Generative adversarial networks for dual-modality electrical tomography in multi-phase flow measurement. (March 2021)

• Record Type: Journal Article
• Authors: Xia, Zihan; Cui, Ziqiang; Chen, Yuxiang; Hu, Yafeng; Wang, Huaxiang
• Is Part Of: Measurement. Volume 173(2021)
• Publication Date: 2021-03
• Subjects: Electrical capacitance tomography -- Electromagnetic tomography -- Dual-modality fusion -- Generative adversarial networks; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.108608
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: In many multi-phase flows, the online measurement and monitoring of phase fractions as well as distributions play a vital role in determining the process efficiency and safety. The dual-modality electrical capacitance tomography (ECT) and electromagnetic tomography (EMT) technique provides an efficient measure to estimate the distribution of electromagnetic property in rapidly changing multi-phase flows. A generative adversarial network (GAN) is designed to solve the fusion problem of ECT and EMT in the gas–liquid–solid (G–L-S) three-phase flow measurement. The fusion model incorporates the features of dual-modality measurements and images to generate the electromagnetic property in high precision, by which the accurate phase volume can be derived. Furthermore, a simulation approach is proposed to provide the sufficient measurement samples that approximate the real three-phase flow measurement. In the numerical study, the fluidization process of a G–L–S fluidized bed (GLSFB) reactor is simulated and measured by the models of ECT and EMT. The simulation validation on samples from GLSFB and experiments on the three-phase flow setup demonstrate the high accuracy of electromagnetic property reconstruction and generalization ability of fusion model that suitable for various flow regimes. The errors of calculated phase fraction are less than 0.15 in both simulations and experiments. Graphical abstract: Highlights: Dual-modality electrical tomography for gas–liquid–solid flow visualization. Fusion model based on generative adversarial networks. A new simulation approach for training sample acquisition. Accurate reconstruction of electromagnetic properties. Volume fraction measurement for various flow regimes.