A non-radioactive flow meter using a new hierarchical Neural network

M. Meribout, N. Al-Rawahi, A. Al-Naamany, A. Al-Bimani, K. Al-Busaidi, A. Meribout

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, a Multilayer neural network has been developed to carry out the fusion of multi-sensor information for a new multiphase flow meter (MPFM) device. The velocity and density of each phase are determined using the fluid electrical and acoustic property signals which are combined with the physical models of multiphase fluids, in addition to the venturi, differential pressure, and absolute pressure sensors. Two rings of high and low frequency ultrasonic sensors are used to overcome the uncertainties of the electrical sensors in the range of 40-60% water-cut for low and high gas fractions respectively. Experimental results on a multiphase flow loop show that real-time classification of phase flow rates for up to 90% gas fraction can be achieved with less than 10% relative error.

Original languageEnglish
Title of host publication14th International Conference on Multiphase Production Technology
Pages65-78
Number of pages14
Publication statusPublished - 2009
Event14th International Conference on Multiphase Production Technology - Cannes, France
Duration: Jun 17 2009Jun 19 2009

Other

Other14th International Conference on Multiphase Production Technology
CountryFrance
CityCannes
Period6/17/096/19/09

Fingerprint

flowmeter
Multiphase flow
Gases
sensor
Neural networks
Ultrasonic sensors
Acoustic properties
Fluids
multiphase flow
Sensors
Pressure sensors
Multilayer neural networks
Electric properties
Fusion reactions
Flow rate
acoustic property
fluid
electrical property
Water
gas

ASJC Scopus subject areas

  • Geochemistry and Petrology
  • Environmental Chemistry

Cite this

Meribout, M., Al-Rawahi, N., Al-Naamany, A., Al-Bimani, A., Al-Busaidi, K., & Meribout, A. (2009). A non-radioactive flow meter using a new hierarchical Neural network. In 14th International Conference on Multiphase Production Technology (pp. 65-78)

A non-radioactive flow meter using a new hierarchical Neural network. / Meribout, M.; Al-Rawahi, N.; Al-Naamany, A.; Al-Bimani, A.; Al-Busaidi, K.; Meribout, A.

14th International Conference on Multiphase Production Technology. 2009. p. 65-78.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Meribout, M, Al-Rawahi, N, Al-Naamany, A, Al-Bimani, A, Al-Busaidi, K & Meribout, A 2009, A non-radioactive flow meter using a new hierarchical Neural network. in 14th International Conference on Multiphase Production Technology. pp. 65-78, 14th International Conference on Multiphase Production Technology, Cannes, France, 6/17/09.
Meribout M, Al-Rawahi N, Al-Naamany A, Al-Bimani A, Al-Busaidi K, Meribout A. A non-radioactive flow meter using a new hierarchical Neural network. In 14th International Conference on Multiphase Production Technology. 2009. p. 65-78
Meribout, M. ; Al-Rawahi, N. ; Al-Naamany, A. ; Al-Bimani, A. ; Al-Busaidi, K. ; Meribout, A. / A non-radioactive flow meter using a new hierarchical Neural network. 14th International Conference on Multiphase Production Technology. 2009. pp. 65-78
@inproceedings{d162369170054dd2b8565a4bcb9bf5a4,
title = "A non-radioactive flow meter using a new hierarchical Neural network",
abstract = "In this paper, a Multilayer neural network has been developed to carry out the fusion of multi-sensor information for a new multiphase flow meter (MPFM) device. The velocity and density of each phase are determined using the fluid electrical and acoustic property signals which are combined with the physical models of multiphase fluids, in addition to the venturi, differential pressure, and absolute pressure sensors. Two rings of high and low frequency ultrasonic sensors are used to overcome the uncertainties of the electrical sensors in the range of 40-60{\%} water-cut for low and high gas fractions respectively. Experimental results on a multiphase flow loop show that real-time classification of phase flow rates for up to 90{\%} gas fraction can be achieved with less than 10{\%} relative error.",
author = "M. Meribout and N. Al-Rawahi and A. Al-Naamany and A. Al-Bimani and K. Al-Busaidi and A. Meribout",
year = "2009",
language = "English",
isbn = "9781855981119",
pages = "65--78",
booktitle = "14th International Conference on Multiphase Production Technology",

}

TY - GEN

T1 - A non-radioactive flow meter using a new hierarchical Neural network

AU - Meribout, M.

AU - Al-Rawahi, N.

AU - Al-Naamany, A.

AU - Al-Bimani, A.

AU - Al-Busaidi, K.

AU - Meribout, A.

PY - 2009

Y1 - 2009

N2 - In this paper, a Multilayer neural network has been developed to carry out the fusion of multi-sensor information for a new multiphase flow meter (MPFM) device. The velocity and density of each phase are determined using the fluid electrical and acoustic property signals which are combined with the physical models of multiphase fluids, in addition to the venturi, differential pressure, and absolute pressure sensors. Two rings of high and low frequency ultrasonic sensors are used to overcome the uncertainties of the electrical sensors in the range of 40-60% water-cut for low and high gas fractions respectively. Experimental results on a multiphase flow loop show that real-time classification of phase flow rates for up to 90% gas fraction can be achieved with less than 10% relative error.

AB - In this paper, a Multilayer neural network has been developed to carry out the fusion of multi-sensor information for a new multiphase flow meter (MPFM) device. The velocity and density of each phase are determined using the fluid electrical and acoustic property signals which are combined with the physical models of multiphase fluids, in addition to the venturi, differential pressure, and absolute pressure sensors. Two rings of high and low frequency ultrasonic sensors are used to overcome the uncertainties of the electrical sensors in the range of 40-60% water-cut for low and high gas fractions respectively. Experimental results on a multiphase flow loop show that real-time classification of phase flow rates for up to 90% gas fraction can be achieved with less than 10% relative error.

UR - http://www.scopus.com/inward/record.url?scp=70450191234&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70450191234&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9781855981119

SP - 65

EP - 78

BT - 14th International Conference on Multiphase Production Technology

ER -