Evaluation of Landweber Coupled Least Square Support Vector Regression Algorithm for Electrical Capacitance Tomography for LN2–VN2 Flow

• Published in: Energies (Basel) Vol. 16; no. 22; p. 7661
• Main Authors: Tian, Ze-Nan, Gao, Xin-Xin, Xia, Tao, Zhang, Xiao-Bin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2023
• Subjects: algorithm; Algorithms; Carbon dioxide; cryogenic; ECT; Electrodes; experiment; Experiments; Fluids; Heat; LSSVR; Measurement techniques; Neural networks; Sensors; Tomography
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en16227661

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 (LN2-VN2). 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.