Comparison of data-based models for prediction and optimization of energy consumption in electric arc furnace (EAF)

Abstract

This paper addresses the problem of data-based optimization of electric arc furnace (EAF) energy consumption. In the steel industry, optimization of production processes could lead to savings in energy and material consumption. Using data from EAF batches produced at the SIJ Acroni steel plant, the consumption of electrical energy during melting was analysed. For each batch, different parameters and signals were measured, such as the weight of the scrap, injected oxygen, added carbon, energy consumption, etc. After the preprocessing phase (detection of anomalies and outliers), the most influential regressors were analysed and selected for further modelling and prediction. In the modelling phase, we focused on evolving fuzzy modelling method in comparison with some established machine learning methods. The obtained static models were used to predict the total energy consumption of the current batch. All models were trained with 70% of data and validated and compared with 30% of data. The experimental results show that the proposed models can efficiently predict the energy consumption, which can be used to reduce the energy consumption and increase the overall efficiency of the electric steel mill.