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首页> 外文期刊>SPE Production and Facilities >Artificial Neural Network Models for Identifying Flow Regimes and Predicting Liquid Holdup in Horizontal Multiphase Flow
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Artificial Neural Network Models for Identifying Flow Regimes and Predicting Liquid Holdup in Horizontal Multiphase Flow

机译：人工神经网络模型识别流态并预测水平多相流中的持液量

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摘要

This paper presents two artificial neural network (ANN) models to identify the flow regime and calculate the liquid holdup in horizontal multiphase flow. These models are developed with 199 experimental data sets and with three-layer back-propagation neural networks (BPNs). Superficial gas and liquid velocities, pressure, temperature, and fluid properties are used as inputs to the model. Data were divided into three portions: training, cross validation, and testing. The results show that the developed models provide better predictions and higher accuracy than the empirical correlations developed specifically for these data groups. The developed flow-regime model predicts correctly for more than 97% of the data points. The liquid-holdup model outperforms the published models; it provides holdup predictions with an average absolute percent error of 9.407, a standard deviation of 8.544, and a correlation coefficient of 0.9896.
机译：本文提出了两种人工神经网络（ANN）模型，用于识别流态并计算水平多相流中的持液量。这些模型使用199个实验数据集和三层反向传播神经网络（BPN）开发。表观气体和液体的速度，压力，温度和流体性质被用作模型的输入。数据分为三个部分：训练，交叉验证和测试。结果表明，与专门为这些数据组开发的经验相关性相比，开发的模型提供了更好的预测和更高的准确性。建立的流态模型可以正确预测97％以上的数据点。持液量模型优于已发布的模型；它提供的保留预测的平均绝对百分比误差为9.407，标准偏差为8.544，相关系数为0.9896。

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