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"Applying subtractive clustering for neuro-fuzzy modelling of a bleaching plant"

Presently, the demands for good paper quality are growing higher and higher. Since one important variable to assess paper quality is paper brightness, pulp bleaching is a most important concern. Therefore, it is extremely important to have a thorough understanding of the bleaching plant, in order to achieve those high standards. In this paper a neuro-fuzzy approach is proposed for modelling of the pulp bleaching plant at Companhia de Celulose do Caima, S.A. (Portugal). This strategy is conducted in two phase: in the first one, subtractive clustering is applied in order to extract a set of fuzzy rules; then, in the second stage, the centres and widths of the membership functions are tuned by means of a fuzzy neural network trained with backpropagation. This technique seems promising since it permits good results with large nonlinear plants. Furthermore, it describes the plant using a set of linguistic rules, which have the advantage of being closer to natural human language, so, more intuitive for operators. The results obtained so far can be acceptable, since the model root mean square error is about 0.2% of the real value.