Quality Prediction in Industrial Processes: Application of a Neuro-Fuzzy System



In chemical industries, as paper pulp, quality control is a decisive task for competitiveness. Quality prediction is determinant in quality control. However the complexity of the production processes, their non-linear and time varying characteristics does not allow to develop reliable prediction models based on first principles. New tools issued from fuzzy systems and neural networks are being developed to overcome these difficulties. In this paper a neuro-fuzzy strategy is proposed to predict bleaching quality by predicting the outlet brightness. Firstly, a fuzzy subtractive clustering technique is applied to extract a set of fuzzy rules; secondly, the centers 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.


pulp industry, fuzzy modelling, neural-networks modeling


Neuro-Fuzzy Modelling


MCPL'2000, July 2000

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