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

✍ Scribed by Jesus Flores-Cerrillo; John F. MacGregor


Publisher
American Institute of Chemical Engineers
Year
2004
Tongue
English
Weight
212 KB
Volume
50
Category
Article
ISSN
0001-1541

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✦ Synopsis


Abstract

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. Β© 2004 American Institute of Chemical Engineers AIChE J, 50: 1219–1228, 2004


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## Abstract The development of a state‐space framework for monitoring batch processes that can complement the existing multivariate monitoring methods is presented. A subspace identification method will be used to extract the dynamic and batch‐to‐batch trends of the process and quality variables fr