Mathematical and numerical results for quality control of hot metal in blast furnace

Abstract

The blast furnace is a very complex industrial equipment producing hot metal from iron oxides. Its measuring, modeling and exploring functionalities is very crucial; this is due to the difficult measurement and control problems related to the unit.

To maintain high efficiency, the current work proposes new adaptive algorithms for data-driven methods. These methods are classified into supervised and unsupervised algorithms, well known in optimization problems as regression, classification and clustering. To extract their limitations, a comparative study between the proposed techniques is presented, where the obtained results are validated on real data from the steel processes ArcelorMittal-Annaba, proving the feasibility and effectiveness of the proposed models and numerical procedures. Keywords: supervised and unsupervised learning; data analysis; modeling.