Multivariate monitoring of batch processes using batch-to-batch information

被引:39
作者
Flores-Cerrillo, J [1 ]
MacGregor, JF [1 ]
机构
[1] McMaster Univ, Dept Chem Engn, Hamilton, ON L8S 4L7, Canada
关键词
batch monitoring; partial-least squares; principal component analysis; statistical process control; on-line monitoring;
D O I
10.1002/aic.10147
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Multiway principal component analysis (MPCA) and multiway partial-least squares (MPLS) are well-established methods for the analysis of historical data from batch processes, and for monitoring the progress of new batches. Direct measurements made on prior batches can also be incorporated into the analysis by monitoring with multiblock methods. An extension of the multiblock MPCA/MPLS approach is introduced to explicitly incorporate batch-to-batch trajectory information summarized by the scores of previous batches, while keeping all the advantages and monitoring statistics of the traditional MPCA/MPLS. However, it is shown that the advantages of using information on prior batches for analysis and monitoring are often small. Its main advantage is that it can be useful for detecting problems when monitoring new batches in the early stages of their operation., the approach and benefits are illustrated with condensation polymerization and emulsion polymerization systems, as examples. (C) 2004 American Institute of Chemical Engineers.
引用
收藏
页码:1219 / 1228
页数:10
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