Capacitance Tomography System Experimental Research

Two-phase flow processes exists widely in industry field and the parameter measurement of these processes is very important for resource exploitation or process control.Having many distinct advantages such as non-invade,swift response,low cost,simple structure,Electrical Capacitance Tomography (ECT) has being the most popular research direction and the main development in flow imaging technique. Electrical capacitance tomography can acquire visible 2D/3D distribution information of closed pipeline or vessel.So this technique has great developing potential in industrial process parameter measurement.In this thesis,the electrical capacitance tomography system was improved on.The structure parameter of sensing array electrode was optimized. The resolving power of the system was enhanced.The new measuring method of realizing increased useful datum was applied. Image reconstruction is one of the most important portion of ECT's technologies. The research of fast and accurate image reconstruction's arithmetic is the base of real-time detecting in ECT system.This paper applied Radial Basis Function Neural Network in image reconstruction.Experimental results illustrate that the new method are valid to enhance the capacity of image reconstruction of ECT system.