Dynamic Quantification of Process Parameters in Viscose Production with Evolving Fuzzy Systems

In viscose production, it is important to monitor three process parameters as part of the spin-bath 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 fibre type that is produced. Thus, conventional chemometric models, 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 based on TS fuzzy systems architecture, which are able to adapt automatically to varying process dynamics by updating their inner structures and parameters in a single-pass incremental manner. Gradual forgetting mechanisms are necessary in order to out-date older learned relations and to account for more flexibility and spontaneity of the models. The results show that our dynamic approach is able to overcome the huge prediction errors produced by various state-of-the-art static chemometric models, which could be elicited over a three months period.