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Modeling of a 1000MW power plant ultra super-critical boiler system using fuzzy-neural network methods
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Liu, X. J., Kong, X. B., Hou, G. L. and Wang, J. H. (2013) Modeling of a 1000MW power plant ultra super-critical boiler system using fuzzy-neural network methods. Energy Conversion and Management, Volume 65 . pp. 518-527. doi:10.1016/j.enconman.2012.07.028 ISSN 01968904.
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Official URL: http://dx.doi.org/10.1016/j.enconman.2012.07.028
Abstract
A thermal power plant is an energy conversion system consisting of boilers, turbines, generators and their auxiliary machines respectively. It is a complex multivariable system associated with severe nonlinearity, uncertainties and multivariable couplings. These characters will be more evident when the system is working at a higher level energy conversion capacity. In many cases, it is almost impossible to build a mathematical model of the system using conventional analytic methods. The paper presents our recent work in modeling of a 1000 MW ultra supercritical once-through boiler unit of a power plant. Using on-site measurement data, two different structures of neural networks are employed to model the thermal power plant unit. The method is compared with the typical recursive least squares (RLSs) method, which obviously demonstrated the merit of efficiency of the neural networks in modeling of the 1000 MW ultra supercritical unit.
| Item Type: | Journal Article | ||||
|---|---|---|---|---|---|
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||
| Journal or Publication Title: | Energy Conversion and Management | ||||
| Publisher: | Elsevier Ltd | ||||
| ISSN: | 01968904 | ||||
| Official Date: | January 2013 | ||||
| Dates: |
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| Volume: | Volume 65 | ||||
| Page Range: | pp. 518-527 | ||||
| DOI: | 10.1016/j.enconman.2012.07.028 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Access rights to Published version: | Restricted or Subscription Access | ||||
| Funder: | National Natural Science Foundation of China (NSFC), Natural Science Foundation of Beijing, China. Guo jia ke xue ji shu bu [Ministry of Science and Technology] (CMST), Engineering and Physical Sciences Research Council (EPSRC) | ||||
| Grant number: | 61273144, 60974051 (NSFC); 4122071 (Natural Science Foundation of Beijing); 2011CB710706 (CMST); EP/G062889 (EPSRC) |
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