Optimized on-line control of MMA polymerization using fast multi-objective DE

Optimized on-line control (OOC) of polymerization reactors combine the optimization with the on-line operation and control. In this, re-optimized control variable trajectories, in the presence of unplanned disturbances, are obtained and implemented on-line to save the batch. Also, the available computational time for the optimization is limited as the re-optimized trajectories need to be implemented in real time on the actual system. In the present study, the OOC of such a system, i.e., bulk polymerization of methyl methacrylate (MMA) in a batch reactor, is carried out in the occurrence of heater malfunction. To solve the underlying multi-objective problem, a multi-objective variant of differential evolution with an improved mutation strategy is developed. The developed algorithm shows faster convergence with respect to other compared algorithms for a large number of benchmark problems. Finally, this algorithm is used to find the optimal temperature trajectories and the OOC with these trajectories found to be successfully countering the effect of heater malfunction.

聚合反应器的优化在线控制（Optimized on-line Control, OOC）将优化过程与在线操作及控制环节相结合。该方法可在遭遇非计划扰动时，在线获取并实施重新优化后的控制变量轨迹，以保障批次生产顺利完成。同时，由于重新优化后的轨迹需在实际生产系统中实时部署，因此优化环节可用的计算时长极为有限。本研究针对加热器故障场景，以间歇反应器内甲基丙烯酸甲酯（Methyl Methacrylate, MMA）本体聚合体系为研究对象，开展优化在线控制研究。为求解该问题对应的多目标优化问题，本文提出一种改进变异策略的多目标差分进化变体算法。相较于其他对比算法，所提算法在大量基准测试问题中展现出更快的收敛速度。最终，利用该算法求解得到最优温度轨迹，并将其应用于优化在线控制，结果表明该方案可有效抵消加热器故障带来的影响。