Multivariate statistical analysis of mid‐infrared spectra for the online monitoring of 2‐ethylhexyl acrylate/styrene emulsion copolymerization

Abstract

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.

4,4′‐Methylenebis(2,6‐di‐t‐butylphenol) as filler in high‐damping chlorinated polyethylene composites

“…CPE/AO‐4426 shows a phase separation, and the peak at above the T g of CPE probably arises from the glass transition of the AO‐4426 phase and the dissociation of intermolecular hydrogen bonds within the AO‐4426‐rich domain . At the same time, the magnitude of the first peak decreases with the increase of AO‐4426, which is probably due to the decrease in CPE content in the hybrids.…”

A Critical Overview of Sensors for Monitoring Polymerizations

“…Stavropoulos et al397 also used in‐line MIR spectroscopy to monitor the emulsion copolymerization of STY/2‐ethyl hexylacrylate. They used partial least squares regression for the on‐line estimation of conversion and copolymer composition.…”

“…Different multivariate statistical approaches have been used in combination with on‐line sensors for calibration purposes such as in IR and Raman spectroscopy (see Sections 3.9.3 and 3.9.5). For instance, in the realm of Near‐ and Mid‐IR spectroscopy, Sasic et al,591 Reshadat and Balke,437 Reshadat et al,242, 250 Apruzzese et al,238 Stavropoulos et al,397 Roberge and Dube396 and Watari and Ozaki413, 592 performed calibrations using multivariate approaches. In Raman spectroscopy, Vickers et al,593 van den Brink,482 van den Brink et al,483–486 Aust et al224 and Reis et al224, 430, 436, 493, 594 have also used these approaches.…”

Development of a prototype MSPC software package for on‐line process monitoring and statistical quality control

“…The ®rst one describes the successful implementation of a spectroscopic MIR sensor in combination with PCA and PLS to a laboratory emulsion copolymerization process of styrene/2-ethylhexyl acrylate (STY/2-EHA) [22]. The second application describes the successful implementation of MSPC techniques to an industrial high-pressure Low Density Polyethylene (LDPE) tubular reactor [23].…”