Structural Monitoring and Maintenance

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Detecting and predicting the crude oil type inside composite pipes using ECS and ANN

  • Altabey, Wael A. (International Institute for Urban Systems Engineering, Southeast University)
  • Received : 2015.09.24
  • Accepted : 2016.11.16
  • Published : 2016.12.25
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Abstract

The present work develops an expert system for detecting and predicting the crude oil types and properties at normal temperature ${\theta}=25^{\circ}C$, by evaluating the dielectric properties of the fluid transfused inside glass fiber reinforced epoxy (GFRE) composite pipelines, by using electrical capacitance sensor (ECS) technique, then used the data measurements from ECS to predict the types of the other crude oil transfused inside the pipeline, by designing an efficient artificial neural network (ANN) architecture. The variation in the dielectric signatures are employed to design an electrical capacitance sensor (ECS) with high sensitivity to detect such problem. ECS consists of 12 electrodes mounted on the outer surface of the pipe. A finite element (FE) simulation model is developed to measure the capacitance values and node potential distribution of ECS electrodes by ANSYS and MATLAB, which are combined to simulate sensor characteristic. Radial Basis neural network (RBNN), structure is applied, trained and tested to predict the finite element (FE) results of crude oil types transfused inside (GFRE) pipe under room temperature using MATLAB neural network toolbox. The FE results are in excellent agreement with an RBNN results, thus validating the accuracy and reliability of the proposed technique.

Keywords

References

  1. Al-Tabey, W.A. (2010), "Effect of Pipeline Filling Material on Electrical Capacitance Tomography", Proceedings of the International Postgraduate Conference on Engineering (IPCE 2010), Perlis, Malaysia, October 16-17.
  2. Al-Tabey, W.A. (2012), Finite Element Analysis in Mechanical Design Using ANSYS: Finite Element Analysis (FEA) Hand Book For Mechanical Engineers With ANSYS Tutorials, LAP Lambert Academic Publishing, Germany, ISBN 978-3-8454-0479-0.
  3. Altabey W.A. (2016), "FE and ANN model of ECS to simulate the pipelines suffer from internal corrosion", Struct. Monit. Maint., 3(3), 297-314, DOI: http://dx.doi.org/10.12989/smm.2016.3.3.297.
  4. Altabey W.A., (2016), "The Thermal Effect on Electrical Capacitance Sensor for Two-Phase Flow Monitoring", Struct. Monit. Maint., 3(4), 335-347, DOI: http://dx.doi.org/10.12989/smm.2016.3.4.335.
  5. ANSYS Low-Frequency Electromagnetic analysis Guide, The Electrostatic Module in the Electromagnetic subsection of ANSYS, (2014), ANSYS, inc. Southpointe 275 Technology Drive Canonsburg, PA 15317, Published in the USA.
  6. Asencio, K., Bramer-Escamilla, W., Gutierrez, G. and Sanchez, I. (2015), "Electrical capacitance sensor array to measure density profiles of a vibrated granular bed", Powder Technol., 270, 10-19. https://doi.org/10.1016/j.powtec.2014.10.003
  7. Buhmann, M.D. (2003). "Radial basis functions: theory and implementations", Cambridge University Press, Cambridge.
  8. Daoye, Y., Bin, Z., Chuanlong, X., Guanghua, T. and Shimin, W. (2009), "Effect of pipeline thickness on electrical capacitance tomography", Proceedings of the 6th International Symposium on Measurement Techniques for Multiphase Flows, Journal of Physics: Conference Series 147, 1-13.
  9. Fasching, G.E. and Smith, N.S. (1988), "High Resolution Capacitance Imaging System", US Dept. Energy, 37, DOE/METC-88/4083
  10. Fasching, G.E. and Smith, N.S. (1991) "A capacitive system for 3-dimensional imaging of fluidized-beds", Rev. Sci. Instr., 62, 2243-2251 https://doi.org/10.1063/1.1142343
  11. Huang, S.M., Plaskowski, A.B., Xie, C.G. and Beck, M.S, (1989), "Tomographic imaging of two-flow component flow using capacitance sensor", J. Phys. E : Sci. Instrum., 22,173-177. https://doi.org/10.1088/0022-3735/22/3/009
  12. Jaworski, A.J. and Bolton, G.T. (2000), "The design of an electrical capacitance tomography sensor for use with media of high dielectric permittivity", Meas. Sci. Technol., 11(6), 743-757. https://doi.org/10.1088/0957-0233/11/6/318
  13. Li, H. and Huang, Z. (2000), "Special measurement technology and application", Zhejiang University Press, Hangzhou.
  14. Mohamad, E.J., Rahim, R.A., Leow, P.L., Fazalul, Rahiman, M.H., Marwah, O.M.F., Nor Ayob, N.M., Rahim, H.A. and Mohd Yunus, F.R. (2012), "An introduction of two differential excitation potentials technique in electrical capacitance tomography", J. Sensors Actuators A, 180, 1-10 https://doi.org/10.1016/j.sna.2012.03.025
  15. Mohamad, E.J., Rahim, R.A., Rahiman, M.H.F., Ameran, H.L.M., Muji, S.Z.M. and Marwah, O.M.F. (2016), "Measurement and analysis of water/oil multiphase flow using Electrical Capacitance Tomography sensor", Flow Meas. Instrum., 47, 62-70. https://doi.org/10.1016/j.flowmeasinst.2015.12.004
  16. Pei, T. and Wang, W. (2009), "Simulation analysis of sensitivity for electrical capacitance tomography", Proceedings of the 9thInternational Conference on Electronic Measurement & Instruments (ICEMI 2009).
  17. Sardeshpande, M.V., Harinarayan, S. and Ranade, V.V. (2015), "Void fraction measurement using electrical capacitance tomography and high speed photography", J. Chem. Eng. Res. Des., 9(4), 1-11.
  18. Saudi Aramco (2008), "Setting new standards for 75 years: Our Legacy, Our Future", Annual Review 2008.
  19. Saudi Aramco (2014), "Energy is opportunity", Annual Review 2014.
  20. Valle, Y., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J. and Harley, R.G. (2008), "Particle swarm optimization: Basic concepts", Variants and Applications in Power Systems. IEEE Transaction on Evolutionary Computation, 12(2), 171-195. https://doi.org/10.1109/TEVC.2007.896686
  21. Xie, C.G., Huang, S.M., Hoyle, B.S., Thorn, R., Lenn, C., Snowden, D. and Beck, M.S. (1992), "Electrical capacitance tomography for flow imaging: system model for development of image reconstruction algorithms and design of primary sensors", IEEE Proceedings-G, 139(1), 89-98.
  22. Yang, W.Q. (1997), "Modelling of capacitance sensor", IEEE proceedings: Measurement Science and Technology, 144(5), 203-208. https://doi.org/10.1049/ip-smt:19971425
  23. Yang, W.Q. and York, T.A. (1999), "New AC-based capacitance tomography system", IEEE proceedings:Measurement Science and Technology, 146(1), 47-53. https://doi.org/10.1049/ip-smt:19990008
  24. Yang, W.Q., Beck, M.S. and Byars, M. (1995b), "Electrical capacitance tomography -from design to applications", Meas. Control, 28(9), 261-266 https://doi.org/10.1177/002029409502800901
  25. Yang, W.Q., Stott, A.L., Beck, M.S. and Xie, C.G. (1995a), "Development of capacitance tomographic imaging systems for oil pipeline measurements", Rev. Sci. Instrum., 66(8), 4326 https://doi.org/10.1063/1.1145322
  26. Zhang, W., Wang, C., Yang, W. and Wang C. (2014), "Application of electrical capacitance tomography in particulate process measurement - A review", J. Adv. Powder Technol., 25, 174-188. https://doi.org/10.1016/j.apt.2013.12.003

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