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Batch process monitoring based on multiple-phase online sorting principal component analysis.

Zhaomin Lv¹, Xuefeng Yan¹, Qingchao Jiang¹

¹Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, P.O. Box 293, MeiLong Road No. 130, Shanghai 200237, PR China.

ISA Transactions

|May 11, 2016

Summary

This study introduces a new multiple-phase online sorting principal component analysis (MPOSPCA) strategy to effectively monitor batch processes. MPOSPCA overcomes challenges in determining phase numbers and data length variations for improved process analysis.

Keywords:

Batch process monitoring Multiple-phase Phase number Principal component analysis

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Area of Science:

Chemical Engineering
Process Monitoring
Data Analysis

Background:

Traditional phase-based batch process monitoring faces challenges with determining the correct number of phases.
Existing methods struggle with the variable data lengths inherent in batch and fed-batch processes.
Accurate online monitoring is crucial for process optimization and quality control in chemical manufacturing.

Purpose of the Study:

To propose a novel multiple-phase online sorting principal component analysis (MPOSPCA) strategy for robust batch process monitoring.
To address the limitations of existing methods in phase identification and handling uneven data lengths.
To enhance the reliability and accuracy of online monitoring for batch and fed-batch operations.

Main Methods:

Developed a new multiple-phase partition algorithm using k-means and average Euclidean radius to determine phase number and datasets.
Applied principal component analysis (PCA) to build models for each identified phase, retaining all components.
Implemented online monitoring using Euclidean distance for model selection, Bayesian inference (BI) for online sorting of components, and T(2) statistics for fault detection.

Main Results:

The proposed MPOSPCA strategy successfully determined the multiple phases and their corresponding datasets from offline normal data.
Online monitoring demonstrated effective model selection and component sorting, leading to accurate calculation of T(2) statistics.
The method was validated using a numerical example and the fed-batch penicillin fermentation process, showing feasibility and effectiveness.

Conclusions:

The MPOSPCA strategy provides an effective solution for monitoring multiple-phase batch and fed-batch processes online.
This approach overcomes key limitations of existing methods, particularly in phase number determination and data length variability.
MPOSPCA offers a reliable tool for real-time process analysis, contributing to improved process control and understanding.