@article{
 title = {Predicting CO and NOx emissions from gas turbines: Novel data and a benchmark PEMS},
 type = {article},
 year = {2019},
 keywords = {CO,Database,Exhaust emission prediction,Extreme learning machine,Gas turbines,NOx,Predictive emission monitoring systems},
 pages = {4783-4796},
 volume = {27},
 websites = {http://online.journals.tubitak.gov.tr/openDoiPdf.htm?mKodu=elk-1807-87},
 month = {11},
 day = {26},
 id = {710ca243-ad29-36a4-86cd-f17f0fc7ae5e},
 created = {2019-11-29T19:28:23.249Z},
 file_attached = {false},
 profile_id = {37fa15c3-e5d0-3212-8e18-e4c72814fd47},
 last_modified = {2021-02-21T14:00:57.531Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Kaya2019},
 private_publication = {false},
 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.},
 bibtype = {article},
 author = {Kaya, Heysem and Tüfekci, Pınar and Uzun, Erdinç},
 doi = {10.3906/ELK-1807-87},
 journal = {Turkish Journal of Electrical Engineering and Computer Sciences},
 number = {6}
}

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