Data-driven predictive control for blast furnace ironmaking process

High performance control of blast furnace (BF) ironmaking process is a difficult problem due to the high temperature and hostile measurement conditions for measuring devices in the process. Previous research focused on developing of accurate predictive models for silicon content in hot metal ([SI]) while control of the whole process is seldom discussed. In the present work, a data-driven predictive control method based on subspace method is presented for the blast furnace ironmaking process. The algorithm is based on input–output data and easy to implement. Simulation results show the algorithm is effective for the control application. Finally, various practical issues concerning predictive control of blast furnace ironmaking process are also addressed, such as constraint handling, control objective and output set-point selection, adaptive strategy, etc.