Improved Process Understanding Using Multiway Principal Component Analysis

TLDR

Uniform polymer production by batch processing is critical for downstream performance, site interchangeability, and competitiveness, and reducing batch‑to‑batch variability through tighter control of key variables helps achieve these goals. The study demonstrates that multiway principal component analysis can identify major sources of variability in processing steps. MPCA was applied to the processing data to uncover key variability sources. The analysis revealed that reactor temperature fluctuations from heating system disturbances and heat‑transfer limitations were the primary source of batch variability, and it identified correlations between processing step variations and product properties, leading to recommendations for reducing these variations.

Abstract

Producing a uniform polymer by batch processing is important for the following reasons: to improve the downstream processing performance, to enable material produced at one site to be used by another, and to remain competitive. Eliminating the sources of batch-to-batch variability and tightening the control of key variables are but two ways to accomplish these objectives. In this work, it is shown that multiway principal component analysis (MPCA) can be used to identify major sources of variability in the processing steps. The results show that the major source of batch-to-batch variability is due to reactor temperature variations arising from disturbances in the heating system and other heat-transfer limitations. Correlations between the variations in the processing steps and the final product properties are found, and recommendations to reduce the sources of variations are discussed.