Michael Jansen

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.

Current and future development in neural computation in steel processing

Abstract A rolling mill process control system calculates the setup for the mill’s actuators based on models of the technological process. Neural networks are applied as components of hybrid neuro/analytical process models. They are the key to fit the general physical models to the needs of the automation of a specific mill. Besides present applications the paper describes future trends in application of neural networks in process control.