Capacitance Tomography Study

Electronic Capacitance Tomography (ECT) is developed as a new technique for monitoring multiphase flow from the middle of 80th year last century. ECT system can reconstruct the image which describes distributing of medium inside the container by measuring capacitance between two discretional electrodes being fixed outside the container.There are three main difficulties in ECT system: The central part in hardware system is the capacitance transducer of which geometrical and material parameters influence the whole system performance effectively, confirming of the optimizing parameter is one of most important points of ECT system; The point and difficulty of Data Check System is the design of the circuitry which is used to measure low value capacitance between 10 fF to some thousands fF, so, excellent ability of anti-jamming and enough wide measuring bound of this system make the design of small capacitance measurement circuit to be more difficult; To confirm the method of image reconstruction algorithm is key to computer image reconstruction system of which the aim is to improve effectively the quality of image on the base of contenting actual needs, because of the limited measuring data from so little electrodes, the image reconstruction using this data is very difficult.On the basis of multi-ingredient, this paper adopts orthogonal designing method to design the optimizing parameter transducer, and achieves a set of optimizing parameters quickly; In comparison with AC measuring circuit, this paper chooses DCmeasuring circuit——charge/discharge method as the checking circuit according toactual hardware level and needs, the charge/discharge circuit has excellent ability of anti-jamming and gets measuring results fast and accurately; In order to confirm the method of image reconstruction, this paper analyses the root of ill-pose property by means of the decomposition of singular value firstly, on base of above analysis, this paper introduces the transcendental information to reduce infection of ill-pose property and completes image reconstruction. Simulation results illustrate that this image reconstruction method has more distinct improvement of quality of image than LBP method, and in comparison with SIRT method or Neural Network method this method is simpler an more excellent adaptability in case of equality image quality, so the method based on multiple linear regression and regularization is very practical.