Application of extreme learning machine to gas flow measurement with multipath acoustic transducers

Abstract

To reduce the large integration errors brought by traditional methods in gas flow measurement under complex flow field, artificial neural network, support vector machine and other intelligent algorithms have been put into use. But these intelligent methods consume much time for training and need intensive user intervention for network design. This paper proposes to apply extreme learning machine to multipath ultrasonic flowmeters, which can analytically determine the output weights of networks instead of error backpropagation algorithm and iterative tuning of the parameters, and therefore provide high metering accuracy at extremely fast learning speed as well as require least human intervention. To test its effectiveness under different flow field and sensitivity to complex flow profiles, extreme learning machine is applied to determine the flow rate under two piping configurations, which can produce mild and severe flow disturbances. The determination errors are compared with a traditional integration method on the position of 5D and 10D as well as with the path orientation of 0° and 90°. Then 7 installation angles and 9 installation positions are respectively configured to study the performance and sensitivity of UFMs to installation effects. Finally, a comparison between extreme learning machine and other two intelligent algorithms is made in training and test time, the mean squared error and the maximal metering error under severe flow disturbance. It is found that extreme learning machine has rather high determination accuracy for flow rate at extremely fast learning speed and it is insensitive to the installation effects of ultrasonic gas flowmeter.