2‐D finite‐element modeling of electrostatic sensor for tomography system

Abstract

Purpose -Circular pipelines are mostly used for pneumatic conveyance in industrial processes. For optimum and efficient production in industries that use a pipeline for conveyance, tomographic image of the transport particles is paramount. Sensing mechanism plays a vital role in process tomography. The purpose of this paper is to present a two-dimensional (2-D) model for sensing the characteristics of electrostatic sensors for electrical charge tomography system. The proposed model uses the finite-element method. Design/methodology/approach -The domain is discretized into discrete shapes, called finite elements, by using a MATLAB. Each of these elements is taken as image pixels, on which the electric charges carried by conveyed particles are transformed into equations. The charges' interaction and the sensors installed around the circumference, at the sensing zone of the conveying pipeline are related by the proposed model equations. A matrix compression technique was also introduced to solve the problem of unevenly sensing characteristics of the sensors due to elements' number's concentration. The model equations were used to simulate the modeled electrostatic charge distribution carried by the particles moving in the pipeline. Findings -The simulated results show that the proposed sensors are highly sensitive to electrostatic charge at any position in the sensing zone, thereby making it a good candidate for tomographic image reconstruction. Originality/value -Tomographic imaging using finite element method is found to be more accurate and reliable compared to linear and filtered back projection method.

Cross-correlation focus method with an electrostatic sensor array for local particle velocity measurement in dilute gas–solid two-phase flow

“…Zhang et al also applied the CC method between ipsilateral electrode pairs in a triple-bed combined with a circulating fluidized bed [8]. On the other hand, the tomography method needs an electrostatic tomography system to reconstruct the particle concentration distribution, and then uses the pixel-to-pixel correlation method to calculate the velocity distribution [9][10][11][12][13][14]. Green et al introduced the linear projection method for image reconstruction [10,11], while Rahmat et al introduced the filter back-projection method [13] and the least squares with regularization method [14].…”