NDLI: A soft-sensing method of gas/liquid mass flow- rate based on hybrid feedback (HF) Elman wavelet neural network

Content Provider	IEEE Xplore Digital Library
Author	Jun Han Feng Dong
Copyright Year	2010
Description	Author affiliation: School of Electrical Engineering & Automation, Tianjin University, China (Jun Han; Feng Dong)
Abstract	In industry, gas/liquid two-phase flow in horizontal pipe is very common in many industry processes, and their measurements are of significance. In our works, a series of gas/water two-phase flow experiments in a horizontal pipe were conducted in which the V-cone differential pressure meter was used, and a soft-sensing method was brought forward in which the hybrid feedback (HF) Elman wavelet neural network was developed to achieve the gas/water mass flow-rate. Experimental results showed that the soft-sensing method combined V-cone with the HF Elman wavelet network could satisfy the demand of gas/liquid mass flow-rate measurement and the error of the mass flow-rate was small. The test results show the adaptability of the HF Elman wavelet network is better. The method is attractive for possible applications in measurement.
Starting Page	760
Ending Page	765
File Size	1826946
Page Count	6
File Format	PDF
ISBN	9781424428328
ISSN	10915281
DOI	10.1109/IMTC.2010.5488292
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurofeedback Hafnium Neural networks Fluid flow Fluid flow measurement Differential equations Pressure measurement Recurrent neural networks Electronic mail Gas industry V-cone hybrid feedback Elman wavelet neural network differential pressure gas/liquid two-phase flow mass flow-rate
Content Type	Text
Resource Type	Article
Subject	Instrumentation Electrical and Electronic Engineering

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