Voidage Measurement of Air-Water Two-phase Flow Based on ERT Sensor and Data Mining Technology  

基于ERT传感器和数据挖掘技术的空气水两相流空隙率测量(英文)

作　　者：王保良 孟振振 黄志尧 冀海峰 李海青 

机构地区：[1]State Key Laboratory of Industrial Control Technology,Department of Control Science and Engineering,Zhejiang University

出　　处：《Chinese Journal of Chemical Engineering》2012年第2期400-405,共6页中国化学工程学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China (60972138)

摘　　要：Based on an electrical resistance tomography(ERT) sensor and the data mining technology,a new voidage measurement method is proposed for air-water two-phase flow.The data mining technology used in this work is a least squares support vector machine(LS-SVM) algorithm together with the feature extraction method,and three feature extraction methods are tested:principal component analysis(PCA),partial least squares(PLS) and independent component analysis(ICA).In the practical voidage measurement process,the flow pattern is firstly identified directly from the conductance values obtained by the ERT sensor.Then,the appropriate voidage measurement model is selected according to the flow pattern identification result.Finally,the voidage is calculated.Experimental results show that the proposed method can measure the voidage effectively,and the measurement accuracy and speed are satisfactory.Compared with the conventional voidage measurement methods based on ERT,the proposed method doesn't need any image reconstruction process,so it has the advantage of good real-time performance.Due to the introduction of flow pattern identification,the influence of flow pattern on the voidage measurement is overcome.Besides,it is demonstrated that the LS-SVM method with PLS feature extraction presents the best measurement performance among the tested methods.Based on an electrical resistance tomography（ERT） sensor and the data mining technology,a new voidage measurement method is proposed for air-water two-phase flow.The data mining technology used in this work is a least squares support vector machine（LS-SVM） algorithm together with the feature extraction method,and three feature extraction methods are tested：principal component analysis（PCA）,partial least squares（PLS） and independent component analysis（ICA）.In the practical voidage measurement process,the flow pattern is firstly identified directly from the conductance values obtained by the ERT sensor.Then,the appropriate voidage measurement model is selected according to the flow pattern identification result.Finally,the voidage is calculated.Experimental results show that the proposed method can measure the voidage effectively,and the measurement accuracy and speed are satisfactory.Compared with the conventional voidage measurement methods based on ERT,the proposed method doesn＇t need any image reconstruction process,so it has the advantage of good real-time performance.Due to the introduction of flow pattern identification,the influence of flow pattern on the voidage measurement is overcome.Besides,it is demonstrated that the LS-SVM method with PLS feature extraction presents the best measurement performance among the tested methods.

关 键 词：two-phase flow voidage measurement electrical resistance tomography sensor data mining feature extraction 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] U416.217[自动化与计算机技术—计算机科学与技术]