Optimization-based Online Decision Support Tool for Electric Arc Furnace Operation **This work is supported by the McMaster Steel Research Center (SRC) and the McMaster Advanced Control Consortium (MACC).

Electric arc furnaces (EAFs) are broadly used in the steel industry for producing different grades of steel by melting steel scrap and modifying its chemistry The EAF process is highly energy intensive and involves a low level of automation. The decisions associated with the amount and timing of injected inputs depend heavily on the EAF operators. Although the operators’ practical experience is crucial in running the EAF, important multivariable interactions and subtle relationships may not be apparent. In this work, a multi-rate moving horizon estimator (MHE) is coupled with an economics-based dynamic optimizer to form an online decision support tool (DST). The tool is able to reconstruct the states and provide optimal decisions to operators in less than 18 CPU seconds on average despite the use of a highly nonlinear large-scale EAF model. This framework is developed using entirely open source tools to have a high appeal to industrial practitioners. A case study is presented which demonstrates the increase in profit through the use of the DST.