Wet Gas Meter Development Based on Slotted Orifice Couple and Neural Network Techniques

Abstract

A slotted orifice has many superiorities over a standard orifice. For single-phase flow measurement, its flow coefficient is insensitive to the upstream velocity profile. For two phase flow measurement, various characteristics of its differential pressure (DP) are stable and closely correlated with the mass flow rate of gas and liquid. The complex relationships between the signal features and the two-phase flow rate are established through the use of a back propagation (BP) neural network. Experiments were carried out in the horizontal tubes with 50mm inner diameter, operated with water flow rate in the range of 0.2m 3.h -1 to 4m 3.h -1, gas flow rate in the range of 100m 3.h -1 to 1000m 3.h -1, and pressure at 400kPa and 850kPa respectively, where the temperature is ambient temperature. This article includes the principle of wet gas meter development, the experimental matrix, the signal processing techniques and the achieved results. On the basis of the results it is suggested that the slotted orifice couple with a trained neural network may provide a simple but efficient solution to the wet gas meter development.