Real-time product quality control for batch processes based on stacked least-squares support vector regression models

A novel real-time final product quality control method for batch operations based on stacked least-squares support vector regression models (stacked LSSVR) is proposed. It combines midcourse correction (MCC) and batch-to-batch control. To enhance the model prediction accuracy and generalization capability, a stacked LSSVR approach is presented. Quality control is achieved by predicting the final product quality using stacked LSSVR models and adjusting process variables at some pre-specified decision points. Then a decision is made on whether or not control action is taken at every decision point. Once the control action is expected, the manipulated variable values are calculated and the control action is taken to bring the off-spec product quality back to the target. Then a batch-to-batch control is used to overcome the model plant mismatches and unmeasured disturbances. At last, the proposed modeling and quality control strategy is illustrated on a simulated batch reactor.

► An integrated strategy for quality control in batch processes is proposed.
► A stacked LSSVR model is proposed to model batch processes.
► A missing data imputation method is used to predict the future trajectories.
► The proposed scheme can overcome the problem of model plant mismatches.