基于PSO_SA算法的聚丙烯熔融指数预报

Melt index prediction based on PSO_SA algorithm

实时准确的熔融指数预报在控制聚丙烯产品质量和提高聚丙烯生产的经济效益上有着举足轻重的作用。本文提出了一种粒子群优化(PSO)算法和模拟退火(SA)算法相结合的PSO_SA算法,该算法利用PSO和SA的优劣势进行互补,提高了算法寻优的能力和效果。利用此算法对建立的RBF聚丙烯熔融指数预报模型进行结构寻优,得到结构最优的预报模型。最后通过该模型对实际聚丙烯生产数据的预报研究,证明了PSO_SA算法寻优得到的预报模型具有很高的预报精度和可靠性能。

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.