A hybrid dynamic model for the prediction of molten iron and slag quality indices of a large-scale blast furnace

Abstract

The stable, economically optimal, and environmental-friendly operation of blast furnaces is still a challenge. Blast furnaces consume huge amounts of energy and are among the biggest sources of CO2 in the metal industry. The operation of industrial blast furnaces is challenging because of their sheer size, multi-phase and multi-scale physics and chemistry, slow dynamics with response times of 8 hours and more, and the lack of direct measurements of most of the important inner variables. Model-based schemes are prime candidates for providing the missing information and improving the operation. However, only recently, such schemes have been applied successfully, and there is still a lot of room for improvements. The spatial extension, the lack of precise mechanistic knowledge about the chemical and physical phenomena, and the presence of unmeasured disturbances make the application of first-principle models to process operations extremely challenging. In this work, a hybrid dynamic model is developed for the prediction of the hot metal silicon content and the slag basicity in the blast furnace process. These two variables are the key indicators of the internal process conditions, and the ultimate goal of our work is to control them by a model-based scheme. The core relationships between the process variables are imposed by a first-principles-based steady-state model, and a parallel data-based model represents the process dynamics and compensates for the deficiencies of the mechanistic model. Validation results for real plant measurements of a world-scale blast furnace show that the hybrid model is more accurate than the rigorous model and a stand-alone data-based model in long-term predictions of the dynamic behavior of the process.