Modeling the thermal properties of H-DRI during melting in an electric arc furnace

Abstract

Understanding the melting behaviour of hydrogen direct-reduced iron (H-DRI) is essential for further developing new ore-based steelmaking routes that utilize hydrogen reduction. This paper introduces a novel approach for deriving simple functions to predict the main thermochemical properties of DRI pellets, most notably the liquidus and solidus temperatures and enthalpy, which are critical for accurately predicting the energy balance of an electric arc furnace (EAF). The approach is demonstrated for a specific H-DRI grade. First, the thermochemical properties of the H-DRI are experimentally determined through differential scanning calorimetry (DSC) from room temperature up to 1600 °C. The experimental results are then compared with computational thermodynamic simulations using FactSage software. After validating the accuracy of the simulations, synthetic data is generated to derive regression equations for thermal properties using machine learning. The functions obtained from this regression analysis apply to EAF process models.