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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 923036, 13 pages
http://dx.doi.org/10.1155/2013/923036
Research Article

Mathematical Model Based on BP Neural Network Algorithm for the Deflection Identification of Storage Tank and Calibration of Tank Capacity Chart

1School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, China
2College of Tourism, Hainan University, Haikou, Hainan 570228, China
3Hefei Rongshida Sanyo Electric Co. Ltd., Hefei, Anhui 230061, China

Received 28 February 2013; Accepted 22 April 2013

Academic Editor: Fuding Xie

Copyright © 2013 Caihong Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Sun, “The discussion of horizontal tank volume about the problematic point of the verification and calculation,” Petroleum Products Application Research, vol. 18, no. 5, pp. 20–24, 2000.
  2. Z. Li, “The calculation of horizontal storage tank volume with elliptic cylinder type,” Mathematics in Practice and Theory, vol. 2, pp. 17–26, 1997.
  3. Y. Ji, S. Song, and Y. Tu, “The current situation and development tendency of liquid level measurement technology of storage tank,” Petroleum Engineering Construction, vol. 32, no. 4, pp. 1–4, 2006.
  4. C. Li, T. Liang, W. zhou, and J. Lu, “The function relation of remain liquid volume and oil height of storage tank,” Petro-Chemical Equipment, no. 6, pp. 25–27, 2001.
  5. C. Li, T. Liang, and M. Jin, “The relation of liquid volume and oil height of storage tank with deformation section,” Petro-Chemical Equipment, no. 1, pp. 21–22, 2003.
  6. G. Si, “Calculate the liquid volume by liquid height of tank with deformation section,” Process Equipment & Piping, no. 2, pp. 63–64, 2000.
  7. B. Gao and X. Su, “The volume calculation of different liquid height of horizontal tank with various end enclosure,” Petro-Chemical Equipment, no. 4, pp. 1–7, 1999.
  8. C. Fu, “The volume calculation of inclined storage tank,” Journal of Heilongjiang Bayi Agricultural University, no. 2, pp. 43–52, 1981.
  9. S. Sivaraman and W. A. Thorpe, “Measurement of tank-bottom deformation reduces volume errors,” Oil and Gas Journal, vol. 84, no. 44, pp. 69–71, 1986. View at Scopus
  10. F. Kelly, “Shore tank measurement,” Quarterly Journal of Technical Papers, vol. 1, pp. 59–62, 1987. View at Scopus
  11. Q. Ai, J. Huang, X. zhang, and M. Zhang, “Model of the identification of oil tank’s position and the calibration of tank capacity table,” Light Industry Design, vol. 4, 2011.
  12. S. Si, F. Hua, X. Tian, and J. Wu, “The study of the relation of any height and volume in Erect Spherical cap body,” Automation & Instrumentation, vol. 154, pp. 15–16, 2011.
  13. Z. Li, “Study on tank capacity of tilted oil tank in elliptic cross-section,” Engineering & Test, vol. 51, no. 2, pp. 38–40, 2011.
  14. Y. Chang, D. Zhou, N. Ma, and Y. Lei, “The problem of deflection identification of horizontal storage tank and calibration of tank capacity chart,” Oil & Gas Storage and Transportation, vol. 31, no. 2, pp. 109–113, 2012.
  15. J. Dou, Y. Mei, Z. Chen, and L. Wang, “Model of the identification of oil tank’s position and the calibration of tank capacity table,” Pure and Applied Mathematics, vol. 27, no. 6, pp. 829–840, 2011.
  16. Y. Ai, “The study of capacity table calibration of storage tank with deflection,” Business Affection, vol. 41, pp. 170–170, 2012.
  17. S. Ou, J. Wang, and S. Han, “Model of the identification of oil tank’s position and the calibration of tank capacity table,” China Petroleum and Chemical Standard and Quality, vol. 31, no. 4, pp. 25–26, 2011.
  18. Z. Wang, Y. Li, and R. F. Shen, “Correction of soil parameters in calculation of embankment settlement using a BP network back-analysis model,” Engineering Geology, vol. 91, pp. 168–177, 2007.
  19. S. Tian, Y. Zhao, H. Wei, and Z. Wang, “Nonlinear correction of sensors based on neural network model,” Optics and Precision Engineering, vol. 14, no. 5, pp. 896–902, 2006.
  20. W. He, J. H. Lan, Y. X. Yin, and Z. H. Zhang, “The neural network method for non-linear correction of the thermal resistance transducer,” Journal of Physics, vol. 48, no. 1, pp. 207–211, 2006. View at Publisher · View at Google Scholar · View at Scopus
  21. W. Liu, MATLAB Program Design and Application, China Higher Education Press, Beijing, China, 2002.
  22. B. H. M. Sadeghi, “BP-neural network predictor model for plastic injection molding process,” Journal of Materials Processing Technology, vol. 103, no. 3, pp. 411–416, 2000. View at Publisher · View at Google Scholar · View at Scopus
  23. W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” The Bulletin of Mathematical Biophysics, vol. 5, no. 4, pp. 115–133, 1943. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  24. L. Sun and Y. Li, “Review of on-line defects detection technique for above ground storage tank floor monitoring,” in Proceedings of the 8th World Congress on Intelligent Control and Automation (WCICA '10), vol. 8, pp. 4178–4181, July 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. T. Hu, P. Yuan, and J. Din, “Applications of artificial neural network to hydrology and water resources,” Advances in Water Science, vol. 11, pp. 76–81, 1995.
  26. China Society for Industrial and Applied Mathematics, 2010 National Mathematical Contest in Modeling the Title of a [DB/OL], China Society for Industrial and Applied Mathematics, Beijing, China, 2010.
  27. Department of pplied Mathematics of Tongji university, Advanced Mathematics (New Paris Interiors), Higher Education Press, Beijing, China, 2006.
  28. D. Zhang, MATLAB Neural Network Application Design, Mechanical Industry Press, Beijing, China, 2009.
  29. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, “Artificial neural networks in hydrology. I: preliminary concepts,” Journal of Hydrologic Engineering, vol. 5, no. 2, pp. 115–123, 2000. View at Publisher · View at Google Scholar · View at Scopus
  30. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, “Artificial neural networks in hydrology. II: hydrologic applications,” Journal of Hydrologic Engineering, vol. 5, no. 2, pp. 124–137, 2000. View at Publisher · View at Google Scholar · View at Scopus
  31. G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Mathematics of Control, Signals, and Systems, vol. 2, no. 4, pp. 303–314, 1989. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  32. R. Hecht-Nielsen, Neurocomputing, Addison-Wesley, Menlo Park, Calif, USA, 1990.
  33. K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks, vol. 2, no. 5, pp. 359–366, 1989. View at Scopus
  34. J. Guan and H. Zhao, “Practical methods of oil volume calibration of horizontal storage tank,” Metrology & Measurement Technique, vol. 31, no. 3, pp. 21–36, 2004.
  35. M. Yang and J. Lin, Mathematica Base and Mathematical Software, Dalian University of Technology Press, Dalian, China, 2007.
  36. Science and Technology Products Research Center of Feisi, Neural Network Theory and the Implementation of Matlab7, pp. 99–108, Electronic Industry Press, Beijing, China, 2005.