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Improving the efficiency of electric arc furnace steelmaking using non-dominated sorting genetic algorithm | 2025

2025.7.10

With the continuous advancement of the national “dual carbon” strategy, green and low-carbon transformation has become the core task of the steel industry. The electric arc furnace (EAF) steelmaking process is gradually becoming an important direction for the structural adjustment of my country’s steel industry with its advantages such as environmental protection, high efficiency and short process. However, how to reduce energy consumption and carbon emissions at the same time without increasing the smelting cost is a key technical problem that the electric arc furnace steelmaking process urgently needs to solve.

In the past, related studies generally had several limitations when constructing optimization models: such as not considering the charge structure of scrap steel, molten iron and direct reduced iron (DRI) at the same time; the optimization target focused on cost or energy consumption, and rarely included carbon emissions in a unified framework; modeling was mostly based on empirical fitting, and the thermodynamics and material conservation characteristics of the smelting process were not fully combined, resulting in insufficient adaptability of the model in actual production.

Recently, Professor Qiang Yue and his team from Northeastern University established a multi-objective optimization model for electric arc furnace steelmaking system based on ternary charge (scrap steel, molten iron, DRI) by introducing the non-dominated sorting genetic algorithm (NSGA-II). The model takes smelting cost, unit steel energy consumption and unit steel carbon emission as optimization targets, and systematically introduces constraints such as molten steel composition, oxygen supply balance, electrode loss and slag basicity into the model. The research team carried out simulation calculations based on actual operating parameters, and used the NSGA-II algorithm to iteratively optimize the charge structure, and screened out the ratio scheme that takes into account both cost and low-carbon performance from the optimal solution set.

The relevant research results were published in the steel research international journal, Vol. 96, No. 2400370, 2025, with the title “Enhancing Efficiency in Electric Arc Furnace Steelmaking: A Multi-Objective Optimization Approach Using the Non-Dominated Sorting Genetic Algorithm II”. The authors of the paper are: Xiaoyu Yi, Qiang Yue*, Zhihe Dou, Qingcai Bu.

The research results and conclusions are as follows:

1. After optimizing the charge structure, the scrap steel ratio increased to 50.9%, the molten iron ratio decreased to 38.8%, and the DRI decreased to about 10%;

2. The unit steel smelting cost decreased by 12 yuan (from 2849.4 yuan to 2837.6 yuan);

3. The unit steel energy consumption decreased by about 4% (from 2232515 kJ to 2144984 kJ);

4. The unit steel carbon emission decreased by 13% (from 944.3 kg to 822.9 kg), that is, a reduction of 121.4 kg;

5. The smelting cycle was shortened by 2 minutes (from 43.2 min to 41.3 min);

6. For every 1% increase in the scrap steel ratio: the smelting cost increased by 5.78 yuan; the unit steel carbon emission decreased by 8.48 kg; the unit steel power consumption increased by 5.41 kWh;

7. The most suitable scrap steel ratio is between 40% and 50%, in which the smelting cycle is the shortest and the production efficiency is the best;

8. The heat balance results show that after optimization, the effective heat utilization rate of molten steel is increased from 64.37% to 66.98%, the generation of slag, furnace gas and smoke is reduced, and the metal consumption per unit steel output is reduced by 10 kg.

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