Publication type: Conference paper
Type of review: Peer review (publication)
Title: A data-driven approach to predict NOx-emissions of gas turbines
Authors: Cuccu, Giuseppe
Danafar, Somayeh
Cudré-Mauroux, Philippe
Gassner, Martin
Bernero, Stefano
Kryszczuk, Krzysztof
DOI: 10.1109/BigData.2017.8258056
Proceedings: 2017 IEEE International Conference on Big Data (BIGDATA)
Pages: 1283
Pages to: 1288
Conference details: 2017 IEEE International Conference on Big Data (IEEE BigData 2017), Boston, 11-14 December 2017
Issue Date: 2017
ISBN: 978-1-5386-2715-0
Language: English
Subjects: Predictive maintenance; Analytics; Emission prediction
Subject (DDC): 003: Systems
Abstract: 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
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Computational Life Sciences (ICLS)
Appears in collections:Publikationen Life Sciences und Facility Management

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