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

Alma A.M. Rahat, Chunlin Wang, Richard M. Everson and Jonathan E. Fieldsend

Applied Energy, 2018, vol. 229, issue C, 446-458

Abstract: 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.

Keywords: Evolutionary multi-objective optimization under uncertainty; Coal combustion optimisation; NOx; Unburned carbon content in fly ash; Gaussian processes; Probabilistic dominance (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (15)

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DOI: 10.1016/j.apenergy.2018.07.101

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