Temperature optimization setting model of the reheating furnace on 1700 line in tangsteel

In this paper, the temperature optimization setting model of the 1# reheating furnace is researched according to the actual production data of the furnace on 1700 line in Tangsteel. The BP neural network is employed to construct a prediction model of billet tapping temperature. Then, the genetic optimization algorithm is adopted to establish the temperature optimization setting model of the furnace on the basis of the prediction model of billet tapping temperature. The optimal setting temperatures of every zone of the furnace are optimized based on the ideal charging temperature and tapping temperature of billets, as well as the billet stay time in each zone of the furnace. The model is currently used for off-line instructional operators to set the temperature values of each zone.