NDLI: The application of the Synchronous improved grey-neural network model in predicting of oil-gas pipeline corrosion rate

Content Provider	IEEE Xplore Digital Library
Author	Furen Zhang Sun Jie Zhang Huamin Zhang Furen
Copyright Year	2010
Description	Author affiliation: School of Urban Construction and environment engineering, Chongqing University, China (Zhang Furen) \|\| School of Mechanical and Electrical Engineering, Chongqing Jiaotong University, China (Furen Zhang; Sun Jie; Zhang Huamin)
Abstract	With much affect factors, the corrosion of pipeline under the complicated surroundings is not avoided. Leakage of gas will affect the running of transportation system, and lead to the happening of accidents, the damage on the personnel and property, as well as the pollution of environmental and waste of energy sources. So, the prediction of the corrosion rate and its' law have important interest for controlling and reducing incidents of gas. The Synchronous improved grey-neural network Model has been established based on the improved grey model and the improved neural network model. This new model was used to predict the tendency of corrosion rate based on actual data. The maximal error and the average error is 1.24% and 0.48%, respectively. Compared with the models of the relative references, it shows this new model can get the best predicting result. It shows this new model is dependable and rational, and has great application value. (Abstract)
Starting Page	1153
Ending Page	1156
File Size	219683
Page Count	4
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5583668
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	corrosion rate Corrosion Computational modeling Pipelines compositive model gas pipeline Artificial neural networks grey-neural network Predictive models prediction Forecasting
Content Type	Text
Resource Type	Article

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