Self-Training Statistical Quality Prediction of Batch Processes with Limited Quality Data

Abstract

Because of expensive cost or large time delay, quality data are difficult to obtain in many batch processes, while the ordinary process variables are measured online and recorded frequently. This paper intends to build a statistical quality prediction model for batch processes under limited quality data. Particularly, the self-training strategy is introduced and combined with the partial least-squares regression model. For multiphase batch processes, a phase-based self-training PLS model is developed for quality prediction in each phase of the process. The feasibility and effectiveness of the developed method is evaluated by an industrial injection molding process.