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Neuro-fuzzy technique application for identifying flow regimes of particles conveying in pneumatic pipeline

The desire to satisfy demand of industrial sector by improving product quality and reducing environmental emission leads up to identify and monitor the behaving of the internal flows inside pipelines. The flow of solid particles through pipeline in vertical gravity flow rig system has been monitored...

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Main Author: Elsawi Khairalla, Mutaz Mohamed Elhassan
Format: Thesis
Language:English
Published: 2012
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/34693/1/MutazMohamedElhassanElsawiKhairallaMFKE2012.pdf
http://eprints.utm.my/id/eprint/34693/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:83152?queryType=vitalDismax&query=+Neuro-fuzzy+technique+application+for+identifying+flow+regimes+of+particles+conveying+in+pneumatic+pipeline++&public=true
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spelling my.utm.346932021-07-21T01:52:15Z http://eprints.utm.my/id/eprint/34693/ Neuro-fuzzy technique application for identifying flow regimes of particles conveying in pneumatic pipeline Elsawi Khairalla, Mutaz Mohamed Elhassan TK Electrical engineering. Electronics Nuclear engineering The desire to satisfy demand of industrial sector by improving product quality and reducing environmental emission leads up to identify and monitor the behaving of the internal flows inside pipelines. The flow of solid particles through pipeline in vertical gravity flow rig system has been monitored by 16-electrodynamic sensors that measure the charge carried by solid particles. The identification model has been built and developed based on the training of the captured data at different flow patterns. The final identification model consists of four ANFIS based fuzzy C-means clustering where every ANFIS is able to identify the presence of the flow inside specific quarter in the cross section of the pipe. It is shown that the four ANFIS models are able to work simultaneously to provide the expected output after applying simple thresholding for the ANFIS’ output. The identification model has been evaluated by ten different types of flow patterns. The accuracy of the identification model has improved at higher flow rate. As a result, the identified flow pattern has been used to acquire the concentration profile by using filtered back projection. The successful ANFIS model can be extended for horizontal pipeline to present the percentage of flow inside the pipe. 2012 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/34693/1/MutazMohamedElhassanElsawiKhairallaMFKE2012.pdf Elsawi Khairalla, Mutaz Mohamed Elhassan (2012) Neuro-fuzzy technique application for identifying flow regimes of particles conveying in pneumatic pipeline. Masters thesis, Universiti Teknologi Malaysia, Faculty of Electrical Engineering. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:83152?queryType=vitalDismax&query=+Neuro-fuzzy+technique+application+for+identifying+flow+regimes+of+particles+conveying+in+pneumatic+pipeline++&public=true
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 TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Elsawi Khairalla, Mutaz Mohamed Elhassan
Neuro-fuzzy technique application for identifying flow regimes of particles conveying in pneumatic pipeline
description The desire to satisfy demand of industrial sector by improving product quality and reducing environmental emission leads up to identify and monitor the behaving of the internal flows inside pipelines. The flow of solid particles through pipeline in vertical gravity flow rig system has been monitored by 16-electrodynamic sensors that measure the charge carried by solid particles. The identification model has been built and developed based on the training of the captured data at different flow patterns. The final identification model consists of four ANFIS based fuzzy C-means clustering where every ANFIS is able to identify the presence of the flow inside specific quarter in the cross section of the pipe. It is shown that the four ANFIS models are able to work simultaneously to provide the expected output after applying simple thresholding for the ANFIS’ output. The identification model has been evaluated by ten different types of flow patterns. The accuracy of the identification model has improved at higher flow rate. As a result, the identified flow pattern has been used to acquire the concentration profile by using filtered back projection. The successful ANFIS model can be extended for horizontal pipeline to present the percentage of flow inside the pipe.
format Thesis
author Elsawi Khairalla, Mutaz Mohamed Elhassan
author_facet Elsawi Khairalla, Mutaz Mohamed Elhassan
author_sort Elsawi Khairalla, Mutaz Mohamed Elhassan
title Neuro-fuzzy technique application for identifying flow regimes of particles conveying in pneumatic pipeline
title_short Neuro-fuzzy technique application for identifying flow regimes of particles conveying in pneumatic pipeline
title_full Neuro-fuzzy technique application for identifying flow regimes of particles conveying in pneumatic pipeline
title_fullStr Neuro-fuzzy technique application for identifying flow regimes of particles conveying in pneumatic pipeline
title_full_unstemmed Neuro-fuzzy technique application for identifying flow regimes of particles conveying in pneumatic pipeline
title_sort neuro-fuzzy technique application for identifying flow regimes of particles conveying in pneumatic pipeline
publishDate 2012
url http://eprints.utm.my/id/eprint/34693/1/MutazMohamedElhassanElsawiKhairallaMFKE2012.pdf
http://eprints.utm.my/id/eprint/34693/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:83152?queryType=vitalDismax&query=+Neuro-fuzzy+technique+application+for+identifying+flow+regimes+of+particles+conveying+in+pneumatic+pipeline++&public=true
_version_ 1706956949749235712
score 13.211869

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