Applied machine learning and optimization in steel melting
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article number
00541_2023_02_03
This article showcases the utilization of data-driven modeling assisted by machine learning (ML) to simulate the melting process of different steel grades in a medium-frequency induction furnace concerning the target variable of energy consumption. The results of the predictive models developed are presented in this article, along with the possibility of producing optimized charge combinations through the use of predictive outcomes and backward analysis.
| Authors | Tim Kaufmann, Dierk Hartmann, Shikun Chen, Johannes Gottschling |
|---|---|
| Publishing Date | 1 May 2023 |
| Format | |
| Publisher | Vulkan-Verlag GmbH |
| Language | German |
| Title | Applied machine learning and optimization in steel melting |
| Description | This article showcases the utilization of data-driven modeling assisted by machine learning (ML) to simulate the melting process of different steel grades in a medium-frequency induction furnace concerning the target variable of energy consumption. The results of the predictive models developed are presented in this article, along with the possibility of producing optimized charge combinations through the use of predictive outcomes and backward analysis. |
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