Research of furnace temperature optimization control method in hot rolling process

摘要

To effectively solve the problems of high energy consumption,low control accuracy and time control-delay for furnace,this paper proposes the intelligent control strategies based on the process feature of walking beam furnace,namely fuzzy-RBF network self-learning and self-optimizing function,which is combined with dynamic PID feedback compensation strategy.The experiment shows that the system not only guarantees the furnace temperature control accuracy and increases the temperature up and down rate under working condition fluctuation,which reduces unit fuel consumption,unit electricity consumption and billet burning loss,but also improves the furnace production capacity.