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
Highlights
The increasing demand for energy had a double negative impact on the environment both through increasing deforestation and increasing carbon and flue gas emissions
Given the hyperparameters, we train 50 models and subsequently apply three fusion strategies: a) we take the average of predictions as the ensemble output b) we stack the predictions to a basic extreme learning machine classifiers (ELMs) with 50 hidden neurons and to c) an (RF) regressor with 50 trees
In this paper, we presented a novel, publicly available exhaust gas emission dataset for future use and comparative analyses
Summary
The increasing demand for energy had a double negative impact on the environment both through increasing deforestation and increasing carbon and flue gas emissions. NOx (NOx = NO2 + NO) are considered the primary pollutants of the atmosphere, since they are responsible for environmental problems such as photochemical smog, acid rain, tropospheric ozone, ozone layer depletion, and eventually global warming [1]. In addition to these environmental catastrophes, they cause various health problems in humans exposed to high concentrations of these gases [1]. We provide background on flue gas emission monitoring systems and on the machine learning paradigm we have used for prediction
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