HCN production process hybrid intelligence based on artificial neural networks and genetic algorithm

Abstract

Complex process modeling and optimization system is a hot area of research. A system model is proposed between process parameters and performance index by using the BP neural network for hydrogen cyanide (HCN) production process. In proposed method, the optimal process parameters would be searched by using genetic algorithm and these optimal parameters could be entered into BP neural network to predict the conversion rate of ammonia. The table about HCN production and process parameters can be produced as optimal results to increase the conversion rate of ammonia. The test result provides that proposed modeling method is a new and effective way for solving optimization problems of multi-dimensional nonlinear system.