Data-driven multi-objective optimisation of coal-fired boiler combustion systems
dc.contributor.author | Rahat, AAM | en |
dc.contributor.author | Wang, C | en |
dc.contributor.author | Everson, RM | en |
dc.contributor.author | Fieldsend, JE | en |
dc.date.accessioned | 2018-10-29T09:32:30Z | |
dc.date.available | 2018-10-29T09:32:30Z | |
dc.date.issued | 2018-11-01 | en |
dc.identifier.issn | 0306-2619 | en |
dc.identifier.uri | http://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.extent | 446 - 458 | en |
dc.language.iso | en | en |
dc.publisher | Elsevier | en |
dc.title | Data-driven multi-objective optimisation of coal-fired boiler combustion systems | en |
dc.type | Journal Article | |
plymouth.volume | 229 | en |
plymouth.journal | Applied Energy | en |
dc.identifier.doi | 10.1016/j.apenergy.2018.07.101 | en |
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.dateAccepted | 2018-07-22 | en |
dc.rights.embargodate | 2019-08-09 | en |
dc.rights.embargoperiod | Not known | en |
rioxxterms.versionofrecord | 10.1016/j.apenergy.2018.07.101 | en |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | en |
rioxxterms.licenseref.startdate | 2018-11-01 | en |
rioxxterms.type | Journal Article/Review | en |