Abstract
In the context of increasing environmental concerns, the iron and steel industry faces large pressure to reduce its energy consumption and carbon footprint while maintaining economic viability. This paper explores the implementation of best practice operations within foundry processes, specifically induction furnace melting, to enhance energy and cost efficiency and reduce CO2 emissions. A digital twin model is developed integrating discrete event simulation, system dynamics modeling, and symbolic regression to simulate the foundry production process and evaluate the impact of various operational practices. A large Danish foundry is used as a case study, providing data for induction furnace production incorporating various electricity market data sources. Symbolic regression models are deployed to accurately predict melt temperatures and energy requirements. Results indicate that adopting best practices can lead to significant savings - up to 21% in electricity costs and 14.2% in CO2 emissions - while improving productivity. The study also highlights a point of diminishing returns at 65% adherence to best practices due to existing production schedules. Furthermore, the study demonstrates the digital twin’s potential as a decision-support tool in optimizing industrial process operations.
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Acknowledgments
This paper is part of the project “DECODE: Data-driven best-practice for energy-efficient operation of industrial processes - A system integration approach to reduce the CO2 emissions of industrial processes” funded by EUDP (Case no. 64020-2108), and Project “Danish participation in IEA IETS Annex Task XVIII - Digitization, artificial intelligence and related technologies for energy efficiency and reduction of greenhouse gas emissions in industry”, the EUDP, Denmark (Case no. 134-21010).
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Howard, D.A., Værbak, M., Ma, Z., Jørgensen, B.N., Ma, Z. (2025). Data-Driven Digital Twin for Foundry Production Process: Facilitating Best Practice Operations Investigation and Impact Analysis. In: Jørgensen, B.N., Ma, Z.G., Wijaya, F.D., Irnawan, R., Sarjiya, S. (eds) Energy Informatics. EI.A 2024. Lecture Notes in Computer Science, vol 15271. Springer, Cham. https://doi.org/10.1007/978-3-031-74738-0_17
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