Mathematical modelling and parameter identification of a stainless steel annealing furnace |
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| Affiliation: | 1. Cologne University of Applied Sciences, Lab Control Engineering and Mechatronics, Betzdorfer Str. 2, 50679 Köln, Germany;2. VDEh-Betriebsforschungsinstitut GmbH, Division Measurement and Automation, Sohnstr. 65, 40237 Düsseldorf, Germany;1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing, China;2. Zhejiang & CAS Application Center for Geoinformatics, Zhejiang, China;3. Key Laboratory of Plant Genetics and Molecular Breeding, Zhoukou Normal University, Henan, China;4. School of Management, Xinxiang University, Henan, China;1. State Key Laboratory of Software Development Environment, Beihang University, Beijing, PR China;2. School of Computer Science and Engineering, Beihang University, Beijing, PR China;3. School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA;1. Institute of High Performance Computing, 1 Fusionopolis Way, Singapore 138632, Singapore;2. Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore;1. Graz University of Technology, Institute of Thermal Engineering, Inffeldgasse 25/b, 8010 Graz, Austria;2. Messer Austria GmbH – Kompetenzzentrum Metallurgie, Industriestraße 5, 2352 Gumpoldskirchen, Austria |
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| Abstract: | A new, comprehensive mathematical model of continuous annealing furnaces is developed, under consideration of both the radiative and convective heat transfer of the furnace components. Based on measured normal operating data from an industrial stainless steel plant, parameter identification is basically carried out using a nonlinear least-squares optimization algorithm for the whole annealing furnace, to estimate optimal values of uncertain parameters, such as emissivities. Due to the complexity of the model, a sequential approach for parameter identification is proposed and implemented, i.e. the parameter set is divided into different subsets, and the parameter estimation is carried out sequentially in several steps and iterations. The performance of the model with the estimated parameters is then evaluated on a different test data set. It is shown that the obtained model can predict temperature evolutions along the furnace in good agreement to measured data, under both steady-state and transient conditions. The presented model is suitable for controller design and process optimization. |
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| Keywords: | Continuous annealing Modelling Parameter identification Optimization |
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