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

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 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.