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dc.contributor.authorRahat, AAMen
dc.contributor.authorWang, Cen
dc.contributor.authorEverson, RMen
dc.contributor.authorFieldsend, JEen
dc.date.accessioned2018-10-29T09:32:30Z
dc.date.available2018-10-29T09:32:30Z
dc.date.issued2018-11-01en
dc.identifier.issn0306-2619en
dc.identifier.urihttp://hdl.handle.net/10026.1/12663
dc.description.abstract

© 2018 Elsevier Ltd 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.

en
dc.format.extent446 - 458en
dc.language.isoenen
dc.publisherElsevieren
dc.titleData-driven multi-objective optimisation of coal-fired boiler combustion systemsen
dc.typeJournal Article
plymouth.volume229en
plymouth.journalApplied Energyen
dc.identifier.doi10.1016/j.apenergy.2018.07.101en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
dcterms.dateAccepted2018-07-22en
dc.rights.embargodate2019-08-09en
dc.rights.embargoperiodNot knownen
rioxxterms.versionofrecord10.1016/j.apenergy.2018.07.101en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2018-11-01en
rioxxterms.typeJournal Article/Reviewen


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