Local gas holdup measurement of a bubble column using SONIA-ultrasonic non-invasive method

Muhammad R. Widyanto; Marsudi B. Utomo; Kazuhiko Kawamoto; Benyamin Kusumoputro; Kaoru Hirota

doi:10.1016/j.sna.2005.10.021

Local gas holdup measurement of a bubble column using SONIA-ultrasonic non-invasive method

Muhammad R. Widyanto^*, Marsudi B. Utomo, Kazuhiko Kawamoto, Benyamin Kusumoputro, Kaoru Hirota

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

12 Citations (Scopus)

Abstract

A non-invasive technique for local gas holdup measurement of a bubble column using Self-Organized Network inspired by Immune Algorithm (SONIA) neural network and ultrasonic method is investigated. The energy attenuation and the transmission time difference of ultrasound are used as measurement parameters to obtain the local gas holdup in an air-water dispersion system using SONIA neural network reconstruction. Bubble size distributions in the bubble column are obtained by using a photographic method. The experimental results and simulations on three different mean bubble size condition show that the errors of SONIA neural network method is 1/9 times lower than those of the conventional back-propagation neural network. The results show a good agreement with measured data.

Original language	English
Pages (from-to)	447-454
Number of pages	8
Journal	Sensors and Actuators A: Physical
Volume	126
Issue number	2
DOIs	https://doi.org/10.1016/j.sna.2005.10.021
Publication status	Published - 14 Feb 2006
Externally published	Yes

Keywords

Local gas holdup
Neural network
Ultrasonic non-invasive method

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10.1016/j.sna.2005.10.021

Cite this

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title = "Local gas holdup measurement of a bubble column using SONIA-ultrasonic non-invasive method",

abstract = "A non-invasive technique for local gas holdup measurement of a bubble column using Self-Organized Network inspired by Immune Algorithm (SONIA) neural network and ultrasonic method is investigated. The energy attenuation and the transmission time difference of ultrasound are used as measurement parameters to obtain the local gas holdup in an air-water dispersion system using SONIA neural network reconstruction. Bubble size distributions in the bubble column are obtained by using a photographic method. The experimental results and simulations on three different mean bubble size condition show that the errors of SONIA neural network method is 1/9 times lower than those of the conventional back-propagation neural network. The results show a good agreement with measured data.",

keywords = "Local gas holdup, Neural network, Ultrasonic non-invasive method",

author = "Widyanto, {Muhammad R.} and Utomo, {Marsudi B.} and Kazuhiko Kawamoto and Benyamin Kusumoputro and Kaoru Hirota",

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T1 - Local gas holdup measurement of a bubble column using SONIA-ultrasonic non-invasive method

AU - Widyanto, Muhammad R.

AU - Utomo, Marsudi B.

AU - Kawamoto, Kazuhiko

AU - Kusumoputro, Benyamin

AU - Hirota, Kaoru

PY - 2006/2/14

Y1 - 2006/2/14

N2 - A non-invasive technique for local gas holdup measurement of a bubble column using Self-Organized Network inspired by Immune Algorithm (SONIA) neural network and ultrasonic method is investigated. The energy attenuation and the transmission time difference of ultrasound are used as measurement parameters to obtain the local gas holdup in an air-water dispersion system using SONIA neural network reconstruction. Bubble size distributions in the bubble column are obtained by using a photographic method. The experimental results and simulations on three different mean bubble size condition show that the errors of SONIA neural network method is 1/9 times lower than those of the conventional back-propagation neural network. The results show a good agreement with measured data.

AB - A non-invasive technique for local gas holdup measurement of a bubble column using Self-Organized Network inspired by Immune Algorithm (SONIA) neural network and ultrasonic method is investigated. The energy attenuation and the transmission time difference of ultrasound are used as measurement parameters to obtain the local gas holdup in an air-water dispersion system using SONIA neural network reconstruction. Bubble size distributions in the bubble column are obtained by using a photographic method. The experimental results and simulations on three different mean bubble size condition show that the errors of SONIA neural network method is 1/9 times lower than those of the conventional back-propagation neural network. The results show a good agreement with measured data.

KW - Local gas holdup

KW - Neural network

KW - Ultrasonic non-invasive method

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JO - Sensors and Actuators A: Physical

JF - Sensors and Actuators A: Physical

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ER -

Local gas holdup measurement of a bubble column using SONIA-ultrasonic non-invasive method

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Keywords

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