Multi-model control of blast furnace burden surface based on fuzzy SVM

Burden distribution is one of the key procedures of blast furnace operation. The improvement in control quality of the entire charging process for blast furnace is very necessary for more competitive and profitable production. In this paper, burden surface data from radars are classified by using k-means clustering algorithm to set up the multiple models set. Given objective burden surface, multiple burden distribution control strategies are obtained. In every charging period, real time burden surface data will be processed to match the model in model set by fuzzy support vector machine, and the optimal control strategy based on the matched model will be switched in action. Finally, blast furnace closed-loop control can be realized by this way. The proposed control method is applied to a blast furnace in an Iron and Steel Plant, energy saving and consumption reduction have been achieved greatly in this operation process.