Abstract and Applied Analysis

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

Caihong Li, Yali Yuan, Lulu Song, Yunjian Tan, and Guochen Wang

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Abstract

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 α and lateral deflection parameter β 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.

Article information

Source
Abstr. Appl. Anal., Volume 2013, Special Issue (2013), Article ID 923036, 13 pages.

Dates
First available in Project Euclid: 26 February 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1393449775

Digital Object Identifier
doi:10.1155/2013/923036

Zentralblatt MATH identifier
07095493

Citation

Li, Caihong; Yuan, Yali; Song, Lulu; Tan, Yunjian; Wang, Guochen. Mathematical Model Based on BP Neural Network Algorithm for the Deflection Identification of Storage Tank and Calibration of Tank Capacity Chart. Abstr. Appl. Anal. 2013, Special Issue (2013), Article ID 923036, 13 pages. doi:10.1155/2013/923036. https://projecteuclid.org/euclid.aaa/1393449775


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