Mass Flow Measurement of Fine Particles in a Pneumatic Suspension using Electrostatic Sensing and Neural Network Techniques

Yan, Y. and Xu, L. and Lee, P. (2006) Mass Flow Measurement of Fine Particles in a Pneumatic Suspension using Electrostatic Sensing and Neural Network Techniques. IEEE Transactions on Instrumentation & Measurement, 55 (6). pp. 2330-2334. ISSN 0018-9456 . (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/10.1109/TIM.2006.887040

Abstract

In this paper, a novel approach is presented to the measurement of velocity and mass How rate of pneumatically conveyed solids using electrostatic sensing and neural network techniques. A single ring-shaped electrostatic sensor is used to derive a signal, from which two crucial parameters-velocity and mass flow rate of solids-may be determined for the purpose of monitoring and control. It is found that the quantified characteristics of the signal are related to the velocity and mass flow rate of solids. The relationships between the signal characteristics and the two measurands are established through the use of backpropagation (BP) neural networks. Results obtained on a laboratory test rig suggest that an electrostatic sensor in conjunction with a trained neural network may provide a simple, practical solution to the long-standing industrial measurement problem.

Item Type: Article
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA166 Instrumentation
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Yiqing Liang
Date Deposited: 12 Sep 2008 07:20
Last Modified: 21 May 2011 23:49
Resource URI: http://kar.kent.ac.uk/id/eprint/9775 (The current URI for this page, for reference purposes)
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