Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks

• The process of stainless steel production in the electric arc furnace was modelled.
• Multilayer perceptron was used as the optimal neural network for modelling.
• The content of carbon, chromium, nickel, silicon and iron was used as inputs.
• The effect of scrap composition on electrical energy consumption was investigated.
• The highest impact on electrical energy consumption has the content of carbon.

The objective of this research was to use state-of-the-art artificial neural network approach to estimate the extent and effect of fluctuations in the chemical composition of stainless steel at tapping of an electric arc furnace, and thus scrap and alloy weights in the charge material mix, on the specific electrical energy consumption. Such an estimation would help to further evaluate process control strategies and optimize overall operation of the electric arc furnace. The multilayer perceptron architecture 5-5-1 with hyperbolic tangent function in the hidden layer and linear function in the output layer was used as an optimal neural network model. The model was built, tested and validated based on experimental melts of the electric arc furnace at a melt shop in Italy. The proposed model was presented as an adequate one based on the coefficient of determination (R2) which was above 0.9 as well as other error parameters calculated. The highest effect on the electrical energy consumption has carbon content.