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

Heysem KAYA; Pinar TÜFEKCİ; Erdinç UZUN

Makale detayları

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

Yazarlar:

Heysem KAYA | Pinar TÜFEKCİ | Erdinç UZUN

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2019 Vol.27 Issue 6, pp.4783-4796

Belge Tipi:

Makale

Yayın Tarihi:

11/2019

Belge Dili:

İngilizce

Document ID (Org No.):

2000733562

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

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.

Makale detayları

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

Yazarlar: Heysem KAYA | Pinar TÜFEKCİ | Erdinç UZUN

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2019 Vol.27 Issue 6, pp.4783-4796

Belge Tipi: Makale

Yayın Tarihi: 11/2019

Belge Dili: İngilizce

Document ID (Org No.): 2000733562

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Heysem KAYA | Pinar TÜFEKCİ | Erdinç UZUN

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2019 Vol.27 Issue 6, pp.4783-4796

Belge Tipi:

Makale

Yayın Tarihi:

11/2019

Belge Dili:

İngilizce

Document ID (Org No.):

2000733562

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632