Universiti Teknologi Malaysia Institutional Repository

Flow regime identification and concentration distribution of solid particles flow in pipelines using electrodynamic tomography and artificial neural networks

Rahmat, Mohd Fuaad and Ahmed Sabit, Hakilo (2005) Flow regime identification and concentration distribution of solid particles flow in pipelines using electrodynamic tomography and artificial neural networks. In: Proceeding of the 9th International Conference on Mechatronics Technology, 5-8 December 2005, Kuala Lumpur.

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Abstract

Solid particles flow in a pipeline is a common means of transportation in industries. This is because pipeline transportation can avoid waste through spillage and minimizes the risk of handling of hazardous materials. Pharmaceutical industries, food stuff manufacturing industries, cement and chemical industries are few of the industries to exploit this transportation technique. For such industries, monitoring and controlling material flow through the pipe is an essential element to ensure efficiency and safety of the system. This paper presents electrical charge tomography which is one of the most efficient, robust, cost-effective and noninvasive tomographic methods of monitoring solid particles flow in a pipeline. Process flow data is captured fitting an array of 16-discrete electrodynamic sensors about the circumference of the flow pipe. The data captured is processed using two tomographic algorithms to obtain tomographic images of the flow. Then a neural network tool is used to improve image resolution and accuracy of measurements. The results from the above technique shows significant improvements in the pipe flow image resolution and measurements.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Solid particles flow, pipeline, pipeline transportation, hazardous materials, Pharmaceutical industries, food stuff manufacturing, cement and chemical industries, material flow control, electrical charge tomography, electrodynamic sensors, tomographic algorithm, tomographic images, neural network, image resolution.
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:1848
Deposited By: Dr Zaharuddin Mohamed
Deposited On:15 Mar 2007 08:13
Last Modified:05 Jan 2012 01:59

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