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  2. Life Sciences und Facility Management
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  4. Publikationen Life Sciences und Facility Management
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dc.contributor.authorCuccu, Giuseppe-
dc.contributor.authorDanafar, Somayeh-
dc.contributor.authorCudré-Mauroux, Philippe-
dc.contributor.authorGassner, Martin-
dc.contributor.authorBernero, Stefano-
dc.contributor.authorKryszczuk, Krzysztof-
dc.date.accessioned2018-07-09T12:49:15Z-
dc.date.available2018-07-09T12:49:15Z-
dc.date.issued2017-
dc.identifier.isbn978-1-5386-2715-0de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/7725-
dc.description.abstractPredicting the state of modern heavy-duty gas turbines for large-scale power generation allows for making informed decisions on their operation and maintenance. Their emission behavior however is coupled to a multitude of operating parameters and to the state and aging of the engine, making the underlying mechanisms very complex to model through physical, first-order approaches. In this paper, we demonstrate that accurate emission models of gas turbines can be derived using machine learning techniques. We present empirical results on a broad range of machine learning algorithms applied to historical data collected from long-term engine operation. A custom data-cleaning pipeline is presented to considerably boost performance. Our best results match the measurement precision of the emission monitoring system, accurately describing the evolution of the engine state and supporting informed decision making for engine adjustment and maintenance scheduling.de_CH
dc.language.isoende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectPredictive maintenancede_CH
dc.subjectAnalyticsde_CH
dc.subjectEmission predictionde_CH
dc.subject.ddc003: Systemede_CH
dc.titleA data-driven approach to predict NOx-emissions of gas turbinesde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
dc.identifier.doi10.1109/BigData.2017.8258056de_CH
zhaw.conference.details2017 IEEE International Conference on Big Data (IEEE BigData 2017), Boston, 11-14 December 2017de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end1288de_CH
zhaw.pages.start1283de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedings2017 IEEE International Conference on Big Data (BIGDATA)de_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedPredictive Analyticsde_CH
Appears in collections:Publikationen Life Sciences und Facility Management

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Cuccu, G., Danafar, S., Cudré-Mauroux, P., Gassner, M., Bernero, S., & Kryszczuk, K. (2017). A data-driven approach to predict NOx-emissions of gas turbines [Conference paper]. 2017 IEEE International Conference on Big Data (BIGDATA), 1283–1288. https://doi.org/10.1109/BigData.2017.8258056
Cuccu, G. et al. (2017) ‘A data-driven approach to predict NOx-emissions of gas turbines’, in 2017 IEEE International Conference on Big Data (BIGDATA), pp. 1283–1288. Available at: https://doi.org/10.1109/BigData.2017.8258056.
G. Cuccu, S. Danafar, P. Cudré-Mauroux, M. Gassner, S. Bernero, and K. Kryszczuk, “A data-driven approach to predict NOx-emissions of gas turbines,” in 2017 IEEE International Conference on Big Data (BIGDATA), 2017, pp. 1283–1288. doi: 10.1109/BigData.2017.8258056.
CUCCU, Giuseppe, Somayeh DANAFAR, Philippe CUDRÉ-MAUROUX, Martin GASSNER, Stefano BERNERO und Krzysztof KRYSZCZUK, 2017. A data-driven approach to predict NOx-emissions of gas turbines. In: 2017 IEEE International Conference on Big Data (BIGDATA). Conference paper. 2017. S. 1283–1288. ISBN 978-1-5386-2715-0
Cuccu, Giuseppe, Somayeh Danafar, Philippe Cudré-Mauroux, Martin Gassner, Stefano Bernero, and Krzysztof Kryszczuk. 2017. “A Data-Driven Approach to Predict NOx-Emissions of Gas Turbines.” Conference paper. In 2017 IEEE International Conference on Big Data (BIGDATA), 1283–88. https://doi.org/10.1109/BigData.2017.8258056.
Cuccu, Giuseppe, et al. “A Data-Driven Approach to Predict NOx-Emissions of Gas Turbines.” 2017 IEEE International Conference on Big Data (BIGDATA), 2017, pp. 1283–88, https://doi.org/10.1109/BigData.2017.8258056.


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