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Translating third-order data analysis methods to
chemical batch processes
Authors:
Karlene A. Hoo1,
Kenneth S. Dahl2, and Michael J. Piovoso2
1Department
of Chemical Engineering, University of South Carolina, Columbia, SC 29208
2Central Research and Development, DuPont Chemical Co., Wilmington,
DE 19880
Abstract
Measurements collected from batch processes naturally produce
a third-order or three-dimensional
data form. Such a structure also results when multiple samples are
measured using hyphenated analysis techniques such as liquid
chromatography-diode array detection. Analysis of third-order
data by a method such as principal components analysis (PCA) is achieved by a
non-unique
rearrangement that produces a two-dimensional
array. This explicitly and preferentially models only one of three possible orders.
In
contrast, methods such as parallel factor analysis
(PARAFAC) apply a particular decomposition that accounts for all three
orders. The results from either method should be related if data are
to be interpreted reliably for applications such as on-line monitoring
and control. This work compares and contrasts these two approaches
from an applied point of view. To accomplish this objective, exemplary methods
are selected from each type of analysis, parallel factor analysis (PARAFAC), and
multiway principal components analysis (MPCA). These are employed to
analyze industrial data taken from the manufacture of a condensation polymer in
a batch reactor.
Publication: Chemometrics and Intelligent Laboratory Systems, Vol. 46, pp 161-180, 1999.
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