Study on prediction method for generation and consumption of coke oven gas

A direct prediction method based on empirical mode decomposition and echo state network is proposed to predict coke oven gas generation and consumption of the steel industry. First, the empirical mode decomposition is used to de-noise the practical data with high noise level. Then the direct relatio...

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Bibliographic Details
Published in2010 8th World Congress on Intelligent Control and Automation pp. 4446 - 4451
Main Authors Ying Liu, Jun Zhao, Wei Wang, Chun-yang Sheng, Li-qun Cong, Wei-min Feng
Format Conference Proceeding
LanguageChinese
English
Published IEEE 01.07.2010
Subjects
Automation
echo state network direct prediction method
empirical mode decomposition
Intelligent control
Job shop scheduling
Noise level
Ovens
prediction method for generation and consumption of coke oven
Prediction methods
Software
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DOI10.1109/WCICA.2010.5554064

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Summary:A direct prediction method based on empirical mode decomposition and echo state network is proposed to predict coke oven gas generation and consumption of the steel industry. First, the empirical mode decomposition is used to de-noise the practical data with high noise level. Then the direct relationship between the prediction origin and prediction horizon using echo state network is established without the need to close the network loop or iterate in the prediction process. Such a method has the advantage of avoiding the iteration error accumulation, and the corresponding forecasting precision is increased. The prediction results using practical production data show the validity of the proposed method and provide the scientific decision support for the gas resources scheduling.
DOI:10.1109/WCICA.2010.5554064