International Journal of Energy Research最新文献

Paraffin wax is a promising phase change material (PCM) for thermal energy storage (TES), but its low thermal conductivity usually hinders its usefulness. While the addition of nanoparticles like carbon nanotubes (CNTs) is a known enhancement strategy, knowledge gaps exist in how CNT types and concentrations affect the performance of solar thermal storage under the same processing conditions. This study systematically compares paraffin composites with three distinct CNTs—2 SWCNTs from different synthesis methods and 1 MWCNT—at concentrations of 0.25, 0.5, and 1.0 wt%. We evaluated microstructure, rheology, thermal properties, as well as performance under simulated solar charging and heat recovery. Results showed that while all CNTs can increase thermal conductivity, performance of the charging rate varied greatly. SWCNTs could accelerate the charging rate by 45% with 16% conductivity gains, but MWCNT composites can hinder the charging rate (−20%) even with 9% conductivity gain. Higher CNT concentrations also led to additional challenges, including processability and localized degradation. This work demonstrates that optimal material selection for solar storage systems requires a holistic approach, balancing thermal conductivity, charging rate, and long-term stability.

A comprehensive mathematical model is constructed of the fuel stack in a proton exchange membrane fuel cell (PEMFC)-based vehicle. The model comprises four main components: an air supply system, a hydrogen supply system, a cooling system, and a fuel cell stack. The model is employed to investigate the effect of the bipolar plate flow field design on the liquid water generation and power output of the fuel cell stack. Four bipolar plate designs are considered: (1) anode serpentine flow field–cathode parallel flow field (AS–CP), (2) anode double serpentine counter-flow field–cathode parallel flow field (ADS–CP), (3) anode serpentine flow field–cathode double serpentine counter flow field (AS–CDS), and (4) anode double serpentine counter flow field–cathode double serpentine counter flow field (ADS–CDS). For each design, the liquid water generation and PEMFC performance are evaluated through WLTC Class 3 test cycle simulations. The results show that the ADS–CP design yields an effective reduction in the liquid water production. In particular, it achieves a reduction of 43.62% on the anode side and 4.07% on the cathode side compared to the AS–CP design.

The growing demand for sustainable urban development underscores the importance of optimizing the performance of smart buildings in local energy communities (LECs), which are key to reducing environmental impact and enhancing quality of life. This paper introduces a unified sustainability index, offering a comprehensive approach for assessing and improving the environmental, economic, and social performance of the local energy community. The novelty of this paper lies in developing a comprehensive sustainability framework for LECs, integrating energy efficiency, renewable adoption, demand response programs (DRPs), and energy-sharing, offering a more holistic evaluation compared to prior works that focus on building performance aspects. A numerical study conducted on a local energy community model illustrates the effectiveness of this approach: incorporating energy efficiency, self-consumption (SC), DRP, and energy sharing strategies leads to a 53.2% sustainability score, significantly outperforming a baseline scenario with 23.0% sustainability. The framework’s application demonstrates how dynamic optimization strategies, supported by adaptive technologies, can continuously enhance sustainability metrics in response to evolving operational conditions. The findings provide actionable insights for architects, energy managers, and policymakers, encouraging the widespread adoption of integrated sustainability strategies in LECs. This work highlights the critical role of advanced technologies and collaborative energy management in achieving long-term, holistic sustainability goals.

H. Poshteh, M. Rezvani, A. N. Shirazi, and B. Yousefi, “Optimal Planning for an Integrating Thermal–CHP–Boiler Units With a High Penetration Wind Farm Considering Economic and Environmental Factors,” International Journal of Energy Research, 2025 (2025): 9954628, https://doi.org/10.1155/er/9954628.

In this article, the affiliation “Department of Electrical Engineering, Islamic Azad University, Nour Branch, Nour, Iran” was incorrect. The corrected affiliation appears below:

Department of Electrical Engineering, No.C., Islamic Azad University, Noor, Iran

We apologize for this error.

Oil and gas remain the primary energy sources globally, even with advancements in renewable energy technologies. Significant oil deposits remain unrecovered after conventional extraction methods. Chemical enhanced oil recovery (EOR) effectively recovers bypassed and residual oil. However, conventional surfactant flooding faces challenges, including instability, high adsorption, and environmental damage, reducing oil recovery efficiency and profitability. This research proposes using benign ionic liquids (ILs) that exhibit high emulsification and electrostatic stabilization at elevated salinity and temperature. Therefore, this study aims to analyze the rheological (flow and deformation) characteristics of Emirati light crude oil and its emulsions for EOR. The addition of four imidazolium-based ILs—C₁₀mimCl, C₁₂mimCl, C₁₂mimBF₄, and C₁₆mimBr—diluted in seawater (SW) and formation brine (FB) was examined at 500 ppm. Additionally, the effects of the IL carbon chain length, salinity, anion type, temperature, shear rate, time, and angular frequency were studied. The prepared emulsions exhibited high stability, reduced viscosity, and shear-thinning behavior, which were accurately characterized by the power law. Furthermore, viscoelastic properties were measured, including storage modulus (G’), loss modulus (G"), crossover frequency, and damping factor (tan δ). Longer alkyl chain ILs, such as (FB-C₁₆mimBr), exhibited the earliest crossover frequency of 13 rad/s and achieved the lowest emulsion viscosities of 1.73 mPa·s and 0.71 mPa·s at 25°C and 80°C, respectively, measured at 1000 s⁻¹. This results from their increased hydrophobicity and ability to disrupt hydrogen bonds between asphaltene molecules. Overall, these findings indicate that ILs—as green alternatives to conventional surfactants—improve the rheological properties of oil emulsions by lowering viscosity, enhancing flowability, and increasing stability while ensuring uniform oil displacement, making them effective for EOR.

The design of support for mining roadways often heavily relies on the expertise of engineering technicians, which can result in inadequate control over the surrounding rock, overly conservative support parameters. Using the 171106 panel of LiuZhuang Mine as the study context, this study optimized an unsupervised machine-learning algorithm, built a machine-learning–based evaluation model for gateroad surrounding rock, developed an intelligent decision-support system for roadway support, and optimized support design parameters; the feasibility of the scheme was cross-validated through numerical simulation and mine pressure monitoring. The results show that, after optimization, the unsupervised algorithm achieved 92% accuracy in surrounding-rock classification. Applied at the working face, the dynamic classification indicated Class III surrounding rock; leveraging the intelligent decision system, a support cross-section was generated, and an optimized support design was produced. Compared with the original scheme in numerical simulations, the optimized design reduced left-rib displacement by 21%, surrounding-rock stress by 9.1%, roof displacement by 10%, and shrank the plastic zone by 1 m. Field measurements further showed that roof bolt stress decreased by 8.98%, rib bolt stress by 7.7%, roof separation by 16%, roof displacement by 25%, and rib displacement by 21%. These findings verify the feasibility and scientific soundness of the optimized support scheme and provided a valuable reference model for the optimization of support systems in similar mining environments.

The Yongbyon reactor in North Korea represents a significant global security threat because of its potential for plutonium production, which can be utilized in nuclear weapons. The nuclear tests conducted at the Yongbyon research reactor from 2006 to 2017 highlight the necessity for accurate assessments of its plutonium production capabilities. This study estimated the plutonium production potential of the Yongbyon reactor to be ~51 kg, based on its operational history and analysis using the Monte Carlo code for advanced reactor design (McCARD) code. Sensitivity analysis indicates that the most critical variable for predicting plutonium production capacity is the integrated thermal power release data from the reactor. Factors such as the temperature of fuel and coolant, and the number of neutron samples in the McCARD have a negligible impact (less than 1%) on the estimates of plutonium production. Regardless of how diverse the history of thermal power is, or what value the maximum power reaches (20 or 25 MWt), the integrated thermal energy consistently determines the amount of plutonium produced, emphasizing its significance in the analysis.

This study presents a front-end engineering design (FEED) methodology for an integrated CO₂ transport–injection–storage system, utilizing multiobjective optimization (MOO) and nodal analysis. The methodology’s performance is validated through a carbon capture and storage (CCS) demonstration project in the Gunsan Basin (GB), South Korea. This approach employs the dynamic inflow performance relationship (IPR)−outflow performance relationship (OPR) technique, applying it to the FEED of the CO₂ transport–injection–storage system to enable CO₂ injection into a saline aquifer via a single injection well connected through an onshore hub terminal and a subsea pipeline. By adjusting decision variables (CO₂ discharge pressure at the onshore hub terminal, pipeline diameter, tubing diameter, and CO₂ temperature at the wellhead), three objectives (CO₂ storage capacity, safety, and economic benefit) are optimized through MOO, identifying the Pareto-optimal front (POF) among objective functions. These trade-off solutions provide reliable ranges for the four decision variables used in the nodal analysis, which considers real-time pressure and temperature variations in the system during CO₂ injection, along with the associated facility qualifications and operating conditions. This analysis determines the IPR−OPR at the bottom of the injection well and the corresponding pressure–flowrate, defining the practical FEED scope for the integrated CO₂ transport–injection–storage system. By integrating optimal solutions from both MOO and nodal analysis, the study identifies the final nondominated solutions for efficient and stable CO₂ geological storage. The proposed methodology offers decision-makers robust scenarios for facility qualifications and operating conditions, considering CO₂ storage capacity, safety, and economic efficiency at the FEED stage of a CCS demonstration project.

This article presents a novel hybrid machine learning time series model (MLTSM) for predicting the electrical output of solar photovoltaic (PV) systems, integrating a physics-based theoretical model with an ensemble of data-driven regressors. The study addresses the challenge of solar energy’s variability by enhancing predictability for grid integration. Using a 34-day dataset from two solar power plants in India, we engineer critical features—including irradiation and ambient temperature, transformed via a third-degree polynomial derived from PV system physics—to improve forecasting accuracy. We conduct a comprehensive evaluation of multiple machine learning (ML) models, including linear regression, ridge regression, decision trees (DTree), random forest (RForest), and K-nearest neighbors, and propose a weighted hybrid ensemble that combines the top performers. Among the individual models, linear and ridge regression demonstrated superior performance. The proposed hybrid model achieved a notable R  ² value of 98% for Plant 1 and 91% for Plant 2, with root mean squared errors (RMSEs) of 36–66 and 42–127, respectively. This study contributes a publicly available dataset, a novel physics-informed feature engineering methodology, and a scalable hybrid forecasting framework that offers a practical balance of accuracy, computational efficiency, and interpretability for real-world solar energy forecasting.

This study proposes a preactivated residual neural network (ResNet) with long short-term memory (LSTM) to predict electric vehicle (EV) charging demand at an individual fast-charging station. While fast-charging stations offer convenience to EV users, the use of fast-charging stations can also threaten the stability and quality of the power system. Therefore, it is important to accurately forecast the charging demand at individual fast-charging stations for the operation of the power system. The proposed model incorporates two deep learning models: ResNet and LSTM. The ResNet is used to perform the feature extraction needed for forecasting fast-charging patterns. The LSTM performs forecasting of fast-charging demand based on sequential input. The proposed model ensures superior forecasting performance without vanishing gradient. Furthermore, the structure of the preactivated ResNet enables optimal parameter updates based on the loss function of mean squared error (MSE). The proposed model was evaluated with real-world data from EV fast-charging stations in Jeju Island, South Korea. The maximum prediction performance of the proposed model was attained with 8.04% in the normalized root MSE and a mean absolute error (MAE) of 4.71 kW.