Model Predictive Control of an Industrial Distillation Column Based on a Hybrid Model: Adapting the Domain of Validity

Abstract

In any model predictive control (MPC) implementation, the key factor is the quality of the employed system model in order to guarantee constraint satisfaction and to optimize the performance of the controlled system. However, it is difficult in practice to model all the phenomena of the true system which leads to plant-model mismatch. We propose to augment a simple first principles based model with a machine learning model to capture the plant dynamics which are not represented in the semi-rigorous model. The use of data-based models however can be problematic outside the region where the model was trained. In this work, we estimate the domain of validity of the data-based model using a one-class support vector machine (SVM) which is trained on a low-dimensional projection of the available training data. During the application of the controller, data of newly visited regions is collected and the domain of validity is adapted based on checking a performance criterion. The hybrid model incorporates a weighted contribution of the data-based model based on its predicted validity. We demonstrate the versatility and the benefits of the proposed adaptive MPC based on a hybrid model (AHMPC) approach via an industrial distillation column case study.