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 |
|---|---|
| Pages (from-to) | 1-6 |
| Number of pages | 7 |
| Journal | Journal of Mathematics in Industry |
| Volume | 7 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 4 May 2017 |
Keywords
- Bayer process
- soft senor
- stepwise multiple linear regression
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