Batch process diagnosis: PLS with variable selection versus block-wise PCR

Data from a batch chemical process have been analysed in order to diagnose the causes of variability of a final quality parameter. The trajectories of 47 process variables from 37 batches have been arranged in a matrix by using alignment methods. Two different approaches are compared to diagnose the key process variables: PLS with variable selection and block-wise PCR. The application of Unfold Partial Least Squares Regression (U-PLS) leads to one significant component. By means of weight plots, the variables most correlated with the final quality are identified. Nevertheless, with observed data, it is not possible to know if correlation is due to causality (and hence related to a critical point) or is due to other causes. Pruning PLS models by using variable selection methods and technical information of the process has allowed the process variables most correlated with the final quality to be revealed. The application of Principal Component Regression to the trajectories of the process variables (block-wise PCR) has given straightforward results without requiring a deep knowledge of the process. The results obtained have been used to propose several hypotheses about the likely key process variables that require a better control, as a previous step to conducting further studies for process diagnosis and optimisation, like experimental designs.