Wet gas metering by cone throttle device with machine learning. (November 2020)

• Record Type: Journal Article
• Authors: Li, Shanshan; Zhao, Fan; Zheng, Xuebo; He, Denghui; Bai, Bofeng
• Is Part Of: Measurement. Volume 164(2020)
• Publication Date: 2020-11
• Subjects: Wet gas -- Flow rate measurement -- Cone throttle device -- Principal component analysis -- Neural networks; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.108080
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A high-accuracy method for measuring flow rates in wet gas is proposed. It is based on the combination of the cone throttle device and the neural networks. PDF and PSD are applied to the differential pressure fluctuation of the cone. PCA are used to extract independent features to the neural networks. Abstract: The online measurement of gas and liquid flow rates in wet gas is of great significance. This paper presents a new method to measure gas and liquid flow rates of wet gas by combining the cone throttle device and machine learning techniques. The equivalent diameter ratio of the cone device is 0.45. Experiments are carried out in a horizontal pipe of diameter 50 mm and the operating pressure ranges from 100 kPa to 250 kPa. The working fluids are the mixture of air and water with the Lockhart-Martinelli parameter (XLM ) less than 0.3. The multilayer feedforward neural network is used for developing the measurement model. The model requires representative features as inputs and uses the gas and liquid flow rates as outputs. In addition to the mean values of the permanent pressure loss and the upstream-throat differential pressure, the probability density function (PDF) and power spectral density (PSD) of the upstream-throat differential pressure fluctuation are also extracted as representative features. With the principal component analysis method, the independent PDF and PSD features are obtained. The untrained dataset is used to evaluate the performance of the neural network model. Predictions of the flow rates are in good agreement with the experiments. The mean relative errors of the gas and liquid flow rates are 0.05% and −3.66%, respectively. The results show that the proposed method is capable of establishing the implicit correlations between the characteristic parameters of wet gas and the corresponding flow rates.