LAPSE:2020.0626
Published Article
LAPSE:2020.0626
Leak Detection in Gas Mixture Pipelines under Transient Conditions Using Hammerstein Model and Adaptive Thresholds
Syed Muhammad Mujtaba, Tamiru Alemu Lemma, Syed Ali Ammar Taqvi, Titus Ntow Ofei, Seshu Kumar Vandrangi
June 23, 2020
Conventional 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.
Keywords
adaptive thresholds, data-driven leak detection, Hammerstein model, OLGA simulator, pipeline system identification, pseudo-random binary signals
Suggested Citation
Mujtaba SM, Lemma TA, Taqvi SAA, Ofei TN, Vandrangi SK. Leak Detection in Gas Mixture Pipelines under Transient Conditions Using Hammerstein Model and Adaptive Thresholds. (2020). LAPSE:2020.0626
Author Affiliations
Mujtaba SM: Department of Mechanical Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia [ORCID]
Lemma TA: Department of Mechanical Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
Taqvi SAA: Department of Chemical Engineering, NED University of Engineering and Technology, Karachi 75270, Pakistan [ORCID]
Ofei TN: Department of Geoscience and Petroleum, Norwegian University of Science and Technology, S.P Andersens veg 15a, 7031 Trondheim, Norway [ORCID]
Vandrangi SK: Department of Mechanical Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
Journal Name
Processes
Volume
8
Issue
4
Article Number
E474
Year
2020
Publication Date
2020-04-17
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr8040474, Publication Type: Journal Article
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LAPSE:2020.0626
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doi:10.3390/pr8040474
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Jun 23, 2020
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Jun 23, 2020
 
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Original Submitter
Calvin Tsay
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