Two-Phase Flow Parameter Measurement Using Multi-Task Learning and Electrical Capacitance Tomography

YANG Haichao; HU Hongli; LU Chengcheng; LU Jiayu; WANG Maojie

doi:10.7652/xjtuxb202303019

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Two-Phase Flow Parameter Measurement Using Multi-Task Learning and Electrical Capacitance Tomography

更新时间：2025-07-09

- Two-Phase Flow Parameter Measurement Using Multi-Task Learning and Electrical Capacitance Tomography
- Vol. 57, Issue 3, Pages: 202-211(2023)
- 作者机构：
  
  西安交通大学电力设备电气绝缘国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202303019
  CLC： TP183
- Online First：10 March 2023，
  
  Published：2023
- 稿件说明：
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YANG Haichao, HU Hongli, LU Chengcheng, et al. Two-Phase Flow Parameter Measurement Using Multi-Task Learning and Electrical Capacitance Tomography[J]. 2023, 57(3): 202-211.
DOI：

YANG Haichao, HU Hongli, LU Chengcheng, et al. Two-Phase Flow Parameter Measurement Using Multi-Task Learning and Electrical Capacitance Tomography[J]. 2023, 57(3): 202-211. DOI： 10.7652/xjtuxb202303019.

摘要

针对电容层析成像技术(ECT)重建的图像精度低、对流动参数测量误差较大的问题

提出了一种采用多任务学习和电容层析成像的两相流参数测量方法。首先

采用残差模块作为特征提取模块对Landweber算法重构的图像进行特征提取

以此消除重建图像的伪影对流动参数测量所带来的干扰; 其次

为了进一步提升模型的抗噪声干扰能力

采用硬参数共享的双路输出神经网络结构进行初次相含率测量; 之后

采用融合神经网络与极端随机森林等分类模型的方法来实现流型识别; 最后

为了进一步提升相含率测量的准确度

采用极端随机森林等回归模型通过融合流型识别和初次相含率测量的结果

获得最终的相含率。实验结果表明:该方法相对于仅采用神经网络的方法

可以提升相含率测量的准确度; 在实测数据测试中

该方法进行相含率测量的相对误差在5%以内

流型识别的准确度可以达到98%以上

表现出了较好的抗噪声干扰能力

能够满足对实际工业应用的需求。

Abstract

A two-phase flow parameter measurement method

which combines multi-task learning and electrical capacitance tomography

is proposed to solve the problem of the interference in parameter measurement caused by the low accuracy of the images reconstructed by the reconstruction algorithm in terms of electrical capacitance tomography. Firstly

the residual module is used as the feature extraction module to extract the features of the images reconstructed by the Landweber algorithm

so as to eliminate the interference of artifacts of reconstructed images on parameter measurement. Secondly

a dual output multi-task neural network with hard parameter sharing is built to finish the task of initial measurement of the phase fraction in order to further improve the anti-noise ability of the model. Then

a method of fusing neural network and classification models

such as extreme random forests

is proposed to achieve flow identification. Finally

regression models such as extreme random forests are used to obtain the final phase fraction by fusing the results of flow pattern identification and initial results of the phase fraction in order to further improve the accuracy of phase fraction measurement. The experimental results show that the proposed algorithm can improve the accuracy of phase fraction measurement when compared with the method of only using the neural network. In the actual measurement

the relative error of phase fraction measured by the proposed algorithm is within 5%

and the accuracy of flow identification is beyond 98%

which shows a good anti-noise ability of the model and can meet the demands of practical industrial applications.

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references

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