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dc.contributor.authorRahat, AAM
dc.contributor.authorChunlin, W
dc.contributor.authorEverson, R
dc.contributor.authorFieldsend, J
dc.date.accessioned2018-08-09T14:19:36Z
dc.date.issued2018-08-09
dc.description.abstractCoal 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.en_GB
dc.description.sponsorshipThis work was supported by the Engineering and Physical Sciences Research Council, United Kingdom [Grant No.: EP/M017915/1], the National Natural Science Foundation of China [Grant Nos.: 61375078 and 61304211], and the China Scholarship Council.en_GB
dc.identifier.citationVol. 229, pp. 446-458en_GB
dc.identifier.doi10.1016/j.apenergy.2018.07.101
dc.identifier.urihttp://hdl.handle.net/10871/33710
dc.language.isoenen_GB
dc.publisherElsevieren_GB
dc.rights.embargoreasonUnder embargo until 10 August 2019 in compliance with publisher policy.en_GB
dc.rights© 2018 Elsevier Ltd. All rights reserved.en_GB
dc.subjectevolutionary multi-objective optimization under uncertaintyen_GB
dc.subjectCoal combustion optimizationen_GB
dc.subjectNOxen_GB
dc.subjectUnburned coal content in fly ashen_GB
dc.subjectGaussian processesen_GB
dc.subjectProbabilistic dominanceen_GB
dc.titleData-driven multi-objective optimisation of coal-fired boiler combustion systemsen_GB
dc.typeArticleen_GB
dc.identifier.issn0306-2619
dc.descriptionThis is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.en_GB
dc.identifier.journalApplied Energyen_GB


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