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Being able to predict future temperatures on the wall lining is key when controlling and scheduling maintenance for large industrial smelting furnaces. In this paper, we propose and test a machine learning approach for predicting lining temperatures in a ferronickel smelting furnace. This approach was deployed and evaluated in a real‐world scenario, i.e., in one of Cerro Matoso S.A.'s (CMSA) industrial...
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