Application of genetic algorithm to improve an electric arc furnace freeboard model based on practical data. (2015) - Kolagar, Atefeh Hassannia; Meier, Thomas; Echterhof, Thomas; Pfeifer, Herbert
Electric arc furnaces are applied to produce steel by melting scrap in a batch process. Despite of the significant role of the off-gas in the electric arc furnace environmental and energy feature, limited research has been carried out specifically on the modelling of the electric arc furnace freeboard. In this research, a reactor base model for electric arc furnace with burner and without burner is extended by considering more inputs to approach real conditions. There are some uncertainties in the measurement or calculation of electric arc furnace inputs due to different constraints. Regarding the multi objective problem and the conflicting nature of objectives, a genetic algorithm is applied to improve the model accuracy. The genetic algorithm found the optimum input parameters in the validated range based on the best compatibility with actual off-gas data. The comparison among different results illustrates the effective performance of genetic algorithm for this problem. This accurate model can be applied in the study of recycling, storage condition and control of the electric arc furnace off-gas for final purpose of energy saving and environmental protection.