Application of electrical resistance tomography to ice-water two-phase flow parameter measurement

2013 ◽  
Vol 21 (8) ◽  
pp. 1981-1987
Author(s):  
张建国 ZHANG Jian-guo ◽  
马福昌 MA Fu-chang ◽  
窦银科 DOU Yin-ke
Keyword(s):  
Electrical Resistance ◽  
Flow Parameter ◽  
Two Phase Flow ◽  
Parameter Measurement ◽  
Phase Flow ◽  
Electrical Resistance Tomography ◽  
Two Phase ◽  
Ice Water

Application of Electrical Resistance Tomography to Ice-Water Two-Phase Flow Parameters Measurement

2013 ◽  
Vol 562-565 ◽  
pp. 686-690 ◽  
Author(s):  
Jian Guo Zhang ◽  
Fu Chang Ma
Keyword(s):  
Electrical Resistance ◽  
Two Phase Flow ◽  
Phase Distribution ◽  
Medical Diagnostics ◽  
Phase System ◽  
Phase Flow ◽  
Electrical Resistance Tomography ◽  
Two Phase ◽  
Cross Sectional ◽  
Ice Water

Abstract. The pipeline transport system of ice-water mixed fluid applies widely (e.g., ice cold storage system). Ice-water two phase flow is a non-settlement flow with viscous particles. Due to its complexity and variability, the correct parameter measurement of ice-water two phase flow, in terms of ice packing factor (IPF) and phase distribution, is an extremely challenging task. Electrical resistance tomography (ERT) is a novel imaging technology that can display cross-sectional electrical conductivity distribution within a pipe by using a tomography reconstruction algorithm. Compared to computerized tomography (CT), which is a standard tool in medical diagnostics and non-destructive testing of materials, ERT has many advantages in cost, speed and safety. The work described here is to study and learn about ERT implementation in measurement parameters of ice-water two-phase flow. In experiment the reconstructed image clearly display the phase distribution of ice and water in two-phase system. The research results prove that it is possible to monitor on-line the transportation process of ice-water two-phase flow using ERT system.

A novel gas/liquid two-phase flow imaging method through electrical resistance tomography with DDELM-AE sparse dictionary

Sensor Review ◽  
2020 ◽  
Vol 40 (4) ◽  
pp. 407-420
Author(s):  
Bo Li ◽  
Jian ming Wang ◽  
Qi Wang ◽  
Xiu yan Li ◽  
Xiaojie Duan
Keyword(s):  
Electrical Resistance ◽  
Transient Flow ◽  
Two Phase Flow ◽  
Flow Imaging ◽  
Phase Flow ◽  
Electrical Resistance Tomography ◽  
Two Phase ◽  
Flow Process ◽  
Content Type ◽  
High Level

Purpose The purpose of this paper is to explore gas/liquid two-phase flow is widely existed in industrial fields, especially in chemical engineering. Electrical resistance tomography (ERT) is considered to be one of the most promising techniques to monitor the transient flow process because of its advantages such as fast respond speed and cross-section imaging. However, maintaining high resolution in space together with low cost is still challenging for two-phase flow imaging because of the ill-conditioning of ERT inverse problem. Design/methodology/approach In this paper, a sparse reconstruction (SR) method based on the learned dictionary has been proposed for ERT, to accurately monitor the transient flow process of gas/liquid two-phase flow in a pipeline. The high-level representation of the conductivity distributions for typical flow regimes can be extracted based on denoising the deep extreme learning machine (DDELM) model, which is used as prior information for dictionary learning. Findings The results from simulation and dynamic experiments indicate that the proposed algorithm efficiently improves the quality of reconstructed images as compared to some typical algorithms such as Landweber and SR-discrete fourier transformation/discrete cosine transformation. Furthermore, the SR-DDELM has also used to estimate the important parameters of the chemical process, a case in point is the volume flow rate. Therefore, the SR-DDELM is considered an ideal candidate for online monitor the gas/liquid two-phase flow. Originality/value This paper fulfills a novel approach to effectively monitor the gas/liquid two-phase flow in pipelines. One deep learning model and one adaptive dictionary are trained via the same prior conductivity, respectively. The model is used to extract high-level representation. The dictionary is used to represent the features of the flow process. SR and extraction of high-level representation are performed iteratively. The new method can obviously improve the monitoring accuracy and save calculation time.

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