Abstract
Based on a simplified model, the underlying temperature criteria is proposed as a method to study the temperature trends in a blast furnace. As, an application, a neural network able to forecast hot metal temperatures from 2 to 16 h in advance (with decreasing precision) has been built. This neural network has been designed to work at real time in a production plant.
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