Modelling the strip thickness in hot steel rolling mills using least‐squares support vector machines |
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| Authors: | Yuri A W Shardt Siamak Mehrkanoon Kai Zhang Xu Yang Johan Suykens Steven X Ding Kaixiang Peng |
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| Affiliation: | 1. Department of Chemical Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada and Institute of Automatic Control and Complex Systems (AKS), University of Duisburg‐Essen, Germany;2. ESAT‐STADIUS, Catholic University of Leuven, Leuven, Belgium;3. Key Laboratory of Knowledge Automation for Industrial Processes of the Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China;4. Institute of Automatic Control and Complex Systems (AKS), University of Duisburg‐Essen, Germany |
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| Abstract: | The development and implementation of better control strategies to improve the overall performance of a plant is often hampered by the lack of available measurements of key quality variables. One way to resolve this problem is to develop a soft sensor that is capable of providing process information as often as necessary for control. One potential area for implementation is in a hot steel rolling mill, where the final strip thickness is the most important variable to consider. Difficulties with this approach include the fact that the data may not be available when needed or that different conditions (operating points) will produce different process conditions. In this paper, a soft sensor is developed for the hot steel rolling mill process using least‐squares support vector machines and a properly designed bias update term. It is shown that the system can handle multiple different operating conditions (different strip thickness setpoints, and input conditions). |
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| Keywords: | soft sensors steel mill support vector machines process systems engineering |
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