Abstract
Electrical Capacitance Tomography (ECT) has more attention in the last few decades due to its importance in many industrial and medical processes. Research has various directions in this field such how reconstruct accurate images of the object under consideration, hardware implantation of both the recognition system and/or the image viewing devices. In this paper, a novel single-stage intelligent approach is designed for reconstructing images that describe the materials distribution of the multi-phase flow in industrial pipelines. The proposed algorithm utilizes Fuzzy Inference System (FIS) to overcome the nonlinear response of the ECT system. The proposed algorithm is fast since it does not need solving the forward problem to update the sensitivity matrix. The reported results show that the proposed FIS image reconstruction algorithm has high accuracy and promising technique.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Wang, A., Marashdeh, Q., Fan, L.-S.: ECVT imaging and model analysis of the liquid distribution inside a horizontally installed passive cyclonic gas–liquid separator. Chem. Eng. Sci. 141, 231–239 (2016)
Rasteiro, M.G., Silva, R.C.C., Garcia, F.A.P., Faia, P.M.: Electrical Tomography: a review of Configurations and Applications to Particulate Processes. KONA Powder Part. J. 29(29), 67–80 (2011)
Wang, A., Marashdeh, Q., Motil, B.J., Fan, L.-S.: Electrical capacitance volume tomography for imaging of pulsating flows in a trickle bed. Chem. Eng. Sci. 119 (2014)
Wang, Z., Chen, Q., Wang, X., Li, Z., Han, Z.: Dynamic Visualization Approach of the Multiphase Flow Using Electrical Capacitance Tomography. Chinese J. Chem. Eng. 20(2), 380–388 (2012)
Yang, Y., Jia, J., Mccann, H.: A Faster Measurement Strategy of Electrical Capacitance Tomography Using Less Sensing Data, (2) (2015)
Isaksen, Ø.: A review of reconstruction techniques for capacitance tomography. Meas. Sci. Technol. 7(3), 325–337 (1996)
Weber, J.M., Layfield, K.J., Van Essendelft, D.T., Mei, J.S.: Fluid bed characterization using Electrical Capacitance Volume Tomography (ECVT), compared to CPFD Software’s Barracuda. Powder Technol. 250, 138–146 (2013)
Yang, W.Q., Peng, L.: Image reconstruction algorithms for electrical capacitance tomography. Meas. Sci. Technol. 14(1), R1–R13 (2003)
Rahman, N.A.A., Rahim, R.A., Bawi, A.M., Leow, P.L., Pusppanathan, J., Mohamad, E.J., Chan, K.S., Din, S.M., Ayob, N.M.N., Yunus, R.R.M.: A Review on Electrical Capacitance Tomography Sensor Development. J. Teknol. 73(3), 35–41 (2015)
Liu, X., Wang, X., Hu, H., Li, L., Yang, X.: An extreme learning machine combined with Landweber iteration algorithm for the inverse problem of electrical capacitance tomography. Flow Meas. Instrum. 45, 348–356 (2015)
Taylor, S.H., Garimella, S.V.: An explicit conditioning method for image reconstruction in electrical capacitance tomography. Flow Meas. Instrum. 46, 155–162 (2015)
Deabes, W.A., Abdelrahman, M.A.: A nonlinear fuzzy assisted image reconstruction algorithm for electrical capacitance tomography. ISA Trans. 49(1), 10–18 (2010)
Ye, J., Wang, H., Yang, W.: Image Reconstruction for Electrical Capacitance Tomography Based on Sparse Representation. IEEE Trans. Instrum. Meas. 64(1) (2015)
Abdelrahman, M.A., Gupta, A., Deabes, W.A.: A Feature-Based Solution to Forward Problem in Electrical Capacitance Tomography of Conductive Materials. IEEE Trans. Instrum. Meas. 60(2), 430–441 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Deabes, W.A., Amin, H.H. (2016). Fast Intelligent Image Reconstruction Algorithm for ECT Systems. In: Omatu, S., et al. Distributed Computing and Artificial Intelligence, 13th International Conference. Advances in Intelligent Systems and Computing, vol 474. Springer, Cham. https://doi.org/10.1007/978-3-319-40162-1_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-40162-1_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-40161-4
Online ISBN: 978-3-319-40162-1
eBook Packages: EngineeringEngineering (R0)