A Two-step Parallel Phase Partition Algorithm for Monitoring Multiphase Batch Processes with Limited Batches

Abstract

For multiphase batch processes, dividing the whole batch process into different phases by time-varying underlying characteristics could simplify model complexity without much loss of precision. The conventional step-wise sequential phase partition (SSPP) algorithm divides the batch process rely on the previous partition phase, which easily results in error accumulation. Besides, it is hardly applied to batch processes with limited training batches. In this paper, a parallel phase partition algorithm with limited batches is proposed for addressing those issues. By analyzing the contextual information of each time-slice, the batch process can be roughly divided using the time-varying characteristics. Based on the division result, parallel phase partition method is conducted to avoid error accumulation and connect the division result with monitoring system. To better capture the changes of process characteristics, we post-process the division result and update the boundaries between major phases and transition patterns. After phase and transition partition, different statistical models are developed so that the online monitoring can be executed. Experiments of process monitor are designed with an injection modeling process, and experimental results illustrate the process understanding ability and online monitoring performance of our proposed method.