Modeling and controlling the temperature profiles of EAFs are critical to the steel quality and energy economy. Better temperature models allow insight into the furnace’s heat distribution and dynamics to regulate steel's metallurgical properties (Kumar & Singh, 2018). Computational models, including finite elements and thermodynamics, can predict temperature gradients by considering factors such as arc heat input, slag behavior, and heat losses.

These models can identify zones where temperature control is critical to achieve the desired steel properties. EAFs regulate temperature control strategies by real-time monitoring with feedback and feedforward control algorithms. Infrared pyrometers and thermocouples provide continuous temperature measurements, which advanced control systems use to optimize arc power, electrode, and gas flow (Wang et al., 2020). An alternative prediction strategy is the MPC, which anticipates future temperature changes and balances the system to remove fluctuations. Effective temperature control prevents defects such as overheating or underheating by ensuring that steel is uniform and energy-efficient.

The integration of modeling and control helps the adaptive process optimize the EAF. Machine learning techniques can handle historical and real-time data to improve the furnace's temperature profile (Zhang et al., 2019). It improves the system responds quickly to disturbances and variations in raw material. Thus, it provides reliable steel quality and efficient operational returns from advanced temperature modeling and control in EAFs. It could be considered an improvement over traditional processes by integrating.

References

Kumar, A., & Singh, R. (2018). Thermal modeling of electric arc furnace and its influence on steel quality. Journal of Materials Processing Technology, 254, 123–132.

Wang, Y., Chen, J., & Li, H. (2020). Real-time temperature control in electric arc furnace using model predictive control. Control Engineering Practice, 102, 104557.

Zhang, L., Wang, T., & Li, X. (2019). Machine learning-based adaptive control for temperature regulation in electric arc furnace steelmaking. IEEE Transactions on Industrial Informatics, 15(7), 4100–4108.