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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Vydané v:	Applied energy Ročník 229; s. 446 - 458
Hlavní autori:	Rahat, Alma A.M., Wang, Chunlin, Everson, Richard M., Fieldsend, Jonathan E.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.11.2018
Predmet:	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
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Abstract	•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.
AbstractList	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. •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.
Author	Wang, Chunlin Rahat, Alma A.M. Everson, Richard M. Fieldsend, Jonathan E.
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Keywords	Probabilistic dominance Evolutionary multi-objective optimization under uncertainty NOx Coal combustion optimisation Gaussian processes Unburned carbon content in fly ash
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Snippet	•Quantification of predictive uncertainty in data-driven models of power plants.•Multi-objective optimisation under uncertainty to find optimal NOx and UBC... Coal remains an important energy source. Nonetheless, pollutant emissions – in particular Oxides of Nitrogen (NOx) – as a result of the combustion process in a...
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SubjectTerms	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
Title	Data-driven multi-objective optimisation of coal-fired boiler combustion systems
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