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Proceedings Paper

Smart sensors for the petroleum sector based on long period gratings supervised by artificial neural networks
Author(s): Gustavo R. C. Possetti; Francelli K. Coradin; Lílian C. Côcco; Carlos I. Yamamoto; Lucia V. R. de Arruda; Rosane Falate; Marcia Muller; José L. Fabris
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Paper Abstract

This work shows the use of long period gratings in the petroleum sector, in two specific applications. The proposed sensors are employed both to identify substances in a simulated flow inside a pipeline, and to assess the gasoline conformity commercialized in gas stations. The gratings responses for each specific case were employed to train and to validate two different topologies of artificial neural networks: perceptron multilayer and radial base function. The obtained results show that fiber optic sensors supervised by artificial neural networks can constitute systems for smart measurement with high applicability in the petrochemical field.

Paper Details

Date Published: 16 May 2008
PDF: 4 pages
Proc. SPIE 7004, 19th International Conference on Optical Fibre Sensors, 70045W (16 May 2008); doi: 10.1117/12.786843
Show Author Affiliations
Gustavo R. C. Possetti, Univ. Tecnológica Federal do Paraná (Brazil)
Francelli K. Coradin, Univ. Tecnológica Federal do Paraná (Brazil)
Lílian C. Côcco, Univ. Federal do Paraná (Brazil)
Carlos I. Yamamoto, Univ. Federal do Paraná (Brazil)
Lucia V. R. de Arruda, Univ. Tecnológica Federal do Paraná (Brazil)
Rosane Falate, Univ. Estadual de Ponta Grossa (Brazil)
Marcia Muller, Univ. Tecnológica Federal do Paraná (Brazil)
José L. Fabris, Univ. Tecnológica Federal do Paraná (Brazil)


Published in SPIE Proceedings Vol. 7004:
19th International Conference on Optical Fibre Sensors

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