Sensing characteristics of electrostatic inductive sensor for flow parameters measurement of pneumatically conveyed particles

Xu, Chen; Wang, Shimin; Tang, Gan; Yang, Dingyi; Zhou, Bin

doi:10.1016/j.elstat.2007.01.001

Cited by 83 publications

(54 citation statements)

References 14 publications

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“…In order to understand the electrodynamics sensor, a suitable mathematical model is inevitable. The corresponding mathematical models are discussed in detail by the researchers (Xu et al, 2007;Williams and Beck, 1995;Woodhead et al, 2005).…”

Section: Electrodynamics Sensormentioning

confidence: 99%

Image Reconstruction Algorithm for Electrical Charge Tomography System

Rahmat¹,

Isa²,

Jusoff³

et al. 2010

American Journal of Applied Sciences

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Problem statement: Many problems in scientific computing can be formulated as inverse problem. A vast majority of these problems are ill-posed problems. In Electrical Charge Tomography (EChT), normally the sensitivity matrix generated from forward modeling is very ill-condition. This condition posts difficulties to the inverse problem solution especially in the accuracy and stability of the image being reconstructed. The objective of this study is to reconstruct the image cross-section of the material in pipeline gravity dropped mode conveyor as well to solve the ill-condition of matrix sensitivity. Approach: Least Square with Regularization (LSR) method had been introduced to reconstruct the image and the electrodynamics sensor was used to capture the data that installed around the pipe. Results: The images were validated using digital imaging technique and Singular Value Decomposition (SVD) method. The results showed that image reconstructed by this method produces a good promise in terms of accuracy and stability. Conclusion: This implied that LSR method provides good and promising result in terms of accuracy and stability of the image being reconstructed. As a result, an efficient method for electrical charge tomography image reconstruction has been introduced.

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Section: Electrodynamics Sensormentioning

confidence: 99%

Image Reconstruction Algorithm for Electrical Charge Tomography System

Rahmat¹,

Isa²,

Jusoff³

et al. 2010

American Journal of Applied Sciences

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“…This would increase difficulties in manufacturing and installation. Xu et al 3,4 studied the sensing characteristics and signal processing methods of ring-shaped electrostatic sensors, which were used for flow parameter monitoring. 1,19,20 However, a ringshaped one only provides the integrated signals from all the particles present in the sensing zone, thus lacking local information for some monitoring objectives, such as flow pattern and fault location.…”

Section: Introductionmentioning

confidence: 99%

“…3 In previous studies, 3,11,31,32 spatial sensitivity was used to describe the sensitivity characteristics. It is defined as the absolute value of induced charges on an electrostatic sensor's probe from a static unit point charge, thus we call it static sensitivity in the following.…”

Section: Introductionmentioning

confidence: 99%

“…3,11,31,32 It reflects a basic relationship between the static charged particles and the corresponding induced charges, on which the definition and analytical model of dynamic sensitivity will be based. Therefore, first of all, static sensitivity has to be studied.…”

Section: Introduction Of Hemisphere-shaped Electrostatic Sensorsmentioning

confidence: 99%

“…Due to the advantages of robustness and low cost, [1][2][3] electrostatic monitoring has been widely used for parameter monitoring of gas-solid two-phase flows, such as flow velocity, mass flow rate, and flow pattern, [1][2][3][4][5][6][7][8][9][10] and detecting abnormal debris present in gas turbines' exhaust gas for online condition monitoring. [11][12][13][14][15][16] Electrostatic sensors are key components in electrostatic monitoring systems.…”

Section: Introductionmentioning

confidence: 99%

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Theoretical analysis and finite element method simulations on dynamic sensitivity of hemisphere-shaped electrostatic sensors

Tang

Chen

et al. 2016

Advances in Mechanical Engineering

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Electrostatic sensors are key components of electrostatic monitoring systems. Their sensitivity characteristics have a direct influence on monitoring accuracy. In previous studies, spatial sensitivity, which is called static sensitivity here, was used to describe the sensitivity characteristics. However, it only reflects a basic relationship between static charged particles and induced charges on an electrostatic sensor's probe. Besides, as a three-dimensional defined parameter, it is difficult to build a unified model if actual boundary conditions are considered. Thus, it is not quite proper for applications that detect moving particles. To solve this problem, dynamic sensitivity is proposed in this article. As for a hemisphereshaped electrostatic sensor, first, a more accurate model of static sensitivity is built. Based on it, dynamic sensitivity is defined and modeled analytically. Then, a calibration method is proposed to improve the model's accuracy under actual boundary conditions. In the end, finite element method simulations are done for validations. The results demonstrate that dynamic sensitivity reflects a relationship between moving charged particles and the actual output signals of a sensor, thus it is direct and practical for moving particles. And the theoretical results are highly consistent with the simulated ones. Moreover, the dynamic sensitivity indicates localized sensing characteristics of hemisphere-shaped electrostatic sensors.

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