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dc.contributor.authorMujtaba, Syed Muhammad
dc.contributor.authorLemma, Tamiru Alemu
dc.contributor.authorTaqvi, Syed Ali Ammar
dc.contributor.authorOfei, Titus Ntow
dc.contributor.authorVandrangi, Seshu Kumar
dc.date.accessioned2022-09-02T11:39:35Z
dc.date.available2022-09-02T11:39:35Z
dc.date.created2021-01-19T12:10:44Z
dc.date.issued2020
dc.identifier.issn2227-9717
dc.identifier.urihttps://hdl.handle.net/11250/3015411
dc.description.abstractConventional leak detection techniques require improvements to detect small leakage (<10%) in gas mixture pipelines under transient conditions. The current study is aimed to detect leakage in gas mixture pipelines under pseudo-random boundary conditions with a zero percent false alarm rate (FAR). Pressure and mass flow rate signals at the pipeline inlet were used to estimate mass flow rate at the outlet under leak free conditions using Hammerstein model. These signals were further used to define adaptive thresholds to separate leakage from normal conditions. Unlike past studies, this work successfully detected leakage under transient conditions in an 80-km pipeline. The leakage detection performance of the proposed methodology was evaluated for several leak locations, varying leak sizes and, various signal to noise ratios (SNR). Leakage of 0.15 kg/s—3% of the nominal flow—was successfully detected under transient boundary conditions with a F-score of 99.7%. Hence, it can be concluded that the proposed methodology possesses a high potential to avoid false alarms and detect small leaks under transient conditions. In the future, the current methodology may be extended to locate and estimate the leakage point and size.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleLeak Detection in Gas Mixture Pipelines under Transient Conditions Using Hammerstein Model and Adaptive Thresholdsen_US
dc.title.alternativeLeak Detection in Gas Mixture Pipelines under Transient Conditions Using Hammerstein Model and Adaptive Thresholdsen_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.volume8en_US
dc.source.journalProcessesen_US
dc.source.issue4en_US
dc.identifier.doi10.3390/pr8040474
dc.identifier.cristin1874220
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode0


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