Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1616
Title: Modelling and measurement accuracy enhancement of flue gas flow using neural networks
Authors: Kang, H
Yang, QP
Butler, C
Keywords: Flow measurement;Flow simulation;Neural nets;Pipe flow
Issue Date: 1998
Publisher: IEEE
Citation: IEEE Transactions on Instrumentation and Measurement. 47 (5): 1379-1384
Abstract: This paper discusses the modeling of the flue gas flow in industrial ducts and stacks using artificial neural networks (ANN's). Based upon the individual velocity and other operating conditions, an ANN model has been developed for the measurement of the volume flow rate. The model has been validated by the experiment using a case-study power plant. The results have shown that the model can largely compensate for the nonrepresentativeness of a sampling location and, as a result, the measurement accuracy of the flue gas flow can be significantly improved.
URI: http://bura.brunel.ac.uk/handle/2438/1616
ISSN: 0018-9456
Appears in Collections:Mechanical and Aerospace Engineering
Advanced Manufacturing and Enterprise Engineering (AMEE)
Dept of Mechanical Aerospace and Civil Engineering Research Papers

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