In this manuscript, we consider the problem of multi-rate modeling and economic model predictive control (EMPC) of electric arc furnaces (EAF), which are widely used in the steel industry to produce molten steel from scrap metal. The two main challenges that we address are the multi-rate nature of the measurement availability, and the requirement to achieve final product of a desired characteristic, while minimizing the operation cost. To this end, multi-rate models are identified that include predictions for both the infrequently and frequently measured process variables. The models comprise local linear models and an appropriate weighting scheme to capture the nonlinear nature of the EAF. The resulting model is integrated into a two-tiered predictive controller that enables achieving the target end-point while minimizing the associated cost. The EMPC is implemented on the EAF process and the closed-loop simulation results subject to the limited availability of process measurements and noise illustrate the improvement in economic performance over existing trajectory-tracking approaches. (C) 2016 Elsevier Ltd. All rights reserved.