Application of neural network to the supervisory control of a reheating furnace in the steel industry

Abstract

The efficient and reliable control of a reheating furnace is a challenging problem, due to: (a) the many different types of billets to be processed, (b) the strong intercorrelation among process variables, (c) the large dimension of the input and output space, (d) the strong interaction among process variables, (e) a large time delay, and (f) highly nonlinear behavior. Thus, conventional reheating furnace operation has been heavily dependent upon look-up tables which list the optimal set points. This paper describes a modified modular neural network for the supervisory control of a reheating furnace. Based on the divide-and-conquer concept, a modular network is capable of dividing a complex task into subtasks, and modeling each subtask with an expert network. To model such activities, a gating network is used for the classification and allocation of the input data to the corresponding expert network. To overcome the correlation effects among process variables and the problem of dimensionality, principal component analysis (PCA) has been employed to remove the correlation and reduce the problem dimension. From PCA analysis, it was possible to decide on the optimal dimension for the problem, to describe the dynamic behavior of the furnace. The proposed neural network has been trained and tested using operational data from the reheating furnace and has been implemented on the wire rod mill process of POSCO TM.