Evolving Chemometric Models for Predicting Dynamic Process Parameters in Viscose Production
Sprache des Titels:
Englisch
Original Kurzfassung:
In viscose production, it is important to monitor three process parameters in order to assure a high quality
of the final product: the concentrations of H2SO4, Na2SO4 and ZnSO4. During on-line production these
process parameters usually show a quite high dynamics depending on the fiber type that is produced.
Thus, conventional chemometric models, which are trained based on collected calibration spectra from
Fourier transform near infrared (FT-NIR) measurements and kept fixed during the whole life-time of the
on-line process, show a quite imprecise and unreliable behavior when predicting the concentrations of
new on-line data. In this paper, we are demonstrating evolving chemometric models which are able to
adapt automatically to varying process dynamics by updating their inner structures and parameters in
a single-pass incremental manner. These models exploit the Takagi?Sugeno fuzzy model architecture,
being able to model flexibly different degrees of non-linearities implicitly contained in the mapping
between near infrared spectra (NIR) and reference values. Updating the inner structures is achieved by
moving the position of already existing local regions and by evolving (increasing non-linearity) or merging
(decreasing non-linearity) new local linear predictors on demand, which are guided by distance-based
and similarity criteria. Gradual forgetting mechanisms may be integrated in order to out-date older
learned relations and to account for more flexibility of the models. The results show that our approach is
able to overcome the huge prediction errors produced by various state-of-the-art chemometric models. It
achieves a high correlation between observed and predicted target values in the range of [0.95,0.98] over
a 3 months period while keeping the relative error below the reference error value of 3%.
Forschungs-