Yao Danya

The use of simultaneous iterative reconstruction technique for electrical capacitance tomography

Abstract High-quality reconstruction of capacitance tomography data is important to get the quantitative information from the cross sectional images of the multiphase pipe flow. Due to the complex nature of the capacitance sensors, the reconstruction algorithms well developed for medical tomography are not applicable. The main problems lie in two aspects. One is the ‘soft-field’ effect, the other is the limited number of measurements. To resolve these problems and get high-quality images, a reconstruction algorithm, named simultaneous iterative reconstruction technique (SIRT), often used in geology investigation, is introduced. According to the fastness of the process tomography and the smoothing effect of SIRT, some improvements have been made. These methods are compared to the well-known linear back projection algorithm (LBP) and the linear back projection thresholded algorithm widely used in capacitance tomography. A computer simulated and real eight-electrode capacitance tomography system has been used for the evaluation.

Recognizing Flow Pattern of Gas/Liquid Two-component Flow Using Fuzzy Logical Neural Network

Publisher Summary Two-component flow is very common in many industry areas such as power plants, steel factories, and chemical manufactures. Because of its complex flowing states and property, it is very difficult to measure two-component flow using traditional detecting method and the measurement accuracy is usually much lower. This is unfavorable to industrial practice. This chapter describes a new method based on fuzzy neural network which is used to recognize the two-component flow pattern. It discusses the structure of the fuzzy neural network, including the selection of the fuzzy logical rule and the training sets. An accelerated learning algorithm is used to train the neural network to shorten its learning time. After computer simulation has been done, it is found that this new method can recognize four typical flow patterns existing in the gas/liquid two-component flow: stratified flow, annular flow, slug flow, and bubble flow. Results useful for the future work are also presented in the chapter.