Mathematical modeling and observer design for the plate temperature in hot rolling

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Ongoing research aims to enhance the quality and efficiency of steel production, particularly in the hot rolling of heavy plates, where understanding plate temperature evolution is crucial for process planning. This thesis presents a first-principles mathematical model that generates a temperature profile through the plate's thickness and a radial profile of the work rolls during production. The thermal model is validated with measurement data, leading to a computationally efficient reduced model that focuses on work rolls during brief contact times with the plate. An Extended Kalman Filter (EKF) is developed using the full model to observe plate temperature, correcting estimates with infrequent pyrometer measurements of surface temperatures, the only data available under normal production conditions. The thesis discusses the EKF's initialization and parametrization, emphasizing their impact on performance. Despite limited measurements, the estimated temperatures align well with additional data from a verification experiment. A second EKF based on the reduced model is also introduced; while both observers demonstrate comparable accuracy, the second is more computationally efficient and suitable for real-time use. The thesis concludes with an analysis of current measurement facilities, addressing potential measurement errors.