scholarly journals Solid Concentration Estimation by Kalman Filter

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2657 ◽  
Author(s):  
Yongguang Tan ◽  
Shihong Yue

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.

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5697
Author(s):  
Chang Sun ◽  
Shihong Yue ◽  
Qi Li ◽  
Huaxiang Wang

Component fraction (CF) is one of the most important parameters in multiple-phase flow. Due to the complexity of the solid–liquid two-phase flow, the CF estimation remains unsolved both in scientific research and industrial application for a long time. Electrical resistance tomography (ERT) is an advanced type of conductivity detection technique due to its low-cost, fast-response, non-invasive, and non-radiation characteristics. However, when the existing ERT method is used to measure the CF value in solid–liquid two-phase flow in dredging engineering, there are at least three problems: (1) the dependence of reference distribution whose CF value is zero; (2) the size of the detected objects may be too small to be found by ERT; and (3) there is no efficient way to estimate the effect of artifacts in ERT. In this paper, we proposed a method based on the clustering technique, where a fast-fuzzy clustering algorithm is used to partition the ERT image to three clusters that respond to liquid, solid phases, and their mixtures and artifacts, respectively. The clustering algorithm does not need any reference distribution in the CF estimation. In the case of small solid objects or artifacts, the CF value remains effectively computed by prior information. To validate the new method, a group of typical CF estimations in dredging engineering were implemented. Results show that the new method can effectively overcome the limitations of the existing method, and can provide a practical and more accurate way for CF estimation.


2007 ◽  
Vol 27 (Supplement1) ◽  
pp. 109-110
Author(s):  
Junichi UEMATSU ◽  
Kazuya ABE ◽  
Xiaoran YU ◽  
Tatsuya HAZUKU ◽  
Masaki OSHIMA ◽  
...  

1982 ◽  
Vol 15 (4) ◽  
pp. 311-313 ◽  
Author(s):  
HIROYASU OHASHI ◽  
TAKUO SUGAWARA ◽  
KEN-ICHI KIKUCHI ◽  
MORITO TAKEDA

2015 ◽  
Author(s):  
Xiangji Dou ◽  
Xinwei Liao ◽  
Xiaofeng Li ◽  
Hongmei Liao ◽  
Xiangnan He ◽  
...  

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