Sensor Field Segmentation And Structural Parameter Optimization Of ECT System

Electrical capacitance tomography(Electrical Capacitance Tomography,ECT)technology is a new type of flow tomography technology with low cost and high safety performance developed from the medical CT technology in the 1980 s.It can use an array capacitive sensor evenly installed around the pipeline to obtain multiple capacitance measurements of the measured area at different angles or directions without destroying the structure of the multiphase flow mixture in the closed pipeline.The capacitance measurement value is used as the projection data by the computer to use an image reconstruction algorithm to calculate the relevant process parameters such as the concentration and distribution of each phase separation medium.Because of its simple structure,fast response and many other advantages,it is widely used in the measurement process of two-phase flow or multi-phase flow in industrial pipelines.This paper focuses on the optimization of sensor field segmentation and structural parameter optimization of ECT system.The main work of the paper is as follows:The composition of the ECT system is studied,and the working principle and sensitive distribution function of the ECT system are analyzed to provide a theoretical basis for the thesis research.Analyze the mathematical model of the capacitive sensor,use the finite element method to simulate the sensitive field of the sensor,according to the distribution characteristics of the sensitive field,combine the triangle and quadrilateral splitting method,complete the splitting of the sensitive field,and select the finite element model with high calculation accuracy and small error to calculate the sensitive field function to get the typical sensitive field distribution between the electrodes.The finite element method was used to analyze the influence of variousstructural parameters of the sensor on the performance,and the optimization function of the sensor structure was determined.The orthogonal design method was used to optimize the structural parameters,and the RBF neural network was used to perform a regression analysis on the experimental results of the orthogonal design.The prediction model was obtained to better reflect the complex relationship between the optimization parameters and the objective function.The chaos simulated annealing particle swarm optimization algorithm is used for optimization,and a set of optimization parameters is obtained.In order to verify the effectiveness of the optimized parameters,the reconstruction results of the LBP image reconstruction algorithm are compared with the reference sensor and the sensor designed by orthogonal test.The experimental results show that the sensor imaging accuracy optimized by the RBF neural network and the chaotic simulated annealing particle swarm optimization algorithm higher.