Processes

20 pages, 11329 KB

Open AccessArticle

Effects of Beetroot Powder and Ascorbic Acid as Nitrite Replacers on Physicochemical Properties, Antioxidant Activity, and Storage Stability of Frankfurters

by Zhanibek Yessimbekov, Anuarbek Suychinov, Eleonora Okuskhanova, Aitbek Kakimov and Zhumatay Urazbayev

Processes 2026, 14(12), 1962; https://doi.org/10.3390/pr14121962 (registering DOI) - 16 Jun 2026

Abstract

This study evaluated beetroot powder as a natural colorant and partial nitrite replacer in frankfurters. Five formulations were prepared: control with nitrite curing salt, T1 and T2 with 25% and 50% nitrite replacement, T3 with complete nitrite replacement, and T4 with complete nitrite [...] Read more.

This study evaluated beetroot powder as a natural colorant and partial nitrite replacer in frankfurters. Five formulations were prepared: control with nitrite curing salt, T1 and T2 with 25% and 50% nitrite replacement, T3 with complete nitrite replacement, and T4 with complete nitrite replacement plus ascorbic acid. The samples were analyzed for physicochemical properties, color, water-holding capacity, cooking yield, sensory quality, antioxidant activity, total phenolic content, and lipid oxidation during 7 days of refrigerated storage at 2–3 °C. Beetroot powder markedly increased redness in uncooked frankfurters, with a* values rising from 13.84 in the control to 37.20 in T3 and 35.31 in T4. T2, T3, and T4 also improved cooking yield, reaching 90.19%, 90.42%, and 89.84%, respectively, compared with 85.85% in the control. T2 showed the highest total sensory score (23.3), while T3 had the lowest acceptability (19.8). During storage, T4 showed the strongest oxidative stability, with TBARS increasing from 0.23 to 1.10 mg MDA/kg, compared with 0.44 to 2.42 mg MDA/kg in T3. T4 also maintained the highest DPPH activity and total phenolic content after 7 days: 2.53 μmol TE/g and 95.68 mg GAE/kg, respectively. Beetroot powder improved color and antioxidant potential, but complete nitrite replacement required ascorbic acid to control oxidation and maintain quality. These findings support the use of beetroot powder–ascorbic acid systems in reduced-nitrite frankfurter formulations. Full article

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

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16 pages, 1406 KB

Open AccessArticle

Prediction of Heat Load in Oil and Gas Gathering Stations Based on CNN–LSTM–Attention

by Zhonglin Hu, Pengzheng Mu, Binyuan Rao, Xiaozhe Ru, Mengkai Lv, Zhiguo Wang, Zhenglong Zhang and Ziyi Wu

Processes 2026, 14(12), 1961; https://doi.org/10.3390/pr14121961 (registering DOI) - 16 Jun 2026

Abstract

Under the national context of energy transition and energy conservation, accurate prediction of thermal load in oil and gas gathering and transportation stations is crucial for ensuring operational safety and reducing energy consumption. To address the limitations of traditional forecasting methods in handling [...] Read more.

Under the national context of energy transition and energy conservation, accurate prediction of thermal load in oil and gas gathering and transportation stations is crucial for ensuring operational safety and reducing energy consumption. To address the limitations of traditional forecasting methods in handling the nonlinear, non-stationary, and long-term temporal dependencies of thermal load data, this paper proposes a hybrid deep learning model that integrates convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and an attention mechanism, namely the CNN–LSTM–Attention model. First, key influencing factors such as ambient temperature, return water temperature, and the previous hour’s thermal load were selected as model inputs through correlation analysis. Subsequently, a CNN was employed to extract spatial features from multi-source data, LSTM to capture temporal dependencies, and an attention mechanism to dynamically focus on critical operational nodes, thereby enhancing the model’s ability to perceive important features. The experimental results show that the proposed model performs excellently in heat load prediction, achieving a root mean square error of 5.98, a mean absolute error of 4.66, and a mean absolute percentage error of 9.66%, with an R-squared (R²) value of 0.9568. Its prediction accuracy and stability are significantly superior to those of the standalone CNN and standalone LSTM models. This study provides an effective algorithmic solution for precise thermal load forecasting in oil and gas gathering and transportation stations and offers insights for optimizing the applicability of deep learning models in industrial scenarios. Full article

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

9 pages, 334 KB

Open AccessCorrection

Correction: Razgonova et al. Supercritical CO₂-Based Extraction and Detection of Phenolic Compounds and Saponins from the Leaves of Three Medicago varia Mart. Varieties by Tandem Mass Spectrometry. Processes 2024, 12, 1041

by Mayya P. Razgonova, Muhammad Amjad Nawaz, Elena P. Ivanova, Elena I. Cherevach and Kirill S. Golokhvast

Processes 2026, 14(12), 1960; https://doi.org/10.3390/pr14121960 (registering DOI) - 16 Jun 2026

Abstract

In the original publication [...] Full article

(This article belongs to the Special Issue Supercritical Technology Applied to Food, Pharmaceutical, Chemical and Energy Industries—2nd Edition)

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21 pages, 4103 KB

Open AccessArticle

DIF-LSTM: A Dual Information Filtering LSTM Network for V/G Value Prediction in Czochralski Silicon Growth

by Yin Wan, Yu-Lin Sun, Ding Liu, Xiao-An Deng and Jun-Chao Ren

Processes 2026, 14(12), 1959; https://doi.org/10.3390/pr14121959 (registering DOI) - 16 Jun 2026

Abstract

In Czochralski (CZ) silicon growth, controlling the ratio of crystal growth velocity to axial temperature gradient (

V / G

) is critical for defect management. However, the

V / G

value is difficult to measure in real-time. Furthermore, it exhibits strong multivariate [...] Read more.

In Czochralski (CZ) silicon growth, controlling the ratio of crystal growth velocity to axial temperature gradient (

V / G

) is critical for defect management. However, the

V / G

value is difficult to measure in real-time. Furthermore, it exhibits strong multivariate coupling and extreme non-stationarity under complex thermal fields. While standard deep learning models like LSTM are used for soft sensing, they often misidentify high-frequency hardware noise as true process dynamics, causing severe error amplification in multi-step predictions. To address this, we propose a Dual Information Filtering LSTM (DIF-LSTM). It utilizes an external context-aware mechanism to screen long-term steady-state redundant information and an internal denoising gate coupled with the LSTM input to explicitly block transient high-frequency noise. Furthermore, a confidence evaluation branch and residual decay fusion ensure stable multi-step forecasting. Experimental results an industrial-scale experimental silicon single crystal furnace show DIF-LSTM achieves superior accuracy, obtaining an

R^{2}

of 0.9935 and a Mean Squared Error of

1.60 \times 10^{- 6}

at a 3-step horizon. Even at a 9-step horizon, it maintains an

R^{2}

of 0.9422, significantly outperforming the baseline IF-LSTM (0.8498). Full article

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

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Graphical abstract

10 pages, 186 KB

Open AccessEditorial

Editorial: Analysis and Optimization Control of Active Distribution Networks and Smart Grids

by Jingrui Zhang, Jian Wang, Po Li and Tengpeng Chen

Processes 2026, 14(12), 1958; https://doi.org/10.3390/pr14121958 (registering DOI) - 16 Jun 2026

Abstract

The global energy landscape is undergoing a profound transformation driven by the imperative to reduce carbon emissions and achieve carbon neutrality [...] Full article

(This article belongs to the Special Issue Analysis and Optimization Control of Active Distribution Networks and Smart Grids)

26 pages, 3990 KB

Open AccessArticle

Resilience Enhancement of Power Systems Integrated with Renewable Energy Considering the Participation of Proton Exchange Membrane Electrolyzers Under Severe Ice Disaster Conditions

by Chengxi Li, Kai Wen, Rongjian Mo, Changyuan Wang, Shiao Wang, Ling Lu and Jie Zhao

Processes 2026, 14(12), 1957; https://doi.org/10.3390/pr14121957 (registering DOI) - 16 Jun 2026

Abstract

Against the background of China’s dual carbon goals, high-renewable-power systems suffer severe resilience threats from destructive ice disasters, and existing recovery approaches fail to fully exploit multi-type flexible resources with unsatisfying computational efficiency. Targeting this gap, this work establishes a resilience enhancement framework [...] Read more.

Against the background of China’s dual carbon goals, high-renewable-power systems suffer severe resilience threats from destructive ice disasters, and existing recovery approaches fail to fully exploit multi-type flexible resources with unsatisfying computational efficiency. Targeting this gap, this work establishes a resilience enhancement framework for ice-affected power grids. This model quantifies line failure probability considering time-varying ice thickness and wind load, generates representative fault scenarios via sequential Monte Carlo and K-means clustering, and innovatively incorporates mobile energy storage systems (MESSs) and low-temperature-corrected PEM electrolyzers into coordinated post-fault dispatch; an improved parrot optimization (PO) algorithm with Chebyshev chaos, random mutation and adaptive t-distribution is designed to boost solving efficiency. Tested on the IEEE 39-bus system, the proposed method reduces average load shedding to 3.7% and raises renewable accommodation to 95.6%, outperforming fixed energy storage and literature-based strategies by cutting load curtailment by 45.6% and 30.2% respectively, while multi-condition sensitivity analyses validate its stable applicability under varying disaster intensity and renewable penetration. This coordinated scheduling strategy supplies feasible technical support for practical anti-icing resilience promotion of new-type power grids. Full article

(This article belongs to the Special Issue Modeling and Advanced Control of Motor Drives and Power Systems)

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21 pages, 42592 KB

Open AccessArticle

An Automatic Detection Method for Coal–Rock Interface Based on YOLOv8 and Cubic Spline Interpolation

by Yang Gao, Yiming Zhang, Xin Huang and Degen Li

Processes 2026, 14(12), 1956; https://doi.org/10.3390/pr14121956 (registering DOI) - 16 Jun 2026

Abstract

It is crucial to accurately extract the continuous coal–rock interface in order to achieve automatic height control of the coal mining machine. This paper proposes an automatic extraction method that combines YOLOv8 detection and cubic spline interpolation to address the problem of discrete [...] Read more.

It is crucial to accurately extract the continuous coal–rock interface in order to achieve automatic height control of the coal mining machine. This paper proposes an automatic extraction method that combines YOLOv8 detection and cubic spline interpolation to address the problem of discrete output and difficulty in directly generating coal–rock interfaces in existing deep learning detection methods. First, YOLOv8 is used to efficiently perceive coal–rock images and output a discrete sequence of bounding box centre points. Then, through cubic spline interpolation, it is fitted into a smooth, continuous coal–rock interface that conforms to the shape of the rock layer. Experiments have shown that this method can achieve end-to-end processing and performs well in typical complex underground scenes. The cubic spline interpolation strategy improves the vertical positioning accuracy of the boundary line, with a 14.9% increase in vertical positioning accuracy compared to linear interpolation. It significantly improves the engineering usability and control accuracy of the output curve, providing a reliable technical solution for intelligent mining in coal mines. Full article

(This article belongs to the Special Issue Adsorption and Pore Structure Analysis in Coalbed Methane and Gas Recovery)

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33 pages, 2319 KB

Open AccessArticle

Coordinated Scheduling of Network Reconfiguration, Photovoltaic Generation, and Intelligent Parking Lots in Active Distribution Systems Using Enhanced Grey Wolf Optimization

by Salman Alotaibi and Ali S. Alghamdi

Processes 2026, 14(12), 1955; https://doi.org/10.3390/pr14121955 (registering DOI) - 15 Jun 2026

Abstract

The large-scale integration of photovoltaic (PV) generation and electric vehicles (EVs) into distribution networks introduces significant operational challenges, including voltage fluctuations, increased energy losses, and feeder congestion. While previous studies have addressed distribution system reconfiguration (DSR), PV scheduling, or EV intelligent parking lot [...] Read more.

The large-scale integration of photovoltaic (PV) generation and electric vehicles (EVs) into distribution networks introduces significant operational challenges, including voltage fluctuations, increased energy losses, and feeder congestion. While previous studies have addressed distribution system reconfiguration (DSR), PV scheduling, or EV intelligent parking lot (IPL) management separately, no unified framework exists that simultaneously optimizes all three flexibility tools. This research therefore aims to develop a coordinated scheduling framework that minimizes both energy losses and voltage deviations over a 24 h horizon. For solving the mathematical formulation, an Enhanced Grey Wolf Optimizer (EGWO) is developed using the concepts of dynamic neighborhood influence and self-adaptive convergence factor to prevent the issue of premature convergence and dynamic balancing of the algorithm during the search process. Simulation results on the IEEE 33-bus system across five scenarios quantify the benefits of each control layer. DSR alone reduces daily energy loss by 30.41%. Photovoltaic scheduling alone reduces loss by 15.40%. When combined, PV scheduling and DSR achieve a 38.29% loss reduction, demonstrating strong synergy. Full integration including IPL further improves voltage deviation by 40.26% compared to the base case, while maintaining loss reduction at 36.20%. Full article

(This article belongs to the Special Issue Recent Advances in Renewable Energy Systems: Integration Challenges, Forecasting, and Grid Optimization for Sustainable Energy Management)

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35 pages, 11161 KB

Open AccessArticle

A Mechanics-Based Recursive Propagation Framework for Modeling Complex Hydraulic Fracture Networks in Naturally Fractured Shale Reservoirs

by Jiangpeng Hu, Pin Jia, Gaojiaxiang Zhang, Gaofei Yan, Binyu Wang, Wenhao Duan and Renyi Cao

Processes 2026, 14(12), 1954; https://doi.org/10.3390/pr14121954 (registering DOI) - 15 Jun 2026

Abstract

Hydraulic fracturing in naturally fractured shale reservoirs commonly generates complex mesh-like fracture networks governed by hydraulic fracture–natural fracture interactions, which strongly affect stimulated volume, fracture connectivity, and early-time production. Existing simulation and monitoring-based methods often cannot simultaneously capture interaction mechanisms, rapidly generate field-scale [...] Read more.

Hydraulic fracturing in naturally fractured shale reservoirs commonly generates complex mesh-like fracture networks governed by hydraulic fracture–natural fracture interactions, which strongly affect stimulated volume, fracture connectivity, and early-time production. Existing simulation and monitoring-based methods often cannot simultaneously capture interaction mechanisms, rapidly generate field-scale fracture networks, and validate production responses. This study proposes a mechanics-constrained recursive propagation framework. A field-constrained stochastic natural-fracture model is first constructed, an explicit hydraulic fracture–natural fracture interaction criterion is incorporated to identify penetration, opening, and shear slipping, and a fully vectorized bidirectional recursive algorithm is developed to efficiently generate complex fracture networks. The method is applied to a 40-stage fractured horizontal well in the Changqing Oilfield, where the target interval has a porosity of 6.1%, a permeability of 0.1 mD, and a horizontal stress contrast of 7.0 MPa. The simulated network reproduces crossing, arrest, unilateral diversion, and bilateral diversion, and agrees well with microseismic observations. EDFM-based fully implicit flow simulation further shows early-time production deviations of 2–10%. These results demonstrate that the proposed framework can efficiently generate physically plausible field-scale fracture networks for fracturing design, post-fracturing evaluation, and short-term production forecasting. Full article

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19 pages, 3884 KB

Open AccessArticle

Linking Dielectric Response with Transformer Moisture Content Through Vector Fitting Analysis and Havriliak–Negami Model

by Giovanni Hernandez, Abner Ramirez and Parminder Panesar

Processes 2026, 14(12), 1953; https://doi.org/10.3390/pr14121953 (registering DOI) - 15 Jun 2026

Abstract

This paper presents a method for estimating moisture content (%MC) in power transformers. It primarily relies on the analysis of the statistical properties of relaxation times characterizing the dielectric frequency response (DFR), which is fitted as a sum of rational functions using the [...] Read more.

This paper presents a method for estimating moisture content (%MC) in power transformers. It primarily relies on the analysis of the statistical properties of relaxation times characterizing the dielectric frequency response (DFR), which is fitted as a sum of rational functions using the Vector Fitting (VF) tool. The DFR is modeled as a sum of Debye terms (accounting for materials exhibiting different relaxation times due to multiple polarization processes) characterized by poles and residues provided by VF. These parameters are then used to derive statistical factors that correlate with the shape of the dielectric response curve in the context of the Havriliak–Negami (HN) model, which is known for its effectiveness in characterizing materials with multiple relaxation times. By correlating the statistical factors with the HN model parameters, substantial insights into the insulation condition can be achieved. A moisture index (MI) is proposed from these parameters, which, when combined with conductivity, allows for accurate %MC estimation in the solid insulation system (cellulose). The combined MI and conductivity capture combined effects on moisture behavior, addressing both conductivity and polarization losses at different frequencies. The proposed method provides an efficient and straightforward non-invasive approach to insulation assessment without complex optimization algorithms. Experimental work on transformers at varying moisture levels provides validation of the proposed approach and demonstrates strong correlation with industry standards. The results confirm its reliability for moisture evaluation in transformer monitoring. Full article

(This article belongs to the Special Issue High-Power-Density and High-Reliability Power Transformers: Design, Operation, Protection, and Enabling Technologies)

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40 pages, 2403 KB

Open AccessArticle

Mechanism and Simulation Analysis of Resonance De-Icing for 100 m High-Voltage Transmission Line

by Yu Zhang, Yinke Dou, Fujia Liu, Liangliang Zhao, Yangyang Jiao and Huajian Li

Processes 2026, 14(12), 1952; https://doi.org/10.3390/pr14121952 (registering DOI) - 15 Jun 2026

Abstract

To address safety hazards such as line damage and operational instability caused by icing on high-voltage overhead transmission lines, this study conducts numerical simulation research on wire vibration de-icing based on the ANSYS finite element platform. Using a 100 m span transmission line [...] Read more.

To address safety hazards such as line damage and operational instability caused by icing on high-voltage overhead transmission lines, this study conducts numerical simulation research on wire vibration de-icing based on the ANSYS finite element platform. Using a 100 m span transmission line as the research model, 49.8 m ice-covered sections are set on both sides of the line, and the 0.4 m range in the middle is designated as the concentrated excitation force area of the vibration motor. By applying intermittent harmonic loads in the excitation stage, the process of mechanical vibration de-icing is accurately reproduced. At the same time, life and death element technology is introduced to remove ice-covered units with stress exceeding the critical failure threshold, accurately realizing the dynamic simulation of the entire process of ice-covering cracking and detachment. This study selects resonance frequency bands that are suitable for the structural characteristics of the transmission line through static analysis, modal analysis, and harmonic response analysis, and preliminarily locks in candidate excitation frequencies. Combined with transient dynamics simulation, the optimal excitation frequency for vibration de-icing of transmission lines is determined by comprehensively considering the efficiency of de-icing and the safety constraints of conductor dancing. A method for determining the optimal de-icing frequency based on multi-step finite element analysis has been developed, which can provide theoretical support and simulation reference for the structural design, frequency matching, and operational parameter optimization of mechanical vibration de-icing devices for high-voltage transmission lines and overhead cables. Full article

(This article belongs to the Special Issue Adaptive Control and Optimization in Power Grids)

18 pages, 6801 KB

Open AccessArticle

Numerical Simulation of Horizontal Well Steering Fracturing Based on the Cohesive Zone Model

by Jian Shi, Peng Song, Jinsheng Zhao, Jun Yang, Jin Wang, Wantao Liu, Qiang Liu, Chen Yang and Mingyong Xu

Processes 2026, 14(12), 1951; https://doi.org/10.3390/pr14121951 (registering DOI) - 15 Jun 2026

Abstract

Horizontal-well steering fracturing is an important completion strategy for unconventional reservoirs, where fracture growth is jointly controlled by wellbore azimuth, natural fractures, and inter-cluster stress interference. In this study, a two-dimensional fluid-solid-coupled hydraulic-fracturing model with embedded cohesive elements was developed to simulate fracture [...] Read more.

Horizontal-well steering fracturing is an important completion strategy for unconventional reservoirs, where fracture growth is jointly controlled by wellbore azimuth, natural fractures, and inter-cluster stress interference. In this study, a two-dimensional fluid-solid-coupled hydraulic-fracturing model with embedded cohesive elements was developed to simulate fracture initiation and growth at steering angles of 0°, 30°, 45°, 60°, and 90°. The Blanton, Warpinski-Teufel, and Blanton-Gao hydraulic-fracture/natural-fracture interaction criteria were used as mechanical benchmarks to interpret simulated capture, deflection, and penetration regimes. The simulations indicate that natural fractures preferentially guide fracture propagation: hydraulic fractures tend to be captured by, or propagate along, natural fractures at approach angles ≤30°, whereas penetration is more likely at approach angles ≥60°. In the single-stage single-cluster model, the 90° case produces the largest simulated fracture length and the highest failed-cohesive-element count. In the single-stage multi-cluster model with 3 m cluster spacing, the 30–45° interval shows more favorable fracture extension and interface activation than the 90° case because inter-cluster stress-shadow effects suppress fracture-network development at large steering angles. The resulting steering-angle window should be interpreted as a comparative result for the fixed mesh, deterministic natural-fracture realization, and baseline cluster-spacing configuration adopted here. These results provide a mechanistic basis for steering-fracturing design in hard-rock reservoirs while clarifying the applicability limits of the two-dimensional plane-strain approximation. Full article

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

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17 pages, 2843 KB

Open AccessArticle

Case Study of Dynamic Stratified Production Allocation and Remaining Oil Evaluation in Offshore Multilayer Commingled Reservoirs with Strong Aquifers

by Fang Ding, Gangxiang Song, Ruidong Wu, Yan Jin, Peng Zhou and Xiukun Wang

Processes 2026, 14(12), 1950; https://doi.org/10.3390/pr14121950 (registering DOI) - 15 Jun 2026

Abstract

After prolonged development, offshore edge- and bottom-water reservoirs have entered an ultra-high water-cut stage. Under long-time multilayer commingled production conditions, accurate dynamic quantification of layer-wise production remains technically challenging, and conventional production allocation approaches often lack the accuracy required for fine-scale reservoir management. [...] Read more.

After prolonged development, offshore edge- and bottom-water reservoirs have entered an ultra-high water-cut stage. Under long-time multilayer commingled production conditions, accurate dynamic quantification of layer-wise production remains technically challenging, and conventional production allocation approaches often lack the accuracy required for fine-scale reservoir management. To address these challenges, this study proposes a production allocation method that integrates static reservoir properties, dynamic production performance, and pressure-based correction. The resulting layer-wise allocation provides a quantitative basis for evaluating remaining oil utilization and delineating the distribution of remaining oil across individual layers. The method is formulated on the basis of a pseudo-steady-state productivity model that incorporates wellbore imperfection effects. The initial production rate of each layer is calculated by combining reservoir transmissibility with production pressure drawdown. To account for unequal pressure responses among layers under commingled production—resulting from pressure imbalance, limited edge- and bottom-water energy support, and interlayer interference—a correction factor is introduced to adjust the effective production contribution of low-pressure layers. Compared with the PLT test results, the average error of the conventional KH method was 16.7%, whereas the average error of the proposed method was reduced to 6.1%. The average error was reduced from 16.7% to 6.1%, corresponding to a 63.5% reduction in prediction error, indicating that the proposed approach can provide a more reliable estimation of layered production contribution. The proposed method enables continuous and dynamic production allocation for commingled wells without the need for frequent surveys, offering a practical tool for identifying multilayer production imbalance, evaluating remaining potential, and supporting development optimization in offshore oilfields. Full article

(This article belongs to the Special Issue Flow Mechanisms and Enhanced Oil Recovery, 2nd Edition)

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24 pages, 7645 KB

Open AccessArticle

Prediction and Control Technology of Trapped Annular Pressure in Gas Storage Wells

by Wei Rong, Xiaoping Yang, Zhi Zhang, Zhong Pan, Xuefeng Dou, Liangwen Liu, Xiaobin Bai, Nan Cai and Huayan Li

Processes 2026, 14(12), 1949; https://doi.org/10.3390/pr14121949 (registering DOI) - 15 Jun 2026

Abstract

In view of the frequent occurrence of trapped annular pressure and the increasingly prominent risk of wellbore integrity under the periodic high-intensity injection and production conditions of gas storage wells, a trapped annular pressure prediction model suitable for deep gas storage wells is [...] Read more.

In view of the frequent occurrence of trapped annular pressure and the increasingly prominent risk of wellbore integrity under the periodic high-intensity injection and production conditions of gas storage wells, a trapped annular pressure prediction model suitable for deep gas storage wells is established based on the comprehensive heat transfer characteristics of the tubing string-cement sheath-formation. The calculation results of the model are in good agreement with field-measured pressure data, with a coincidence degree of about 95%. Based on the established model, the influence laws of four major factors, including tubing specification and dimension, thermophysical properties of annular fluid, casing material characteristics and daily gas production rate, on trapped annular pressure are systematically analyzed. Meanwhile, the pressure control effects of three measures, namely Annulus A pressure relief, application of insulated tubing and nitrogen injection into Annulus B, are quantitatively compared for the case well. The research results show that adopting tubing with larger outer diameter and thinner wall thickness, injecting fluid with lower thermal expansion coefficient or higher isothermal compressibility coefficient into the annulus and appropriately reducing daily gas production can effectively decrease trapped annular pressure. Among them, the influence of fluid properties on trapped annular pressure is far greater than that of pipe material parameters. Among the three pressure control measures, nitrogen injection into Annulus B presents the optimal pressure control effect; when the nitrogen volume accounts for approximately 3% of the total annular fluid volume, the trapped annular pressure is reduced by about 82%. The research findings provide a theoretical basis and technical guidance for the prediction and control of trapped annular pressure in gas storage wells. It is recommended to prioritize the nitrogen injection technology for Annulus B in the well construction stage, and realize pressure management for producing wells by combining Annulus A pressure relief and production regulation. Full article

(This article belongs to the Section Energy Systems)

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21 pages, 4019 KB

Open AccessArticle

Relative Permeability Characteristics of Natural Gas and CO₂ Mixtures in Matrix and Fractured Cores: An Experimental Study

by Hongyou Zhang, Wenzheng Liu, Guangyi Sun, Xin Liu, Zhihui Wei, Lei Zhang and Hai Sun

Processes 2026, 14(12), 1948; https://doi.org/10.3390/pr14121948 (registering DOI) - 15 Jun 2026

Abstract

To clarify the oil–gas multiphase flow behavior of natural gas/CO₂ composite flooding in the dual-medium system of the BZ26-6 fractured reservoir, systematic oil–gas relative permeability experiments were conducted under reservoir temperature and pressure conditions. Using the steady-state method, the effects of core [...] Read more.

To clarify the oil–gas multiphase flow behavior of natural gas/CO₂ composite flooding in the dual-medium system of the BZ26-6 fractured reservoir, systematic oil–gas relative permeability experiments were conducted under reservoir temperature and pressure conditions. Using the steady-state method, the effects of core type, gas composition, and reservoir pressure on relative permeability behavior were investigated. The results show that the relative permeability curves are characterized by relatively high oil-phase permeability and low gas-phase permeability. Increasing the CO₂ fraction generally enhances oil mobilization and displacement efficiency, whereas the two-phase co-flow zone may reach an optimum at an intermediate CO₂ fraction, depending on the core structure. Specifically, with increasing CO₂ fraction, displacement efficiency increased from 37.05% to 43.70% in fractured metamorphic cores and from 60.74% to 64.63% in fractured carbonate cores. In contrast, decreasing reservoir pressure may induce stress-sensitive fracture compression, narrow the co-flow zone, and reduce flow capacity. Oil–gas two-phase flow behavior is strongly controlled by reservoir structure, with fractured carbonate cores exhibiting higher displacement efficiency and a wider co-flow region than fractured metamorphic cores. Within the scope of this study, a CO₂ fraction of 40% appears to be a comparatively favorable composite-gas composition when both displacement performance and gas-source economics are considered. Full article

(This article belongs to the Special Issue Advances in Reservoir Simulation and Multiphase Flow in Porous Media)

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20 pages, 21925 KB

Open AccessArticle

Multi-Criteria Optimization of Face Milling of Al7075 Hybrid Metal Matrix Composites Using TOPSIS and CODAS Under Hybrid MQL-Cryogenic CO₂ Cooling

by Jie Yang, Qingzhe Meng, Youlei Zhao and Vinothkumar Sivalingam

Processes 2026, 14(12), 1947; https://doi.org/10.3390/pr14121947 (registering DOI) - 15 Jun 2026

Abstract

Face milling of aluminum 7075 hybrid metal matrix composites with 10 wt.% TiO₂ and 3 wt.% graphite (HMMCs) are needed to improve performance and sustainability. This study focuses on optimizing the milling process for Al7075 HMMCs using the desirability approach and advanced [...] Read more.

Face milling of aluminum 7075 hybrid metal matrix composites with 10 wt.% TiO₂ and 3 wt.% graphite (HMMCs) are needed to improve performance and sustainability. This study focuses on optimizing the milling process for Al7075 HMMCs using the desirability approach and advanced multi-criteria decision-making (MCDM) methodologies, including the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Combined Distance-based Assessment (CODAS). Surface roughness (SR), cutting force (CF), carbon emissions (CE), and energy consumption (EC) were systematically evaluated and ranked using the L₁₈ Taguchi Orthogonal Array. Minimum Quantity Lubrication (MQL) and cryogenic CO₂ cooling techniques were used to achieve a superior surface finish and reduce friction at the tool-workpiece interface, thereby minimizing scratches and thermal damage. Desirability evaluation results showed the optimal machining conditions for milling of Al7075 (HMMCs) occurred at a cutting speed (V_c) of 200 m/min, a feed rate (f) of 0.02 mm/rev, and a depth of cut (a_p) of 0.3 mm, proving the potential of integrating MCDM tools with effective cooling strategies. The desirability method favored a balanced compromise, while entropy-weighted TOPSIS/CODAS emphasized energy and carbon-related responses. Improvements of 6% in cutting force, 7% in surface roughness, and a 7% reduction in energy consumption, along with 8% lower carbon emissions, were achieved, demonstrating the effectiveness of hybrid cooling strategies in promoting eco-friendly and resource-efficient processes. Full article

(This article belongs to the Section Process Control, Modeling and Optimization)

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27 pages, 12721 KB

Open AccessArticle

Polymer Controlled Oil Bank Dynamics: A Hybrid Physics-Informed Machine Learning Quantitative Framework

by Wenyang Shi, Yunpeng Gong, Shaokai Rong, He Li, Lei Tao, Jiajia Bai, Zhengxiao Xu and Qingjie Zhu

Processes 2026, 14(12), 1946; https://doi.org/10.3390/pr14121946 (registering DOI) - 14 Jun 2026

Abstract

To address the lack of systematic quantitative characterization of oil bank dynamic evolution and unclear dominant controlling factors in polymer flooding, this study combines reservoir numerical simulation with Python-based quantitative analysis and a machine learning framework (random forest + SHAP). We established 1D [...] Read more.

To address the lack of systematic quantitative characterization of oil bank dynamic evolution and unclear dominant controlling factors in polymer flooding, this study combines reservoir numerical simulation with Python-based quantitative analysis and a machine learning framework (random forest + SHAP). We established 1D and 2D reservoir models: the 1D model develops a precise quantitative characterization method for oil bank width (defined by front/rear edge saturation offsets Pf < 1.0% and Pb < 1.0%, fitted with a cubic polynomial, R² > 0.95) and height (derived from optimal oil saturation difference time curves and integral calculation); the 2D model investigates the regulatory mechanism of reservoir heterogeneity. Based on 15,000 sets of physically consistent simulation data, the random forest model achieves high prediction accuracy (R² = 0.98). Sensitivity analysis reveals that main flow direction permeability, reservoir temperature, and water-phase exponent (nw) of the Corey model are the dominant controlling parameters, exhibiting substantially higher sensitivity than polymer adsorption capacity and residual resistance coefficient. The oil bank height shows a negative correlation with the first two parameters, while it displays a peak-type variation with the water-phase exponent. Under heterogeneous conditions, permeability anisotropy amplifies the regulatory effect of relative permeability exponents, leading to unbalanced oil bank migration (quantified by front ratio R). This study breaks through the limitations of traditional qualitative characterization, elucidates the spatiotemporal evolution laws and heterogeneous regulatory mechanisms of the oil bank, and provides reliable theoretical and dataset support for optimizing polymer flooding schemes. Full article

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

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21 pages, 2494 KB

Open AccessArticle

Aerodynamic Performance and Noise Optimization of a Parallel Multi-Blade Centrifugal Fan via RBF-Assisted Bayesian Surrogate Optimization

by Han Wu, Weiyu Chen, Yue Pan, Jihong Wang and Yunfeng Gu

Processes 2026, 14(12), 1945; https://doi.org/10.3390/pr14121945 (registering DOI) - 14 Jun 2026

Abstract

Parallel multi-blade centrifugal fans present a challenge in simultaneously reducing aerodynamic noise and maintaining efficiency. This study presents a multi-objective optimization using a radial basis function (RBF)-assisted Bayesian optimization framework, with three volute parameters (tongue radius, tongue clearance, and axial gap) as design [...] Read more.

Parallel multi-blade centrifugal fans present a challenge in simultaneously reducing aerodynamic noise and maintaining efficiency. This study presents a multi-objective optimization using a radial basis function (RBF)-assisted Bayesian optimization framework, with three volute parameters (tongue radius, tongue clearance, and axial gap) as design variables. Computational fluid dynamics (CFD) combined with the Ffowcs Williams–Hawkings (FW-H) acoustic analogy was employed to evaluate noise and total pressure efficiency. To reduce computational cost, an RBF surrogate model was constructed from 30 Latin hypercube samples, achieving leave-one-out cross-validation (LOOCV) R² values of 0.978 and 0.995 for noise and efficiency, respectively. A Bayesian search using the log expected hypervolume improvement (logEHVI) acquisition function was performed on the RBF response surfaces, converging to a hypervolume of approximately 0.72, consistent with an NSGA-II benchmark. Based on household fan requirements, a 70/30 noise-efficiency weighting was adopted, yielding RBF-predicted values of 59.04 dB and 0.545 for the selected low-noise-preference candidate. An independent CFD recalculation yielded 59.19 dB and 0.554. The SPL at the characteristic frequency of 2550 Hz was reduced by 9.9 dB. Flow field analysis revealed that the optimized tongue clearance weakened the impingement on the volute tongue and suppressed unsteady vortex shedding. This framework provides an efficient strategy for multi-objective aerodynamic and acoustic optimization of parallel centrifugal fan systems. Full article

(This article belongs to the Topic Fluid Mechanics, 3rd Edition)

23 pages, 6518 KB

Open AccessArticle

Multi-Criteria Evaluation and Scenario-Driven Selection of Grounding Connectors Across Materials and Joining Processes

by Junjie Chen, Zhigao Wang, Fan Wang, Mei Wang, Tao Liu, Xinsheng Lan and Jigang Huang

Processes 2026, 14(12), 1944; https://doi.org/10.3390/pr14121944 (registering DOI) - 14 Jun 2026

Abstract

Grounding connectors critically influence the safety and long-term reliability of earthing systems through coupled electro-thermal, mechanical, and corrosion behaviors, yet no standardized quantitative framework exists for jointly evaluating these performance dimensions across diverse deployment scenarios. This study introduces a unified multi-criteria evaluation framework [...] Read more.

Grounding connectors critically influence the safety and long-term reliability of earthing systems through coupled electro-thermal, mechanical, and corrosion behaviors, yet no standardized quantitative framework exists for jointly evaluating these performance dimensions across diverse deployment scenarios. This study introduces a unified multi-criteria evaluation framework applied to six grounding connector configurations spanning four alloy families and three joining technologies. Electro-thermal response was characterized by coupled finite element simulations (0–100 A), mechanical reliability by quasi-static tensile testing (n = 10 per configuration), and corrosion durability by accelerated salt-spray exposure with image-based corroded area fraction quantification. Performance metrics were normalized and aggregated using equal-weight, Analytic Hierarchy Process, and Shannon entropy weighting schemes, with the Technique for Order of Preference by Similarity to Ideal Solution applied for multi-scenario ranking. One-way analysis of variance confirmed statistically significant effects of connector type on tensile performance (F(5, 54) = 3154.90, p < 0.001). The exothermic welded joint achieved the highest mean ultimate tensile load (61.5 ± 1.5 kN), while copper mechanical connectors exhibited the lowest steady-state temperature rise (~2 K above ambient at 100 A). Compression-crimped connectors ranked first under both equal and Analytic Hierarchy Process weighting (closeness coefficients 0.737 and 0.807, respectively), while stainless steel connectors ranked first under corrosion-critical deployment scenarios. Scenario-weighted analyses demonstrate that the optimal material–process combination shifts with environmental severity, current duty, and mechanical demand, providing a reproducible, evidence-based basis for context-dependent connector specification. Full article

(This article belongs to the Special Issue Green Manufacturing and Low-Carbon Control of Mechanical and Electrical Products)

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