基于粒子羣算法的 BP 神经网络在催化裂化反应再生过程中的应用

The catalytic cracking reaction-regeneration process is a highly nonlinear and strongly coupled operating productive process. It is difficult to accurately describe the model due to its complex process. Owing to artificial neural network’s powerful self-adaptive, self-organizing, self-learning and nonlinear prediction ability, a 5-11-1 BP neural network structure was built for modelling the catalytic cracking reaction regeneration process in which the machining load, 5 operating conditions were set as input variables, and diesel production was set as the output variables. Then the optimal weight and threshold of BP neural network are optimized by a particle swarm algorithm (PSO) for improving the prediction accuracy of neural network. The results show that the prediction model of the catalytic cracking reaction regeneration process based on PSO-BP neural network is significantly higher than that of BP neural network without optimization.