Fuzzy Phase Partition and Hybrid Modeling Based Quality Prediction and Process Monitoring Methods for Multiphase Batch Processes

Abstract

A novel fuzzy phase partition method and a hybrid modeling strategy are proposed for quality prediction and process monitoring in batch processes with multiple operation phases. The fuzzy phase partition method is proposed on the basis of a sequence-constrained fuzzy c-means (SCFCM) clustering algorithm. It divides the batch process into several fuzzy operation phases by performing the SCFCM algorithm on trajectory data of phase-sensitive process variables. This SCFCM-based partition method not only has high computation efficiency and good partition accuracy but also is easy to implement and popularize. In addition, it generates “soft” partition results, where a “transition” phase exists between two adjacent “steady” operation phases. A hybrid modeling strategy is developed to build appropriate models for all operation phases according to their own characteristics. Phase-based multiway PLS models are built for regular steady phases that have longer durations and stable process behaviors. Just-in-time PLS models are built for those phases with shorter durations but time-varying or nonlinear process behaviors, including all transition phases and several irregular steady phases. This hybrid modeling strategy significantly enhances the modeling accuracy, resulting in better quality prediction and process monitoring performance. Advantages of proposed methods are illustrated by case studies in a fed-batch penicillin fermentation process.

Monitoring Uneven Multistage/Multiphase Batch Processes using Trajectory‐Based Fuzzy Phase Partition and Hybrid MPCA Models

“…Therefore, phase partition can be carried out by detecting changes in trajectories of phase‐sensitive variables. According to this idea, a SCFCM‐based fuzzy phase partition method has been proposed for the batch process with even durations . This method divides the batch process into several fuzzy operation phases by clustering time slices of all batches (Figure ) using the SCFCM algorithm.…”

“…The representative data‐driven phase partition methods include the multiphase (MP) algorithm, the sub‐PCA method, the stepwise sequential phase partition (SSPP) algorithm, the Gaussian mixture model (GMM)‐based method, and so on. Some data‐driven phase partition methods were also proposed to handle the uneven duration problem in multiphase batch processes . For example, Lu et al developed a revised sub‐PCA phase partition method for multiphase batch processes with uneven‐length operation phases.…”

Multi-Phase Support Vector Regression Soft Sensor for Online Product Quality Prediction in Glutamate Fermentation Process

“…Compared with the aforementioned two methods, data-driven methods are easier to perform because of their data-driven property. However, their phase partition results obtained by data-driven methods may or may not always consistent with actual operation phases (Sun et al, 2011;Luo et al, 2016).…”

“…As a result, it is of critical importance to on-line identification of phases in fermentation process, phase partition is a crucial procedure before multi-phase modeling. The effectiveness of a multi-phase model is problematical without a proper phase division (Doan et al, 2007;Sun et al, 2011;Yao and Gao, 2009;Luo et al, 2016).…”

“…Another method for detecting phase change uses singular points detection based only on online measurements (Maiti et al, 2009;Régis et al, 2008). The reader can find more multi-phase analysis methods in (Yao and Gao, 2009;Camacho et al, 2008;Doan et al, 2007;Luo et al, 2016), which give different kinds of phase identification methods. Knowledge based phase identification fails when the process prior knowledge is not enough to divide processes into phases legitimately and difficult to customise for diverse fermentation processes.…”