Abstract
Physics-based models are commonly used for process controls in steel industry. However, parameter adjustments of these models are often difficult due to a large number of parameters and complexity of the models. In this paper, a parameter adjustment method for such physics-based control models is proposed. The adjustment of each parameter is estimated systematically from the process data, avoiding co-linearity problem with partial least squares regression. An application for a hot rolling mill is presented to show its effectiveness.
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