Unsupervised-feature extraction of gas-liquid two-phase flow pattern based on convolutional autoencoder: principle and application

doi:10.11949/0438-1157.20231038

Abstract

Abstract:

In two-phase flow measurements, the accurate identification of the flow pattern is the basis for the measurement of pressure drop, heat transfer and other thermal parameters. Traditional two-phase flow methods have limited applicability under different operating conditions due to the limitations of test conditions and data. Artificial intelligence algorithms can take into account both efficiency and accuracy, but the feature extraction method is still the difficulty in its identification. The accurate identification of flow patterns is of great significance for interpreting data, improving models, and improving application effects. Therefore, this paper proposes a feature extraction method based on unsupervised learning of convolutional autoencoder, which inputs the extracted features into random forest, support vector machine, and back propagation neural network classifiers for classification, respectively. The experimental results show that the recognition accuracy for all four stream types reaches more than 99%, which indicates that the convolutional autoencoder feature extraction method can significantly improve the accuracy of the classification algorithm, has good compatibility with different classifiers, and also provides help for the feature extraction method for subsequent popular recognition.

Key words: gas-liquid flow, bubble, neural networks, algorithm

摘要：

在气液两相流的测量中，流型的准确识别是压降、换热等热工参数测量的基础。传统的两相流方法由于试验条件和数据的局限性使得在不同工况下的适用性有限。人工智能算法可以同时兼顾效率和精度，但特征提取方法仍是识别的难点。流型的准确识别对于解释数据，改进模型，以及提高应用效果方面具有重要意义。因此提出了一种基于卷积自编码器的无监督学习的特征提取方法，将特征提取后分别输入到随机森林，支持向量机以及前馈神经网络分类器中进行分类。实验结果表明，对四种流型的识别精度都达到了99%以上，说明卷积自编码器特征提取方法能显著地提高分类算法的准确率，对不同的分类器具有很好的兼容性，也为今后流型识别的特征提取方法提供了帮助。

关键词: 气液两相流, 气泡, 神经网络, 算法

CLC Number:

TQ 028.8

Sirui CHEN, Jingliang BI, Lei WANG, Yuanyuan LI, Gui LU. Unsupervised-feature extraction of gas-liquid two-phase flow pattern based on convolutional autoencoder: principle and application[J]. CIESC Journal, 2024, 75(3): 847-857.

陈思睿, 毕景良, 王雷, 李元媛, 陆规. 气液两相流流型特征无监督提取的卷积自编码器：机理及应用[J]. 化工学报, 2024, 75(3): 847-857.

Figures/Tables 23

Fig.1 Autoencoder of two-layer network structure

Fig.2 Deep autoencoder network structure

Fig.3 Training process of deep autoencoder

Fig.4 Convolutional autoencoder network structure

Fig.5 Random forest network structure

Fig.6 Network structure of BP classifier

Fig.7 Data preprocessing process

Fig.8 Flow pattern recognition structure

Table 1 Numerical representation of flow pattern

流型	数值表示
泡状流	[1, 0, 0, 0]
塞状流	[0, 1, 0, 0]
弥散流	[0, 0, 1, 0]
环状流	[0, 0, 0, 1]

Fig.9 Autoencoder training loss function

Fig.10 Feature extraction visualization

Table 2 Classifier index

指标	概念
精确度（precision）	查准率，表示所有被预测少数类的样本中，真正的少数类所占的比例。精确度越低代表判错了越多的多数类。是将多数类判错后所需付出成本的衡量
召回率（recall）	查全率，表示所有真实为少数类的样本中，被预测正确的样本所占的比例。越低代表捕捉到了越少的少数类
F1分数（F1 score）	精确度和召回率的调和平均

Fig.11 Hyperparameter learning curve of random forest classification

Fig.12 Results of random forest classification

Fig.13 Classification results after feature extraction by convolutional autoencoder

Table 3 Evaluation of the effect of convolutional autoencoder

流型类别	Random forest	RF+Con_AE
泡状流	90.16%	98.39%
塞状流	89.09%	98.15%
弥散流	91.34%	100.00%
环状流	95.17%	100.00%
样本个数	252	252

Fig.14 Comparison of feature extraction effects of convolutional autoencoder

Fig.15 Overall recognition effect of convective type on convolutional autoencoder

Fig.16 Confusion matrix of feature classifier

Table 4 Effect evaluation of different classifiers

分类器	查准率/%	查全率/%	F1分数/%	样本个数
random forest	99.09	99.21	99.13	252
SVM	99.21	99.21	99.13	252
BP	99.18	99.21	99.18	252

Fig.17 Training loss function of BP classifier

Fig.18 Training accuracy of BP classifier

Fig. 19 Classification results of four kinds of stream images by different classifiers

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