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

Authors: HEYSEM KAYA, PINAR TÜFEKCİ, ERDİNÇ UZUN

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 NO_x 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, NO_x, exhaust emission prediction, gas turbines, extreme learning machine, database

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