Evaluation of Landweber Coupled Least Square Support Vector Regression Algorithm for Electrical Capacitance Tomography for LN 2 –VN 2 Flow

Ze-Nan Tian, Xin-Xin Gao, Tao Xia and Xiao-Bin Zhang ()
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Ze-Nan Tian: Institute of Refrigeration and Cryogenics, Zhejiang University, Hangzhou 310027, China
Xin-Xin Gao: Institute of Refrigeration and Cryogenics, Zhejiang University, Hangzhou 310027, China
Tao Xia: Institute of Refrigeration and Cryogenics, Zhejiang University, Hangzhou 310027, China
Xiao-Bin Zhang: Institute of Refrigeration and Cryogenics, Zhejiang University, Hangzhou 310027, China

Energies, 2023, vol. 16, issue 22, 1-17

Abstract: The electric capacitance tomography (ECT) technique has been widely used in phase distribution reconstruction, while the practical application raised nonideal noise and other errors for cryogenic conditions, requiring a more accurate algorithm. This paper develops a new image reconstruction algorithm for ECT by coupling the traditional Landweber algorithm with the least square support vector regression (LSSVR) for cryogenic fluids. The performance of the algorithm is quantitatively evaluated by comparing the inversion images with the experimental results for both the room temperature working medium with the dielectric constant ratio close to cryogenic fluid and the cryogenic fluid of liquid nitrogen/nitrogen vapor (LN 2 -VN 2 ). The inversion images based on the conventional LBP and Landweber algorithms are also presented for comparison. The benefits and drawbacks of the developed algorithms are revealed and discussed, according to the results. It is demonstrated that the correlated coefficients of the images based on the developed algorithm reach more than 0.88 and a maximum of 0.975. In addition, the minimum void fraction error of the algorithm is reduced to 0.534%, which indicates the significant optimization of the LSSVR coupled method over the Landweber algorithm.

Keywords: ECT; LSSVR; cryogenic; experiment; algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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