Process parameter optimization for refining electric arc furnaces (EAFs) holds critical importance in steel purity and mechanical characteristics. Processed parameters (temperature control, oxygen injection, and melting time) significantly affect impurity (sulfur, phosphorus, non-metallic inclusions) removal. Impurity removal significantly affects steel cleanliness, mechanical characteristics (Kumar & Singh, 2018).

Actual parameter control promotes critical metallurgical reactions ensuring molten steel attains critical chemical composition/microstructure, needed, strengths, and tensile steel. Optimization is essential in refining EAFs within, including timing and airflow rates of oxygen blowing, and carbon injection. These determine decarburization, slag formation, controlling unwanted element removal, and minimizing reoxidation (Wang et al., 2020). Variation occurs in steel quality but optimization of timing and intensity of carbon and oxygen can bypass this variation. A strategy that includes the current process control systems, data, and predictive models improves steel quality. There is a recommendation to maintain and develop steel quality through machine learning and prediction (Zhang et al., 2019).

Integrating and refining EAF process parameters, these variables increase steel purity and mechanical characteristics. By evaluating historical production, these variables can predict data and other factors. They can simulate the outcomes for reference and optimize these variables (Liu et al., 2021). Balancing energy and units of waste helps improve production and reduce the volume of waste. energy consumption by reducing waste materials. Parameter optimization in EAFs is a PDOA required for the production of high-purity steel with enhanced mechanical characteristics.

References

Kumar, A., & Singh, R. (2018). Process optimization in electric arc furnace steelmaking for improved steel quality. Journal of Materials Processing Technology, 254, 123–132.

Liu, X., Wang, T., & Chen, J. (2021). Machine learning-based optimization of electric arc furnace process parameters for steel quality enhancement. IEEE Transactions on Industrial Informatics, 17(4), 2895–2904.

Wang, Y., Chen, J., & Li, H. (2020). Control of oxygen and carbon injection in electric arc furnace refining. Metallurgical and Materials Transactions B, 51(5), 2456–2465.

Zhang, H., Chen, Y., & Wang, T. (2019). Effect of melt stirring on homogenization and mechanical properties in electric arc furnace steelmaking. Ironmaking & Steelmaking, 46(5), 417–425.