期刊
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
卷 27, 期 6, 页码 4783-4796出版社
Tubitak Scientific & Technological Research Council Turkey
DOI: 10.3906/elk-1807-87
关键词
Predictive emission monitoring systems; CO; NOx; exhaust emission prediction; gas turbines; extreme learning machine; database
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.
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