Application of neural computing in basic oxygen steelmaking

The use of computer control in the basic oxygen steelmaking (BOS) process is essential to obtain accurate end-point temperature (EPT) and carbon control in liquid steel. The current computer model employed to execute this task is a procedural model that must be maintained by a person with considerable steelmaking knowledge. The requirement for an improved, maintenance reduced model is becoming increasingly important as expertise in this area is dwindling. The steelmaking process is highly complex and volatile. Artificial neural networks (ANNs) have been used to model this type of non-linear system. This paper describes an investigation into the use of ANNs to predict oxygen and coolant requirements during the end-blow period of the steelmaking process. During the end-blow period, a temperature measurement and sample are taken using a probe. These measurements are then used as inputs to the ANN model in order to predict how much oxygen to blow and how much coolant to add in order to achieve the desired end-point conditions in the steel at the end of the process. The software used to perform most of the modelling was the Clementine Data Mining System. This paper discusses the results from the ANN trials at Port Talbot BOS plant, which is part of the Corus Group.