Abstract:

Accurate and reliable prediction of polypropylene melt index is crucial in the control of propylene polymerization process, as well as in prompting the profit of the product.An optimization algorithm, the PSO_SA, based on particle swarm optimization (PSO) and simulated annealing (SA) is proposed, which uses the strength of PSO and SA to make up with the weaknesses of each other, and then the optimization capability and performance are improved.The PSO_SA is used to optimize the structure of the RBF neural network which is employed to predict the melt index of polypropylene.A research on the optimized model is carried out based on the data from a real plant and the model achieves a prediction accuracy of 0.75% in MRE.The result shows that the proposed approach has great prediction accuracy and reliability.