Thermal management of methanol steam reforming reactor by back propagation neural network and particle swarm optimization algorithm

Methanol steam reforming (MSR) reactor is a device that converts the methanol into hydrogen. Since the MSR is an endothermic reaction, the methanol conversion is usually affected by the heating conditions. So, to improve the methanol conversion, the heating condition should be managed. This study aims to present a framework and methodology for studying the thermal management of a determined MSR reactor by controlling the heating conditions. Firstly, the characteristics of a 3-D MSR reactor are carried out, and the effects of the inlet condition parameters e.g. heating parameters and fuel supply parameters, on the methanol conversion are investigated. Then, the performance prediction of the MSR reactor is studied using several common intelligence algorithms and the corresponding surrogate models are obtained. The back propagation neural network surrogate model shows the best prediction accuracy than stepwise linear, radial basis function neural network, linear support vector machines, and random forest algorithms. Finally, the thermal management of the MSR reactor is carried out by combining the surrogate model and particle swarm optimization algorithm to obtain better performance. The results show that hydrogen production of the MSR reactor can be generally guaranteed by the characteristic curves of the heating parameters and fuel supply parameters, and the methanol conversion can be maintained steadily above 93% within the whole period of the hydrogen production requirements.