A self-adaptive deep learning algorithm for intelligent natural gas pipeline control

Tao Zhang, Hua Bai, Shuyu Sun

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


Natural gas has been recognized as a promising energy supply for modern society due to its relatively less air pollution in consumption, while pipeline transportation is preferred especially for long-distance transmissions. A simplified pipeline control scenario is proposed in this paper to deeply accelerate the management and decision process in pipeline dispatch, in which a direct relevance between compressor operations and the inlet flux at certain stations is established as the main dispatch logic. A deep neural network is designed with specific input and output features for this scenario and the hyper-parameters are carefully tuned for a better adaptability of this problem. The realistic operation data of two pipelines have been obtained and prepared for learning and testing. The proposed algorithm with the optimized network structure is proved to be effective and reliable in predicting the pipeline operation status, under both the normal operation conditions and abnormal situations. The successful definition of "ghost compressors" make this algorithm to be the first self-adaptive deep learning algorithm to assist natural gas pipeline intelligent control.
Original languageEnglish (US)
Pages (from-to)3488-3496
Number of pages9
JournalEnergy Reports
StatePublished - Jun 15 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-06-17
Acknowledged KAUST grant number(s): BAS/1/1351-01-01
Acknowledgements: The work of Tao Zhang and Shuyu Sun was supported by funding from the National Natural Scientific Foundation of China (Grants No. 51874262) and King Abdullah University of Science and Technology (KAUST), Saudi Arabia through the grants BAS/1/1351-01-01.

ASJC Scopus subject areas

  • General Energy


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