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ORGANIZER;CN=ESTAD 2023:mailto:info@metec-estad.com
LOCATION:Room 7
SUMMARY:Reliable digital twins for the transition to a CO2-free steel industry
DESCRIPTION:As a result of strong globalization and rapid scientific progress in almost all subfields relevant to industry, large industrial companies increasingly embrace the advantages accompanying the use of digital twins’ automation, and of analysis and optimization of previous process structures.

 In addition to these changes, the industry faces challenges such as reducing CO2 emissions, efficiency and optimization processes across the entire value chain driven by global competition, and the minimization of waste products. This is particularly relevant in the steel industry, 
where the identification of optimal process parameters for complex control models is often a limiting factor for improving efficiency and reducing CO2 emissions. 

To achieve a carbon-free steel industry and further improve their gas and steam networks and management practices, established approaches need to be reconsidered to reduce CO2 emissions and waste and increase overall efficiency. In this paper, we propose an approach that leverages Machine Learning and the Koopmann approach to improve the accuracy of digital twins. By demonstrating this approach using case studies from recent projects within the Research Fund for Coal and Steel (RFCS). 

CLASS:PUBLIC
DTSTART:20230615T102000
DTEND:20230615T104000
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