Otto Gramckow

Neural Network Control for Steel Rolling Mills

Worldwide, steel and aluminum production and manufacturing is still one of the major basic industries with a huge amount of material and energy consumption. Hence, optimization of the various process control schemes which are involved can lead to significant savings. Artificial Neural Networks are a new information processing technique which provides a novel approach to process control problems and promises major improvements. Therefore, Siemens together with FORWISS has been studying and developing neural control schemes for a number of different process control problems which occur at hot line rolling mills (Lindhoff et al., 1994). In this paper we give a brief survey of the different control aspects which were tackled with this new approach and comment on their current status.

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.