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

Authors

HEYSEM KAYA
PINAR TÜFEKCİ
ERDİNÇ UZUN

DOI

10.3906/elk-1807-87

Abstract

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

First Page

4783

Last Page

4796

Recommended Citation

KAYA, HEYSEM; TÜFEKCİ, PINAR; and UZUN, ERDİNÇ (2019) "Predicting CO and NOx emissions from gas turbines: novel data and a benchmark PEMS," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 27: No. 6, Article 53. https://doi.org/10.3906/elk-1807-87
Available at: https://journals.tubitak.gov.tr/elektrik/vol27/iss6/53