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
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id	my-utm-ep.32099
record_format	uketd_dc
spelling	my-utm-ep.320992017-09-30T08:34:44Z Flow regime identification of particles conveying in pneumatic pipeline using electric charge tomography and neural network techniques 2012-07 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 http://eprints.utm.my/id/eprint/32099/ http://eprints.utm.my/id/eprint/32099/5/AliMohamedAhmedMFKE2012.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:67947?site_name=Restricted Repository masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
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
qualification_level	Master's degree
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
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2012
url	http://eprints.utm.my/id/eprint/32099/5/AliMohamedAhmedMFKE2012.pdf
_version_	1747815921128308736

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

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