Industrial Batch Data Analysis Using Latent Variable Methods

Abstract

Currently most batch processes run in an open loop manner with respect to final product quality, regardless of the performance obtained. This fact, allied with the increased industrial importance of batch processes, indicates that there is a pressing need for the development and dissemination of automated batch quality control techniques that suit present industrial needs. Within this context, the main objective of the current work is to exemplify the use of empirical latent variable methods to reduce product quality variability in batch processes. These methods are also known as multiway principal component analysis (MPCA) and partial least squares (MPLS) and were originally introduced by Nomikos and MacGregor (1992, 1994, 1995a and 1995b ). Their use is tied with the concepts of statistical process control (SPC) and lead to incremental process improvements. Throughout this thesis three different sets of industrial sets of data, originating from different batch process were analyzed. The first section of this thesis (Chapter 3) demonstrates how MPCA and multi-block, multiway, partial least squares (MB-MPLS) methods can be successfully used to troubleshoot an industrial batch unit in order to identify optimal process conditions with respect to quality. Additionally, approaches to batch data laundering are proposed. The second section (Chapter 4) elaborates on the use of a MPCA model to build a single, all-encompassing, on-line monitoring scheme for the heating phase of a multi-grade batch annealing process. Additionally, this same data set is used to present a simple alignment technique for batch data when on-line monitoring is intended (Chapter 5). This technique is referred to as pre-alignment and it relies on the use of a PLS model to predict the duration of new batches. Also, various methods for dealing with matrices containing different sized observations are proposed and evaluated. Finally, the last section (Chapter 6) deals with end-point prediction of a condensation polymerization process.