Energy Science & Engineering最新文献

This study investigates the micro-pore structure characteristics and genesis of low-resistivity reservoirs in the Wufeng and Longmaxi Formation of the Sichuan Basin. A comprehensive analytical approach—combining core analysis, gas adsorption, high-pressure mercury intrusion, and X-ray photoelectron spectroscopy (XPS) was employed to systematically characterize the pore structure of low-resistivity shale reservoirs and their relationship with electrical resistivity. The results reveal that low-resistivity shale reservoirs typically exhibit smaller pore volume and specific surface area, along with a higher degree of organic matter graphitization. This organic matter graphitization process significantly reduces the rock's resistivity. Pore structure evolution is governed by both compaction and tectonic deformation, leading to macropore reduction and meso-/micropore redistribution. Morphological transformations in organic matter pores—including pore collapse and wall contact—further facilitate electron migration and contribute to resistivity decline. By analyzing microstructural features of the Wufeng–Longmaxi shale, this study highlights the dominant influence of organic matter maturity, graphitization, and pore structure dynamics on resistivity, offering a theoretical framework for understanding the genesis and guiding exploration of low-resistivity shale gas reservoirs.

Gas turbine exhaust temperatures typically exceed 500°C, with waste heat recovery significantly improving thermal efficiency. As a mainstream recovery technology, the organic rankine cycle (ORC) utilizes cyclopentane working fluid that has high evaporation temperatures but carries flammability risks. The combined dry and flooded heat exchangers stabilize flow while ensuring superheat, requiring strict liquid level safety. This study investigates dynamic characteristics and control strategies of a flooded ORC system with cyclopentane. Within safe liquid level ranges, pump speed affects system power by merely 0.48% maximum, eliminating the need for regulation; cooling water flow control yields no benefits, while an optimal 0.1 split ratio exists in heat transfer oil. The system maintains safe levels through pump speed adjustment according to operating condition variations and maximizes output power via heat transfer oil split ratio modulation. This study provides theoretical foundations for the operation and control of cyclopentane and flooded ORC systems.

This paper presents an adaptive population size (NP)–based accelerated Particle Swarm Optimization (AAPSO) algorithm for duty cycle–based maximum power point tracking (MPPT) in photovoltaic (PV) systems. The proposed method directly modulates the duty cycle of a DC–DC converter, enabling rapid and precise adjustments to the maximum power point (MPP) under both uniform and partial shading conditions. AAPSO enhances conventional PSO by adopting a social-only variant and an adaptive Population size (NP) mechanism that begins with a large population for exploration and gradually reduces it to balance exploration and exploitation. To ensure robustness, the algorithm is executed 100 times, and performance is analyzed using statistical metrics and run-length distribution (RLD). Simulation results demonstrate approximately 99.8% tracking efficiency with a 100% tracking accuracy across all runs, while convergence counts are reduced nearly threefold compared to conventional Particle Swarm Optimization (CPSO) and two recent adaptive PSO-based MPPT methods from the literature. Experimental validation using a Ćuk converter prototype further confirms its practical feasibility. Overall, this study contributes an adaptive, duty cycle–based constrained PSO framework that integrates robustness, scalability, and statistical reliability for MPPT in large-scale PV systems.

In petroleum recovery processes, crude oil emulsions serve a crucial yet complex dual role. While facilitating hydrocarbon transport from subterranean reservoirs to surface facilities, excessively stable emulsions create significant challenges in downstream dehydration operations. The heightened stability of these colloidal systems necessitates increased demulsifier dosages and elevated separation temperatures, thereby substantially escalating operational expenditures. This technological dichotomy underscores the critical need for a comprehensive understanding of emulsion formation mechanisms, comparative evaluation of demulsification methodologies, and fundamental insights into destabilization processes—all essential for optimizing field operations. Building upon systematic analysis of emulsion characteristics and stabilization mechanisms, this study presents a critical synthesis of contemporary physical and chemical demulsification technologies. We conduct a comparative assessment of their technical advantages and operational limitations, with particular emphasis on advancing chemical demulsification strategies. The paper provides a rigorous classification and mechanistic analysis of diverse demulsifier categories, elucidating their interfacial activity and molecular-level interactions at oil–water interfaces. Looking toward future developments, we propose promising directions for next-generation demulsifier design and emerging hybrid separation technologies. These forward-looking perspectives aim to inform the development of cost-effective dehydration solutions while addressing current technological gaps in heavy crude processing and environmentally sustainable demulsification.

In this paper, the research on casing running is analyzed. By analyzing the stress and deformation of casing string with centralizer, the calculation model of friction, bending and centralizer pointing force in the process of horizontal down-hole running of casing string is derived, and the friction between casing and running hole wall is solved by iterative method. The mathematical model is used to entangle the bending force of casing. When the string is bent along the bending direction of borehole trajectory, the bending force is related to the casing outer diameter, cross-sectional area and string length. Then, the influence of casing additional force, such as drilling fluid viscous resistance, keyway rock breaking resistance, casing buckling additional load, casing running dynamic load and rotating casing running, on casing friction is analyzed in detail. The dynamic load of running casing makes the casing in curved and vertical sections bear large alternating load of tension and pressure, and rotating casing running may be an effective measure to reduce the friction of running casing. To run casing safely in extended reach horizontal well, the floating casing technology was simulated and analyzed. The safety of the horizontal well casing has been compromised, providing a casing cement sheath safety guarantee for future CO₂ injection production measures in oil and gas wells.

Accurately estimating the state of health (SOH) of power batteries is beneficial for their maintenance, delaying aging, ensuring safety, and providing a basis for their secondary use to enhance resource utilization efficiency. However, existing data-driven methods rely heavily on laboratory data and lack adequate adaptability to real-world vehicle conditions. Moreover, traditional gradient boosting algorithms such as gradient boosting decision trees (GBDT) and LogitBoost encounter precision and generalization issues when faced with the complex operating conditions of real vehicles, thereby limiting their practical applications. To address these challenges, this paper proposes a method for estimating the SOH of power batteries in pure electric vehicles using an extreme gradient boosting (XGBoost) model optimized by the grid search cross-validation (GSCV) method, based on data from a vehicle manufacturer's monitoring platform. First, data are divided according to a “discharge + charge” pattern, and 16 capacity degradation feature factors from six categories are extracted from the discharge-charge segments as input variables for the XGBoost model, while partial charged capacity is extracted from the charge segments as the output label for the model. Subsequently, to overcome the XGBoost model's sensitivity to hyperparameters and its susceptibility to overfitting, the GSCV method is employed for parameter optimization of the XGBoost model, and the GSCV-XGBoost model is used to estimate partial charged capacity. Finally, an SOH correction method is applied to the output of the GSCV-XGBoost model to obtain the corrected SOH. Experimental results demonstrate that the SOH estimated by the GSCV-XGBoost model combined with the SOH correction method exhibits smaller errors and remains consistently below 2% compared to SOH corrected based on the Ampere-hour integral method. In estimating partial charged capacity, the GSCV-XGBoost model significantly outperforms the XGBoost model. Compared to the CBDT and linear regression (LR) models, the GSCV-XGBoost model achieves the highest goodness of fit (R²), with the smallest mean absolute error (MAE) and root mean squared error (RMSE). The research findings presented in this paper are expected to provide effective solutions for real-world vehicle power battery SOH monitoring.

As decentralized energy systems gain momentum, microgrids (MGs) have become a vital component of the modern power landscape. Yet, maintaining power quality (PQ) within these systems presents ongoing challenges due to the presence of nonlinear loads, variable renewable energy sources, and frequent switching operations. These factors contribute to PQ disturbances, such as harmonic distortion, voltage instability, and synchronization issues. Conventional mitigation methods often struggle to cope with such dynamic and complex environments. This review investigates the emerging role of artificial intelligence (AI) as a powerful tool for optimizing PQ in MGs. It presents a detailed overview of various AI-based methods, including machine learning (ML), metaheuristics, deep learning, fuzzy logic, and hybrid approaches and their implementation in areas like harmonic suppression, voltage and frequency regulation, islanding detection, renewable energy coordination, and predictive diagnostics. The study evaluates these techniques based on key performance indicators, such as precision, scalability, and suitability for real-time operation, while also addressing challenges related to data reliability, interpretability, and cybersecurity. The article concludes by highlighting future research directions, such as AI integration with Internet of Things (IoT), edge computing, and decentralized intelligence. Overall, the review illustrates how AI can play a pivotal role in transforming MG PQ optimization for the evolving smart grid era.

Prompt and accurate fault detection in extra high voltage transmission lines is required for guaranteeing the steadiness of power system. This study describes the performance of BiLSTM, GRU, and TCN as deep learning models for the detection and classification of faults in transmission lines through synthetic and real-time sequential datasets in 500 kV transmission line between Jamshoro and Karachi (NKI), in Sindh, Pakistan. Testing models' performance on simulated faults versus real fault events, the study concludes a major space and suggests insights for their practical applicability. The results show that deep learning models can reach vast level of accuracy in classifying different faults in transmission lines. This study forms the basis for exploiting modern fault detection practices in operating grids to improve their dependability and flexibility. The results revealed an accuracy of 98.31%, achieved by the BiLSTM, 94.27% for GRU and TCN as 99.8% through simulated data set, whereas using real-time fault data BiLSTM scored 62.05% accuracy, while GRU accuracy score achieved 96.43%, and TCN attained 100% accuracy. The results demonstrate that the deep learning models used in this study work well analyzing time series data by achieving high fault accuracy for fault classification in transmission lines. In general, the study was conducted to identify the best model in managing the fault over extra high voltage transmission lines under different conditions.

The proliferation of high-frequency wireless power transfer (WPT) technology in smart grid applications—particularly dynamic charging infrastructure, distributed device powering, and electrical fault diagnostics—has intensified concerns regarding leakage magnetic field effects on electromagnetic compatibility and operational integrity of critical grid components. Conventional electromagnetic shielding solutions suffer from the dual limitations of excessive spatial footprint and suboptimal material efficiency, proving inadequate for contemporary power systems requiring compact, resource-efficient electromagnetic protection. The study proposed a paradigm-shifting geometric optimization framework employing passive electromagnetic shielding to simultaneously enhance shielding performance and material utilization efficiency. Initially, through systematic finite element analysis (FEA) of four distinct configurations (disc, ring, concentric ring, and fan), the study establishes the concentric-ring topology as superior in achieving optimal balance between mass reduction and shielding efficiency. Parametric analysis reveals critical design interdependencies: shielding effectiveness (SE) demonstrates direct proportionality to ring width and inverse proportionality to inter-ring gap distance. An intelligent prediction model based on a deep belief–back propagation neural network (DBN-BP) was subsequently developed to generate customized parameter combinations, demonstrating either 113% SE or 71.4% material volume or 106% effectiveness at 43.36% material consumption. A practical solution for electromagnetic management in WPT-enabled power systems has been provided, and a physics-based machine learning research perspective for high-efficiency shielding design has been offered.

The HC method for hydropower is a commonly used rock mass quality classification technique in China's hydropower industry. Due to the anisotropic nature of the layered schist in the study area, and the varying angles between different tunnel layers and the tunnel axis, significant discrepancies arise between the HC method's classification results and actual rock mass classifications when these angles are parallel. This study employs uniaxial compression tests on schist to reveal its anisotropic characteristics under loading directions at 0°, 45°, and 90° angles relative to the bedding planes. The compressive strength exhibits a V-shaped variation with changes in angle between loading direction and schistosity plane, while the elasticity modulus shows a linear decrease as this angle varies. Numerical simulation experiments were conducted to monitor deformations of surrounding rock masses around tunnels. The findings indicate that as the angle between bedding orientation and tunnel axis decreases, both wall and roof deformations increase progressively. Under conditions of 0°, 30°, 45°, 60°, and 90° angles, the ratios of wall deformation values are approximately 1:3.73:4.74:5.44:7.7; whereas for roof deformation values, they are about 1:1.3:1.94:4.7:6.7. When applying traditional HC methods for classifying surrounding rock quality in parallel schist tunnels, a low agreement rate of only 13.33% was observed. However, by incorporating adjustments based on scoring criteria related to major structural plane orientations into numerical simulation results—specifically modifying weights assigned to structural planes—the agreement rate improved significantly to an impressive 100%. These research outcomes effectively enhance both accuracy and applicability in classifying layered rock masses, providing reliable foundations for tunneling construction practices.