A generalized reconstruction algorithm for electrical capacitance tomography

J. Lei, S. Liu

Key Laboratory of Power System Protection and Dynamic Security Monitoring and Control, Ministry of Education, North China Electric Power University, Changping District, Beijing 102206, China, leijing2002@gmail.com.

ABSTRACT

Owing to the advantages, such as high speed, low cost, high safety, easy implementation and non-intrusive sensing, electrical capacitance tomography (ECT) is considered as a promising process tomography technology. Successful applications of ECT depend on the speed and precision of the image reconstruction algorithms. In essence, ECT image reconstruction process belongs to a typical ill-posed problem. Methods that ensure a stable numerical solution while enhancing the quality of the reconstructed images should be employed. In this paper, based on the semiparametric model, a generalized objective functional that considers the inaccuracy nature in the measured capacitance data and the model error, which has been developed using the combination estimation and a novel stabilizing functional, is created. An efficient algorithm, which integrates the beneficial advantages of the homotopy method where the homotopy equation is designed by the fixed point homotopy and solved using the fixed point iteration algorithm based on the alternate iteration scheme, the self organizing migration algorithm and the harmony search algorithm, is proposed for searching a possible global optimal solution. This algorithm is tested by the noise-free capacitance data and the noise-contaminated capacitance data, and excellent numerical performances and encouraging results are observed. Numerical simulation results reveal that the proposed algorithm is very successful in solving the ECT image reconstruction problem. In the cases considered in this paper, the reconstructed results by the proposed algorithm show salient improvement in the spatial resolution and accuracy. The spatial resolution of the reconstructed images by the proposed algorithm is enhanced, and the artifacts in the reconstructed images can be removed effectively. Furthermore, the reconstructed results under the noise-contaminated capacitance data indicate that the proposed algorithm is very competent to deal with the inaccuracy in the capacitance data. Consequently, a promising algorithm is introduced for ECT image reconstruction.

Keywords Electrical capacitance tomography; Semiparametric model; self organizing migration algorithm; Harmony search

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