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International Journal of Energy for a Clean Environment
SJR: 0.195 SNIP: 0.435 CiteScore™: 0.74

ISSN Imprimir: 2150-3621
ISSN On-line: 2150-363X

International Journal of Energy for a Clean Environment

Anteriormente Conhecido Como Clean Air: International Journal on Energy for a Clean Environment

DOI: 10.1615/InterJEnerCleanEnv.v8.i3.30
pages 221-237

COMPARING EMPIRICAL, STATISTICAL, AND NEURAL NETWORK MODELS CALCULATING NOx EMISSIONS FROM GAS TURBINES

0. Skreiberg
Department of Energy and Process Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway
J. E. Hustad
Department of Energy and Process Engineering, The Norwegian University of Science and Technology (NTNU), Kolbjorn Hejes v. 1A, N-7491 Trondheim, Norway
E. I. Garnaes
Axess Energy and Environment, Gloshaugen Innovation Centre, 7491 Trondheim, Norway
P. Reinermann
Pavilion Technologies Inc., Austin, Texas, USA
T. Bjorge
Statoil R&D, 7005 Trondheim, Norway

RESUMO

Measurements of NOx levels and process data have been performed on two offshore gas turbine installations in Norway, a standard aeroderivative system and a dry low-emission system. The data have been used to develop statistical and neural network NOx emission models, and comparisons with existing empirical models, i.e., so-called physical or first principles models, have been made. Model comparison results show that the neural network models perform best, but also, some statistical models are able to reproduce the measured emission levels quite well. Empirical models are not able to reproduce the emission levels from the two gas turbines satisfactorily.


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