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.
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.
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.
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.
This work is supported by the National Natural Science Foundation of China (Grant nos. 61402330, 61405143, 61601324, 61872269, 61903273), Natural Science Foundation of Tianjin Municipal Science and Technology Commission (18JCYBJC85300), Tianjin enterprise science and technology correspondent project (18JCTPJC61600), Tianjin Science and Technology Program (19PTZWHZ00020).
Li, B., Wang, J.m., Wang, Q., Li, X.y. and Duan, X. (2020), "A novel gas/liquid two-phase flow imaging method through electrical resistance tomography with DDELM-AE sparse dictionary", Sensor Review, Vol. 40 No. 4, pp. 407-420. https://doi.org/10.1108/SR-01-2019-0018Download as .RIS
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