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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.