Enhanced Batch Process Monitoring and Quality Prediction Using Multi-phase Dynamic PLS

作     者：QI Yongsheng~(1,2),WANG Pu~1,GAO Xuejin~1 1.College of Electronic Information and Control Engineering,Beijing University of Technology,Beijing 100124,P.R.China 2.College of Electric Power,Inner Mongolia University of Technology,Huhhot,010051,P.R.China 

会议名称：《第三十届中国控制会议》

会议日期：2011年

学科分类：0810[工学-信息与通信工程] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0835[工学-软件工程] 081002[工学-信号与信息处理] 

基　　金：supported by National Natural Science Foundation(NNSF) of China under Grant 60704036 

关 键 词：Batch Process Dynamic PLS Gaussian Mixture Model Quality Prediction 

摘      要：正In industrial manufacturing,most batch processes are multi-phase and uneven-length batch processes in nature, phase-based approaches are intuitively well suited for batch process monitoring and quality *** this paper,a new strategy is proposed using multi-phase dynamic partial least squares（DPLS） for batch processes monitoring and quality ***,batch process data was automatically divided into several phases using Gaussian mixture model（GMM） clustering *** run-to-run variations among different instances of a phase are synchronized by using dynamic time warping（DTW）.Finally,multi-phase DPLS model is built between each phase and the quality *** proposed method easily handles the following problems:（1）static single model;（2）process and its model do not match;（3） linear method may not be efficient in compressing and extracting dynamic nonlinear process *** idea and algorithm are illustrated with respect to the typical data collected from a benchmark simulation of fed-batch penicillin fermentation production. The simulation results demonstrate the effectiveness of the proposed method in comparison to original DPLS.