Modeling and optimization of slopping prevention and batch time reduction in basic oxygen steelmaking

Because of increasingly stricter environmental regulations, steel plants are at- tempting to reduce the occurrence of (heavy) slopping, which can be accompa- nied by large ejections of dust. They are also aiming to increase their produc- tion capacity by e.g. investments in additional equipment and by improving logistics. Reduction of the batch time in basic oxygen steelmaking might con- tribute to the desired increase in production capacity if the converters are the bottleneck in production. Currently the desired temperature and steel composition are met by appli- cation of a ¯rst principles static model, which determines the required raw material input. This model is sometimes perceived as complicated. The set- points of the control variables such as the addition rates, the lance height and the oxygen blowing rate are based on standard operating procedures, which have been developed during many years of practical experience. Operators only deviate from these standard operating procedures when it is necessary, for instance, when slopping occurs. It may be expected, that both the batch time and the occurrence of slopping can signi¯cantly be reduced by optimizing operating settings. The objective of this thesis is to develop a dynamic control strategy for basic oxygen steelmaking which both reduces the occurrence of slopping and in- creases the production capacity by reducing the batch time. The development of this strategy would greatly bene¯t from the continuous measurement of im- portant process variables. However, due to the high temperatures and dusty environment involved, measuring of important process variables is di±cult. It is therefore necessary to develop a dynamic process model that predicts im- portant process variables. Dynamic modeling of the process enables dynamic optimization. The feasibility of measurements, modeling of the process and dynamic optimization are studied subsequently in this thesis.