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

Abstract

In this paper, a novel approach is presented to the measurement of velocity and mass flow rate of pneumatically conveyed fine particles using electrostatic sensing and neural network techniques. A single ring-shaped electrostatic sensor is used to obtain a signal from which the two crucial parameters, velocity and mass flow rate of particles, may be derived 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 particles. The relationships between the signal characteristics and the two measurands are established through the use of a back-propagation (BP) neural network. 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