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Flow regime identification of particles conveying in pneumatic pipeline using electric charge tomography and neural network techniques

A plastic beads (solid particles) flow in a pipeline is a common means of transportation in industries. Monitoring and controlling materials flow through the pipeline is essential to ensure plant efficiency and safety of the system. The pipeline transportation used in this project makes use of elect...

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Main Author: Ahmed Abuassal, Ali Mohamed
Format: Thesis
Language:English
Published: 2012
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/32099/5/AliMohamedAhmedMFKE2012.pdf
http://eprints.utm.my/id/eprint/32099/
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spelling my.utm.320992017-09-30T08:34:44Z http://eprints.utm.my/id/eprint/32099/ Flow regime identification of particles conveying in pneumatic pipeline using electric charge tomography and neural network techniques Ahmed Abuassal, Ali Mohamed TA Engineering (General). Civil engineering (General) A plastic beads (solid particles) flow in a pipeline is a common means of transportation in industries. Monitoring and controlling materials flow through the pipeline is essential to ensure plant efficiency and safety of the system. The pipeline transportation used in this project makes use of electrodynamic sensors which are charge to voltage converters. The process flow data is captured fitting an array of 16 sensors around the circumference of the pipe to capture the inherent charge on the flowing solid materials. A high speed data acquisition card DAS1800HC is used as the interface between the sensors and a personal computer which processes the data. A Radial Basis Function (RBF) neural network based flow regime identifier program is developed in Matlab environment. Baffles of different shapes are inserted to artificially create expected flow regimes and data captured in this way are used in training and evaluating the network’s performance. The results of this work show significant improvments, the dataset which was check as the input gave good results, especially for full flow, three quarter flow and inverse quarter flow are 100%, and 95% has been succeed for each of quarter flow inverse three quarter flow and inverse half flow, and for the others flow regimes (center half and half flow) 90% succeed. 2012-07 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/32099/5/AliMohamedAhmedMFKE2012.pdf Ahmed Abuassal, Ali Mohamed (2012) Flow regime identification of particles conveying in pneumatic pipeline using electric charge tomography and neural network techniques. Masters thesis, Universiti Teknologi Malaysia, Faculty of Electrical Engineering. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:67947?site_name=Restricted Repository
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Ahmed Abuassal, Ali Mohamed
Flow regime identification of particles conveying in pneumatic pipeline using electric charge tomography and neural network techniques
description A plastic beads (solid particles) flow in a pipeline is a common means of transportation in industries. Monitoring and controlling materials flow through the pipeline is essential to ensure plant efficiency and safety of the system. The pipeline transportation used in this project makes use of electrodynamic sensors which are charge to voltage converters. The process flow data is captured fitting an array of 16 sensors around the circumference of the pipe to capture the inherent charge on the flowing solid materials. A high speed data acquisition card DAS1800HC is used as the interface between the sensors and a personal computer which processes the data. A Radial Basis Function (RBF) neural network based flow regime identifier program is developed in Matlab environment. Baffles of different shapes are inserted to artificially create expected flow regimes and data captured in this way are used in training and evaluating the network’s performance. The results of this work show significant improvments, the dataset which was check as the input gave good results, especially for full flow, three quarter flow and inverse quarter flow are 100%, and 95% has been succeed for each of quarter flow inverse three quarter flow and inverse half flow, and for the others flow regimes (center half and half flow) 90% succeed.
format Thesis
author Ahmed Abuassal, Ali Mohamed
author_facet Ahmed Abuassal, Ali Mohamed
author_sort Ahmed Abuassal, Ali Mohamed
title Flow regime identification of particles conveying in pneumatic pipeline using electric charge tomography and neural network techniques
title_short Flow regime identification of particles conveying in pneumatic pipeline using electric charge tomography and neural network techniques
title_full Flow regime identification of particles conveying in pneumatic pipeline using electric charge tomography and neural network techniques
title_fullStr Flow regime identification of particles conveying in pneumatic pipeline using electric charge tomography and neural network techniques
title_full_unstemmed Flow regime identification of particles conveying in pneumatic pipeline using electric charge tomography and neural network techniques
title_sort flow regime identification of particles conveying in pneumatic pipeline using electric charge tomography and neural network techniques
publishDate 2012
url http://eprints.utm.my/id/eprint/32099/5/AliMohamedAhmedMFKE2012.pdf
http://eprints.utm.my/id/eprint/32099/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:67947?site_name=Restricted Repository
_version_ 1643648940574244864
score 13.211869

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