半间歇聚合反应分子量分布周期操作的迭代学习控制

摘要

The iterative learning control under periodic operation is used to control molecular weight distribution (MWD) in semi-batch polymerization process in this work. Firstly, the effect of periodic control on the MWD of the semi-batch polymerization reactor is analyzed. Simulation results show that the broadening of molecular weight distribution is achieved through periodic operation, therefore the performance of polymerization is improved. Then the optimal control strategy based on particle swarm optimization (PSO) with an improved performance index is used to control the shape of MWD, in which the control variable is duty cycle of the periodic feeding. In simulation experiment, the control inputs can reach to the optimal values from batch to batch in the presence of model plant mismatches, and the MWD of the polymer approximate the desired MWD gradually, thus the effectiveness of the periodic operation controlling method on MWD is verified.%通过对半间歇聚合反应的引发剂进料实施周期操作,研究了这类操作方式对聚合物分子量分布的影响.研究结果显示,周期操作能改善聚合反应过程,对分子量分布有明显的加宽作用.对性能指标进行改进,以引发剂周期进料的占空比为控制变量,采用基于粒子群优化的迭代学习算法,对分子量分布进行了优化控制.仿真分析表明,在实际对象和模型存在不匹配的情况下,运用迭代粒子群算法,控制输入随着批次学习的进行而逐渐趋于最优解,聚合反应的分子量分布则不断逼近希望的分子量分布.实验结果验证了以周期操作方式对半间歇聚合过程分子量分布进行迭代优化控制的可行性.