Adaptive quality prediction of batch processes based on PLS model

Abstract

There are usually no on-line product quality measurements in batch and semi-batch processes, which make the process control task very difficult. In this paper, a model for predicting the end-product quality from the available on-line process variables at the early stage of a batch is developed using partial least squares (PLS) method. Furthermore, some available mid-course quality measurements are used to rectify the final prediction results. To deal with the problem that the process may change with time, recursive PLS (RPLS) algorithm is used to update the model based on the new batch data and the old model parameters after each batch. An application to a simulated batch MMA polymerization process demonstrates the effectiveness of the proposed method.