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 (X_LM) 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.

在线测量湿气中的气体和液体流速具有重要意义。本文提出了一种通过结合锥形节流阀装置和机器学习技术来测量湿气的气体和液体流速的新方法。锥形装置的当量直径比为0.45。实验在直径为50 mm的水平管中进行，工作压力范围为100 kPa至250 kPa。工作流体是具有Lockhart-Martinelli参数（X _LM）小于0.3。多层前馈神经网络用于开发测量模型。该模型需要具有代表性的特征作为输入，并使用气体和液体的流速作为输出。除了永久压力损失和上游喉管压差的平均值外，上游喉管压差波动的概率密度函数（PDF）和功率谱密度（PSD）也被提取为代表特征。使用主成分分析方法，可以获得独立的PDF和PSD特征。未经训练的数据集用于评估神经网络模型的性能。流速的预测与实验非常吻合。气体和液体流速的平均相对误差分别为0.05％和-3.66％。