Predicting CO and NOxemissions from gas turbines: novel data and abenchmark PEMS

Abstract

Predictive emission monitoring systems (PEMS) are important tools for validation and backing up of costlycontinuous emission monitoring systems used in gas-turbine-based power plants. Their implementation relies on theavailability of appropriate and ecologically valid data. In this paper, we introduce a novel PEMS dataset collected overfive years from a gas turbine for the predictive modeling of the CO and NOxemissions. We analyze the data using arecent machine learning paradigm, and present useful insights about emission predictions. Furthermore, we present abenchmark experimental procedure for comparability of future works on the data.