International Journal of Energy Research

In the transition towards sustainable energy sources, hydrogen has emerged as the predominant alternative after fossil fuels. This comprehensive literature review investigates into the evolving landscape of hydrogen launching strategies within the energy market. Numerous countries have strategically initiated efforts to introduce products based on hydrogen, highlighting the global acknowledgment of the potential inherent in hydrogen. Despite witnessing significant growth in the adoption of hydrogen energy worldwide, barriers to its widespread commercialization persist. This review identifies key barriers, including cost structures, technical complexities, and marketing challenges, hindering the establishment of a robust hydrogen energy market. The article critically examines the actions already undertaken to harness hydrogen energy for commercial purposes, looking at scientific insights and statistical data. By carefully examining the current state of hydrogen energy initiatives, the review highlights gaps in existing strategies that require thoughtful consideration. Insights derived from scientific and statistical analyses inform the identification of barriers and contribute to a nuanced understanding of the challenges faced in the journey towards hydrogen energy commercialization. Ultimately, the article presents a strategic proposal outlining marketing actions geared towards expediting the commercialization and widespread adoption of hydrogen energy. Grounded in a thorough review of existing literature and supported by scientific and statistical evidence, this comprehensive analysis offers valuable insights for stakeholders seeking to navigate the intricate landscape of hydrogen energy in the global energy market.

To enhance the overall performance of recompression supercritical carbon dioxide- (sCO₂-) based systems, two new double-effect absorption reheat power cycles (DARPC) were developed in this study. These methods are based on the typical absorption power cycle (APC). For the proposed sCO₂/DARPC systems, a parametric analysis of the thermodynamic and economic performances, as well as additional parametric optimisations, were performed quantitatively. The results indicate that replacing the APC subsystem with DARPC subsystems can enhance the total function of the sCO2 system even further, owing to the increased H₂O vapour created in the separator and the reheating process, which adds to the greater net power output. Furthermore, compared to the DARPC2 subsystem, the DARPC1 subsystem may produce more H₂O vapour from the generator and separator, resulting in an increase in net output power. When compared to a single sCO₂ power cycle, multiobjective optimisations showed that the sCO₂/DARPC1 and sCO₂/DARPC2 systems could increase the exergy efficiency by 12.95% and 11.51% and decrease the total product unit cost by 9.67% and 8.37%, respectively. Furthermore, the sCO₂/DARPC1 and sCO₂/DARPC2 systems can achieve improvements in exergy efficiency of 4.95% and 3.61% and a total product unit cost of 4.52% and 3.15%, respectively, compared with the sCO₂/APC system.

The study is aimed at investigating the effects of nuclear fusion reactors, human development, and economic growth on nuclear energy consumption in the United States from 1990 to 2019 using time and frequency causality analyses. The time domain causality analysis examined the relationship between variables over time using a single test statistic, while the frequency domain analysis explored causality in the short and long term at different frequencies. The findings from the time domain analysis indicated that nuclear energy consumption had a unidirectional causal effect on the human development index. Conversely, nuclear fusion reactors had a unidirectional causal impact on nuclear energy consumption. The results from the frequency domain analysis revealed that economic growth had a permanent unidirectional causal effect on nuclear energy consumption. In contrast, nuclear energy consumption had a temporary unidirectional causal impact on the human development index. Additionally, there was a bidirectional temporary and permanent causal effect between nuclear fusion reactors and nuclear energy consumption. Based on these findings, the study recommends that the United States continue providing financial incentives to develop nuclear energy technologies, such as constructing new nuclear power plants and offering subsidies to encourage the use of nuclear energy.

Buildings play a critical role in energy consumption, representing one of the primary consumers of power. Heating load (HL) and cooling load (CL) are essential for determining the energy efficiency of buildings. Several research projects attempt to address the critical challenge of enhancing energy efficiency in residential buildings, focusing on accurately estimating HL and CL using solutions that implement statistical prediction or typical building control management. This study, however, looked into advanced machine learning (ML) models for sustainable building design based on harnessing the potential of artificial intelligence and explainable AI (AIX) technologies. The proposed model was trained and tested using a dataset of 768 buildings based on feature engineering methods with various ML algorithms (including cutting-edge emotional neural learning (ENN), nonparametric kernel-based probabilistic models known as Gaussian process regression (GPR), and boosted tree (BT) algorithm). In addition, the output of the model was fed to standard building energy performance software (Ecotect) that utilizes the dataset from twelve different building shapes to perform various building energy efficiency analyses. The overall performance of the proposed model was measured using different performance metrics, including MAPE, MAE, RMSE, and PCC to measure the performance of HL- and CL-based building energy efficiency. The performance evaluation results indicate that the M3 variants, especially GPR-M3, consistently outperformed their counterparts across heating and cooling scenarios. The three models indicated reliability for modeling HL and CL. However, for HL, the GPR-M3 model emerged as the best model, outperforming GPR-M1 by 9.2% and GPR-M2 by 3.9%. Similarly, GPR-M3 is superior to CL, with the highest PCC at 0.9858, marking an 8.1% and 1.9% improvement over GPR-M1 and GPR-M2, respectively.

The present study investigates the characteristics and periodic behavior of H₂/air tubular flames in a 1 mm diameter microtube under exciting and nonexciting inlet conditions. Under unstimulated inlet conditions, increasing inlet velocity positively impacted flow stratification and self-sustaining of the tubular flames, leading to higher maximum temperatures within the flame kernel due to reduced flow temperature gradients near the wall. Conversely, under stimulated conditions, varying excitation amplitudes resulted in two flame propagation modes: flame with semi-repetitive extinction/ignition (FSREI) and pulsating flame, observed across different exciting amplitude ranges. It was found that the formation of recirculation fields generated by negative propagation speed temporarily stored the released heat of combustion and prevented it from extinguishing in the flowing phase. From the kinetics point of view, the maximum reaction rate during the pulsating mode belongs to H + O₂ = HO₂, while competition between H + O₂ = HO₂ and H₂ + OH = H₂O + H occurred in the FSREI mode. Results revealed that in the pulsating mode, fluctuations in mass fractions of the heavier species are more considerable near the outlets. However, radical mass fraction fluctuations were significant near the inlet slot in pulsating mode.

An equilibrium-based steady-state simulator model that predicts and optimizes hydrogen production from steam gasification of biomass is developed using ASPEN Plus software and artificial intelligence techniques. Corn cob’s chemical composition was characterized to ensure the biomass used as a gasifier and with potential for production of hydrogen. Artificial intelligence is used to examine the effects of the significant input variables on response variables, such as hydrogen mole fraction and hydrogen energy content. Optimizing the steam-gasification process using response surface methodology (RSM) considering a variety of biomass-steam ratios was carried out to achieve the best results. Hydrogen yield and the impact of main operating parameters were considered. A maximum hydrogen concentration is found in the gasifier and water-gas shift (WGS) reactor at the highest steam-to-biomass (S/B) ratio and the lowest WGS reaction temperature, while the gasification temperature has an optimum value. ANFIS was used to predict hydrogen of mole fraction, 0.5045 with the input parameters of S/B ratio of 2.449 and reactor pressure and temperature of 1 bar and 848°C, respectively. With the steam-gasification model operating at temperature (850°C), pressure (1 bar), and S/B ratio of 2.0, an ASPEN simulator achieved a maximum of 0.5862 mole fraction of hydrogen, while RSM gave an increase of 19.0% optimum hydrogen produced over the ANFIS prediction with the input parameters of S/B ratio of 1.053 and reactor pressure and temperature of 1 bar and 850°C, respectively. Varying the gasifier temperature and S/B ratio have, on the other hand, a crucial effect on the gasification process with artificial intelligence as a unique tool for process evaluation, prediction, and optimization to increase a significant impact on the products especially hydrogen.