Two-Stage Variable Selection Using the Wavelet Transform of Batch Trajectories for Data Interpretation and Construction of Parsimonious Quality-Estimation Models

We propose a two-stage variable-selection strategy performed in the wavelet domain in order to extract quality-related information from batch trajectories of process variables and to build parsimonious quality-estimation models. The proposed variable-selection method proceeds in two stages and uses the discrete wavelet transform of the batch trajectories. This approach greatly reduces the computation time required for finding those wavelet coefficients related to product quality. A quality-estimation model built with the selected wavelet coefficient subset is shown for the case study we discuss to exhibit satisfactory estimation accuracy. The application of the method to an industrial PVC polymerization process was found to achieve the same prediction accuracy as a multiway partial least-squares (MPLS) based model that uses all variables in the time domain.