English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

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.

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