Multivariate Trajectory-Based Local Monitoring Method for Multiphase Batch Processes

This paper proposes a new method combining the multivariate trajectory analysis and the principal component analysis (PCA) for multiphase batch process monitoring. To handle the uneven length problem, the trajectories of process variables are calculated instead of the original samples. For online monitoring, similar trajectories are extracted by just-in-time learning (JITL) with historical trajectories and the PCA model is constructed, which can deal with the missing data problem as well. Furthermore, to acquire a more reliable monitoring performance, a new distance-based measurement is proposed to show the location of samples. For performance evaluation, case studies of a numerical example and a simulated penicillin fermentation process are provided, with detailed comparisons to traditional methods.