Abstract
About 70% of the steel used worldwide is produced via the Basic Oxygen Furnace (BOF). This process has limited automation, and normal operation relies on invaluable operators’ knowledge and past experience. In this paper, we present a framework for dynamic optimization of BOF operation. The dynamic optimization problems utilize a first-principles based dynamic model, and are solved using a hybrid method in which the states are integrated using a differential–algebraic equation (DAE) solver over an initial time interval and full discretization over the remainder of the time horizon. Optimization case studies are presented in which the impact of constraints and different objective formulations are explored. The results suggest that the proposed framework can potentially aid steelmakers to significantly reduce process costs while meeting production and quality targets. The framework is implemented in Python using the open-source software tool, CasADi.
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