Modeling and Control of Electric Arc Furnace Systems

Electric arc furnaces (EAFs) lie at the heart of contemporary steelmaking, converting electrical energy into thermal energy via plasma arcs to melt scrap metal. The intrinsic nonlinearity of the arc, coupled with rapid fluctuations in power and thermal load, poses significant challenges for both accurate mathematical modelling and robust control. Researchers have developed multi-domain models that capture electrical characteristics, thermal dynamics and electrode mechanics, often integrating data-driven methods such as neural networks or fuzzy logic with first-principles descriptions to enhance predictive fidelity. Control strategies range from traditional proportional-integral regulators for electrode positioning to advanced model predictive control schemes that anticipate arc behaviour and constrain unproductive electrode movements. Beyond process performance, emphasis has grown on mitigating power-quality disturbances—flicker, voltage asymmetry and harmonic injection—through coordinated converter and compensation systems. Real-time simulation platforms employing hardware-in-the-loop further bridge laboratory development with plant-scale implementation. Progress in this field promises substantial energy savings, improved steel quality and reduced grid impact, underscoring the global importance of sophisticated modelling and control of EAF systems.

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Modeling and Control of Electric Arc Furnace Systems publication trend

The graph below shows the total number of articles in modeling and control of electric arc furnace systems across all publications each year (not limited to Nature Index journals).

Technical terms

Electric arc furnace (EAF): A steelmaking vessel that generates heat by sustaining electric arcs between electrodes and metal charge to melt scrap or feedstock.

Arc length: The distance between an electrode tip and the molten pool surface, which strongly influences arc voltage, current distribution and furnace power.

Fuzzy arc length identifier: A soft-computing system using fuzzy-set rules to estimate arc length from electrical measurements despite process nonlinearity and disturbance.

Model predictive control (MPC): A control strategy that uses a dynamic model to predict future process outputs and compute optimal control moves over a rolling horizon.

Dynamic arc resistance: A time-varying resistance of the electric arc, derived from averaged volt-ampere data, critical for accurate simulation of furnace electrical behaviour.

References

1. Electric Arc Furnace Electrode Movement Control System Based on a Fuzzy Arc Length Identifier. Energies (2023).
2. Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage Gradient. Energies (2017).
3. Estimating the Impact of Arc Furnaces on the Quality of Power in Supply Systems. Energies (2020).
4. Power Quality Enhancement in Electric Arc Furnace Using Matrix Converter and Static VAR Compensator. Electronics (2021).
5. A Mathematical Model of Electrical Arc Furnaces for Analysis of Electrical Mode Parameters and Synthesis of Controlling Influences. Energies (2022).