Process analytics to predict arc loss in an electric arc furnace**

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Smelting is an energy intensive operation in metallurgical processes where ore is refined into base metal in units such as an electric arc furnace that can draw up to 80MW of power. Disruptions to the furnace operation, such as loss of the electric arc, can result in expensive costs associated with lost production time and reduced energy efficiency. In this project we aim to combine process analytics, machine learning and large amounts of historical data to develop an inferential sensor that can predict loss of the furnace arc. Specifically, our goal is to study patterns in upstream metallurgical and electrical variables in order to develop a supervised learning classification model that can inform operators when there is a high probability of the onset of arc loss. Operators can then use this information to take corrective actions and perform predictive maintenance to ensure that stable operation and constant production is maintained.