Troubleshooting of an Industrial Batch Process Using Multivariate Methods

Abstract

Multivariate statistical methods are used to analyze data from an industrial batch drying process. The objective of the study was to uncover possible reasons for major problems occurring in the quality of the product produced in the process. Partial least-squares (PLS) methods were able to isolate which group of variables in the chemistry, in the timing of the various stages of the batch, and in the shape of the time-varying trajectories of the process variables were related to a poor-quality product. The industrial study illustrates the approach and the power of these multivariate methods for troubleshooting problems occurring in complex batch processes. Several new variations in the multivariate PLS methodology for the analysis of batch data are also implemented. In particular, an application utilizing a novel approach to the time warping of the trajectories for batches, and the subsequent use of the time-warping information, is presented. The use of the time history of the PLS weights of the process variable trajectories to diagnose problems in the dynamic operation of the batches is also clearly illustrated, as is the use of contribution plots for finding features which distinguished between the operating histories of good and bad batches.