Enhancing Efficiency in Electric Arc Furnace Steelmaking: A Multi‐Objective Optimization Approach Using the Non‐Dominated Sorting Genetic Algorithm II

Abstract

To realize the overall optimization of electric arc furnace (EAF) steelmaking system, a multi‐objective optimization model including smelting cost, energy consumption per ton of steel, and carbon emission per ton of steel is established. The model is optimized by multi‐objective genetic algorithm to improve the charging structure. At the same time, the data in the optimal solution set are used to analyze the influence of the change of scrap ratio on smelting cost, carbon emission per ton of steel, and smelting cycle. According to the actual working conditions and the demand of steel plant, the optimized results are selected. Compared with the actual production data, the proportion of scrap steel increases to 50.9%, the ratio of molten iron decreases to 38.8%, the smelting cost per ton of steel decreases by 12 Yuan, the energy consumption per ton of steel decreases by 4%, the carbon emission per ton of steel significantly decreases by 13%, and the smelting cycle is shortened by 2 min, but at the cost of increasing the power consumption per ton of steel. The optimized results and the analysis of the change of scrap ratio provide reference for the optimization of EAF steelmaking system.