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Abstract and Applied Analysis
Volume 2013, Article ID 923036, 13 pages
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.


The tank capacity chart calibration problem of two oil tanks with deflection was studied, one of which is an elliptical cylinder storage tank with two truncated ends and another is a cylinder storage tank with two spherical crowns. Firstly, the function relation between oil reserve and oil height based on the integral method was precisely deduced, when the storage tank has longitudinal inclination but has no deflection. Secondly, the nonlinear optimization model which has both longitudinal inclination parameter and lateral deflection parameter was constructed, using cut-complement method and approximate treatment method. Then the deflection tank capacity chart calibration with a 10 cm oil level height interval was worked out. Lastly, the tank capacity chart was corrected by BP neural network algorithm and got proportional error of theoretical and experimental measurements ranges from 0% to 0.00015%. Experimental results demonstrated that the proposed method has better performance in terms of tank capacity chart calibration accuracy compared with other existing approaches and has a strongly practical significance.