Phase-based recursive regression for quality prediction of multiphase batch processes

Abstract

Batch processes have been widely applied in industry. Quality prediction plays an important role in batch process quality control. Multiple phases within a batch have correlated contributions to final qualities. So, previous phases should be considered in the quality-regression modeling for the current phase. In this paper, a new phase-based recursive statistical quality regression method is proposed for the quality prediction of multiphase batch processes. First, within each batch, main phases are obtained by a basic process analysis. Second, by analyzing phase characteristics, phases which have significant impacts on final qualities are identified as critical-to-quality phases. Then, a recursive quality regression algorithm is proposed using each quality residual of the regression models built in those critical-to-quality phases. Besides, phase characteristics are represented by the average trajectories of process variables within each phase. Quality prediction is performed at each sample point for online prediction. The application to a typical multiphase batch process, injection molding process, illustrates the feasibility and performance of the proposed algorithm.