International Journal of Energy Research最新文献

Coal-fired boilers continue to serve as a primary energy source worldwide, yet their operational efficiency and environmental impact present persistent challenges. This critical review examines recent advancements in performance prediction models and optimization strategies aimed at enhancing the efficiency and sustainability of coal-fired boilers. A comprehensive analysis is conducted on predictive methodologies, encompassing both conventional thermodynamic models and emerging artificial intelligence (AI)-driven approaches, including artificial neural networks (ANNs) and machine learning (ML) algorithms. Key optimization strategies related to combustion control, sensor-based operations, and emissions mitigation are systematically reviewed. Through a detailed evaluation of current research trends, this study identifies critical knowledge gaps and proposes future research directions to advance the environmental performance and operational viability of coal-fired power generation.

Severe accidents in nuclear power plants (NPPs) pose critical challenges due to heightened environmental harshness that can impair instrumentation functionality. This impairment leads to “blind conditions,” where operators lack essential thermal-hydraulic data, hindering decision-making during pivotal moments, as exemplified by the Fukushima Daiichi accident. To address this, Operator Support Tools enhancing nuclear safety are essential for substituting failed instruments, requiring reliability, prompt responsiveness, and situational resilience. This study proposes a deep learning-based surrogate methodology to predict severe accident progression in real-time, enhancing Operator Support Tool capabilities. By constructing a comprehensive dataset using the Modular Accident Analysis Program (MAAP) 5.0.3, the surrogate model approximates complex severe accident analysis codes without the computational burden. Advanced deep learning models, including Transformer and Mamba architectures, are employed to handle multivariate time series forecasting of thermal-hydraulic variables and reactor pressure vessel (RPV) status with variable-length inputs. The developed surrogate models enable rapid and accurate prediction of key variables, operating on portable devices and meeting the Operator Support Tool requirements. This approach advances previous work by improving accuracy through state-of-the-art methodologies and enhancing flexibility in input handling. Performance evaluations demonstrate the models’ effectiveness in supporting operators during severe accidents, mitigating blind conditions, and contributing to the safety and resilience of operations.

The rising global demand for renewable energy and dietary sources has brought about rekindled interest in recent years in marine microalgae as a prospective feedstock for next-generation biofuels. In this research, a novel marine microalgal strain Picochlorum sp. PQ504913.1 was isolated and characterized from the Sonadia Island of Bangladesh for its suitability as sustainable biofuel in a preliminary laboratory-scale evaluation. The isolate was morphologically and molecularly identified based on 18S rRNA phylogeny. The isolated species was cultured in Conway medium at a controlled temperature (24 ± 1 °C), light intensity (152 µE/m²/s), aeration (4.55 ± 0.58 mg/L), and salinity (25 ppt). The maximum cell density and specific growth rate (SGR) of the strain were found to be 32.2 × 10⁶ cells/mL and 0.61 ± 0.03 mg/day, respectively. The strain exhibited a favorable biochemical composition with a higher protein content (30.22 ± 0.47 %) along with moderate lipid (14.56 ± 1.18 %) and carbohydrate (12.42 ± 0.32 %) levels. The fatty acid profile comprised of high proportions of C16:1 (29.19 ± 0.15 %), C14:0 (20.36 ± 1.34 %), and C18:0 (19.34 ± 0.7 %). Moreover, the FAME profiling revealed that saturated fatty acids (SAFAs) were the dominant group of the lipid fraction. Furthermore, the most abundant essential amino acid was leucine (7.87 ± 0.55 %), while aspartic acid and glutamic acid excelled the nonessential amino acids (NEAAs). The biodiesel properties of the investigated Picochlorum sp. were adhered to the international standards of ASTM D6751-02 and EN 14214. Based on biochemical composition and biomass yield, this strain can be considered as promising strain for biodiesel production. This study highlights the potential of this marine microalgae as a sustainable bioresource in aspect of environmental and commercial value, contributing to energy crisis mitigation and acceleration of bioresource development in the global context.

Forecasting the potential and output of building-integrated photovoltaic (BIPV) and traditional photovoltaic (PV) systems, including rooftop, ground-mounted and industrial-shed installations, has become increasingly important, as these technologies hold substantial potential for meeting a significant share of energy demand. Artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) models, are widely recognised as powerful tools for forecasting solar resource potential and system performance. These models play an essential role in accelerating the integration of renewable energy within urban energy planning frameworks. In this context, forecasting for BIPV–PV systems can be broadly classified into three domains: potential, power and energy (PPE). Given the rapid advances in the field of DL over the past few years, numerous studies have made targeted efforts to improve the forecasting accuracy for both BIPV–PV systems by enhancing input data quality and applying advanced, complex and hybrid models. Most of these efforts have mainly narrowed their focus to one of the three forecasting domains rather than adopting a more integrated approach. This systematic literature review (SLR) aims to provide a comprehensive review of PPE forecasting approaches to enable more robust assessment and deeper insights into the feasibility and viability of BIPV–PV systems. The review further highlights key methodological challenges, outlines limitations and offers practical guidance for researchers, policymakers and developers, while identifying emerging trends and future opportunities in AI-based forecasting for BIPV–PV applications.

The shear rheological behavior of rock mass discontinuities dictates the long-term stability of rock engineering. However, the interplay between shear creep, stress relaxation, and long-term strength of red sandstone discontinuities, particularly under the influence of morphological characteristics, remains inadequately understood. This study systematically investigates these time-dependent properties through graded loading shear creep and stress relaxation tests on discontinuities with varying morphologies, quantified by the slope root mean square (Z₂). Key findings reveal that the steady-state creep rate decreases, while the stress relaxation rate increases with Z₂, both exhibiting exponential growth with shear stress. Novel semiempirical rate equations incorporating Z₂ and shear stress were proposed to predict these behaviors. The long-term strength, determined via improved methods (transition creep, isochronous curves, and relaxation), ranged from 66.4% to 82.3% of the instantaneous shear strength (9.71 MPa), with values derived from stress relaxation tests being slightly higher. Although the Burgers model effectively captured the attenuation and steady-state stages of both shear creep and stress relaxation (average R² > 0.945), significant disparities in the fitted parameters indicated that these two processes are related but not entirely equivalent. The findings provide quantitative insights and predictive tools for assessing the long-term deformation and stability of rock masses.

In a decade ~7 times, enhanced efficiency was achieved for perovskite solar cells (PSCs) 3.5%–27%. The charge extraction by the selective contacts controls the efficiency. By its performance, the hole transport materials (HTMs) for PSC have attracted worldwide researchers. Organic HTMs have been studied and employed magnificently, but poor stability against humidity and high-cost organic HTMs remained a significant challenge. Consequently, alternate inorganic HTMs are being studied. Recently, chalcogenide-based HTMs are showing features such as tunable bandgap and appropriate band-edge position, high hole conductivity, mobility, and low production cost. This assessment presents advancement in the studies of inorganic HTM material based on chalcogenide for PSCs. The focus is on the effects of embodying chalcogenide as HTM in PSC and chances for further enhancement in garnering technologies. The optoelectronic features are highlighted in this review, including band structure, bandgap tuning, and hole mobility. The PSC community has been on the search for inorganic HTMs that might lead to a suitable approach.

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.