Abstract
Statistical modeling, also known as machine learning, has gained increased attention in part due to the Industry 4.0 development. However, a review of the statistical models within the scope of steel processes has not previously been conducted. This paper reviews available statistical models in the literature predicting the Electrical Energy (EE) consumption of the Electric Arc Furnace (EAF). The aim was to structure published data and to bring clarity to the subject in light of challenges and considerations that are imposed by statistical models. These include data complexity and data treatment, model validation and error reporting, choice of input variables, and model transparency with respect to process metallurgy. A majority of the models are never tested on future heats, which essentially renders the models useless in a practical industrial setting. In addition, nonlinear models outperform linear models but lack transparency with regards to which input variables are influencing the EE consumption prediction. Some input variables that heavily influence the EE consumption are rarely used in the models. The scrap composition and additive materials are two such examples. These observed shortcomings have to be correctly addressed in future research applying statistical modeling on steel processes. Lastly, the paper provides three key recommendations for future research applying statistical modeling on steel processes.
Highlights
The Electric Arc Furnace (EAF) is the second most common process in steelmaking and accounted for 28% of the total world production of steel, on average, between 2008 and 2017 [1]
Multivariate Linear Regression (MLR)—mean furnace values: The reported research started somewhere back in the 1980s, but it is the research done by Köhle and associates, in the 1990s that constitutes the most significant start to the field
In the first European Coal and Steel Community (ECSC) report of the research, the goal was to gain a better understanding of the reasons behind a better economic operating procedure of the electrical power system in the EAF [32]
Summary
The Electric Arc Furnace (EAF) is the second most common process in steelmaking and accounted for 28% of the total world production of steel, on average, between 2008 and 2017 [1]. The cost of raw materials and Electrical Energy (EE) can account for 80%, or more, of the total cost per metric ton produced steel. It is important to improve both current operational strategies and possibly to invent novel strategies to reduce the energy and raw material consumption. One approach to improve or invent new operational strategies in the EAF is through the use of mathematical modeling. The energy balance of the EAF process makes it possible to express EE consumption, EEl , as a sum of ingoing and outgoing energy factors (see Equations (1)–(3)).
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