Skip to main content
Kent Academic Repository

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

Xu, Lijun, Carter, Robert M., Yan, Yong (2005) Mass Flow Measurement of Fine Particles in a Pneumatic Suspension using Electrostatic Sensing and Neural Network Techniques. In: IMTC 2005- Instrumentation and Measurement Technology Conference. . pp. 1365-1368. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:8918)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.

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: Conference or workshop item (Paper)
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA165 Engineering instruments, meters etc. Industrial instrumentation
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Yiqing Liang
Date Deposited: 15 Aug 2009 20:21 UTC
Last Modified: 16 Nov 2021 09:46 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/8918 (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.