Adaptive Model Predictive Batch Process Monitoring and Control

The present work addresses the problem of loss of model validity in batch process control via online monitoring and adaptation based model predictive control. To this end, a state space subspace-based model identification method suitable for batch processes is utilized and then a model predictive controller is designed. To monitor model performance, a model validity index is developed for batch processes. In the event of poor prediction (observed via breaching of a threshold by the model validity index), reidentification is triggered to identify a new model and thus adapt the controller. In order to capture the most recent process dynamics, the identification is appropriately designed to emphasize more the recent process data. The efficacy of the proposed method is demonstrated using an electric arc furnace as a simulation test bed.