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

In this paper, a novel approach is presented to the measurement of velocity and mass flow 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