基于MKPLS和SQP方法的间歇过程迭代优化控制

Iterative optimal control for batch processes based on MKPLS and SQP methods

采用多向核偏最小二乘(MKPLS)方法建立间歇过程的模型并进行操作条件的优化。由于存在模型失配和未知扰动,基于MKPLS模型的最优控制轨迹在实际对象上往往难以实现最优的产品质量指标。本文利用间歇过程批次间的重复特性与序贯二次规划(SQP)优化算法中迭代计算的相似特点,提出了一种基于MKPLS模型的批次间优化调整策略,使得经过逐步优化调整得到的控制轨迹作用于实际对象时,可以得到更优的质量指标。该方法的有效性在苯乙烯聚合反应器和乙醇流加发酵过程的仿真对象上得到了验证。

Multiway kernel partial least squares（MKPLS）can be used for modeling and optimal control of batch processes.But the calculated optimal control policy based on the model may no longer be optimal,when applied to the actual process due to model-plant mismatches and unknown disturbances.Based on the similar idea between repetitive batch runs and iterations during numerical optimization,a batch-to-batch optimization correction strategy coupled with MKPLS model and sequential quadratic programming（SQP）was proposed.During gradient calculation,the plant data,in place of the predictions of MKPLS model,were used to correct the iterative searching direction.The proposed strategy was illustrated by simulations of bulk polymerization of styrene and fed-batch ethanol fermentation.