conversion. Overall, E. trilobatus bark is a promising material for value-added biorefinery pathways, enabling processes for the production of biochars, CaO-based catalysts, phenolic extracts, and sustainable energy. The valorization of E. trilobatus bark not only enhances the economic potential of forestry residues but also provides environmental co-benefits through carbon soil amendment and landscape applications. Full article

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

► Show Figures

Graphical abstract

18 pages, 5592 KB

Open AccessFeature PaperArticle

Development and Optimization of Green Extraction Process of Greek Mountain Tea (Sideritis scardica)

by Maria-Anna Karadendrou, Anna Karantoni, Ioanna Pitterou, Kyriaki Safari, Georgios Stavropoulos, Andromachi Tzani and Anastasia Detsi

Processes 2025, 13(12), 3945; https://doi.org/10.3390/pr13123945 (registering DOI) - 6 Dec 2025

Abstract

Medicinal and aromatic plants continue to attract attention as rich sources of natural bioactive compounds with potential health benefits. Among them, Greek mountain tea (Sideritis scardica) is widely recognized for its high content of phytochemicals, which have been associated with various [...] Read more.

Medicinal and aromatic plants continue to attract attention as rich sources of natural bioactive compounds with potential health benefits. Among them, Greek mountain tea (Sideritis scardica) is widely recognized for its high content of phytochemicals, which have been associated with various biological activities. In this study, Natural Deep Eutectic Solvents (NADESs) were investigated as a sustainable and efficient alternative to conventional solvents for the extraction of such compounds, aiming to the development of a more efficient extraction process. Six task-specific designed NADESs were prepared and evaluated for their extraction efficiency, based on the Total Phenolic Content (TPC) and Total Flavonoid Content (TFC) of the extract. The most promising NADES, comprising betaine and 1,3-propandeiol, was selected for process optimization using a Box–Behnken design and key extraction parameters were systematically examined to maximize TPC and TFC. The extract obtained under the proposed conditions (S/L = 20 mg/g, 240 min, 40% water as cosolvent) exhibited a TPC and TFC value of 49.2 mg_GAE/g and 45.9 mg_CAE/g, respectively, approximately two times higher than the values of a hydroethanolic extract, obtained under the same conditions (TPC = 26.6 mg_GAE/g, TF = 19.9 mg_CAE/g). The optimum extract was further analyzed using HPLC-DAD to determine its phytochemical profile and was compared with the conventional hydroethanolic extract, revealing the role of the selected media on the extracted compounds. Full article

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

► Show Figures

Figure 1

16 pages, 3364 KB

Open AccessArticle

Study on Jet Characteristics of Novel Coherent Tuyeres and Injection of Hydrogen-Rich Gas in Blast Furnace

by Yongwen Fan, Yunmeng Wang, Yingshi Xu, Peng Xu, Peng Han and Junhong Zhang

Processes 2025, 13(12), 3944; https://doi.org/10.3390/pr13123944 - 5 Dec 2025

Abstract

This study designed a novel coherent tuyere device capable of adjusting the core length of the jet flow. Physical experiments were first conducted to investigate how the number of secondary nozzles in the coherent tuyere affects the gas–solid two-phase flow behavior within the [...] Read more.

This study designed a novel coherent tuyere device capable of adjusting the core length of the jet flow. Physical experiments were first conducted to investigate how the number of secondary nozzles in the coherent tuyere affects the gas–solid two-phase flow behavior within the raceway during the blasting process. Subsequently, the Computational Fluid Dynamics (CFD) method was employed to examine the influence of structural parameters on jet morphology in coherent tuyere. Finally, computational fluid dynamics and discrete phase method (CFD-DPM) was adopted, and the velocity, temperature, and composition distribution patterns within the raceway were analyzed following the injection of hydrogen-rich gas through the coherent tuyere. The results of the physics experiment indicate that increasing the number of secondary nozzles in the coherent tuyere can significantly enlarge the raceway size and broaden the particle kinematic zone, thereby enhancing particle fluidization at the periphery of the raceway. CFD numerical simulation results indicate that increasing the number of secondary nozzles of the tuyere can effectively extend the length of the velocity jet core region. Compared with conventional tuyeres, a six-nozzle coherent tuyere can increase the core length of the blast velocity by about 40%. When the diameter of the secondary nozzles in the coherent tuyere is doubled, the core length of the blast velocity increases by 10%. The results of the CFD-DPM coupled simulation show that unburned carbon particles flow and combust along the periphery of the raceway with the hot air, leading to the formation of a high-temperature region in this area. After the injection of hydrogen-rich gas through the coherent tuyere, the temperature in the raceway decreased significantly. A high-concentration region of H₂ appeared at the periphery of the raceway, while the high-concentration CO region increased in concentration and gradually extended toward the upper part of the raceway. This research achievement is of significant importance for optimizing blast furnace blast kinetic energy and hydrogen-rich gas injection. Full article

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

31 pages, 6065 KB

Open AccessReview

Selecting the Front-Runners: Comparative Evaluation of Emerging Technologies for Microplastic Removal from Drinking Water

by Simeng Li

Processes 2025, 13(12), 3943; https://doi.org/10.3390/pr13123943 - 5 Dec 2025

Abstract

Microplastics (MPs) have emerged as persistent and ubiquitous contaminants in aquatic and terrestrial environments, yet existing reviews often focus narrowly on conventional removal methods and lack an integrated assessment of rapidly emerging technologies. This review addresses this critical gap by providing a comprehensive [...] Read more.

Microplastics (MPs) have emerged as persistent and ubiquitous contaminants in aquatic and terrestrial environments, yet existing reviews often focus narrowly on conventional removal methods and lack an integrated assessment of rapidly emerging technologies. This review addresses this critical gap by providing a comprehensive and comparative synthesis of both established and next-generation approaches for MP removal from water and wastewater systems. Conventional methods such as coagulation–flocculation, sedimentation, and filtration are compared with advanced approaches including membrane separation, adsorption using engineered biochar and nanomaterials, advanced oxidation processes (AOPs), and biodegradation using microbial or enzymatic pathways. Particular emphasis is placed on hybrid and integrated systems, an area seldom summarized in prior reviews, highlighting their synergistic potential to enhance removal efficiency, reduce energy demand, and improve operational stability. Promising front-runner technologies including membrane filtration coupled with coagulation pretreatment and biochar-based magnetic adsorption systems have been identified based on a balanced performance across the key criteria of removal efficiency, scalability, energy demand, cost, byproduct risk, and environmental sustainability. The review concludes by outlining key research priorities such as standardized testing protocols, scalable biophysicochemical integration strategies, and sustainability-oriented life-cycle assessments to guide future innovation in MP management. Full article

(This article belongs to the Special Issue Processes Development for Wastewater Treatment)

► Show Figures

Figure 1

27 pages, 1897 KB

Open AccessArticle

Numerical Geometric Evaluation of an L-Shaped Oscillating Water Column Wave Energy Converter Under the Realistic Sea State Found in Rio Grande-RS

by Maycon da Silveira Paiva, Ana Paula Giussani Mocellin, Elizaldo Domingues dos Santos, Luiz Alberto Oliveira Rocha, Bianca Neves Machado and Liércio André Isoldi

Processes 2025, 13(12), 3942; https://doi.org/10.3390/pr13123942 - 5 Dec 2025

Abstract

This study conducts a numerical investigation of the geometry of the oscillating water column (OWC) wave energy converter under realistic irregular wave conditions found off the coast of Rio Grande, southern Brazil. Two OWC models were compared: the conventional design and the L-shaped [...] Read more.

This study conducts a numerical investigation of the geometry of the oscillating water column (OWC) wave energy converter under realistic irregular wave conditions found off the coast of Rio Grande, southern Brazil. Two OWC models were compared: the conventional design and the L-shaped configuration (L-OWC). The OWC structure consists of a hydropneumatic chamber and an air duct, where a turbine is coupled to an electric generator. Additionally, in the L-shaped chamber configuration, a water intake duct is considered. The constructal design method was employed for the geometric evaluation of the devices. For the L-OWC, the influence of the height-to-length ratio of the water intake duct on the obtained hydropneumatic power available was analyzed. In parallel, for the conventional OWC, the free-board submergence was investigated. Subsequently, the optimal geometry for each OWC model was selected to study the height-to-length ratio of the hydropneumatic chamber. Numerical simulations were performed using ANSYS Fluent software. Thus, the performance of the converters was improved by approximately 35.76 times for the L-OWC and 3.78 times for the conventional OWC. However, it is noteworthy that the optimal configuration of the conventional OWC achieved a performance 2.62 times higher than the optimal L-OWC geometry. Full article

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

19 pages, 2414 KB

Open AccessArticle

An Adaptive Early Warning Method for Wind Power Prediction Error

by Li Zhang, Facai He, Mouyuan Chen, Chun He, Zhigang Huang, Chao Wang and Lei Yan

Processes 2025, 13(12), 3941; https://doi.org/10.3390/pr13123941 - 5 Dec 2025

Abstract

Despite the continuous development of wind power forecasting methods, forecasting errors remain unavoidable, especially during extreme weather events. However, current research on quantifying these errors is quite limited. This paper proposes an adaptive error risk early warning method that can directly predict the [...] Read more.

Despite the continuous development of wind power forecasting methods, forecasting errors remain unavoidable, especially during extreme weather events. However, current research on quantifying these errors is quite limited. This paper proposes an adaptive error risk early warning method that can directly predict the magnitude of forecast errors and classify and warn of risks, thereby achieving proactive risk management. This method comprises three core designs. First, mechanism-based feature engineering captures the driving factors of error generation, including numerical weather prediction bias, atmospheric instability, and meteorological dynamics, all of which are key factors leading to forecast bias. Second, a stacked ensemble method integrates quantile regression, random forest, and gradient booster, utilizing complementary learning capabilities to handle high-dimensional non-stationary error patterns. Third, K-means clustering establishes a dynamic risk threshold that adapts to changes in seasonal error distribution, overcoming the limitations of fixed thresholds. Validation using actual wind farm operation data demonstrates significant improvements: the proposed ensemble model reduces the Root Mean Square Error (RMSE) by 2.5% compared to the best single model, and the dynamic threshold mechanism increases the High-Risk Recall rate from 89.7% to 96.9%. These results confirm that the method can effectively warn of high-error events and provide timely and actionable decision support to enhance grid stability and security. Full article

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

18 pages, 5646 KB

Open AccessArticle

Study on Optimization of 3DP Rock Elastoplastic Properties: Coupling Experimental and DEM Simulation

by Jiecheng Song, Jianjun Liu and Rui Song

Processes 2025, 13(12), 3940; https://doi.org/10.3390/pr13123940 - 5 Dec 2025

Abstract

Three-dimensionally printed (3DP) samples with quartz sand effectively avoid the heterogeneity of reservoir rocks in underground gas storage (UGS), providing reliable supports for rock mechanics research under cyclic injection–production pressures. A study on the mechanical properties of 3DP rock samples was conducted by [...] Read more.

Three-dimensionally printed (3DP) samples with quartz sand effectively avoid the heterogeneity of reservoir rocks in underground gas storage (UGS), providing reliable supports for rock mechanics research under cyclic injection–production pressures. A study on the mechanical properties of 3DP rock samples was conducted by coupling triaxial tests with discrete element method (DEM) simulation. Key results are as follows: (1) The graded particle model (GPM) based on actual particle size distribution (PSD) closely matched experimental data, with an average peak strength error of 1.13%. (2) Cyclic saturation post-processing with silica sol significantly enhanced mechanical properties, increasing peak strength from 5.70 to 52.84 MPa and inducing a plastic-to-brittle failure transition. A power-law relationship was identified between saturation cycles and macroscopic strength. (3) DEM simulations revealed that bond effective modulus linearly controls Young’s modulus. The influence of cohesion on peak strength is greater than that of the friction angle, and the bond stiffness ratio regulates shear failure threshold. The cohesion force is 50 MPa, and the peak strength has been increased to 107.89 MPa. (4) Enhancing particle cohesive strength was key to improving the mechanical properties of 3DP rock samples. This study provides a reliable framework for customized 3DP rock preparation and UGS-related mechanical simulations. Full article

(This article belongs to the Topic Exploitation and Underground Storage of Oil and Gas)

► Show Figures

Graphical abstract

Journal Description

Processes

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI