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 | |
| Publication status | Published - 4 May 2017 |
| Externally published | Yes |
Access to Document
Fingerprint
Dive into the research topics of 'A soft sensor for the Bayer process'. Together they form a unique fingerprint.Profiles
-
William Lee
- Department of Computer Science - Professor
- School of Computing and Engineering
- Centre for Mathematics and Data Science - Deputy Director
Person: Academic