Multivariate analysis and optimization of process variable trajectories for batch processes

A new methodology for analyzing batch and semi-batch process variable trajectories is proposed in this paper for process development and optimization. It is aimed at identifying trajectory features such as cumulative effects and time-specific effects of process variables on final product quality. A new pathway multi-block PLS algorithm, valid under the assumption of linear and additive effects, is proposed to efficiently incorporate information provided by intermediate quality measurements, which help in identifying time-specific effects. Extraction of trajectory features is illustrated using designed experiments on a fundamental simulation model for SBR emulsion copolymerization. The methodology is shown to provide information useful for improving final product quality through trajectory modifications. It is also shown that intermediate quality measurements can significantly reduce the number of batch runs necessary for feature extraction (than when only final product quality is available).