Pusher reheating furnace control: a fuzzy-neural model predictive strategy
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KünyeIcev, Z. A., Stankovski, M. J., Kolemishevska, T. D., Zhao, J., Dimirovski, G. M. (2006). Pusher reheating furnace control: A fuzzy-neural model predictive strategy. In Erbe, H. H., Nikolov, E. K. (Eds.). International IFAC Workshop on Energy Saving Control in Plants and Buildings, Volume 1, Part 1, (pp. 165-170), Red Hook, NY: Curran. http://dx.doi.org/10.3182/20061002-4-BG-4905.00028
A design of fuzzy model-based predictive control for industrial furnaces has been derived and applied to the model of three-zone 25 MW RZS pusher furnace at Skopje Steelworks. The fuzzy-neural variant of Takagi-Sugeno fuzzy model, as an adaptive neuro-fuzzy implementation, is employed as a predictor in a predictive controller. In order to build the predictive controller the adaptation of the fuzzy model using dynamic process information is carried out. Optimization procedure employing a simplified gradient technique is used to calculate predictions of the future control actions.