ABSTRACT
A high solids content n-butyl acrylate/methyl methacrylate emulsion copolymerization process carried out under starved semi-batch conditions was for the first time monitored on-line by means of Fourier transform (FT)-Raman spectroscopy. Partial least squares regression was employed to build calibration models that allowed relating the spectra with solids content (overall conversion), free amounts of both n-butyl acrylate (n-BA) and methyl methacrylate (MMA), and cumulative copolymer composition. In spite of the heterogeneous nature of the polymerization, the similarities of the spectra for MMA, n-BA, and for the copolymer, and the low monomer concentrations in the reactor, the FT-Raman spectroscopy has been shown to be a suitable noninvasive sensor to accurately monitor the process. Therefore, it is well suited for on-line control of all-acrylic polymerization systems.
ABSTRACT
Fourier transform (FT)-Raman combined with partial least squares regression (PLS-R) calibration models allows the accurate monitoring of solids content, copolymer composition, and free amounts of monomers in starved semi-batch emulsion copolymerizations. The calibration models remain valid as long as the spectrometer and the measuring conditions are unchanged. Unfortunately, maintenance and/or repairing of the spectrometer result in changes in the relative intensities of the peaks of the Raman spectrum, reducing the performance of the calibration models. Therefore, a strategy for the up-date of the PLS-R calibration models is needed. Strategies for calibration model maintenance were assessed, and we found that the best strategy was to build a new model composed of the old PLS-R model plus a PLS-R model able to account for the model mismatch of the old model.
