Online monitoring of multi-phase batch processes using phase-based multivariate statistical process control

Online monitoring of batch processes using multivariate statistical methods has attracted enormous research interests due to its practical importance. In this paper, we focus on an important issue that continues to confound online batch process monitoring—run-to-run variations that do not confirm to a normal distribution around a reference trajectory. Here, we show that a phase-based decomposition of the trajectory offers a systematic way to overcome this challenge. In our approach, phase changes are detected online using Singular points in key variables. Run-to-run variations among different instances of a phase are synchronized by using time warping. Finally, phased-based multivariate statistical process control models are used to monitor the execution of the batch and detect abnormalities. This phase-based monitoring approach is robust to run-to-run variations arising from changes in initial conditions and event timings as is illustrated using a well-known fermentation process simulation.