Einar Broese

Application of Neural Networks to Process Control in Steel Industry

Abstract The task of the “level-2᾿ process control system is the pre-calculation and setup of the mill’s actuators prior to processing. Today’s rolling mill process models are fairly mature. Typically, the adaptation of these mathematical models to process variations has been done using a fragmented, look-up table based approach. This approach has drawbacks due to the required size for the look-up tables for large product ranges and the lack of interpolation capability. The application of neural networks within a new control strategy has reduced these problems significantly. The application strategy for neural networks within hybrid systems, their role in various rolling mill applications (rolling force, stock temperature, width) and the benefits achieved by implementations of this technology are described.