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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
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
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:•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 for repeated optimisation.•Methods are demonstrated on the data collected from Jianbi power plant, China. Coal remains an important energy source. Nonetheless, pollutant emissions – in particular Oxides of Nitrogen (NOx) – as a result of the combustion process in a boiler, are subject to strict legislation due to their damaging effects on the environment. Optimising combustion parameters to achieve a lower NOx emission often results in combustion inefficiency measured with the proportion of unburned coal content (UBC). Consequently there is a range of solutions that trade-off efficiency for emissions. Generally, an analytical model for NOx emission or UBC is unavailable, and therefore data-driven models are used to optimise this multi-objective problem. We introduce the use of Gaussian process models to capture the uncertainties in NOx and UBC predictions arising from measurement error and data scarcity. A novel evolutionary multi-objective search algorithm is used to discover the probabilistic trade-off front between NOx and UBC, and we describe a new procedure for selecting parameters yielding the desired performance. We discuss the variation of operating parameters along the trade-off front. We give a novel algorithm for discovering the optimal trade-off for all load demands simultaneously. The methods are demonstrated on data collected from a boiler in Jianbi power plant, China, and we show that a wide range of solutions trading-off NOx and efficiency may be efficiently located.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2018.07.101