Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/1653
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dc.contributor.authorKang, H-
dc.contributor.authorYang, QP-
dc.contributor.authorButler, C-
dc.contributor.authorXie, T-
dc.contributor.authorBenati, F-
dc.coverage.spatial6en
dc.date.accessioned2008-02-15T14:32:11Z-
dc.date.available2008-02-15T14:32:11Z-
dc.date.issued2000-
dc.identifier.citationIEEE Transactions on Instrumentation and Measurement. 49 (2): 228-233en
dc.identifier.issn0018-9456-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/1653-
dc.description.abstractThis paper presents a novel application of neural network modeling in the optimization of sensor locations for the measurement of flue gas flow in industrial ducts and stacks. The proposed neural network model has been validated with an experiment based upon a case-study power plant. The results have shown that the optimized sensor location can be easily determined with this model. The industry can directly benefit from the improvement of measurement accuracy of the flue gas flow in the optimized sensor location and the reduction of manual measurement operation with Pitot tube.en
dc.format.extent469726 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherIEEEen
dc.subjectData acquisitionen
dc.subjectFluid flow measurementen
dc.subjectNeural networksen
dc.subjectOptimizationen
dc.subjectSensor locationen
dc.titleOptimization of sensor locations for measurement of flue gas flow in industrial ducts and stacks using neural networksen
dc.typeResearch Paperen
dc.identifier.doihttp://dx.doi.org/10.1109/19.843054-
Appears in Collections:Advanced Manufacturing and Enterprise Engineering (AMEE)
Dept of Mechanical Aerospace and Civil Engineering Research Papers



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