Visualization Detection of Solid-Liquid Two-Phase Flow in Filling Pipeline by Electrical Capacitance Tomography Technology  

作　　者：Ningbo Jing Mingqiao Li Lang Liu Yutong Shen Peijiao Yang Xuebin Qin 

机构地区：[1]College of Electrical and Control Engineering,Xi’an University of Science and Technology,Xi’an,710054,China [2]College of Energy Engineering,Xi’an University of Science and Technology,Xi’an,710054,China

出　　处：《Computer Modeling in Engineering & Sciences》2022年第4期465-476,共12页工程与科学中的计算机建模（英文）

基　　金：This research was supported by the National Natural Science Foundation of China(No.51704229);Outstanding Youth Science Fund of Xi’an University of Science and Technology(No.2018YQ2-01).

摘　　要：During mine filling,the caking in the pipeline and the waste rock in the filling slurry may cause serious safety accidents such as pipe blocking or explosion.Therefore,the visualization of the innermine filling of the solid-liquid two-phase flow in the pipeline is important.This paper proposes a method based on capacitance tomography for the visualization of the solid-liquid distribution on the section of a filling pipe.A feedback network is used for electrical capacitance tomography reconstruction.This reconstruction method uses radial basis function neural network fitting to determine the relationship between the capacitance vector and medium distribution error.In the reconstruction process,the error in the linear back projection is removed;thus,the reconstruction problem becomes an accurate linear problem.The simulation results showthat the reconstruction accuracy of this algorithm is better than that of many traditional algorithms;furthermore,the reconstructed image artifacts are fewer,and the phase distribution boundary is clearer.This method can help determine the location and size of the caking and waste rock in the cross section of the pipeline more accurately and has great application prospects in the visualization of filling pipelines in mines.

关 键 词：Electrical capacitance tomography mine backfilling visualization detection image reconstruction radial basis function neural network 

分 类 号：O35[理学—流体力学]