| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | A data-driven approach to predict NOx-emissions of gas turbines |
| Autor/-in: | Cuccu, Giuseppe Danafar, Somayeh Cudré-Mauroux, Philippe Gassner, Martin Bernero, Stefano Kryszczuk, Krzysztof |
| DOI: | 10.1109/BigData.2017.8258056 |
| Tagungsband: | 2017 IEEE International Conference on Big Data (BIGDATA) |
| Seite(n): | 1283 |
| Seiten bis: | 1288 |
| Angaben zur Konferenz: | 2017 IEEE International Conference on Big Data (IEEE BigData 2017), Boston, 11-14 December 2017 |
| Erscheinungsdatum: | 2017 |
| ISBN: | 978-1-5386-2715-0 |
| Sprache: | Englisch |
| Schlagwörter: | Predictive maintenance; Analytics; Emission prediction |
| Fachgebiet (DDC): | 003: Systeme |
| Zusammenfassung: | Predicting 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/7725 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | Life Sciences und Facility Management |
| Organisationseinheit: | Institut für Computational Life Sciences (ICLS) |
| Enthalten in den Sammlungen: | 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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