Data-driven multi-objective optimisation of coal-fired boiler combustion systems

•Quantification of predictive uncertainty in data-driven models of power plants.•Multi-objective optimisation under uncertainty to find optimal NOx and UBC trade-off.•Novel solution selection method based on maximum probability of dominance.•Optimisation for all load demands in a single run: no need...

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Veröffentlicht in:	Applied energy Jg. 229; S. 446 - 458
Hauptverfasser:	Rahat, Alma A.M., Wang, Chunlin, Everson, Richard M., Fieldsend, Jonathan E.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.11.2018
Schlagworte:	algorithms China coal Coal combustion optimisation combustion data collection emissions energy Evolutionary multi-objective optimization under uncertainty Gaussian processes laws and regulations nitrogen oxides NOx pollutants power plants prediction Probabilistic dominance Unburned carbon content in fly ash uncertainty China Probabilistic dominance Evolutionary multi-objective optimization under uncertainty NOx Coal combustion optimisation Gaussian processes Unburned carbon content in fly ash
ISSN:	0306-2619, 1872-9118
Online-Zugang:	Volltext
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