Multi-way PCA applied to an industrial batch process

Batch and semi-batch processes are common in most chemical companies. These processes are characterized by a prescribed processing of materials for a finite duration of time. Feedback control often cannot be applied to correct for disturbances in a timely manner during the batch. Techniques which can provide insights into correlations among variables and their relationships to product quality will provide insights in the design of a control strategy that may improve product quality and minimize batch to batch variations. In this paper, the authors apply the statistical technique of multi-way principal component analysis to analyze the data from an industrial batch process. Using this technique, the authors were able to associate several significant causes of variability with the recipe imposed by the process. This information gave rise to a different control strategy which is presented. Subtle effects among batches were also uncovered and identified.