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ORGANIZER;CN=ESTAD 2023:mailto:info@metec-estad.com
LOCATION:Room 8
SUMMARY:A novel decision support system for online optimization of calcium additions using a data-centric machine learning approach
DESCRIPTION:Considering the advantage of achieving rapid and efficient process control with artificial intelligence (AI), the use of applied data-driven strategies is in demand among today's steelmakers. One important application is the online optimization of material additions during ladle refining. As every steel grade is unique, developing distinctly trained machine learning (ML) models seem significantly demanding. A data-centric ML approach can solve this problem by focusing on what AI systems must learn from unique datasets generated by specific steel grade production. Moreover, such data-driven process models may be simpler to integrate into existing production servers compare to the physics-driven models. In the proposed work, an operator-assisted decision support system (DSS) was developed using applied data-centric ML to optimize calcium additions required for producing ultra-clean low alloyed calcium-treated steels. Real industrial process parameters were collected before and after calcium additions for three steel grades. The collected process data were examined to design a base algorithm by continuously monitoring the characteristics of non-metallic inclusions at various stages of ladle refining. The predictive result obtained using the proposed DSS, and the response obtained from the actual process were compared and verified for each studied steel grade. Special efforts were taken to generate self-adapting output for each produced "heat" of a specific steel grade to avoid any degradation in predictive performance. The optimizer named "ClogCalc" was introduced based on the programming architecture of DSS for utilization and integration inside the industrial environment. Initial attempts were made to deploy this novel optimizer inside the production environment. The authors believe that this proposed approach could support operators in making dynamic decisions to optimize calcium additions. 

Keywords: Artificial intelligence, Industry 4.0, Online-monitoring, Steelmaking, Machine learning 

CLASS:PUBLIC
DTSTART:20230615T092000
DTEND:20230615T094000
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