Abstract
The composition of the distillation column is a very important quality value in refineries, unfortunately, few hardware sensors are available on-line to measure the distillation compositions. In this paper, a novel method using sensitivity matrix analysis and kernel ridge regression (KRR) to implement on-line soft sensing of distillation compositions is proposed. In this approach, the sensitivity matrix analysis is presented to select the most suitable secondary variables to be used as the soft sensor’s input. The KRR is used to build the composition soft sensor. Application to a simulated distillation column demonstrates the effectiveness of the method.
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This work was supported by National Basic Research Program of China (973 Program) (No. 2007CB714006)
Qi Li received his B. Sc. and Ph.D. degrees in automatic control engineering at Dalian University of Technology, Dalian, PRC in 2002 and 2008, respectively. He is currently a lecturer in the Institute of Advanced Control Technology at Dalian University of Technology.
His research interests include soft sensing and optimizing control for chemical processes.
Cheng Shao graduated from Liaoning University, PRC in 1981. He received his M. Sc. and Ph.D. degrees in automatic control engineering at Northeastern University, Shenyang, PRC in 1986 and 1992, respectively. He is currently a professor in the Institute of Advanced Control Technology at Dalian University of Technology, PRC.
His research interests include robust adaptive control, intelligent learning control, and optimizing control for chemical processes.
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Li, Q., Shao, C. Soft sensing modelling based on optimal selection of secondary variables and its application. Int. J. Autom. Comput. 6, 379–384 (2009). https://doi.org/10.1007/s11633-009-0379-x
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DOI: https://doi.org/10.1007/s11633-009-0379-x