Petroleum Pipeline Leakage Forecasting Based on Artificial Neural Networks

Abstract:

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The technologies of artificial neural networks can be used to complete information processing of the networks through the interaction of neural cells. The mappings of the stimuli effects and the input and output estimates are obtained via combinations of nonlinear functions. This offers the advantages of self-learning, self-organization, self-adaptation and fault tolerance. It also has the possibility of use in applications for petroleum pipeline leakage forecasting. First we use the flow of the inlet in the pipe as input, and the flow of the outlet in the pipe as output to build a artificial neural network for diagnosing the pipe. This method has been shown to offer better results in performance and efficiency. It is expected that the application of this system will increase sensitivity and further increase petroleum pipeline leakage forecasting performance.

Info:

Periodical:

Advanced Materials Research (Volumes 219-220)

Edited by:

Helen Zhang, Gang Shen and David Jin

Pages:

1003-1007

DOI:

10.4028/www.scientific.net/AMR.219-220.1003

Citation:

X. B. Zhang and J. S. Chen, "Petroleum Pipeline Leakage Forecasting Based on Artificial Neural Networks", Advanced Materials Research, Vols. 219-220, pp. 1003-1007, 2011

Online since:

March 2011

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Price:

$35.00

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