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

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...

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Bibliographic Details
Published inMetals (Basel ) Vol. 11; no. 9; p. 1348
Main Authors Reimann, Alexander, Hay, Thomas, Echterhof, Thomas, Kirschen, Marcus, Pfeifer, Herbert
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2021
Subjects
Algorithms
artificial neural network
Cost control
Data processing
electric arc furnace
Electric arc furnaces
Energy
energy demand
Furnaces
Gaussian process
Gaussian process regression
Köhle formula
Mathematical analysis
Mathematical models
Optimization
regression
Regression analysis
Regression models
Standard deviation
Standardization
Statistical analysis
Statistical models
Steel making
Steel production
Variables
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Summary: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.
ISSN:2075-4701
2075-4701
DOI:10.3390/met11091348