Model Predictive Control of Molten Iron and Slag Quality Indices in a Large-scale Ironmaking Blast Furnace using a Hybrid Dynamic Model

The stable operation and the optimal thermal control of industrial blast furnaces are challenging due to the complexity of the multi-phase and multi-scale physical and chemical phenomena, the presence of fast and extremely slow dynamics with latency periods of more than 8 hours, the absence of direct measurements of key inner variables, and the occurrence of a wide range of unknown disturbances. Industrial blast furnaces are still operated in a semi-automated manner and the quality of the control depends on the skills and dedication of the operators. Model-based control schemes, operated either in closed-loop or as advisory systems, are an obvious option to achieve a smooth and energetically efficient operation of blast furnaces. This work proposes a hybrid dynamic model-based optimizing control scheme for achieving the desired operational objectives by tightly controlling the hot metal silicon content ([Si]) and the slag basicity (SB) at their desired set-points. These two variables are key product quality indices and indicators of the internal thermal status of the blast furnace process. Within the proposed framework, the optimizer regulates the fast dynamics of the blast furnace to counteract the unmeasured disturbances that are caused by the variations in the solid feed, subject to operational constraints. Simulation results on a large-scale industrial blast furnace demonstrate the potential of the proposed approach for an improved operation of the blast furnace process. Copyright (C) 2022 The Authors.