Minimizing power consumption in EAF steelmaking has the potential to substantially lower expenditures and carbon footprint. To achieve this goal, the operation's primary objective is to enhance the utilization of electrical energy in the steelmaking process. In this approach, optimization of the arc state and electrode placement would be critical for maximizing the performance of electric energy transfer from the source to the furnace. For example, a stable arc helps to minimize unfavorable operating conditions such as unsteady fluctuations and overheating, which significantly increase energy loss and reduce efficiency of melting (Zhang et al., 2018). Monitoring and adjusting electrode gaps in real-time improve arc length (gap) to achieve the desirable power consumption and heat generation within the furnace. The best methods reveal the required energy for melting scrap metal and define the power consumption variation.

To accentuate energy optimization, energy-efficient charge mix and scrap preheating and charging sequence would be considered. The extensive heating of scrap through off-gas gas recovery or direct electric heating would reduce the energy required for melting. In other words, reducing the temperature to the scrap is proportional to reducing the electrical energy needed to melt the same mass of scrap. (Wang & Li, 2019). The mixture of the charge, such as scrap, DRI, and other carbon feed in balance, would optimize the thermal properties of the mixture. Such optimization would consider the rate of melting, and equilibrium heating could be measured to understand the requirements for power and its availability. Moreover, optimal electric arc operation would be obtained through batch optimization, process scheduling, and effective waste heat recovery. Batch optimization refers to the reduction of the furnace's idle time and improvement of the energy utilization.

Through this, operational waste of electrical energy making heat is reduced, and the procedure is more organized (Zhang et al., 2018). Examples of a comprehensive approach to EAF energy optimization include waste heat recovery systems, real-time data analytics, and machine learning. Waste heat recovery systems and energy management could be integrated into EAF steelmaking processes to enhance energy savings significantly. They could help lower the overall fuel and electricity usage by recovering thermal energy from gases and reusing it. For instance, a regenerative type burner would be used to capture 50% to 60% of the energy by heat exchange. The regenerative heat would be carried on heat exchangers located in the center of the furnace, where the regenerative flue gas re-circulation system would preheat scrap by heat exchange.

This method would be used together with the flue gas recovery system, whereby the heat exchangers that are placed at the last outlet of the furnace would use the entire flue gas in the recovery heat transfer stage by the 3 chambers used (Singh & Kumar, 2020). In this context, real-time data analytics and machine learning optimization would be necessary. Real-time data would be collected and analyzed to evaluate different parameters and determine a forward-looking system for improving energy efficiency. For example, the real-time data would be collected to help identify and improve the system functionality.

References

Singh, R., & Kumar, S. (2020). Energy efficiency improvements in electric arc furnace steelmaking. Journal of Cleaner Production, 248, 119241.

Wang, X., & Li, Q. (2019). Scrap preheating and charging optimization for energy saving in electric arc furnaces. Metallurgical and Materials Transactions B, 50(5), 2382–2390.

Zhang, H., Chen, Y., & Liu, J. (2018). Arc stability control for energy optimization in electric arc furnace steelmaking. IEEE Transactions on Industrial Electronics, 65(2), 1625–1634.