A soft sensor for the Bayer process

Vincent Cregan; William Lee; Louise Clune

doi:10.1186/s13362-017-0037-9

A soft sensor for the Bayer process

Vincent Cregan, William Lee, Louise Clune

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

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.

Original language	English
Number of pages	6
Journal	Journal of Mathematics in Industry
Volume	7
Issue number	7
DOIs	https://doi.org/10.1186/s13362-017-0037-9
Publication status	Published - 4 May 2017
Externally published	Yes

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10.1186/s13362-017-0037-9Licence: CC BY

Cite this

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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.",

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KW - stepwise multiple linear regression

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A soft sensor for the Bayer process

Abstract

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