Kadlec, P. and Gabrys, B., 2011. Local learning-based adaptive soft sensor for catalyst activation prediction. AIChE Journal, 57 (5), pp. 1288-1301.
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Official URL: http://onlinelibrary.wiley.com/doi/10.1002/aic.123...
This work presents an algorithm for the development of adaptive soft sensors. The method is based on the local learning framework, where locally valid models are built and maintained. In this framework it is possible to model non-linear relationship between the input and output data by the means of a combination of linear models. The method provides the possibility to perform adaptation at two levels: (i) recursive adaptation of the local models; and (ii) the adaptation of the combination weights. The data set used for evaluation of the algorithm describes a polymerisation reactor where the target value is a simulated catalyst activity in the reactor. This data set is also used to evaluate the performance of the proposed algorithm. The results show that the traditional RPLS algorithm struggles to deliver accurate predictions. In contrast to this, by exploiting the two level adaptation scheme, the proposed algorithm delivers more accurate results.
|Uncontrolled Keywords:||soft sensor;adaptive predictive modeling;polymerization process;local learning;ensemble methods|
|Subjects:||Generalities > Computer Science and Informatics > Artificial Intelligence|
Generalities > Computer Science and Informatics
Technology > Engineering > General Engineering
|Group:||School of Design, Engineering & Computing > Smart Technology Research Centre|
|Deposited By:||Professor Bogdan Gabrys|
|Deposited On:||06 Jun 2011 09:36|
|Last Modified:||07 Mar 2013 15:45|
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- Local learning-based adaptive soft sensor for catalyst activation prediction. (deposited 02 Feb 2009 18:02)
- Local learning-based adaptive soft sensor for catalyst activation prediction. (deposited 06 Jun 2011 09:36) [Currently Displayed]
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