Estimation of Electric Arc Furnace Parameters Using Least-Square Support Vector Machine

Electric arc furnace (EAF) serves as a major contributor for the global industrialization due to its wide versed application for manufacturing of high-grade steel. The dynamic operation of EAF is considered to be highly nonlinear and chaotic. Hence, to examine their operations and effects on the electrical network, it is necessary to create an accurate model of an EAF, several strategies including mathematical techniques and data-driven models have already been utilized to simulate the V–I behavior of electric arc furnaces. The paper focuses on examining the data-driven modelling techniques, especially least-square support vector machines (LS-SVM) for estimation of Electric Arc Furnace Parameters. The outcomes demonstrate that the suggested approach using a radial base function kernel offers a model to forecast both the arc current and arc voltage of EAFs.