Improved knowledge extraction and phase-based quality prediction for batch processes

This paper develops a process analysis and quality prediction scheme for the improvement of quality estimation performance in batch processes. Combined with prior phase division algorithm, correlation measure criteria are employed to identify critical phases and key variables with respect to quality prediction. As an effective process understanding and knowledge extraction tool, correlation analyses focusing on each phase help one reveal the phase-specific effect of process operation on product quality prediction without any requirement of prior expertise. The spoiling influences on quality inferential models caused by inclusion of variable redundancy and autocorrelation is well alleviated by the phase-based variablewise unfolding technique and key variable selection procedure. Meanwhile, the proposed method does not demand estimating the unavailable future process observations when used for online quality predicting. On the basis of specific phase analyses and variable selections, the applications of the proposed scheme to injection molding show its effectiveness and feasibility.