International Journal of Energy Research最新文献

This study investigates the combustion characteristics of hydrogen, methane, and coke oven gas (COG) blended with ammonia (NH₃) using one-dimensional simulations. The laminar burning velocity (LBV), adiabatic flame temperature, and nitrogen oxide (NO_x) emissions were analyzed across different NH₃ blending ratios (0%–100%). Hydrogen had the highest burning velocity, dropping sharply with NH₃, while methane’s decrease was gradual. COG showed intermediate behavior, resembling hydrogen. The addition of NH₃ reduced the adiabatic flame temperature in all mixtures, with COG/NH₃/air exhibiting a trend between H₂/NH₃ and CH₄/NH₃. Flame thickness increased with NH₃ content, with COG following trends similar to those of H₂/NH₃/air combustion. NO_x emissions were initially low for all fuels, but increased significantly with NH₃ addition, peaking at 25% NH₃ for H₂ and COG, and 50% NH₃ for CH₄, after which emissions declined owing to the weakening of the HNO pathway. COG/NH₃ combustion characteristics align closely with H₂/NH₃ but trend toward CH₄/NH₃ as NH₃ content rises. The study introduces a method to predict COG/NH₃/air combustion characteristics by fitting the combustion data of H₂/NH₃/air and CH₄/NH₃/air. This method provides accurate predictions of LBV, flame temperature, and nitric oxide (NO) emissions. Methane’s influence is most significant on flame temperature, followed by NO emissions and LBV.

Recent studies confirm the potential efficiency of geothermal resources for maintaining comfortable building temperatures through cooling and heating solutions. In addition, a variety of initiatives have been promoted to effectively reduce both environmental concerns and energy crisis by the integration of renewables. Under this framework, this paper describes and assesses hybrid geothermal and photovoltaic (PV) installations to provide sustainable solutions. Hybrid geothermal–PV systems are evaluated as a strategic opportunity to increase the presence of geothermal energy within the hybrid heating, ventilation, and air-conditioning (HVAC) market. While upfront capital costs associated with geothermal technology can suppose some barriers, the synergistic coupling with PV installations can provide a compelling solution and reliable energy supply, in terms of both economic feasibility and the energy (electricity) consumption. With this aim, a detailed economic and energy analysis is then conducted to evaluate such hybrid solutions connected to the grid, including potential energy storage system. HVAC demand optimized battery energy storage system (ESS) and PV installation connected to the grid are considered as hybrid renewable solution in a Mediterranean location case study based on the tertiary and industry sectors. It is carried out by the authors from real energy demand data collected for 3 years. Simulation results demonstrate that the proposed hybrid system reduces electricity consumption by 25% compared to a conventional air-to-air HVAC configuration, while geothermal operation achieves 34% lower heating demand and 26% lower cooling demand. These improvements highlight the hybrid system potential for enhanced energy efficiency and load-shifting capability in tertiary and industry sectors.

Aqueous zinc-ion batteries (AZIBs) are promising candidates for next-generation large-scale energy storage systems due to their high safety, low cost, and environmental compatibility. However, the practical use of zinc metal anodes is limited by critical challenges, including dendrite growth, corrosion, and the formation of insulating by-products such as Zn₄SO₄(OH)₆·xH₂O (ZHS), which cause capacity fading and shorten cycle life. Recently, biomass-derived materials have attracted significant attention as sustainable, eco-friendly interfacial modification layers for zinc anodes, owing to their abundance, structural tunability, and diverse functional groups. This review categorizes biomass-based interfacial materials into three groups: (1) organic biomass polymers, including chitosan, lignin, cellulose, and hyaluronate; (2) biomass-derived carbons, valued for their high conductivity and mechanical strength; and (3) inorganic biomass materials, such as diatomite and transition-metal complexes. The mechanisms by which these materials suppress dendrite formation, inhibit corrosion, and enhance electrochemical performance are systematically analyzed, with representative advances highlighted. Current limitations, such as low ion conductivity, interfacial degradation during prolonged cycling, high manufacturing costs, limited scalability, and dependance on electrolyte composition, are also critically evaluated. Finally, future research directions are discussed, including the design of composite materials, surface functionalization strategies, operando characterization techniques, co-optimization of electrolytes and protective coatings, and scalable manufacturing processes. Overall, this review provides a comprehensive overview of biomass-based strategies for zinc anode engineering and establishes a foundation for the sustainable development of high-performance AZIBs.

The electrochemical hydrogen compressor (EHC) operates without the oxygen reduction reaction, inherently lacking water generation at the cathode. As a result, the polymer electrolyte membrane undergoes continuous dehydration via electroosmotic drag, leading to increased ohmic resistance and reduced system performance. This study investigates the relationship between membrane hydration and ionic conductivity using a custom-designed conductivity cell with a Nafion 117 membrane. The results confirm a strong correlation between water content and proton conductivity. An empirical relationship is also derived to quantitatively estimate the ionic conductivity of the Nafion 117 membrane as a function of its hydration level. Furthermore, comprehensive analysis of hydrogen output, water vapor products, and impedance characteristics under various applied voltages reveals that operating the EHC at 1.70 and 1.95 V, where water electrolysis occurs, leads to decreases in hydrogen output by 4.2% and 15.7%, respectively, compared to operation at 1.45 V, where electrolysis is absent. Electrochemical impedance spectroscopy (EIS) further demonstrates increases in both ohmic and charge transfer resistances under electrolysis conditions. These findings underscore the importance of maintaining membrane hydration and avoiding water electrolysis to ensure optimal EHC performance. While the onset potential for water electrolysis was found to be 1.53 V in this system, this value is dependent on specific catalytic material and system configuration; thus, EHCs should be operated below the electrolysis threshold appropriate to their configuration. Overall, this study establishes a mechanistic understanding of how water transport and membrane hydration influence efficiency and degradation, advancing the scientific basis for next-generation proton exchange membrane (PEM)-based EHC design.

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.