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

Solid particles flow in a pipeline is a common means of transportation in industries. Pharmaceutical industries, food stuff manufacturing industries, cement and chemical industries are some of the industries to exploit this transportation technique. For such industries, monitoring and controlling ma...

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Main Author:	Ahmed Sabit, Hakilo
Format:	Thesis
Language:	English
Published:	2006
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/5146/1/HakiloAhmedSabitMFKE2006.pdf
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spelling	my-utm-ep.51462018-02-28T07:55:08Z Flow regime identification of particles conveying in pneumatic pipeline using electric charge tomography and neural network techniques 2006-03 Ahmed Sabit, Hakilo TK Electrical engineering. Electronics Nuclear engineering Solid particles flow in a pipeline is a common means of transportation in industries. Pharmaceutical industries, food stuff manufacturing industries, cement and chemical industries are some of the industries to exploit this transportation technique. For such 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 research makes use of electrodynamic sensors which are charge to voltage converters. The process flow data is captured fitting an array of 16 such 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 to interface the sensors to a personal computer which processes the data using linear back projection algorithm (LBPA) and filtered back projection algorithm (FBPA). Data captured for this purpose is in the range of mass flow rates 26 g/s to 204 g/s. A Visual C++ programming language is used to develop an application program to compute the image reconstruction algorithms and display the tomograms which represent the concentration profiles at a measurement crosssection of the pipe. A 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 networkâ€™s performance. This research has produced filtered back concentration profiles of each flow regimes owing to the technique of neural network method of flow regime identification. 2006-03 Thesis http://eprints.utm.my/id/eprint/5146/ http://eprints.utm.my/id/eprint/5146/1/HakiloAhmedSabitMFKE2006.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Ahmed Sabit, Hakilo Flow regime identification of particles conveying in pneumatic pipeline using electric charge tomography and neural network techniques
description	Solid particles flow in a pipeline is a common means of transportation in industries. Pharmaceutical industries, food stuff manufacturing industries, cement and chemical industries are some of the industries to exploit this transportation technique. For such 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 research makes use of electrodynamic sensors which are charge to voltage converters. The process flow data is captured fitting an array of 16 such 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 to interface the sensors to a personal computer which processes the data using linear back projection algorithm (LBPA) and filtered back projection algorithm (FBPA). Data captured for this purpose is in the range of mass flow rates 26 g/s to 204 g/s. A Visual C++ programming language is used to develop an application program to compute the image reconstruction algorithms and display the tomograms which represent the concentration profiles at a measurement crosssection of the pipe. A 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 networkâ€™s performance. This research has produced filtered back concentration profiles of each flow regimes owing to the technique of neural network method of flow regime identification.
format	Thesis
qualification_level	Master's degree
author	Ahmed Sabit, Hakilo
author_facet	Ahmed Sabit, Hakilo
author_sort	Ahmed Sabit, Hakilo
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	2006
url	http://eprints.utm.my/id/eprint/5146/1/HakiloAhmedSabitMFKE2006.pdf
_version_	1747814567588659200

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

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