A soft sensor for the Bayer process

Vincent Cregan, William Lee, Louise Clune

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.
Original languageEnglish
Number of pages6
JournalJournal of Mathematics in Industry
Issue number7
Publication statusPublished - 4 May 2017
Externally publishedYes


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