Intelligent Control on Urban Natural Gas Supply Using a Deep-Learning-Assisted Pipeline Dispatch Technique

Tao Zhang, Hua Bai, Shuyu Sun

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Natural gas has been attracting increasing attentions all around the world as a relatively cleaner energy resource compared with coal and crude oil. Except for the direct consumption as fuel, electricity generation is now another environmentally-friendly utilization of natural gas, which makes it more favorable as the energy supply for urban areas. Pipeline transportation is the main approach connecting the natural gas production field and urban areas thanks to the safety and economic reasons. In this paper, an intelligent pipeline dispatch technique is proposed using deep learning methods to predict the change of energy supply to the urban areas as a consequence of compressor operations. Practical operation data is collected and prepared for the training and validation of deep learning models, and the accelerated predictions can help make controlling plans regarding compressor operations to meet the requirement in urban natural gas supply. The proposed deep neutral network is equipped with self-adaptability, which enables the general adaption on various temporal compressor conditions including failure and maintenance.
Original languageEnglish (US)
JournalFrontiers in Energy Research
StatePublished - 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-02-07
Acknowledged KAUST grant number(s): BAS/1/1351-01-01
Acknowledgements: This work was supported by funding from King Abdullah University of Science and Technology (KAUST) through the grants BAS/1/1351-01-01.


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