Optimal temperature range identification for a hydrogen production cycle using genetic algorithm-based Monte Carlo simulation method
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Nuclear-based hydrogen generation is accumulating more and more interest, both from academia and industry. In nuclear hydrogen production system based on 4-step Cu-Cl cycle, proper temperature inputs play an essential role to ensure the heat transfer process performance as expected. Three major temperature variations exist in this process: room temperature, hydrolysis temperature and oxygen decomposition temperature. The heat requirement of the system varies with temperatures. Therefore, it is important to identify the optimal ranges of the temperatures to ensure the heat requirement satisfied when the temperatures are fluctuating. A Genetic-Algorithm-based Monte-Carlo Simulation method is developed in this thesis to identify the optimal temperature ranges. The process of establishing the model of GA-based MCS is illustrated. Essential parameters of the method are decided through experiments. The confidence interval estimation of the results are presented to improve the reliability. The final result indicates that this method can be applied to identify the optimal ranges and the results are reliable.