Trajectory‐based phase partition and multiphase multilinear models for monitoring and quality prediction of multiphase batch processes

Abstract

New process monitoring and quality prediction methods are proposed for the batch process with multiple operation phases. First, a trajectory‐based phase partition method is developed to divide a batch process into different operation phases by clustering the time slices of reference batches using the warped K‐means algorithm. Multilinear modeling methods, eg, parallel factor analysis and N‐way partial least squares (NPLS), are then used to model the 3‐way batch data in each operation phase. An online process monitoring method is proposed based on the multiphase parallel factor analysis models. An online quality prediction method is developed based on 2‐level quality prediction models, consisting of the first‐level multiphase multiway partial least squares models and the second‐level multiphase NPLS models. The first‐level multiway partial least squares models carry out real‐time quality prediction at each sampling time in different operation phases. At the end of each operation phase, the second‐level multiphase NPLS model is used to compute a more accurate quality prediction by taking into account the phase accumulative effect on the final produce quality. The implementation, effectiveness, and advantages of the proposed methods are illustrated with a case study on a penicillin fermentation process.

LSTM Soft Sensor Development of Batch Processes With Multivariate Trajectory-Based Ensemble Just-in-Time Learning

“…Hence, the quality prediction of batch processes becomes a significant task of process industry. Meanwhile, the real-time property of quality prediction is also required to avoid the control failure and quality corruption during productions [6]- [8]. To achieve the quality prediction scheme of batch processes, data-driven soft sensor techniques are developed by making use of the process data [9]- [14].…”