Optimizing Energy Efficiency in EAF Steel Production: A Data-Driven Approach

Yubin Zhao; Yize Qi; Jose Diogo Franco Viveiros

doi:10.54097/08ey9j73

Authors

Yubin Zhao
Yize Qi
Jose Diogo Franco Viveiros

DOI:

https://doi.org/10.54097/08ey9j73

Keywords:

EAF, Weka, Data analysis, Data modelling.

Abstract

The article’s main research mission is making predictions about energy usage within an EAF steel production operation and being able to reliably predict energy consumption. Besides, we analysed the relationship between energy consumption in electric arc furnace (EAF) production processes by the method of data mining, so we could obtain accurate forecasts to optimize production costs and maximize energy efficiency. Last but not least, by using decision tree, random tree algorithms and multilayer perceptron models, we can easily achieve the intended goals of efficient use of energy and provide a better plan for electric arc furnace (EAF).

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