Regression modeling and quality prediction for multiphase batch processes with inner-phase analysis

Many batch processes have multiple phases which may exhibit significantly different underlying behaviors. Besides, within each phase, processes in general evolve following certain underlying rules, called inner-phase evolution here. In this paper, a new statistical process analysis and quality prediction method is proposed for multiphase batch processes. A two-level phase division algorithm is proposed to capture the changes of relationship between process variables and quality variables within each phase. It reveals that the quality-related inner-phase evolutions in general goes through three statuses sequentially, i.e., transition, steady-phase and transition. Partial least squares (PLS), canonical correlation analysis (CCA) and qualitative trend analysis (QTA) are effectively combined to distinguish different inner-phase process statuses. Their different characteristics are then analyzed respectively for regression modeling and quality analysis. Meanwhile, the uneven-length problem of batch processes caused by operation conditions is handled properly according to their different characteristics in each inner-phase parts. Cumulative effect is considered and modeled both within inner-phase parts and between inner-phase parts for quality perdition. During online application, different quality-related process behaviors within each phase are tracked, revealing the inner-phase evolution. Online quality prediction is performed at each time by adopting different regression models. The application to a typical multiphase batch process, injection molding, illustrates the feasibility and performance of the proposed algorithm for uneven-length batch group quality prediction.