基于支持向量回归模型的间歇过程优化

Batch Process Optimization Based on Support Vector Regression Model

针对间歇过程的优化问题,提出了一种基于支持向量回归模型的批次到批次的优化控制策略。通过对支持向量回归模型在当前控制轨迹处的线性化,构造了一种批次到批次的优化控制方法。在苯乙烯聚合反应器的仿真实验中,该方法能够在存在模型失配与过程扰动的情况下,逐批次地改善过程性能。

A support vector regression model based on batch to batch optimal control strategy was proposed. Because of model mismatching and unknown disturbances, the control performance of optimal control profile calculated from empirical model is deteriorated. Due to the repetitive nature of batch processes, it is possible to improve the operation of the next batch using the information of the current and previous batch runs. A batch to batch optimal control strategy based on the linearization of the model around the control profile is proposed. Applications to a simulated batch styrene polymerization reactor demonstrate that the proposed method can improve process performance from batch to batch in the presence of model mismatching and unknown disturbances.