Processes

14 pages, 2978 KB

Open AccessArticle

The Application of Dual-Branch Multi-Layer Perceptron Intelligent Algorithm in the Prediction of Sweet Spots in Tight Gas Exploration and Development

by Kunjian Wang, Fei Zhang, Fan Yang, Zhanglong Tan, Yinbo Qi, Lisha Sun and Shanyong Liu

Processes 2026, 14(10), 1673; https://doi.org/10.3390/pr14101673 - 21 May 2026

Viewed by 239

Abstract

Due to the complex issues of low porosity and low permeability in tight sandstone reservoirs, non-unified data measurement, and the limitation of traditional methods by empirical formulas and simple statistical models, which make it difficult to couple the correlation of parameters, how to [...] Read more.

Due to the complex issues of low porosity and low permeability in tight sandstone reservoirs, non-unified data measurement, and the limitation of traditional methods by empirical formulas and simple statistical models, which make it difficult to couple the correlation of parameters, how to quickly clean data, establish a comprehensive geological-engineering sweet spot evaluation method, and improve prediction accuracy and engineering decision-making effectiveness have become an urgent technical challenge. This study takes the logging and fracturing construction data in the L area as the data set, uses the Pearson correlation coefficient method to verify the nonlinear characteristics of features, and constructs a geological-engineering integrated intelligent decision-making algorithm based on the collaborative optimization of a dual-branch multi-layer perceptron and attention mechanism. The training results of the dual-branch multi-layer perceptron model and traditional machine learning methods are compared and analyzed. The results show that the prediction error of the adopted dual-branch multi-layer perceptron neural network model is 5.44%. The weight of geological factors in this area accounts for 51.71%, and the engineering factors account for 48.29%. This method has been field-applied in 25 wells in the L area, with a production coincidence rate reaching 94.66%. The sweet spots of tight sandstone reservoirs are mainly the H5 and H6 submembers. The deep integration of machine learning interpretability and geological engineering practice provides a new approach for sweet spot prediction. Full article

(This article belongs to the Special Issue Emerging Technologies for Unconventional Reservoir Development and Geothermal Resource Utilization)

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24 pages, 3608 KB

Open AccessArticle

Hierarchical Adjustable Potential Assessment of Electric Vehicles for Transmission–Distribution–Microgrid Coordination

by Mingshen Wang, Wenjun Ruan, Yi Pan, Xiaodong Yuan, Haiqing Gan and Kemin Dai

Processes 2026, 14(10), 1672; https://doi.org/10.3390/pr14101672 - 21 May 2026

Viewed by 295

Abstract

Electric vehicles (EVs) provide fast charging/discharging flexibility; however, single-layer assessments may overestimate the flexibility that can be physically delivered under downstream distribution-network constraints. This paper proposes a process-oriented hierarchical adjustable-potential assessment framework for transmission–distribution–microgrid coordination. At the microgrid/station layer, a chance-constrained vehicle feasible [...] Read more.

Electric vehicles (EVs) provide fast charging/discharging flexibility; however, single-layer assessments may overestimate the flexibility that can be physically delivered under downstream distribution-network constraints. This paper proposes a process-oriented hierarchical adjustable-potential assessment framework for transmission–distribution–microgrid coordination. At the microgrid/station layer, a chance-constrained vehicle feasible set is constructed to capture user uncertainty, and probabilistic Minkowski-sum aggregation is used to obtain a station-level theoretical envelope. At the distribution layer, voltage and line-thermal constraints are modeled using LinDistFlow and intersected with the theoretical envelope to derive an effective potential satisfying network security limits. At the transmission layer, the effective feasible region is further packaged into a time-varying generalized-battery parameter set for consistent upward reporting without introducing dispatch optimization. In addition, a bottleneck truncation effect (BTE) metric is defined to quantify how distribution constraints reduce upstream-usable flexibility. Case studies show that hierarchical network constraints compress both peak EV flexibility and the all-day feasible-region area. Specifically, the microgrid-layer theoretical envelope reaches 432 kW on the charging side, 124 kW on the discharging side, and 3799 kWh in feasible-region area. After distribution-layer security clipping, the effective envelope becomes 299 kW, 124 kW, and 2063 kWh, corresponding to reductions of 30.79%, 0.00%, and 45.70%, respectively, relative to the microgrid layer. After transmission-layer packaging, the deliverable envelope is further reduced to 285 kW, 118 kW, and 1946 kWh, i.e., reductions of 34.03%, 4.84%, and 48.78%, respectively, relative to the microgrid baseline. These results demonstrate that the proposed workflow provides verifiable and time-varying deliverable capability boundaries for cross-layer EV flexibility assessment. Full article

(This article belongs to the Special Issue Advances in Optimal Operation of Modern Power Systems for Flexibility Enhancement)

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22 pages, 9800 KB

Open AccessArticle

A Physics-Constrained Dual-Stream Dynamic Framework for Wind Power Forecasting Under Extreme Weather

by Yunzhi Hao and Jing Cao

Processes 2026, 14(10), 1671; https://doi.org/10.3390/pr14101671 - 21 May 2026

Viewed by 387

Abstract

Accurate wind power forecasting is essential for ensuring power grid stability and facilitating the large-scale integration of renewable energy, yet it faces significant challenges due to the randomness, variability, and intermittency of wind resources and the increasing frequency of extreme weather events. Existing [...] Read more.

Accurate wind power forecasting is essential for ensuring power grid stability and facilitating the large-scale integration of renewable energy, yet it faces significant challenges due to the randomness, variability, and intermittency of wind resources and the increasing frequency of extreme weather events. Existing data-driven approaches often struggle to balance temporal continuity with meteorological sensitivity, leading to lag effects during rapid fluctuations, and frequently generate predictions that violate physical domain knowledge. To address these limitations, this paper proposes a dual-stream architecture to decouple temporal dependencies and spatial–meteorological mappings, utilizing a Physics-Informed GRU (PI-GRU) and an Enhanced Random Forest (ERF). Both streams are strictly bounded by physical constraints. Furthermore, a scenario-aware adaptive fusion mechanism is introduced to dynamically adjust the model’s reliance on each stream based on real-time wind speed gradients and volatility indices. Extensive experiments were conducted using a comprehensive dataset from three coastal wind farms over 8 months, encompassing stable regimes and extreme weather events. Evaluating across both 1-day and 4-day forecast horizons, the results demonstrate that our method significantly outperforms state-of-the-art baselines, proving its robustness and practical value for grid security and dispatch optimization. Full article

(This article belongs to the Special Issue Data-Based Prediction Models in Energy Systems: From Principles to Applications)

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21 pages, 5262 KB

Open AccessArticle

Virtual Calibration of Steady-State Emissions for Heavy-Duty Diesel Engines Based on Regression Models

by Dongwei Liu, Tianyou Wang, Wenjian Jiao, Xiaowen Xu and Liangtao Xie

Processes 2026, 14(10), 1670; https://doi.org/10.3390/pr14101670 - 21 May 2026

Viewed by 437

Abstract

To promote the green and low-carbon transition and achieve sustainable development in the transportation sector, virtual calibration technology was employed for the efficient and precise control of emissions from heavy-duty diesel engines and aftertreatment systems. A data-driven, semi-empirical and semi-physical simulation modeling method [...] Read more.

To promote the green and low-carbon transition and achieve sustainable development in the transportation sector, virtual calibration technology was employed for the efficient and precise control of emissions from heavy-duty diesel engines and aftertreatment systems. A data-driven, semi-empirical and semi-physical simulation modeling method was proposed. By constructing core modules based on physical mechanisms and refining empirical parameters using experimental data, the method improves computational efficiency while maintaining the prediction accuracy of key parameters. Additionally, a collaborative architecture combining physical actuators and virtual sensor signals was introduced, laying the foundation for the validity of virtual calibration. By innovatively introducing a closed-loop system with real actuators and virtual sensors, the dynamic response characteristics of the control system are faithfully reproduced, providing a reliable environment for validating the results of virtual calibration. Under steady-state conditions, the results demonstrated an average relative error of 1.7% for brake-specific fuel consumption (BSFC) and 6.1% for NOx emissions. An open-loop system for the virtual calibration testing platform was constructed for steady-state calibration. Using the main injection timing and common rail pressure as independent variables, a D-optimal design was utilized to generate 43 sets of experimental points, from which a polynomial regression model was established (R² ≥ 98%). Under the constraints of NOx and pre-turbine temperature, fuel consumption in the low-load range is reduced by 0.5–3 g/kW·h, aftertreatment NOx emissions are reduced by 0.5–3 g/kW·h, and exhaust temperature is increased by 10 °C. This study establishes a complete development workflow consisting of “operating condition design-virtual optimization-bench validation,” significantly enhancing calibration efficiency and engineering applicability. This method shortens the calibration cycle and reduces the number of physical bench tests, providing the industry with a comprehensive calibration methodology tailored to engine operating conditions that is both reproducible and scalable. Full article

(This article belongs to the Section Energy Systems)

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27 pages, 1784 KB

Open AccessArticle

Action-Oriented Programming and Automatic Agent Generation for Adaptive Data Collection in Decentralized Data Ecosystems

by Mustafa Tayyip Bayram, Houssam Razouk and Kyandoghere Kyamakya

Processes 2026, 14(10), 1669; https://doi.org/10.3390/pr14101669 - 21 May 2026

Viewed by 272

Abstract

The semiconductor manufacturing industry depends on effective data collection and analysis for critical processes such as Root Cause Analysis (RCA) and Risk Assessment (RA). Both processes involve software-driven data collection and subsequent analysis by domain experts to support informed decision-making. However, the increasing [...] Read more.

The semiconductor manufacturing industry depends on effective data collection and analysis for critical processes such as Root Cause Analysis (RCA) and Risk Assessment (RA). Both processes involve software-driven data collection and subsequent analysis by domain experts to support informed decision-making. However, the increasing complexity, volume, and decentralized nature of manufacturing data pose significant challenges for effective data collection. Data is distributed across multiple systems with varying formats and ownership, making conventional programming paradigms and manual data collection scripts inadequate for handling this decentralized data landscape. To address these challenges, this study proposes integrating Action-Oriented Programming (AcOP) with Automatic Agent Generation (AAG) as a novel solution. AcOP emphasizes actions as fundamental execution units, separating system behavior and data. Complementing this, AAG uses large language models (LLMs) to autonomously generate intelligent agents, which manage these actions and perform preliminary data analysis with domain-specific knowledge. Our experimental setup compares three microservice applications supporting RCA and RA: Object-Oriented Programming (OOP), AcOP, and AcOP integrated with AAG. Evaluation results indicate that AcOP improves modularity, adaptability, and error handling in decentralized systems. Integrating AAG enhances automation, provides a flexible, low-maintenance solution for data collection and analysis pipelines, and promotes autonomous microservice architectures in data-intensive environments. 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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18 pages, 1922 KB

Open AccessArticle

Selective Synthesis of Nitrite and Nitrate by Liquid-Phase Plasma Using a Dual-Cell: Role of Active Species

by Uijun Kim, Changhyeon Park and Seunghyo Lee

Processes 2026, 14(10), 1668; https://doi.org/10.3390/pr14101668 - 21 May 2026

Viewed by 257

Abstract

Plasma-assisted nitrogen fixation has emerged as a promising strategy for sustainable nitrate production. However, the coexistence of multiple interfaces and complex multi-step reaction pathways within the plasma-liquid system often leads to the formation of mixed nitrogen species, posing a significant challenge for achieving [...] Read more.

Plasma-assisted nitrogen fixation has emerged as a promising strategy for sustainable nitrate production. However, the coexistence of multiple interfaces and complex multi-step reaction pathways within the plasma-liquid system often leads to the formation of mixed nitrogen species, posing a significant challenge for achieving high product selectivity. In this study, a dual-cell reactor was introduced in liquid-phase plasma (LPP) system, enabling selective product distribution. Optical emission spectroscopy revealed pronounced signals corresponding to the second positive system (SPS) of N₂ and the first negative system (FNS) of N₂⁺, indicative of strong plasma excitation and ionization processes that facilitated the formation of reactive nitrogen oxide intermediates. These species were subsequently converted into aqueous NO₂⁻ and further oxidized into NO₃⁻ only in the reaction cell where reactive species are generated. The effects of key parameters, including electrode material, treatment time, solution pH, and discharge conditions, were comprehensively evaluated. As a result, the reaction cell achieved a nitrate selectivity of 98.9%, whereas the absorption cell achieved a nitrite selectivity of 100%. Findings from EPR and scavenger analyses collectively provide a detailed mechanistic understanding of LPP-driven nitrogen fixation and highlight the importance of controlling plasma parameters to achieve highly selective production of nitrogen compounds. Full article

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

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17 pages, 5130 KB

Open AccessArticle

Coupled Effects of Obstacle Distribution and Blockage Ratio on Flame Propagation and Pressure Rise in Propane–Air Premixed Deflagration

by Ning Zhou, Rongkun Rao, Xue Li, Bing Chen, Chunhai Yang, Guangping Zhou, Xuanya Liu, Weiqiu Huang and Xiongjun Yuan

Processes 2026, 14(10), 1667; https://doi.org/10.3390/pr14101667 - 21 May 2026

Viewed by 238

Abstract

To reveal the mechanisms by which obstacle distribution affects propane–air premixed deflagration under different blockage ratios, large eddy simulation (LES) was employed to investigate flame propagation and pressure rise in a confined duct with four obstacle distributions and four blockage ratios. The coupled [...] Read more.

To reveal the mechanisms by which obstacle distribution affects propane–air premixed deflagration under different blockage ratios, large eddy simulation (LES) was employed to investigate flame propagation and pressure rise in a confined duct with four obstacle distributions and four blockage ratios. The coupled effects of obstacle layout and blockage ratio on flame morphology, propagation velocity, vorticity evolution, and pressure rise rate were analyzed. The results show that obstacle distribution significantly changes flame front structures: One-side obstacles produce claw-like flames, center layout obstacles generate tongue-like flames with large vortex regions at low-to-moderate blockage ratios, and both-side or around layout obstacles form mushroom-like flames. At high blockage ratios, around layout obstacles redirect the flow into a high-speed axial jet, leading to the highest flame velocity and maximum pressure rise rate. These findings indicate that the dominant flame acceleration mechanism shifts from vortex-induced flame wrinkling at low-to-moderate blockage ratios to axial-jet-driven flame acceleration at high blockage ratios, providing guidance for obstacle layout optimization and explosion risk mitigation in confined propane–air systems. Full article

(This article belongs to the Section Process Safety and Risk Management)

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30 pages, 2515 KB

Open AccessReview

Unconventional Technologies for Starch Modification: A Critical Review of Recent Advances and Applications in Paste Property Improvement

by Flaviana Coelho Pacheco, Ana Flávia Coelho Pacheco, Irene Andressa, Jeferson Silva Cunha, Fabio Ribeiro dos Santos, Handray Fernandes de Souza, Hiasmyne Silva de Medeiros, Kátia Silva Maciel, Paulo Henrique Costa Paiva and Bruno Ricardo de Castro Leite Júnior

Processes 2026, 14(10), 1666; https://doi.org/10.3390/pr14101666 - 21 May 2026

Cited by 1 | Viewed by 376

Abstract

Starches from various botanical sources are extensively utilized across food applications due to their functional and technological properties. However, native starches exhibit limitations under processing conditions involving heat, pH shifts, or mechanical stress, which restrict their application. In response, the demand for “clean-label” [...] Read more.

Starches from various botanical sources are extensively utilized across food applications due to their functional and technological properties. However, native starches exhibit limitations under processing conditions involving heat, pH shifts, or mechanical stress, which restrict their application. In response, the demand for “clean-label” products has driven interest in sustainable and non-chemical modification strategies. This review aims to provide a critical overview of the effects of unconventional technologies—including ozone, ultrasound, high-pressure processing, high-pressure homogenization, pulsed electric fields, and cold plasma—on starch granule structure and the resulting pasting properties. A bibliometric analysis based on 1679 documents from Scopus and Web of Science^® highlighted a lack of previous studies integrating quantitative trends with in-depth technical discussion. The selected technologies demonstrate potential to enhance starch functionality through distinct modification mechanisms, although their effects are highly dependent on starch source, structure, and processing parameters. Despite promising advances, most applications remain restricted to laboratory scale, and further research is required to optimize conditions and promote industrial feasibility. Full article

(This article belongs to the Special Issue Advanced Technology in Food Processing)

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33 pages, 9010 KB

Open AccessArticle

Reduced-Order Modeling of Transient Events in Data Centers Using Dimensionality Reduction Techniques

by Julio Cesar Ramírez Acero, Ricardo Isaza-Ruget and Javier Rosero-García

Processes 2026, 14(10), 1665; https://doi.org/10.3390/pr14101665 - 21 May 2026

Viewed by 679

Abstract

The present paper proposes a methodology for the analysis and modelling of transient events in a data center based on real-world high-resolution voltage and current measurements. The proposed approach includes the identification of relevant events, temporal segmentation, multivariate representation, and the application of [...] Read more.

The present paper proposes a methodology for the analysis and modelling of transient events in a data center based on real-world high-resolution voltage and current measurements. The proposed approach includes the identification of relevant events, temporal segmentation, multivariate representation, and the application of dimensionality reduction techniques to obtain compact representations of the observed dynamics. A total of eight representative transient events were identified in the available dataset. These events were characterized by short-duration disturbances of moderate magnitude, which is consistent with the operation of highly reliable infrastructures. Three main methods were evaluated: PCA/POD, Kernel PCA, and Autoencoder. The results show that all three approaches are capable of reconstructing the event dynamics with low reconstruction errors, suggesting the presence of a low-dimensional structure in the analyzed data. Among the evaluated methods, PCA/POD provided the best balance between compactness, interpretability, and computational efficiency, while Kernel PCA and Autoencoder offered advantages for representing nonlinear behaviors. The results provide case-study evidence on the feasibility of constructing reduced-order representations for the analysis and monitoring of transient events in data centers under limited-data conditions. Full article

(This article belongs to the Special Issue Advanced Processes for Sustainable Energy Conversion and Utilization)

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3 pages, 145 KB

Open AccessEditorial

Separation Processes for Environmental Preservation: Advances in Sustainable Technologies, Waste Valorization, and Circular Economy

by Maria Angélica Simões Dornellas de Barros and Thiago Peixoto de Araújo

Processes 2026, 14(10), 1664; https://doi.org/10.3390/pr14101664 - 21 May 2026

Viewed by 217

Abstract

Environmental pollution remains one of the most pressing challenges faced by modern society, particularly due to the increasing occurrence of emerging contaminants, hazardous compounds, pathogenic microorganisms, toxic metals, dyes, and complex industrial effluents [...] Full article

(This article belongs to the Special Issue Separation Processes for Environmental Preservation)

18 pages, 4370 KB

Open AccessArticle

Dynamic Evolution of Gas–Water Displacement and Microscopic Fluid Occurrence in Deep Coalbed Methane

by Yuan Wang, Dong Chen, Wei Sun, Yanqing Feng, Shirui Liu, Zengping Zhao, Hongxing Huang, Xiaosong Shi, Mansheng Wu and Dong Feng

Processes 2026, 14(10), 1663; https://doi.org/10.3390/pr14101663 - 21 May 2026

Viewed by 522

Abstract

Deep coalbed methane (CBM) has become an important contributor to natural gas production worldwide. Its fluid occurrence characterized by high free gas content and low water saturation suggests substantial gas-driven displacement caused by hydrocarbon generation overpressure. However, the microscopic evolution of this process [...] Read more.

Deep coalbed methane (CBM) has become an important contributor to natural gas production worldwide. Its fluid occurrence characterized by high free gas content and low water saturation suggests substantial gas-driven displacement caused by hydrocarbon generation overpressure. However, the microscopic evolution of this process and the corresponding occurrence remain poorly understood. To address these issues, we combined centrifugation experiments, nuclear magnetic resonance (NMR) monitoring, and theoretical modeling to systematically investigate pore-scale displacement dynamics and the associated fluid distribution. A dynamic evolution model for gas–water displacement in nanopores is developed by incorporating the capillary pressure and disjoining pressure, and validated against the centrifugation experimental data. At the pore scale, gas–water displacement is governed by critical displacement pressure and water film thickness. Water saturation declines sharply once the displacement pressure exceeds a critical threshold, after which it decreases slowly as the water film progressively thins. At the porous media scale, water saturation continuously decreases with increasing displacement pressure. For the high-rank coal samples in this study, the overall water saturation decreases to 49.15% as the displacement pressure increases to 10 MPa. The water film is negligible for pores larger than 20 nm, but significant for pores smaller than 20 nm. This critical pore size is not fixed, but is a dynamic threshold controlled by the disjoining pressure parameter. The occurrence of free gas in deep CBM is governed by the relative matching between hydrocarbon generation overpressure and reservoir pore structure. These findings provide a theoretical basis for resource assessment and efficient development of deep CBM. Full article

(This article belongs to the Special Issue Advances in Enhancing Unconventional Oil/Gas Recovery, 3rd Edition)

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42 pages, 2355 KB

Open AccessReview

Intelligent Fault Discrimination in Power Transformers: A Comprehensive Review of Methods

by Mohammed Alenezi, Fatih Anayi, Michael Packianather and Mokhtar Shouran

Processes 2026, 14(10), 1662; https://doi.org/10.3390/pr14101662 - 20 May 2026

Viewed by 433

Abstract

The reliable discrimination between magnetizing inrush currents and internal faults is essential for effective power transformer protection and has a direct impact on the security and stability of modern power systems. Although the second-harmonic restraint method has been widely adopted in transformer differential [...] Read more.

The reliable discrimination between magnetizing inrush currents and internal faults is essential for effective power transformer protection and has a direct impact on the security and stability of modern power systems. Although the second-harmonic restraint method has been widely adopted in transformer differential protection, its dependability can be affected by several operating conditions, including asymmetric energization, current transformer saturation, and the use of modern low-loss cores with reduced harmonic content. This paper presents a comprehensive and critical review of advanced techniques for distinguishing inrush currents from internal faults. The reviewed methods are classified into five main methodological categories: harmonic-based methods, time-domain approaches, signal-processing techniques, artificial intelligence-based schemes, and hybrid strategies. For each category, the fundamental operating principles, key advantages, and inherent limitations are discussed. A comparative assessment is also provided to highlight the trade-offs among detection accuracy, operating speed, robustness under adverse conditions, and practical implementation feasibility. The review shows a clear shift toward intelligent and data-driven protection schemes that combine effective feature extraction or deep learning with fast decision-making mechanisms. However, several challenges remain, particularly in relation to cross-site generalization, guaranteed response time, and hardware implementation constraints. Finally, the paper outlines a future research agenda for adaptive and computationally efficient transformer protection, emphasizing the need for benchmark datasets that include field cases, reproducible evaluation protocols, and the co-design of protection algorithms with embedded hardware platforms. Full article

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18 pages, 3919 KB

Open AccessArticle

CFD Modeling of Cuttings Transport Efficiency in Wellbore Annuli: Effects of Inclination Angle and Drilling Fluid Density

by Mo Wang, Shuanggui Li, Bei Yin, Weixing Yang, Jiancheng Luo, Zhiwei Zhong, Ke Zhang and Dezhi Zeng

Processes 2026, 14(10), 1661; https://doi.org/10.3390/pr14101661 - 20 May 2026

Viewed by 251

Abstract

Hole cleaning ensures drilling safety and efficiency. Well inclination angle and drilling fluid density are important parameters affecting cuttings transport. To reveal their coupled interaction mechanism, this study employs the Euler–Euler multiphase flow model to conduct CFD simulations of cuttings transport in a [...] Read more.

Hole cleaning ensures drilling safety and efficiency. Well inclination angle and drilling fluid density are important parameters affecting cuttings transport. To reveal their coupled interaction mechanism, this study employs the Euler–Euler multiphase flow model to conduct CFD simulations of cuttings transport in a 3D eccentric annulus with an eccentricity of 0.6 under various inclination angles (30°, 45°, 60°, 75°) and drilling fluid densities (1200~1800 kg/m³). Using the cuttings transport ratio (CTR), annulus cuttings volume concentration (CVT), outlet cuttings volume fraction, and annulus pressure drop as evaluation indicators, the influence mechanism of these parameters on hole cleaning efficiency is systematically analyzed. The results show that the effect of drilling fluid density on the CTR is regulated by inclination angle, with 45° being the critical angle for the extreme value of the CTR. Increasing density can significantly reduce cuttings deposition in the annulus, with a more pronounced improvement effect in high-inclination sections. Effective cuttings transport can be achieved by increasing the density to 1500, 1650, 1800, and 1800 kg/m³ for inclination angles of 30°, 45°, 60°, and 75°, respectively. The annulus pressure drop increases approximately linearly with density, and first rises then falls as the inclination angle increases from 30° to 75°, with 45° being the critical angle for peak pressure drop. This study clarifies the coupled regulation law of inclination angle and drilling fluid density, and determines the critical drilling fluid density under different inclinations, providing a numerical basis for optimizing hydraulic parameters and improving hole cleaning efficiency in directional drilling. Full article

(This article belongs to the Special Issue Oil and Gas Well Engineering: Experimental and Numerical Investigation, 2nd Edition)

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23 pages, 11493 KB

Open AccessArticle

Variable-Frequency Ventilation Monitoring System Based on Collaborative Wind Speed Prediction Using Environmental Parameters

by Zhongan Jiang, Mingli Si and Ya Chen

Processes 2026, 14(10), 1660; https://doi.org/10.3390/pr14101660 - 20 May 2026

Viewed by 217

Abstract

In order to predict the wind speed of the excavation roadway, control the frequency conversion operation of the local fan in real-time, and realize the real-time monitoring, collaborative prediction, and frequency conversion control of the ventilation state of the excavation face, the frequency [...] Read more.

In order to predict the wind speed of the excavation roadway, control the frequency conversion operation of the local fan in real-time, and realize the real-time monitoring, collaborative prediction, and frequency conversion control of the ventilation state of the excavation face, the frequency conversion ventilation control system of the excavation face is designed. Based on the theory of frequency conversion control, the genetic-neural network wind speed prediction optimization model was established, and the frequency conversion ventilation control system of the excavation face was designed by using S7-200 SMART PLC. The system test results show that the genetic-neural network optimization model can collaboratively predict wind speed according to the environmental parameters (dust concentration, methane concentration, temperature, and humidity, etc.) of different working conditions. The frequency conversion ventilation control system realizes the real-time monitoring of the environmental parameters of the excavation surface, and also provides two control modes: automatic and manual. Compared with the traditional constant power frequency control fan wind speed, the PID wind speed closed-loop control technology can control the fan wind speed by frequency conversion, so that the actual wind speed of the roadway continues to approach the predicted value stably. The variable frequency ventilation control system can be widely used in different types of mines to realize the adaptive control response of ventilation equipment. Full article

(This article belongs to the Special Issue Research Progress in Dust Control Technology)

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25 pages, 817 KB

Open AccessArticle

Cellular- to Plant-Scale Techno-Economic and Strain Design Analysis for Batch and Fed-Batch Process with the DySEEP Framework

by Willians de Oliveira Santos, Rafael David de Oliveira, Dafni Giannari, Rana Ahmed Barghout, Alexandre Tremblay, Radhakrishnan Mahadevan, José Gregório Cabrera Gomez and Galo Antonio Carrillo Le Roux

Processes 2026, 14(10), 1659; https://doi.org/10.3390/pr14101659 - 20 May 2026

Viewed by 259

Abstract

As the world recognizes the need for more sustainable practices, greater effort has been put into improving the economic viability of bioprocesses. Fields like metabolic engineering explore strategies to improve the production of bioproducts of industrial relevance, such as the use of alternative [...] Read more.

As the world recognizes the need for more sustainable practices, greater effort has been put into improving the economic viability of bioprocesses. Fields like metabolic engineering explore strategies to improve the production of bioproducts of industrial relevance, such as the use of alternative carbon sources. Regarding the performance assessment of a bioprocess, the product yield can be helpful, but other parameters like product titer and productivity (rate of production) are just as important. Furthermore, the highest performance may not come from a scenario with the highest yield, titer or productivity, but from a titer, rate, yield (TRY) set that maximizes profitability when the production costs are considered. Our previous work introduced the DySEEP approach, which uses an economic metric based on TRY parameters for evaluation of the potential of bioprocesses. This work expands the DySEEP program in key areas such as adding the possibility of exploring fed-batch processes and using strain design algorithms. Taking batch and fed-batch poly-3-hydroxybutyrate (PHB) production with E. coli as the case study, the scenarios that would lead to economic viability and the theoretical maximum performance for three different carbon sources, glucose, glycerol, and xylose, were identified. The results were then set in a strain design algorithm using Minimal Cut Sets (MCS) to find genetic interventions that could achieve the desired performance. The genetic interventions found with the MCS algorithm successfully led to strains that achieve the economic viability scenario, based on in silico simulations. This work illustrates how the expanded approach can be used to guide metabolic engineering strategies to improve the production of important bioproducts. Full article

(This article belongs to the Special Issue Modeling and Optimization for Multi-Scale Integration, 2nd Edition)

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19 pages, 3646 KB

Open AccessArticle

Intelligent Diagnosis Method for Constrained Primary Frequency Regulation Capacity of Coal-Fired Units Based on ISO-MLRF

by Yuliang Dong, Hongkun Lv, Huahua Wu, Jinghui Yang, Zhenya Lai, Yi Zhang, Jing Li and Dongyu Hua

Processes 2026, 14(10), 1658; https://doi.org/10.3390/pr14101658 - 20 May 2026

Viewed by 234

Abstract

To address the challenges of low diagnostic accuracy of constrained primary frequency regulating (PFR) capacity for coal-fired units due to complex and strongly coupled restricting factors, an intelligent diagnosis method based on an improved snake optimizer-based multi-label random forest classification algorithm is proposed. [...] Read more.

To address the challenges of low diagnostic accuracy of constrained primary frequency regulating (PFR) capacity for coal-fired units due to complex and strongly coupled restricting factors, an intelligent diagnosis method based on an improved snake optimizer-based multi-label random forest classification algorithm is proposed. By analyzing the factors restricting PFR capability, a set of characterization parameters and constraint factors for unit regulating capacity is established. The snake optimizer is enhanced by introducing dynamic update mechanisms and novel search strategies to improve its convergence speed and accuracy. The improved algorithm is then applied to optimize the hyperparameters of the multi-label random forest algorithm, enabling online diagnosis of PFR capacity limitations. Simulation results demonstrate that the proposed algorithm exhibits superior convergence performance, with lower medians of false alarm rate and missing alarm rate across all labels, coupled with reduced result dispersion compared to alternative algorithms. Tests on real operational data show an average false alarm rate of 0.029% and an average missing alarm rate of 0.053 for all labels. The results indicate that the proposed method is feasible and effective, enabling accurate online diagnosis of constrained PFR capacity of coal-fired units. Full article

(This article belongs to the Special Issue Design and Optimization of Heat Engines and Thermal Power Plants)

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15 pages, 3017 KB

Open AccessArticle

Study on the Influence of Alkane C Chain Length on Coal Slime Flotation Based on Interfacial Thermodynamic Analysis and Characterization

by Wei Zhou, Jiahua Su and Yu Wu

Processes 2026, 14(10), 1657; https://doi.org/10.3390/pr14101657 - 20 May 2026

Viewed by 265

Abstract

The reagent regime is a key means to regulate mineral flotation behavior, with collectors being particularly crucial for enhancing the flotation process. This paper systematically investigates the action mechanisms of hydrocarbon oil components such as n-Nonane, n-Dodecane, n-Tridecane, n-Tetradecane, and n-Pentadecane in coal [...] Read more.

The reagent regime is a key means to regulate mineral flotation behavior, with collectors being particularly crucial for enhancing the flotation process. This paper systematically investigates the action mechanisms of hydrocarbon oil components such as n-Nonane, n-Dodecane, n-Tridecane, n-Tetradecane, and n-Pentadecane in coal slime flotation through a combined approach of molecular dynamics simulation and experimental verification. The simulation results show that as the alkane chain length increases, the absolute value of the adsorption energy between the alkane and coal gradually increases (the adsorption energy is negative, indicating that the adsorption process can occur spontaneously), with n-Pentadecane exhibiting the highest adsorption energy. Experimentally, the oil–water mixture achieved optimal dispersity after ultrasonic treatment and standing for 10 min. This dispersity is characterized by the average oil droplet diameter and the most uniform droplet size distribution under the test conditions. The wetting heat test further verified that pentadecane exhibits the strongest interaction with coal slime and the fastest adsorption rate. In flotation tests, n-Tetradecane demonstrated the best actual flotation performance, with a clean coal yield of 70.88%, a combustible recovery of 82.55%, and a flotation perfection index of 50.75%. This study reveals the influence mechanism of alkane chain length on coal slime flotation behavior, providing a theoretical basis for the screening and compounding of efficient collectors. Full article

(This article belongs to the Section Separation Processes)

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19 pages, 3404 KB

Open AccessArticle

Uncertainty Analysis of Two-Phase Relative Permeability in Porous Media via Pore-Scale Simulation: The Impact of Initial Fluid Distribution

by Rui Zhang, Shaokai Tong, Shuang Zhang, Wentong Zhang, Yuanhao Chang and Zhilin Cheng

Processes 2026, 14(10), 1656; https://doi.org/10.3390/pr14101656 - 20 May 2026

Viewed by 409

Abstract

Accurate prediction of steady-state relative permeability via pore-scale modeling is fundamental to understanding multiphase flow processes in diverse engineering applications. However, the stochastic nature of the initial fluid distribution (IFD) in simulations is frequently overlooked, creating uncertainties that may obscure the physical influence [...] Read more.

Accurate prediction of steady-state relative permeability via pore-scale modeling is fundamental to understanding multiphase flow processes in diverse engineering applications. However, the stochastic nature of the initial fluid distribution (IFD) in simulations is frequently overlooked, creating uncertainties that may obscure the physical influence of critical parameters on transport behavior. In this study, a color-gradient lattice Boltzmann method was employed to conduct extensive steady-state simulations across two porous media of varying geometric complexity. The investigation focused on evaluating three representative IFD patterns across different capillary numbers (Ca) and viscosity ratios (M). By introducing the coefficient of variation (CV) and distribution interval overlap analysis, the IFD-induced uncertainty was systematically quantified. The results demonstrate that the IFD is a primary source of statistical variance in relative permeability, exhibiting a strong nonlinear coupling with Ca, M, and structural complexity. CV analysis reveals that uncertainty peaks within specific saturation windows, which shift according to the pore geometry. Specifically, the peak uncertainty window for total relative permeability shifts from S_w

\in

[0.5, 0.7] in the simple model to S_w

\in

[0.3, 0.5] in the heterogeneous model. Notably, the wetting phase exhibits pronounced instability in the low-saturation regime, with the wetting-phase CV reaching its maximum at S_w = 0.3 in the simple model. At low Ca conditions, IFD-induced errors can entirely mask the physical sensitivity of relative permeability to Ca and M within certain saturation intervals. Furthermore, variations in initial configurations lead to divergent evolutions of the fluid-fluid interfacial area relative to wetting saturation, highlighting the role of microscopic topological memory in governing flow behavior. This research provides a quantitative foundation for IFD sensitivity in pore-scale modeling and proposes the integration of a CV-based uncertainty framework into macro-scale models to enhance the robustness and reliability of multiphase flow predictions. Full article

(This article belongs to the Special Issue Advances in Enhancing Unconventional Oil/Gas Recovery, 3rd Edition)

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20 pages, 2734 KB

Open AccessArticle

Development of a Kinematic Model Based on Simulation Data for a Three Symmetrical Wheeled Pipeline Robot

by Manuel Cardona, Ian Sevilla, Jose Luis Ordoñez-Avila, Alberto Max Carrasco and Hector Moreno

Processes 2026, 14(10), 1655; https://doi.org/10.3390/pr14101655 - 20 May 2026

Viewed by 270

Abstract

This study presents the development and validation of a simulation-calibrated kinematic formulation for a three-wheeled symmetric pipeline inspection robot operating under cylindrical confinement. The proposed model integrates analytical implementation in MATLAB 2023b with multibody simulation in SolidWorks 2023 to identify semi-empirical correction terms [...] Read more.

This study presents the development and validation of a simulation-calibrated kinematic formulation for a three-wheeled symmetric pipeline inspection robot operating under cylindrical confinement. The proposed model integrates analytical implementation in MATLAB 2023b with multibody simulation in SolidWorks 2023 to identify semi-empirical correction terms that improve motion prediction under straight and curved pipe conditions. The formulation incorporates curvature-dependent and asymmetry-related effects derived from structured simulation datasets, ensuring consistency between analytical predictions and simulated behavior within the evaluated operating range. Quantitative comparison using statistical indicators demonstrates strong agreement between both approaches, with MAE values of 0.0547 for linear velocity and 13.96 for displacement, RMSE values of 0.0681 and 19.0401, and coefficients of determination of

R^{2} = 0.9997

and

R^{2} = 0.9476

, respectively. Slightly larger deviations are observed at higher rotational speeds. The results provide a consistent analytical representation of the robot’s motion under the studied geometric constraints and establish a basis for future experimental validation and control-oriented extensions in confined pipeline environments. Full article

(This article belongs to the Section Automation Control Systems)

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24 pages, 2811 KB

Open AccessArticle

A Non-Sorted Metaheuristic Method for the Multi-Objective Job-Flow-Shop Scheduling Problem in Conflict-Free Robot Swarm Manufacturing

by Zhengying Cai, Jiahui Jin, Jingyi Li, Zhuimeng Lu, Zeya Liu and Chen Yu

Processes 2026, 14(10), 1654; https://doi.org/10.3390/pr14101654 - 20 May 2026

Viewed by 213

Abstract

Robot swarm manufacturing is a promising direction in smart manufacturing that aggregates multiple robots to collaboratively complete production jobs; however, achieving conflict-free scheduling remains a significant challenge. Traditional methods struggle to address this issue since robot swarms are inherently prone to conflicts. This [...] Read more.

Robot swarm manufacturing is a promising direction in smart manufacturing that aggregates multiple robots to collaboratively complete production jobs; however, achieving conflict-free scheduling remains a significant challenge. Traditional methods struggle to address this issue since robot swarms are inherently prone to conflicts. This article puts forward a non-sorted metaheuristic method to solve it. First, the conflict-free robot swarm manufacturing problem—integrating a multi-objective optimization problem (MOP), a flexible job-shop scheduling problem (FJSP) for job processing, and a flow-shop scheduling problem (FSP) for robot travel—is formulated as a multi-objective job-flow-shop scheduling problem (MJFSP). The robot swarm must accomplish all manufacturing jobs while achieving high manufacturing performance, energy efficiency, and conflict-free operations. Second, a non-sorted metaheuristic algorithm based on an artificial plant community (APC) is proposed. It employs a sequential-pairwise single-elimination tournament system (SSTS) to select elites with a time complexity of

O (n)

, which scales linearly with the population size

(n)

. This surpasses the sorting-based elite selection with polynomial time complexity employed in most metaheuristic methods, such as the

O (n^{2})

of the non-dominated sorting genetic algorithm-III (NSGA-III). Third, an MJFSP benchmark dataset is built, and the experimental results uncover the complex dependencies between the FJSP for job processing and the FSP for robot traveling. The proposed method improves the makespan by up to 13.10% and reduces non-loaded energy consumption by up to 13.49%, achieving zero collision time and an average solution time 11.18% faster than NSGA-III. Full article

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

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31 pages, 8172 KB

Open AccessArticle

Research on Structural Optimization and Process Parameter Response Surface Optimization of Vacuum Low-Temperature Fish Meal Dryer

by Xuchu Chen, Wei Wang, Wuwei Feng, Danyu Li and Rongsheng Lin

Processes 2026, 14(10), 1653; https://doi.org/10.3390/pr14101653 - 20 May 2026

Viewed by 269

Abstract

To address the industry pain points of domestic traditional fish meal processing equipment, such as low protein retention, low drying efficiency, and poor operational reliability, this study focuses on high-moisture, heat-sensitive cod meal as the test material to investigate the structural improvement and [...] Read more.

To address the industry pain points of domestic traditional fish meal processing equipment, such as low protein retention, low drying efficiency, and poor operational reliability, this study focuses on high-moisture, heat-sensitive cod meal as the test material to investigate the structural improvement and synergistic optimization of process parameters for vacuum low-temperature fish meal dryers. The conventional uniform-pitch heating coil was optimized into a three-section differentiated structure, with a wear-resistant protective structure additionally incorporated to fundamentally resolve issues including insufficient heat transfer at the feed end, coking at the discharge end, and coil wear-induced leakage. Verification via COMSOL Multiphysics simulation revealed that the axial temperature gradient of the optimized equipment decreased from 8.6 °C/m to 6.2 °C/m, while the thermal fatigue life of the coil was extended from 2–3 years to over 10 years. A three-factor, three-level response surface methodology (RSM) was employed to design the experiments, with the heating temperature, vacuum degree, and drying time as independent variables and the fish meal protein content as the response variable. A total of 17 experimental runs were constructed, including 12 factorial points and 5 central points; each run was replicated three times in parallel, and data were reported as mean values. Analysis of variance (ANOVA) demonstrated that the regression model was highly statistically significant (p < 0.0001), with a coefficient of variation (CV) of 0.2464% and a coefficient of determination (R²) of 0.9944, indicating excellent fitting accuracy. The determined optimal process parameters were as follows: a drying temperature of 65 °C, vacuum degree of 0.08 MPa, and drying time of 75 min. Compared with the traditional process, the optimized process shortened the drying cycle by 37.5%, reduced unit energy consumption by 29.2%, and increased the fish meal protein content by 6.6%. This research provides a reliable technical solution for the localized processing of high-end fish meal. Full article

(This article belongs to the Section Food Process Engineering)

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27 pages, 18914 KB

Open AccessArticle

First Results on the Production of Natural Colorants by Amazonian Freshwater Fungi: Influence of Carbon Sources and Biological Potential

by Anne Terezinha Fernandes de Souza, Dorothy Ívila de Melo Pereira, Cleudiane Pereira de Andrade Negreiros, Italo Pereira de Lima, Rayssa Souza dos Santos, Liss Stone de Holanda Rocha, Yuliana Padrón-Antonio, Cleiton Fantin, António M. Jordão and Patrícia Melchionna Albuquerque

Processes 2026, 14(10), 1652; https://doi.org/10.3390/pr14101652 - 20 May 2026

Viewed by 408

Abstract

The increasing demand for safer and environmentally sustainable products has intensified the search for natural alternatives to synthetic dyes. Filamentous fungi are promising sources of natural pigments due to their metabolic diversity and the feasibility of large-scale production. In this study, filamentous fungi [...] Read more.

The increasing demand for safer and environmentally sustainable products has intensified the search for natural alternatives to synthetic dyes. Filamentous fungi are promising sources of natural pigments due to their metabolic diversity and the feasibility of large-scale production. In this study, filamentous fungi isolated from Amazonian freshwater environments were evaluated for their potential to produce natural pigment-associated metabolites under different nutritional conditions. Forty-five fungal isolates were screened in solid media and subsequently cultivated in submerged fermentation using three media: potato dextrose broth supplemented with yeast extract (BD + YE); malt extract broth (ME); and yeast extract–sucrose broth supplemented with magnesium sulfate (YES). Among the 39 pigment-producing isolates, seven were selected for further investigation. Sucrose favored the highest absorbance values of pigment extracts, particularly for isolates identified as Talaromyces amestolkiae. In addition, the extract of T. amestolkiae TA10P5-3 exhibited the highest absorbance value (6.83 abs. units at 400 nm) when cultivated in YES medium, indicating stronger chromophore-associated spectral signals. This extract also showed antimicrobial activity against Pseudomonas aeruginosa (625 μg/mL), Staphylococcus epidermidis (312 μg/mL), and Candida tropicalis (625 μg/mL). Finally, the TA10P5-3 extract presented high total phenolic content (246.30 mg GAE/g) and antioxidant activity (EC₅₀ = 5470 μg/mL). These findings highlight Amazonian freshwater fungi as promising sources of natural pigments with potential industrial applications. Full article

(This article belongs to the Special Issue Novel Concepts for Future Biotechnology and Bioprocesses: Plants and Microorganisms for Sustainable Industries)

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22 pages, 4807 KB

Open AccessArticle

Flow Regime-Driven Adaptive Imaging for Oil–Water Two-Phase Flow in Horizontal Wells

by Yuqing Guo, Haimin Guo, Yongtuo Sun, Wenfeng Pen, Ao Li and Dudu Wang

Processes 2026, 14(10), 1651; https://doi.org/10.3390/pr14101651 - 20 May 2026

Viewed by 283

Abstract

Cross-sectional imaging of two-phase oil–water flow in horizontal wells is essential for optimising production, yet conventional deterministic interpolation cannot adapt to varying flow regimes: Kriging smooths chaotic textures while stochastic simulation introduces spurious noise into stable flows. This paper proposes a Flow-Regime-driven Framework [...] Read more.

Cross-sectional imaging of two-phase oil–water flow in horizontal wells is essential for optimising production, yet conventional deterministic interpolation cannot adapt to varying flow regimes: Kriging smooths chaotic textures while stochastic simulation introduces spurious noise into stable flows. This paper proposes a Flow-Regime-driven Framework for Adaptive Cross-sectional Imaging (FR-FACI) that couples flow-regime identification with image reconstruction. Six physically meaningful features extracted from capacitance (CAT) and turbine (SAT) array signals feed a support vector machine (SVM) classifier that assigns each sampling window to one of three regimes: stratified (SF), stratified-froth (SFF), or froth (FR). A chaos weight derived from the calibrated classifier probability continuously blends detrended ordinary kriging with sequential Gaussian simulation, eliminating hard-switching artefacts. Experiments covering 12 operating conditions yield 95.83% classification accuracy under leave-one-condition-out validation. Variogram ranges differ by more than 26-fold across regimes, confirming the physical necessity of dual-path design. FR-FACI achieves an overall MAE of 0.105 and RMSE of 0.160, matching Kriging in stable flows while recovering chaotic textures that all single-model methods miss. Directions for future work, including uncertainty propagation, field-scale validation, and real-time monitoring integration, are discussed. Full article

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

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21 pages, 8673 KB

Open AccessArticle

Investigation of the Friction Reduction Performance of Hydraulic Oscillator Based on the Hybrid Nonlinear Friction Model

by Chao Yang, Jinsheng Sun and Yun Yang

Processes 2026, 14(10), 1650; https://doi.org/10.3390/pr14101650 - 20 May 2026

Viewed by 267

Abstract

Hydraulic oscillator tools (HOTs) are effective solutions for mitigating excessive drag encountered during sliding drilling in horizontal wells. However, their field performance remains unpredictable due to theoretical limitations in modeling nonlinear friction behavior under axial vibration. To address this gap, a series of [...] Read more.

Hydraulic oscillator tools (HOTs) are effective solutions for mitigating excessive drag encountered during sliding drilling in horizontal wells. However, their field performance remains unpredictable due to theoretical limitations in modeling nonlinear friction behavior under axial vibration. To address this gap, a series of friction tests was conducted on sandstone–steel pairs under water-based mud lubrication. Experimental results demonstrate that steady-state sliding friction follows the velocity-dependent Dieterich–Ruina model, while vibration–sliding coupled friction is accurately described by the Dahl model. Integrating these findings, a comprehensive drillstring dynamic model was developed. The model was solved using an explicit central difference method and validated against field hook load data from Well XX-1, with prediction errors below 9%. Parametric studies further quantified HOT performance, revealing that excitation force amplitude and HOT placement significantly impact drag reduction, whereas vibration frequency exerts a relatively modest influence. Meanwhile, the effective propagation distance induced by the hydraulic oscillator is relatively limited, resulting in a drag reduction rate of no more than 30% even under optimal parameter conditions. This work establishes a validated theoretical framework for optimizing hydraulic oscillator parameters in horizontal drilling. Full article

(This article belongs to the Special Issue Research Progress in Oil and Gas Well Engineering)

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19 pages, 2919 KB

Open AccessArticle

Methane Production Using Anaerobic Co-Digestion of Swine and Nejayote Wastewater: Synergic Effects and Kinetic Modeling Studies

by Perla A. González-Tineo, Juan F. Maldonado-Escalante, Eduardo Castro-Payán, Edna R. Meza-Escalante, Luis H. Álvarez, Rigoberto Plascencia-Jatomea and Denisse Serrano-Palacios

Processes 2026, 14(10), 1649; https://doi.org/10.3390/pr14101649 - 20 May 2026

Viewed by 284

Abstract

Anaerobic co-digestion of substrates offers synergistic benefits, enhancing methane production and improving the operational stability of wastewater treatment. The present study, for the first time, evaluated the biochemical methane potential and kinetics modeling performance of two regional wastewater streams—swine wastewater (SW) and nejayote [...] Read more.

Anaerobic co-digestion of substrates offers synergistic benefits, enhancing methane production and improving the operational stability of wastewater treatment. The present study, for the first time, evaluated the biochemical methane potential and kinetics modeling performance of two regional wastewater streams—swine wastewater (SW) and nejayote wastewater (NW)—under mesophilic batch conditions. Five substrate ratios (SW/NW: 100/0 to 0/100) were tested, and interaction effects were measured using the co-digestion performance index (CPI). All mixtures demonstrated synergistic effects, with CPI values ranging from 1.12 to 1.26. NW exhibited the highest methane yield (438 ± 25 NL-CH₄/kgCOD_T-removed), nearly twice that obtained for SW (227 ± 18 NL-CH₄/kgCOD_T-removed). In addition, co-digestion improved the methane yield of SW as mono-digestion, with production increasing from 281.8 ± 12.4 to 304.7 ± 27.8 NL-CH₄/kgCOD_T-removed in all mixtures. The methane production kinetics were analyzed using six mathematical models. The multi-phase Gompertz model provided the best fit (R² > 0.99), while the two-phase model offered the best balance of accuracy and simplicity according to Akaike’s criterion. The present model effectively described the diauxic patterns of methane production resulting from substrate heterogeneity with an error of <8% for all experimental assays. Full article

(This article belongs to the Special Issue Waste Biorefinery Technologies for Sustainable Energy Processes)

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16 pages, 2851 KB

Open AccessArticle

Comparison of Mathematical and Intelligent Prediction Models of Directional Wellbore Collapse

by Yu Fan, Weian Huang, Xihui Hu, Qiutong Wang, Yijia Tang and Hao He

Processes 2026, 14(10), 1648; https://doi.org/10.3390/pr14101648 - 20 May 2026

Viewed by 249

Abstract

Given the great burial depth, ancient depositional age, and multi-phase tectonic evolution of deep formations, drilling operations are highly susceptible to wellbore instability. The design and deployment of directional wells further exacerbate this risk, underscoring the need for quantitative risk assessments for directional [...] Read more.

Given the great burial depth, ancient depositional age, and multi-phase tectonic evolution of deep formations, drilling operations are highly susceptible to wellbore instability. The design and deployment of directional wells further exacerbate this risk, underscoring the need for quantitative risk assessments for directional drilling operations. Based on linear poroelasticity theory, a mechanical model for directional wellbore stability is established to enable wellbore stability evaluation and trajectory optimization design. Furthermore, an intelligent prediction method for collapse pressure is proposed using the XGBoost algorithm. The results indicate that the prediction accuracy of collapse pressure reaches 93%. Under strike-slip in situ stress regimes, wellbore stability is most critical for vertical wells, whereas horizontal and directional wells exhibit lower collapse pressure. The optimal wellbore trajectory is determined to be a horizontal well with an azimuth approximately 36° deviated from the maximum horizontal principal stress direction. The intelligent prediction results show a 98% goodness-of-fit with theoretical calculations, reducing the calculation time from hours to seconds. This study provides a novel approach for wellbore stability analysis and offers a practical tool for the rapid risk assessment of wellbore collapse during directional drilling operations. Full article

(This article belongs to the Special Issue Advanced Computational and Experimental Methods for Subsurface Energy Systems: From Drilling Optimization to CO₂ Storage)

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17 pages, 2747 KB

Open AccessArticle

A Numerical Investigation of Inner Flow in a Turbine with Special Emphasis on Its Pressure and Velocity Distributions

by Yongbo Li, Zhi Zhang, Ke Liu, Huaiyu Cheng and Bin Ji

Processes 2026, 14(10), 1647; https://doi.org/10.3390/pr14101647 - 20 May 2026

Viewed by 248

Abstract

A three-dimensional numerical investigation is conducted to clarify the internal pressure and velocity distributions in a hydraulic turbine under multiple operating conditions. The study aims to identify the main high-gradient regions and the influence of operating parameters on the internal flow field. The [...] Read more.

A three-dimensional numerical investigation is conducted to clarify the internal pressure and velocity distributions in a hydraulic turbine under multiple operating conditions. The study aims to identify the main high-gradient regions and the influence of operating parameters on the internal flow field. The incompressible single-phase Navier–Stokes equations are solved using the SST k-ω turbulence model. Eleven operating conditions with different guide vane openings, net heads, output powers, and discharges are simulated using a full-passage turbine model with mass flow inlet and static pressure outlet boundary conditions. The numerical results are validated against experimental performance data. The results show that the pressure and velocity fields exhibit generally symmetric distributions in the circumferential and axial directions, whereas strong local gradients appear in the rotor–stator interaction region. Local high-pressure and high-velocity zones are mainly observed near the blade leading edges, while low-pressure and low-velocity regions develop near the trailing edges, runner cone, and draft tube. Increasing the net head raises the overall pressure and velocity levels and enhances the low-pressure and low-velocity regions in the draft tube. Under a fixed head, increasing the guide vane opening mainly affects the flow distribution around the stay and guide vanes and modifies the flow structure in the runner cone and draft tube. These findings provide a systematic numerical characterization of the pressure and velocity distributions in the turbine and help identify critical regions for further hydraulic performance analysis and flow field optimization. Full article

(This article belongs to the Special Issue Experimental Research and Numerical Simulations in Turbomachinery)

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29 pages, 8624 KB

Open AccessArticle

Optimal Geomechanical Parameter Selection for Enhanced ROP Modeling: A Systematic Field-Based Comparative Study

by Ahmed S. Alhalboosi, Musaed N. J. AlAwad, Faisal S. Altawati, Mohammed A. Khamis and Mohammed A. Almobarky

Processes 2026, 14(10), 1646; https://doi.org/10.3390/pr14101646 - 19 May 2026

Viewed by 380

Abstract

Accurate prediction of Rate of Penetration (ROP) in carbonate formations remains constrained by the arbitrary selection of geomechanical input parameters in empirical drilling models. This study presents the first systematic field-based evaluation of sixteen geomechanical properties—grouped into three categories: strength parameters [...] Read more.

Accurate prediction of Rate of Penetration (ROP) in carbonate formations remains constrained by the arbitrary selection of geomechanical input parameters in empirical drilling models. This study presents the first systematic field-based evaluation of sixteen geomechanical properties—grouped into three categories: strength parameters (uniaxial compressive strength (UCS), confined compressive strength (CCS), shear strength, thick-walled cylinder strength (TWC), friction angle, and cohesion), elastic moduli (Young’s modulus, shear modulus, bulk modulus, bulk compressibility, dynamic combined modulus (DCM), Poisson’s ratio, brittleness index), and in situ stress parameters (overburden pressure, minimum, and maximum horizontal stresses)—to identify optimal predictors for ROP modeling across PDC bit sizes of 12.25″ and 8.5″. Continuous wireline log data from two vertical carbonate wells in the Middle East (Well A: 1000–3370 m; Well B: 1945 to 3128 m; total intervals of 2370 m and 1183 m, respectively) penetrating formations comprising limestone, dolomite, sandstone, shale, anhydrite, and marly limestone were used. All sixteen geomechanical properties were computed using Interactive Petrophysics (IP) software with lithology-specific empirical correlations and validated against laboratory core measurements (R² = 0.79–0.95). Pearson and Spearman correlation analyses quantified parameter–ROP relationships, and the Al-Abduljabbar empirical model, recalibrated via multiple nonlinear regression, served as the evaluation framework. DCM consistently exhibited the strongest negative correlation with ROP across both bit sizes and achieved the highest model accuracy (R² = 0.54, AAPE = 25.33%), significantly outperforming the Bourgoyne and Young model (R² = 0.26, AAPE = 36.55%). A statistically validated scale-dependent effect was identified: Fisher’s Z-transformation tests confirmed that the correlation reversal between CCS and UCS across bit sizes is statistically significant (CCS: Z = −16.84, p < 0.001; UCS: Z = −6.75, p < 0.001), establishing CCS as the superior predictor at 12.25″ and UCS as the superior predictor at 8.5″—a finding not previously reported in the ROP literature. This reversal is attributed to the larger contact area of the 12.25″ bit, which promotes confinement-dominated rock failure better described by CCS, whereas the smaller bit produces localized stress concentration better represented by UCS. These results establish that (1) optimal geomechanical input selection is bit-size dependent, (2) nonlinear modeling outperforms linear frameworks for strength–ROP relationships, and (3) parameter relevance outweighs coefficient tuning in model robustness. DCM is recommended as the most operationally practical universal input, requiring only conventional compressional sonic and density logs. This study provides a systematic framework for geomechanical parameter selection with direct implications for drilling optimization in heterogeneous carbonate reservoirs. Full article

(This article belongs to the Special Issue Development of Advanced Drilling Engineering)

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21 pages, 1536 KB

Open AccessArticle

Effects of a Complex Functional Ingredient Based on Beef Offal Paste and Plant Ingredients on the Quality, Fatty Acid Profile, Texture, and Storage Stability of Meat Cutlets

by Anuarbek Suychinov, Eleonora Okuskhanova, Zhanibek Yessimbekov and Guldana Kapasheva

Processes 2026, 14(10), 1645; https://doi.org/10.3390/pr14101645 - 19 May 2026

Viewed by 296

Abstract

This study developed a complex functional ingredient based on beef offal paste, whey, rapeseed and sunflower cake powder, and flax flour, and evaluated its effect on beef cutlets formulated with 0, 5, 10, and 15% additive. The study examined chemical composition, pH, water [...] Read more.

This study developed a complex functional ingredient based on beef offal paste, whey, rapeseed and sunflower cake powder, and flax flour, and evaluated its effect on beef cutlets formulated with 0, 5, 10, and 15% additive. The study examined chemical composition, pH, water activity, functional and technological properties, color, fatty acid profile, texture, sensory quality, and refrigerated storage stability. The additive improved the nutritional profile of the cutlets by increasing the protein content from 16.20% in the control to 17.78% at the highest inclusion level, while reducing fat content from 12.50% to 11.20%. The lipid fraction also became more favorable, as total polyunsaturated fatty acids increased from 7.03% to 13.34%, and α-linolenic acid appeared only in additive-containing samples. The additive also modified the functional and structural characteristics of the products. The 10% formulation showed the most pronounced improvement in texture, with the highest hardness, gumminess, and chewiness values, while sensory quality remained comparable to the control at 5 and 10% inclusion but declined at 15%. During 7 days of refrigerated storage, additive-containing samples showed lower acid and peroxide values than the control, together with a slight reduction in microbial growth. Overall, the developed additive acted as a multifunctional ingredient that improved nutritional and technological quality. Among the tested formulations, the 10% inclusion level provided the best balance between quality, storage stability, and sensory acceptability. Full article

(This article belongs to the Special Issue Novel Technologies for the Processing and Quality Assessment of Meat Production)

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18 pages, 6817 KB

Open AccessArticle

An Investigation of the Influence of the Main Wellbore on the Wellbore Stability of Sidetracked Wellbore of the Deep Earth TK-1

by Xuwu Luo, Ning Li, Yan Jin, Jiaqi Luo, Wentong Fan, Yang Xia and Yunhu Lu

Processes 2026, 14(10), 1644; https://doi.org/10.3390/pr14101644 - 19 May 2026

Viewed by 357

Abstract

Deep Earth TK-1, China’s first 10,000 m scientific exploration well, encountered severe wellbore instability during sidetracking at a depth of approximately 9500 m under ultra-deep, high-stress conditions (maximum horizontal stress

σ_{H}

= 230 MPa, minimum horizontal stress

σ_{h}

= 200 MPa). [...] Read more.

Deep Earth TK-1, China’s first 10,000 m scientific exploration well, encountered severe wellbore instability during sidetracking at a depth of approximately 9500 m under ultra-deep, high-stress conditions (maximum horizontal stress

σ_{H}

= 230 MPa, minimum horizontal stress

σ_{h}

= 200 MPa). To clarify how the original wellbore affects the stability of the sidetracked wellbore, single- and dual-well numerical models were established in COMSOL Multiphysics using the solid mechanics module and finite element method. The stress redistribution around the wellbore was analyzed before and after the collapse of the main wellbore, and the influences of well spacing and breakout geometry were quantified. The results show that a stress-relief “safe zone” forms along the direction of maximum horizontal stress before collapse and expands after collapse, allowing safer sidetracking within this range. In the dual-well model, the maximum stress difference around the sidetracked wellbore increases with well spacing and eventually approaches that of a single circular wellbore. The safe zone boundary was quantified for well spacings between 2.0 m and 3.5 m, depending on the major-axis enlargement ratio of the collapsed main wellbore. A larger major-axis enlargement ratio reduces far-field stress interference and expands the safe zone, whereas changes in the minor-axis enlargement ratio have little effect. These findings provide theoretical support for optimizing sidetracking design in ultra-deep wells. Full article

(This article belongs to the Special Issue Advances in Evaluation, Development, Simulation and Utilization of Geo-Energy Resources and Underground Space)

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