Visualization detection of slurry transportation pipeline based on electrical capacitance tomography in mining filling

Abstract

In the long distance transportation of slurry filled for mining filling, there exist complex variation rules of pressure and flow velocity, pipe distribution location and other influencing factors. Electrical capacitance tomography (ECT) is a technique for visualizing two-phase flow in a pipe or closed container. In this paper, a visual detection method was proposed by image reconstruction of core, laminar, bubble and annular flow based on ECT technology, which reflects distribution of slurry in deep filling pipeline and measures the degree of blockage. There is an error between the measured and the real two-phase flow distribution due to two factors, which are immature image reconstruction algorithm of ECT and difference of flow patterns leading to degrees of error. In this paper, convolutional neural networks (CNN) is used to recognize flow patterns, and then the optimal image is calculated by the improved particle swarm optimization (PSO) algorithm with weights using simulated annealing strategy, and the fitness function is improved based on the results of the shallow neural network. Finally, the reconstructed binary image is further processed to obtain the position, size and direction of the blocked pipe. The realization of this method provides technical support for pipeline detection technology.

摘要

在矿山充填过程中使用料浆的长距离运输中，存在着压力、流速、管道分布位置等复杂的变化规律。电容层析成像(ECT)是一种用于可视化管道或封闭容器中两相流的技术。在本文中，提出了一种基于ECT技术的心流、层流、泡状流和环形流的图像重建可视化检测方法。反映了充填管道中料浆的分布，并测量了堵塞程度。引起测量结果与真实两相流之间误差的原因有两个，一是ECT的图像重建算法不成熟，二是流型的差异性，这两个因素导致了测量的两相流量分布与真实的两相流量分布之间存在误差。在本文中，利用卷积神经网络(CNN)识别流型模式，然后通过改进的粒子群优化(PSO)算法计算出优化图像。引入模拟退火策略优化权重，并根据浅层神经网络的结果改进参数。最后，基于重建的二进制图像计算堵塞的管道的位置、大小和方向。该方法的实现为充填管道可视化提供了技术支持。