Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1653
Title: Optimization of sensor locations for measurement of flue gas flow in industrial ducts and stacks using neural networks
Authors: Kang, H
Yang, QP
Butler, C
Xie, T
Benati, F
Keywords: Data acquisition;Fluid flow measurement;Neural networks;Optimization;Sensor location
Issue Date: 2000
Publisher: IEEE
Citation: IEEE Transactions on Instrumentation and Measurement. 49 (2): 228-233
Abstract: This 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.
URI: http://bura.brunel.ac.uk/handle/2438/1653
DOI: http://dx.doi.org/10.1109/19.843054
ISSN: 0018-9456
Appears in Collections:Advanced Manufacturing and Enterprise Engineering (AMEE)
Dept of Mechanical Aerospace and Civil Engineering Research Papers



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