The development of alternate clean energy resources is among the most pressing issues in the energy sector in order to preserve the global natural environment. One of the ideal candidates is the utilization of hydrogen as a primary fuel in lieu of fossil fuels. It can be safely stored in liquid organic hydrogen carrier (LOHC) materials and recovered on demand. A uniform supply of hydrogen is essential for power production systems for their smooth operation. This study was conducted to determine the operating conditions of the dehydrogenation of perhydro-dibenzyltoluene (H18-DBT) to ensure that hydrogen supply in a continuous flow reactor remains stable over a wide range of temperatures. The hydrogen flow rate from the dehydrogenation reaction was measured and correlated with the degree of dehydrogenation (DoD) evaluated from the refractive index of reactant liquid samples at various temperatures, WHSV and the initial reactant concentrations. Moreover, a kinetic model is presented holding validity up to a WHSV of 67 h−1. The results acquired present a range for an order of reaction from 2.3 to 2.4 with the required activation energy of 171 kJ/mol.
Hybrid renewable energy systems with photovoltaic and energy storage systems have gained popularity due to their cost-effectiveness, reduced dependence on fossil fuels and lower CO2 emissions. However, their techno-economic advantages are crucially dependent on the optimal sizing of the system. Most of the commercially available optimization programs adopt an algorithm that assumes repeated weather conditions, which is becoming more unrealistic considering the recent erratic behavior of weather patterns. To address this issue, a data-driven framework is proposed that combines machine learning and hybrid metaheuristics to predict weather patterns over the lifespan of a hybrid renewable energy system in optimizing its size. The framework uses machine learning tree ensemble methods such as the cat boost regressor, light gradient boosting machine and extreme gradient boosting to predict the hourly solar radiation and load demand. Nine different hybrid metaheuristics are used to optimize the hybrid renewable energy system using forecasted data over 15 years, and the optimal sizing results are compared with those obtained from 1-year data simulation. The proposed approach leads to a more realistic hybrid renewable energy system capacity that satisfies all system constraints while being more reliable and environmentally friendly. The proposed framework provides a robust approach to optimizing hybrid renewable energy system sizing and performance evaluation that accounts for changing weather conditions over the lifespan of the system.
To increase the efficiency of a fuel processor and HT-PEMFC (high temperature-proton exchange membrane fuel cell) combined system, it is essential to improve the efficiency of the fuel processor. In this research, the fuel processor was simulated by the Aspen Hysys® simulator, and the effect of the various operating conditions on the total efficiency was investigated. The thermal efficiency of the fuel processor increased as the temperature and S/C (steam-to-carbon) ratio increased, and the efficiency was higher at an S/C ratio of 3 than at an S/C of 4 with a reformer temperature of 700 °C and higher. Under the selected operating conditions of the fuel processor, the recycling of unreacted hydrogen from the anode off-gas (AOG) of the HT-PEMFC improved the overall efficiency of the combined fuel processor and HT-PEMFC by a factor of 1.28. The operating conditions where the AOG supplied more heat than was required for fuel processor operation were excluded. The high-efficiency operating conditions of the fuel cell system were proposed with the target of 5 kW of output as the capacity of the household HT-PEMFC.
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