NIR-based quantification of process parameters in polyetheracrylat (PEA) production using flexible non-linear fuzzy systems

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 piecewise 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. Results on a concrete data set show that it can outperform state-of-the-art calibration methods as well as support vector regression as alternative non-linear model.

Research Highlights
► Quality assurance of process parameters in Polyetheracrylat (PEA) production.
► Automatic quantification of parameters by chemometric models based on FTNIR spectra.
► Saving laboratory expenses by omitting time-intensive analysis efforts of experts.
► Improving statistical state-of-the-art calibration methods by non-linear fuzzy systems.
► Automatic wavelength reduction and selection of appropriate model complexity.