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dc.contributor.authorZhang, Tao
dc.contributor.authorBai, Hua
dc.contributor.authorSun, Shuyu
dc.date.accessioned2022-02-06T13:28:35Z
dc.date.available2022-02-06T13:28:35Z
dc.date.issued2022
dc.date.submitted2021-08-16
dc.identifier.citationZhang, Bai, H., & Sun, S. (2022). Intelligent Control on Urban Natural Gas Supply Using a Deep-Learning-Assisted Pipeline Dispatch Technique. Frontiers in Energy Research, 9. https://doi.org/10.3389/fenrg.2021.759498
dc.identifier.issn2296-598X
dc.identifier.doi10.3389/fenrg.2021.759498
dc.identifier.urihttp://hdl.handle.net/10754/675371
dc.description.abstract<jats:p>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.</jats:p>
dc.description.sponsorshipThis work was supported by funding from King Abdullah University of Science and Technology (KAUST) through the grants BAS/1/1351-01-01.
dc.publisherFrontiers Media SA
dc.relation.urlhttps://www.frontiersin.org/articles/10.3389/fenrg.2021.759498/full
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectnatural gas
dc.subjectpipeline control
dc.subjecturban energy supply
dc.subjectdeep learning
dc.subjectcompressor operation
dc.titleIntelligent Control on Urban Natural Gas Supply Using a Deep-Learning-Assisted Pipeline Dispatch Technique
dc.typeArticle
dc.contributor.departmentComputational Transport Phenomena Laboratory (CTPL), Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalFRONTIERS IN ENERGY RESEARCH
dc.identifier.wosutWOS:000748044300001
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionPipeChina, Beijing, China.
dc.identifier.volume9
kaust.personZhang, Tao
kaust.personSun, Shuyu
kaust.grant.numberBAS/1/1351-01-01
dc.date.accepted2021-12-17
refterms.dateFOA2022-02-06T13:30:58Z
kaust.acknowledged.supportUnitBAS


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This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Except where otherwise noted, this item's license is described as This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.