International Journal of Energy Research最新文献

Ru/Al₂O₃ catalysts were synthesized via a chemical reduction method using ruthenium chloride as the precursor and subsequently subjected to different calcination temperatures. The catalysts’ physicochemical properties were characterized, and their catalytic performance in ammonia decomposition was evaluated. For comparison, Ru/Al₂O₃ catalysts were also prepared via a wet impregnation method to assess the effectiveness of the chemical reduction approach in removing Cl residues. In the chemical reduction process, ruthenium precursor was fully converted to metallic ruthenium using a NaBH₄ solution, which was subsequently dispersed onto an alumina support. Nevertheless, there was residual Cl on the catalysts prepared by wet impregnation even after calcination process, which has negative effect on the ammonia cracking reaction. As the calcination temperature increased, Ru dispersion decreased owing to the agglomeration of Ru particles. The uncalcined catalyst synthesized via chemical reduction exhibited excellent and sustained catalytic activity in the ammonia decomposition reaction. It consistently maintained an ammonia conversion rate of approximately 97% over 100 h at 550°C.

This study addresses the performance limitations of standalone desiccant cooling systems in extreme climates by developing and optimizing a solar-assisted hybrid desiccant evaporative cooling (SHDEC) system specifically for the hot and humid coastal climate of Saudi Arabia. The novel system configuration integrates a solid desiccant wheel, an indirect evaporative cooler (IEC), a heat pump, and a solar–thermal array for regeneration. Through extensive transient TRNSYS simulations and a detailed parametric analysis, key system parameters were optimized. The final SHDEC system achieved a solar fraction (SF) of 69%, maintained comfortable indoor conditions for 88% of the year, and demonstrated a coefficient of performance (COP) of 2.1, which rose to 4.9 when considering only grid-supplied energy. Key findings from the parametric study identified an 80 m² glazed flat plate (FP) collector array, a 4 m³ thermal storage tank, a 400 mm desiccant rotor, and a 2-ton heat pump as the optimal configuration. The results confirm the SHDEC system as a highly viable and sustainable alternative to conventional vapor-compression systems, offering significant energy savings and a path to reduced carbon emissions for cooling-demanding regions.

An integrated energy system (IES) can alleviate energy crises, promote multi-energy complementarity, and enhance finer-grained energy development. Nuclear power is clean and efficient, mainly when using small modular reactors (SMRs), which increase power generation, improve system flexibility, and promote a low-carbon economy. This paper proposes a bi-layer scheduling framework for a SMR-connected integrated energy system (SMR-IES) to optimize operating cost, carbon emissions, and average demand-side flexibility during the peak period index. The first layer optimizes the multi-objective operation of SMR-IES using a hybrid of the improved augmented ε-constraint method and the modified technique for order preference by similarity to the ideal solution approach. This framework incorporates a ladder-type carbon trading mechanism alongside a multi-energy demand response program with a comprehensive user satisfaction index to account for carbon emissions throughout the entire process while enhancing demand-side flexibility for the SMR-IES. The second layer handles uncertainties using the information gap decision theory approach. The proposed method can determine a scheduling operation with predicted renewable energy sources, load, and energy price errors while keeping optimal objective values within acceptable bounds not higher than 35% of the nominal optimal values (β = 0.35). Moreover, the proposed method offers a more efficient approach to managing uncertainty than stochastic and robust optimization methods.

Gas hydrate (GH) resources in the Ulleung Basin hold promise for enhancing South Korea’s energy security; however, their commercial development remains constrained by technical uncertainties. This study presents a hybrid artificial intelligence (AI) framework combining supervised and unsupervised learning to improve the interpretation of GH behavior during laboratory depressurization experiments. A convolutional neural network (CNN) is trained to predict three-phase saturations—water, GH, and gas—using X-ray computed tomography (CT) images. Physically consistent labels were generated using a material balance equation incorporating phase-specific densities to ensure saturation summation constraints. Latent features extracted from the CNN’s flattened layer were visualized using t-distributed stochastic neighbor embedding (t-SNE) to reveal distinct clusters corresponding to GH formation and dissociation stages. Compared to t-SNE applied directly to raw CT images, the CNN-based embeddings demonstrated markedly improved cluster compactness and separation. This improvement was quantified using the simplified Davies–Bouldin and within (S-DBW)-cluster scatter metrics, which demonstrated enhanced clustering performance—showing a 37.5% reduction in the average S-DBW value and a 56.0% reduction in standard deviation compared to the base case. Sensitivity analysis further confirmed the robustness of the CNN-based visualization across a wide range of t-SNE perplexity settings. The resulting cluster distributions aligned well with known physical transitions in GH systems, such as the dissociation threshold near 16 MPa and corresponding shifts in phase saturations. These findings demonstrate the CNN’s ability to extract meaningful, physically relevant features from high-dimensional image data, enabling more interpretable and reliable analysis of multiphase systems. This hybrid framework offers not only improved predictive accuracy but also a robust and interpretable tool for analyzing GH experimental data. The methodology is readily extendable to other geoscience applications involving complex pore-scale imaging and fluid behavior, providing a novel pathway for integrating deep learning with domain expertise in subsurface energy research.

One of the challenges in supplying electricity to remote areas is deciding whether to use standalone systems or extend the grid line. This article investigates a standalone hybrid renewable system versus extending the grid line to meet a proposed residential load demand of 6000 kWh/day, in a case study located 145 km from the grid. The study identifies the optimum possible grid extension distances, taking into account environmental factors such as carbon dioxide (CO₂) penalty and CO₂ emissions during the optimization process. Results indicate that, at the current distance from the grid, grid extension is not an economical solution. Instead, a standalone hybrid renewable energy system (HRES)—comprising photovoltaic (PV), wind turbine (WT), diesel generator (DG), and battery—is the optimal energy supply option, with net present cost (NPC) and cost of energy (COE) values of $4.55 M and $0.136/kWh, respectively. For the system considered, the optimal grid extension distance is 12 km. Load demand, grid extension cost, and distance from the grid are discussed as three main parameters affecting grid extension feasibility. Increasing load demand raises the optimal grid extension distance, while capacity shortage (CS) has a greater influence on this distance. Additionally, when the grid extension cost is held constant, a higher CS reduces the optimal grid extension distance.

The melt-quenching process was applied to create new sets of glass made of 70 B₂O₃-5SiO₂-10Li₂O-(5-x)PbO-10ZnO-xBi₂O₃, where x = 0.0 : 5 mol%. The glassy behavior is shown by the X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses. For each sample, x is the quantity of bismuth oxide (Bi)₂O₃, and the code for those samples is Bi-x. By replacing lead oxide with Bi₂O₃, nonbridging [BO₃] groups were produced. The UV region’s reflectance and UV cut-off wavelengths both raise with Bi₂O₃ replacement. In low-frequency zones up to 600 Hz, research glasses show a notable reduction in dielectric constant (ɛ′) with increasing frequency, while, at higher frequencies, it seems to be almost constant. Ɛ′ significantly decreases when bismuth is used in sample Bi-5 in place of lead Bi-5.0 had the largest effective atomic numbers (Z_eff) among all of the energies mentioned, while Bi-0.0 had the lowest. The Bi-5.0 sample’s exposure buildup factors (EBFs) at 1 MeV were 1.673, 4.541, 8.604, 18.293, and 28.597 at 1, 5, 10, 15, and 30 mfp, in that order. The corresponding fast neutron removal cross-section (FNRC, cm⁻¹) for Bi-0.0, Bi-1.0, Bi-2.0, Bi-3.0, Bi-4.0, and Bi-5.0 were 0.0925, 0.09345, 0.09345, 0.09504, 0.09502, and 0.09471 cm⁻¹. A glass system is recommended as a photon attenuation shielding material.

The joint dip angle has a significant influence on the mechanical behavior of coal, and revealing its influence mechanism is a scientific premise for analyzing mining-induced mechanical behavior of coal mining. However, the geometric shape of coal joints is complex, and the previous research methods of artificially prefabricated cracks are difficult to accurately reshape the initial structural characteristics of coal. Therefore, the realization of the identification of the in situ occurrence of coal joints and the characterization of the distribution law is the basis for revealing the control effect of joint dip angle on mechanical behavior. Through the combination of CT scanning, three-dimensional reconstruction, rock mechanics test and numerical simulation, the equivalent digital rock mass based on geometric probability distribution model is constructed, and on this basis, the control effect of joint dip angle on the mechanical behavior of coal body is studied. The results show that: (1) The average error of joint dip angle and bulk density between the equivalent digital rock mass and the actual coal sample is 2.26%, the error of uniaxial compressive strength is 14.17%, and the error of elastic modulus is 8.45%. The results are relatively consistent. (2) According to the sensitivity coefficient, the joints with an angle in the range of 45°–60° have the greatest influence on the uniaxial compressive strength. The joint angle in the range of 30°–45° has the greatest influence on the tensile and shear strength. (3) The influence degree of joint dip angle on the strength characteristics of digital rock mass is different. According to the sensitivity coefficient, the influence degree from strong to weak is shear strength, compressive strength, and tensile strength. (4) In terms of failure mode, different angles of joints have different control effects on different forms of fracture modes. Joints with angles of 45°–60° and 75°–90° play a major role in controlling the failure modes of model compression and tensile tests, respectively.

While deep learning-based approaches for state of health (SOH) estimation in lithium-ion batteries have been actively studied, most models face deployment constraints in on-device applications due to their high complexity and large number of parameters. Although previous studies have introduced knowledge distillation (KD) for model compression, single-teacher architectures exhibit limited performance improvement due to insufficient knowledge diversity. To resolve this issue, this study proposes a multi-teacher knowledge distillation (MTKD) framework to simultaneously achieve efficient SOH estimation and model compression. From raw charging data, a total of 18 health indicators (HIs) were obtained from diverse perspectives, including temporal information, statistical features, equivalent circuit model (ECM) parameters, and incremental calculation. Key features were selected through Pearson correlation analysis and the maximal information coefficient (MIC), and were utilized as inputs for the deep learning models. Subsequently, large-scale teacher models based on deep neural network (DNN), long short-term memory (LSTM), and one-dimensional convolution neural network (1D CNN) architectures were trained to capture various degradation characteristics, including nonlinear relationships, temporal dependencies, and local patterns. The lightweight student model was then trained using soft targets obtained from the teacher models along with ground truth labels. Experimental results demonstrate that the student model trained with the proposed MTKD achieved a 45.98% reduction in root mean square error (RMSE) and a 15.72% improvement in coefficient of determination (R²) compared to single-teacher KD (STKD). This study successfully extends KD research beyond traditional computer vision and image processing domains, demonstrating practical applicability in battery data-driven applications.

Underground thermal energy storage (UTES) integrated with building foundations is an emerging pathway to decarbonize space conditioning by shifting low-carbon heat across seasons. This review evaluates copper slag, a high-density, thermally stable byproduct of smelting, as a dual-function medium for thermally active foundations. We synthesize evidence on physicochemical, thermal, mechanical, and environmental performance, emphasizing properties most relevant to foundation-integrated sensible heat storage. Reported specific heat capacities of approximately 0.8–1.5 kJ/kg K combined with densities >3000 kg/m³ yield volumetric energy storage that can exceed typical aquifer-based systems, while cycling studies indicate stable round-trip efficiencies (≈80% over ≥100 cycles) and structural tests show that partial slag substitution in concrete (≈50%) can satisfy strength requirements. A comparative life-cycle perspective suggests that meaningful benefits can be achieved: global warming potential (GWP) reductions of 60%–74% relative to natural-gas baseline systems and 15%–20% embodied-energy savings compared to virgin aggregates, contingent upon design and electricity mix. We also identify the principal constraints to deployment, namely, heavy-metal leaching, thermo-mechanical compatibility under cyclic loads, and the absence of explicit code pathways for foundation-integrated storage, and outline mitigation strategies that span pretreatment, mix design, and containment/barrier engineering. Valorizing an industrial residue in building foundations, copper slag UTES links circular-economy objectives with practical, scalable thermal storage. Targeted research on durability, environmental safety, and standards development is now pivotal for translating this to practice.

Accurately predicting the remaining lifespan of lithium-ion batteries (LIBs) is crucial for manufacturing processes and safe, reliable usage. Battery lifespan prediction continues to face major challenges due to varying degradation processes, fluctuating operating conditions, and differences in electrode materials. Here, we combine commercial battery data charged and discharged under different electrodes and temperature conditions to build a data-driven machine learning model for cycle life prediction. The datasets include three types of commercial cathodes: LiFePO₄ (LFP), LiNi_0.86Co_0.11Al_0.03O₂ (NCA), and LiNi_0.83Co_0.11Mn_0.07O₂ (NCM), which were cycled under various conditions and temperatures. The charging and discharging dataset under a single cathode material, trained using the Elastic Net model, shows that the root mean square error (RMSE) reaches over 1528 cycles under different electrodes. Furthermore, our findings reveal that temperature plays a critical role in predictive accuracy, emphasizing the importance of incorporating cycling conditions into prediction models. With both cathode diversity and temperature effects considered during model training, all RMSE values dropped below 200 cycles. Notably, the mean absolute percentage error (MAPE) for NCA decreased from 64% to 27%. These outcomes highlight a promising approach for developing robust machine learning models capable of accurate battery performance prediction across varied conditions, contributing to safer and more reliable battery technology.