Application of electrical capacitance tomography and artificial neural networks to rapid estimation of cylindrical shape parameters of industrial flow structure

Abstract

Abstract:A new approach to solve the inverse problem in electrical capacitance tomography is presented. The proposed method is based on an artificial neural network to estimate three different parameters of a circular object present inside a pipeline, i.e. radius and 2D position coordinates. This information allows the estimation of the distribution of material inside a pipe and determination of the characteristic parameters of a range of flows, which are characterised by a circular objects emerging within a cross section such as funnel flow in a silo gravitational discharging process. The main advantages of the proposed approach are explicitly: the desired characteristic flow parameters are estimated directly from the measured capacitances and rapidity, which in turn is crucial for online flow monitoring. In a classic approach in order to obtain these parameters in the first step the image is reconstructed and then the parameters are estimated with the use of image processing methods. The obtained results showed significant reduction of computations time in comparison to the iterative LBP or Levenberg-Marquard algorithms.

A hybrid tomography for assessing the moisture level of walls and building condition

“…Building porous materials (eg bricks or concrete), both natural and made, has pores (like a sponge). The data collection system collects the measured voltage from the electrode and then processes the data [1][2][3][5][6][7][8][9][10][11][13][14][15][28][29][30][31][32][33]. Figure 1 presents the model of a hybrid tomography system.…”

Using Statistical Algorithms for Image Reconstruction in EIT

“…The object's conductivity is reconstructed on the basis of known voltages and measured potentials. Reconstruction of electrical impedance requires accurate modelling [6,11,17,18,20]. EIT is a method of imaging in which the conductivity distribution of the tested object is estimated on the basis of measurements of electrical voltages and potentials of electrodes at the boundary.…”

A new data-processing approach to study particle motion using ultrafast X-ray tomography scanner: case study of gravitational mass flow

“…For the last decades, the silo discharging process has been studied using different imaging techniques, for instance, X-ray radiography or computed tomography (CT) (Babout et al 2013;Chen et al 2016;Grantham and Forsberg 2004), electrical capacitance tomography (ECT) (Garbaa et al 2016;Grudzien et al 2012a, b;Grudzien 2017;Grudzień et al 2017) and high-speed cameras (Drescher and Ferjani 2004;Steingart and Evans 2005). However, due to the scanning speed limitation of CT and low resolution of ECT as compared to X-ray or lack of visualizing the interior flow part in the case of a high-speed camera, the local velocity and centrifugal effects of granular material flow were not measured effectively.…”