The temperature field model of steel slabs in a reheating furnace is pivotal in quality control of the steel rolling process. Computationally efficient models have been developed for online temperature control. However, accuracy of these models is often not satisfactory and thus causes quality control issues in the steel rolling process. This paper presents a model validation approach to improve accuracy of a temperature field model while maintaining its computational efficiency. Deterministic validation is firstly conducted by ignoring material property uncertainties through building a bias regression model with respect to different geometrical locations of a steel slab. With foreseeable accuracy improvement, statistical model validation is then further conducted by considering material property uncertainties so that reliable temperature control can be achieved for different batches of steel slabs in production.

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