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Modelling the strip thickness in hot steel rolling mills using least‐squares support vector machines
Author(s) -
Shardt Yuri A. W.,
Mehrkanoon Siamak,
Zhang Kai,
Yang Xu,
Suykens Johan,
Ding Steven X.,
Peng Kaixiang
Publication year - 2018
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.22956
Subject(s) - strip steel , process (computing) , soft sensor , rolling mill , steel mill , engineering , key (lock) , mill , process control , least squares function approximation , variable (mathematics) , process engineering , least squares support vector machine , support vector machine , control engineering , mechanical engineering , computer science , mathematics , materials science , metallurgy , artificial intelligence , mathematical analysis , statistics , computer security , estimator , operating system
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).