Genetic-algorithm-based fault detection of the heat transfer process in nuclear-based hydrogen production based ON Cu-Cl cycle

In the heat transfer process of nuclear hydrogen production system based on a 4-step Cu-Cl cycle, there are three major varying temperatures, namely room temperature, hydrolysis temperature and oxygen decomposition temperature. The heat requirement of the system will change with the temperatures. Therefore, it is important to identify the optimal ranges of the varying temperatures to make sure the heat requirement will be satisfied by the heat from a nuclear power plant, when the temperatures fluctuating in the optimal ranges. Genetic-algorithm-based Monte Carlo simulation method is developed in this paper to identify the optimal ranges. This method is able to obtain the optimal ranges through random sampling. The final result indicates that GA-based MCS method can be applied to identify the optimal ranges of the varying temperatures in the heat transfer process, and the ranges are highly trustable.