关键词: two-phase flow, voidage measurement, electrical resistance tomography sensor, data mining, feature extraction

Abstract: 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 in-dependent component analysis (ICA). In the practical voidage measurement process, the flow pattern is firstly iden-tified 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 over-come. Besides, it is demonstrated that the LS-SVM method with PLS feature extraction presents the best measure-ment performance among the tested methods.

Key words: two-phase flow, voidage measurement, electrical resistance tomography sensor, data mining, feature extraction