Prediction of Al2O3 leaching recovery in the Bayer process using statistical multilinear regresion analysis

This paper presents the results of defining the mathematical model which describes the dependence of leaching degree of Al2O3 in bauxite from the most influential input parameters in industrial conditions of conducting the leaching process in the Bayer technology of alumina production. Mathematic...

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Main Authors: Đurić I., Đorđević P., Mihajlović I., Nikolić Đ., Živković Ž.
Format: Article
Language:English
Published: University of Belgrade, Technical Faculty, Bor 2010-01-01
Series:Journal of Mining and Metallurgy. Section B: Metallurgy
Subjects:
Online Access:http://www.doiserbia.nb.rs/img/doi/1450-5339/2010/1450-53391002161D.pdf
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author Đurić I.
Đorđević P.
Mihajlović I.
Nikolić Đ.
Živković Ž.
author_facet Đurić I.
Đorđević P.
Mihajlović I.
Nikolić Đ.
Živković Ž.
author_sort Đurić I.
collection DOAJ
description This paper presents the results of defining the mathematical model which describes the dependence of leaching degree of Al2O3 in bauxite from the most influential input parameters in industrial conditions of conducting the leaching process in the Bayer technology of alumina production. Mathematical model is defined using the stepwise MLRA method, with R2 = 0.764 and significant statistical reliability - VIF<2 and p<0.05, on the one-year statistical sample. Validation of the acquired model was performed using the data from the following year, collected from the process conducted under industrial conditions, rendering the same statistical reliability, with R2 = 0.759.
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id doaj-art-9ba6cc1d5a6747afa64567e8799211b8
institution Kabale University
issn 1450-5339
language English
publishDate 2010-01-01
publisher University of Belgrade, Technical Faculty, Bor
record_format Article
series Journal of Mining and Metallurgy. Section B: Metallurgy
spelling doaj-art-9ba6cc1d5a6747afa64567e8799211b82025-02-02T21:28:38ZengUniversity of Belgrade, Technical Faculty, BorJournal of Mining and Metallurgy. Section B: Metallurgy1450-53392010-01-0146216116910.2298/JMMB1002161DPrediction of Al2O3 leaching recovery in the Bayer process using statistical multilinear regresion analysisĐurić I.Đorđević P.Mihajlović I.Nikolić Đ.Živković Ž.This paper presents the results of defining the mathematical model which describes the dependence of leaching degree of Al2O3 in bauxite from the most influential input parameters in industrial conditions of conducting the leaching process in the Bayer technology of alumina production. Mathematical model is defined using the stepwise MLRA method, with R2 = 0.764 and significant statistical reliability - VIF<2 and p<0.05, on the one-year statistical sample. Validation of the acquired model was performed using the data from the following year, collected from the process conducted under industrial conditions, rendering the same statistical reliability, with R2 = 0.759.http://www.doiserbia.nb.rs/img/doi/1450-5339/2010/1450-53391002161D.pdfpredictionMLRAstepwiseleachingBayer process
spellingShingle Đurić I.
Đorđević P.
Mihajlović I.
Nikolić Đ.
Živković Ž.
Prediction of Al2O3 leaching recovery in the Bayer process using statistical multilinear regresion analysis
Journal of Mining and Metallurgy. Section B: Metallurgy
prediction
MLRA
stepwise
leaching
Bayer process
title Prediction of Al2O3 leaching recovery in the Bayer process using statistical multilinear regresion analysis
title_full Prediction of Al2O3 leaching recovery in the Bayer process using statistical multilinear regresion analysis
title_fullStr Prediction of Al2O3 leaching recovery in the Bayer process using statistical multilinear regresion analysis
title_full_unstemmed Prediction of Al2O3 leaching recovery in the Bayer process using statistical multilinear regresion analysis
title_short Prediction of Al2O3 leaching recovery in the Bayer process using statistical multilinear regresion analysis
title_sort prediction of al2o3 leaching recovery in the bayer process using statistical multilinear regresion analysis
topic prediction
MLRA
stepwise
leaching
Bayer process
url http://www.doiserbia.nb.rs/img/doi/1450-5339/2010/1450-53391002161D.pdf
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AT đorđevicp predictionofal2o3leachingrecoveryinthebayerprocessusingstatisticalmultilinearregresionanalysis
AT mihajlovici predictionofal2o3leachingrecoveryinthebayerprocessusingstatisticalmultilinearregresionanalysis
AT nikolicđ predictionofal2o3leachingrecoveryinthebayerprocessusingstatisticalmultilinearregresionanalysis
AT zivkovicz predictionofal2o3leachingrecoveryinthebayerprocessusingstatisticalmultilinearregresionanalysis