Finding the optimal time resolution for batch-end quality prediction: MRQP - A framework for multi-resolution quality prediction

Batch-end quality prediction is of paramount importance in industries producing high added-value products. However, all available methods hereto only use raw data at its native resolution. This paper proposes a new multi-resolution quality prediction (MRQP) methodology for batch-end quality, which exploits structured correlation in both the time and variables dimensions. The implementation of this methodology results in a more parsimonious and robust model structure, with a predictive performance bounded to be at least as good as the current standard approach. The implementation of MRQP is illustrated in three different case studies, where improvements over the standard single-resolution approach were found to be in the range of 10%-50%. From an interpretation standpoint, multi-resolution models are more robust with respect to the selection of too many predictors, facilitating the identification of key process variables, and providing information on the process time scales that influence final product quality, which can be further exploited for diagnosis, control, and optimization.