A neural network developed in a Foundation Fieldbus environment to calculate flow rates for compressible fluid

This paper proposes the development of an artificial neural network multilayer perceptron, implemented in a Foundation Fieldbus environment, to calculate the flow rate of natural gas by using an orifice plate in a closed pipe. The principal benefit of using neural networks lies in their low computat...

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Bibliographic Details
Published in:Flow measurement and instrumentation 2014-12, Vol.40, p.142-148
Main Authors: Borg, Denis, Suetake, Marcelo, Brandão, Dennis
Format: Article
Language:English
Subjects:
Flow computer
Flow rate calculation
Foundation Fieldbus
Neural network
Orifice plate
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Summary:This paper proposes the development of an artificial neural network multilayer perceptron, implemented in a Foundation Fieldbus environment, to calculate the flow rate of natural gas by using an orifice plate in a closed pipe. The principal benefit of using neural networks lies in their low computational cost and simplicity of implementation, which allows just standard blocks to be used, making the technology independent of the Foundation Fieldbus system manufacturer. To perform the calculation, the proposed methodology relies on static pressure, temperature and differential pressure measurements, which are typically available in industrial plants. The developed methodology generates highly accurate results, and this approach can be implemented at a relatively low cost for Foundation Fieldbus system users. •I would like to thank the reviewers for their comments to improve my paper. I have reviewed it and tried to follow the recommendations.•I have shortened the introduction and taken Refs. [14,15] off. In addition, I complemented the conclusion.•Please, find the answers for your questions in blue inside the document named “Letter_for_reviewers” I have attached to EES website.
ISSN:0955-5986
1873-6998
DOI:10.1016/j.flowmeasinst.2014.09.007