NIR-based Quantification of Process Parameters in Polyetheracrylat (PEA) Production using Flexible Non-linear Fuzzy Systems
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In polyetheracrylat (PEA) production, it is important to monitor three process parameters in order to assure a high quality of the final product: hydroxyl (OH) number, viscosity and
acidity (acid number). Due to the high resolution and high sensitivity, it has been shown in the past that the Fourier transform near infrared (FTNIR) process spectrum measurements can be used to obtain spectra with precise content information about these process parameters.
In order to perform an automatic supervision and to reduce the (off-line, laboratory) analysis effort of experts and operators of these substances, chemometric quantification models have to be used. In this paper, we investigate the usage of a specific type of fuzzy systems, so-called Takagi-Sugeno fuzzy systems, for calibrating the chemometric models.
This type of model architecture supports the usage of piece-wise local linear predictors, being able to model flexibly different degrees of non-linearities implicitly contained in the mapping between NIR spectra and reference values. The training of these models is conducted
by an evolving clustering method (adding new local linear models on demand) and a local (weighted) least squares estimation of the consequent parameters, and connected with a wavelength (dimensionality) reduction mechanism. We compare our modeling approach with state-of-the-art techniques (MLR, PCR, PLS) w.r.t. quantification errors based
on two data sets from the real-world process, one used for calibration and the other used for validation purposes. The results show that our approach is able to perform slightly
better than these methods in case of hydroxyl (OH) number, significantly better in case of viscosity and similarly in case of acidity (acid number), in all cases achieving a lower dimensionality of the models.