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A soft sensor for the Bayer process

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dc.contributor.author Cregan, Vincent
dc.contributor.author Lee, William T.
dc.contributor.author Clune, Louise
dc.date.accessioned 2017-07-04T11:56:40Z
dc.date.available 2017-07-04T11:56:40Z
dc.date.issued 2017
dc.identifier.uri http://hdl.handle.net/10344/5879
dc.description peer-reviewed en_US
dc.description.abstract A soft sensor for measuring product quality in the Bayer process has been developed. The soft sensor uses a combination of historical process data recorded from online sensors and laboratory measurements to predict a key quality indicator, namely particle strength. Stepwise linear regression is used to select the relevant variables from a large dataset composed of monitored properties and laboratory data. The developed sensor is employed successfully by RUSAL Aughinish Alumina Ltd to predict product strength five days into the future with R-squared equal to 0.75 and to capture deviations from standard operating conditions en_US
dc.language.iso eng en_US
dc.publisher SpringerOpen en_US
dc.relation.ispartofseries Journal of Mathematics in Industry;7:7
dc.relation.uri http://dx.doi.org/10.1186/s13362-017-0037-9
dc.subject Bayer process en_US
dc.subject soft sensor en_US
dc.subject stepwise multiple linear regression en_US
dc.title A soft sensor for the Bayer process en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.1186/s13362-017-0037-9
dc.contributor.sponsor SFI en_US
dc.contributor.sponsor Embark Initiative postgraduate Award en_US
dc.relation.projectid 06/MI/005 en_US
dc.relation.projectid RS/2006/41 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US


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