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

6 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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Access to Document

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

Cite this

@article{74b44e3aa8444d689af6c991ea9f49df,

title = "A soft sensor for the Bayer process",

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.",

keywords = "Bayer process, soft sensor, stepwise multiple linear regression",

author = "Vincent Cregan and William Lee and Louise Clune",

year = "2017",

month = may,

day = "4",

doi = "10.1186/s13362-017-0037-9",

language = "English",

volume = "7",

journal = "Journal of Mathematics in Industry",

issn = "2190-5983",

publisher = "Springer Science and Business Media Deutschland GmbH",

number = "7",

}

TY - JOUR

T1 - A soft sensor for the Bayer process

AU - Cregan, Vincent

AU - Lee, William

AU - Clune, Louise

PY - 2017/5/4

Y1 - 2017/5/4

N2 - 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.

AB - 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.

KW - Bayer process

KW - soft sensor

KW - stepwise multiple linear regression

U2 - 10.1186/s13362-017-0037-9

DO - 10.1186/s13362-017-0037-9

M3 - Article

SN - 2190-5983

VL - 7

JO - Journal of Mathematics in Industry

JF - Journal of Mathematics in Industry

IS - 7

ER -

A soft sensor for the Bayer process

Abstract

UN SDGs

Access to Document

Fingerprint

Cite this