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A soft sensor for the Bayer process

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journal contribution
posted on 2022-12-12, 11:43 authored by Vincent Cregan, William T. Lee, Louise Clune
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

History

Publication

Journal of Mathematics in Industry;7:7

Publisher

SpringerOpen

Note

peer-reviewed

Other Funding information

SFI, Embark Initiative postgraduate Award

Language

English

Also affiliated with

  • MACSI - Mathematics Application Consortium for Science & Industry

Department or School

  • Mathematics & Statistics

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