Premium
Multivariate statistical analysis of mid‐infrared spectra for the online monitoring of 2‐ethylhexyl acrylate/styrene emulsion copolymerization
Author(s) -
Stavropoulos Y.,
Kammona O.,
Chatzi E. G.,
Kiparissides C.
Publication year - 2001
Publication title -
journal of applied polymer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.575
H-Index - 166
eISSN - 1097-4628
pISSN - 0021-8995
DOI - 10.1002/app.2020
Subject(s) - copolymer , styrene , infrared , multivariate statistics , materials science , acrylate , infrared spectroscopy , monomer , partial least squares regression , butyl acrylate , principal component analysis , polymer chemistry , emulsion , polymer , computer science , chemistry , organic chemistry , optics , physics , artificial intelligence , composite material , machine learning
This article describes the application of multivariate statistical process control techniques to a series of mid‐infrared spectra collected online from a styrene/2‐ethylhexyl acrylate emulsion copolymerization process. Principal component analysis of the mid‐infrared spectral data indicated that in situ monitoring of the complex copolymerization process was feasible in the spectral region of interest. It was also observed that projection to latent structures or partial least squares (PLS) could be used for the effective indirect online prediction of individual monomer conversions and copolymer compositions over a substantial range of process operating conditions. A combination of the developed PLS methodology with a mid‐infrared attenuated total reflection probe proved to be an effective tool for the efficient online characterization of polymer quality, thereby overcoming the lack of robust online conversion and composition measuring devices. © 2001 John Wiley & Sons, Inc. J Appl Polym Sci 82: 1776–1787, 2001