Application and Evaluation of Mathematical Models for Prediction of the Electric Energy Demand Using Plant Data of Five Industrial-Size EAFs

Metals, vol. 11, pp. 1348

Abstract

The electric arc furnace (EAF) represents the most important process route for recycling of steel and the second most productive steelmaking process overall. Considering the large production quantities, the EAF process is subject to continuous optimization, and even small improvements can lead to a significant reduction in resource consumption and operating cost. A common way to investigate the furnace operation is through the application of mathematical models. In this study the applicability of three different statistical modeling approaches for prediction of the electric energy demand is investigated by using more than 21,000 heats from five industrial-size EAFs. In this context, particular consideration is given to the difference between linear and nonlinear regression models. Detailed information on the treatment of the process data is provided and the applied methods for regression are described in short, including information on the choice of hyperparameters. Subsequently, the results of the models are compared. Gaussian process regression (GPR) was found to yield the best overall accuracy; however, the benefit of applying nonlinear models varied between the investigated furnaces. In this regard, possible reasons for the inconsistent performance of the methods are discussed.

Authors 5

1. Alexander Reimann corresponding Aachen Department of Industrial Furnaces and Heat Engineering

   RWTH Aachen University

   Affiliation as printed

   Department of Industrial Furnaces and Heat Engineering, RWTH Aachen University, Kopernikusstraße 10, 52074 Aachen, Germany

2. Thomas Hay Aachen Department of Industrial Furnaces and Heat Engineering

   RWTH Aachen University

   Affiliation as printed

   Department of Industrial Furnaces and Heat Engineering, RWTH Aachen University, Kopernikusstraße 10, 52074 Aachen, Germany

3. Thomas Echterhof Aachen Department of Industrial Furnaces and Heat Engineering

   RWTH Aachen University

   Affiliation as printed

   Department of Industrial Furnaces and Heat Engineering, RWTH Aachen University, Kopernikusstraße 10, 52074 Aachen, Germany

4. Marcus Kirschen

   University of Bayreuth

   Affiliation as printed

   Thermal Process Engineering, University of Bayreuth, Universitätsstraße 30, 95447 Bayreuth, Germany

5. Herbert Pfeifer Aachen Department of Industrial Furnaces and Heat Engineering

   RWTH Aachen University

   Affiliation as printed

   Department of Industrial Furnaces and Heat Engineering, RWTH Aachen University, Kopernikusstraße 10, 52074 Aachen, Germany