Low Computational-complexity Model of EAF Arc-heat Distribution

Abstract

The paper presents a computational model and the corresponding algorithm for estimating the arc energy distribution to conductive, convective and radiative heat transfer in an electric arc furnace (EAF). The proposed algorithm uses channel arc model (CAM) in order to compute the distribution of the arc energy through empirical equations (to approximate arc radius), ideal gas law (to approximate arc density) and results of magneto-hydro-dynamic (MHD) models (to approximate arc pressure, temperature and velocity). Results obtained using the proposed algorithm are comparable with other similar studies; how-ever, in contrast to other arc-energy distribution models, this model requires only two input variables (arc length and arc current) in order to calculate the energy distribution. Furthermore, simple algebraic equa-tions used in the algorithm ensure minimal computational load and consequently lead to short calculation times which are approximately one hundred thousand (100 000) times smaller than solving the MHD model equations,  making  the algorithm suitable  for  real-time applications,  such as smart monitoring and model-based control. The algorithm has been validated by two different approaches. First, the simulation results have been compared to a study dealing with arc-heat distribution in plasma arc furnace; and sec-ond, the proposed arc module has been integrated into the frame of a comprehensive EAF model in order to estimate the EAF temperature levels and compare them with operational EAF measurements. Bot