Solid Concentration Estimation by Kalman Filter †
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in Tan, Y.; Yue, S. Solid Component Fraction in Multi-Phase Flows Using Electrical Resistance Tomography and Kalman Filter. In Proceedings of the 11th International Symposium on Measurement Techniques for Multiphase Flows (ISMTMF), Zhenjiang, China, 3–7 November 2019.
Sensors 2020, 20(9), 2657; https://doi.org/10.3390/s20092657
Received: 31 March 2020
/
Revised: 25 April 2020
/
Accepted: 4 May 2020
/
Published: 6 May 2020
(This article belongs to the Special Issue Selected papers from ISMTMF-2019)
One of the major tasks in process industry is solid concentration (SC) estimation in solid–liquid two-phase flow in any pipeline. The γ-ray sensor provides the most used and direct measurement to SC, but it may be inaccurate due to very local measurements and inaccurate density baseline. Alternatively, under various conditions there are a tremendous amount of indirect measurements from other sensors that can be used to adjust the accuracy of SC estimation. Consequently, there is complementarity between them, and integrating direct and indirect measurements is helpful to improve the accuracy of SC estimation. In this paper, after recovering the interrelation of these measurements, we proposed a new SC estimation method according to Kalman filter fusion. Focusing on dredging engineering fields, SCs of representative flow pattern were tested. The results show that our proposed methods outperform the fused two types of measurements in real solid–liquid two-phase flow conditions. Additionally, the proposed method has potential to be applied to other fields as well as dredging engineering.
View Full-Text
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Tan, Y.; Yue, S. Solid Concentration Estimation by Kalman Filter. Sensors 2020, 20, 2657. https://doi.org/10.3390/s20092657
AMA Style
Tan Y, Yue S. Solid Concentration Estimation by Kalman Filter. Sensors. 2020; 20(9):2657. https://doi.org/10.3390/s20092657
Chicago/Turabian StyleTan, Yongguang, and Shihong Yue. 2020. "Solid Concentration Estimation by Kalman Filter" Sensors 20, no. 9: 2657. https://doi.org/10.3390/s20092657
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
