Economic Model Predictive Control of the Electric Arc Furnace Using Data-Driven Multi-Rate Models

This work considers the problem of economic model predictive control (EMPC) of electric arc furnaces (EAF), subject to the limited availability of process measurements and noise. The key issues addressed are: (1) the multi-rate sampling of process variables; and (2) the requirement of optimized operation that achieves desired product specifications and also minimizes the operating costs. To this end, we identify data-driven models that capture the temporal dynamics of process measurements sampled at different rates. The resulting multirate models are used to design a two-tiered predictive controller that enables achieving the target end-point while minimizing the associated costs. The EMPC is implemented on the EAF process and the closed-loop simulation results illustrate the improvement in economic performance over existing trajectory-tracking approaches.