A data-driven approach to predict NOx-emissions of gas turbines

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 unde...

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Bibliographic Details
Published in2017 IEEE International Conference on Big Data (Big Data) pp. 1283 - 1288
Main Authors Cuccu, Giuseppe, Danafar, Somayeh, Cudre-Mauroux, Philippe, Gassner, Martin, Bernero, Stefano, Kryszczuk, Krzysztof
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2017
Subjects
Data models
Engines
Load modeling
Maintenance engineering
Monitoring
Training
Turbines
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Summary: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.
DOI:10.1109/BigData.2017.8258056