Heysem KAYA, Pinar TÜFEKCİ, Erdinç UZUN

175174

Predicting CO and NOx emissions from gas turbines: novel data and a benchmark PEMS

Predictive emission monitoring systems (PEMS) are important tools for validation and backing up of costly continuous emission monitoring systems used in gas-turbine-based power plants. Their implementation relies on the availability of appropriate and ecologically valid data. In this paper, we introduce a novel PEMS dataset collected over five years from a gas turbine for the predictive modeling of the CO and NOx emissions. We analyze the data using a recent machine learning paradigm, and present useful insights about emission predictions. Furthermore, we present a benchmark experimental procedure for comparability of future works on the data
Keywords:

Predictive emission monitoring systems, CO, NOx, exhaust emission prediction gas turbines, extreme learning machine, database,

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Turkish Journal of Electrical Engineering and Computer Science-Cover
  • ISSN: 1300-0632
  • Yayın Aralığı: Yılda 6 Sayı
  • Yayıncı: TÜBİTAK
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