Neural Computation For Flow Regime Classification Based On Electrical Capacitance Tomography.

Recognition of gas-liquid flow regimes in pipelines is important in an industrial control process such as for oil production. In oil production, gas-liquid flows are normally concealed in a pipe the actual type of flows cannot be easily determined. Also, obtaining measurements corresponding to the...

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Main Authors: Mat-Dan, A. A., Mohamad-Saleh, J, Ahmad, M. A.
Format: Conference or Workshop Item
Language:English
Published: 2004
Subjects:
Online Access:http://eprints.usm.my/8612/
http://eprints.usm.my/8612/1/Neural_Computation_for_Flow_Regime_Classification_Based_on_Electrical_%28PPKEElektronik%29_2004.pdf
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author Mat-Dan, A. A.
Mohamad-Saleh, J
Ahmad, M. A.
author_facet Mat-Dan, A. A.
Mohamad-Saleh, J
Ahmad, M. A.
author_sort Mat-Dan, A. A.
building USM Institutional Repository
collection Online Access
description Recognition of gas-liquid flow regimes in pipelines is important in an industrial control process such as for oil production. In oil production, gas-liquid flows are normally concealed in a pipe the actual type of flows cannot be easily determined. Also, obtaining measurements corresponding to the flow distribution becomes almost impossible.
first_indexed 2025-11-15T15:25:39Z
format Conference or Workshop Item
id usm-8612
institution Universiti Sains Malaysia
institution_category Local University
language English
last_indexed 2025-11-15T15:25:39Z
publishDate 2004
recordtype eprints
repository_type Digital Repository
spelling usm-86122017-11-20T07:22:10Z http://eprints.usm.my/8612/ Neural Computation For Flow Regime Classification Based On Electrical Capacitance Tomography. Mat-Dan, A. A. Mohamad-Saleh, J Ahmad, M. A. TK1-9971 Electrical engineering. Electronics. Nuclear engineering Recognition of gas-liquid flow regimes in pipelines is important in an industrial control process such as for oil production. In oil production, gas-liquid flows are normally concealed in a pipe the actual type of flows cannot be easily determined. Also, obtaining measurements corresponding to the flow distribution becomes almost impossible. 2004 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.usm.my/8612/1/Neural_Computation_for_Flow_Regime_Classification_Based_on_Electrical_%28PPKEElektronik%29_2004.pdf Mat-Dan, A. A. and Mohamad-Saleh, J and Ahmad, M. A. (2004) Neural Computation For Flow Regime Classification Based On Electrical Capacitance Tomography. In: 1st National Postgraduate Colloquium School of Chemical Engineering, USM, 2004, School of Chemical Engineering, USM.
spellingShingle TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Mat-Dan, A. A.
Mohamad-Saleh, J
Ahmad, M. A.
Neural Computation For Flow Regime Classification Based On Electrical Capacitance Tomography.
title Neural Computation For Flow Regime Classification Based On Electrical Capacitance Tomography.
title_full Neural Computation For Flow Regime Classification Based On Electrical Capacitance Tomography.
title_fullStr Neural Computation For Flow Regime Classification Based On Electrical Capacitance Tomography.
title_full_unstemmed Neural Computation For Flow Regime Classification Based On Electrical Capacitance Tomography.
title_short Neural Computation For Flow Regime Classification Based On Electrical Capacitance Tomography.
title_sort neural computation for flow regime classification based on electrical capacitance tomography.
topic TK1-9971 Electrical engineering. Electronics. Nuclear engineering
url http://eprints.usm.my/8612/
http://eprints.usm.my/8612/1/Neural_Computation_for_Flow_Regime_Classification_Based_on_Electrical_%28PPKEElektronik%29_2004.pdf