Machine learning-guided prediction and optimization of precipitation efficiency in the Bayer process

Bakhtom, Abbas; Bariki, Saeed Ghasemzade; Movahedirad, Salman; Sobati, Mohammad Amin

doi:10.1007/s11696-022-02642-x

Chem. Pap.

2023

DOI: 10.1007/s11696-022-02642-x

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Machine learning-guided prediction and optimization of precipitation efficiency in the Bayer process

Abbas Bakhtom

Saeed Ghasemzade Bariki

Salman Movahedirad

et al.

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Cited by 5 publications

References 39 publications

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Exploring innovative strategies for precipitation extent enhancement in a downscaled Bayer process tank

Bakhtom,

Ghasemzade Bariki,

Movahedirad

2024

Can J Chem Eng

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The Bayer process is a cornerstone of alumina production, and its precipitation stage holds the key to both efficiency and product quality. In this study, we embarked on a comprehensive exploration of strategies to enhance the precipitation extent of aluminium hydroxide, a pivotal step in the Bayer process. Utilizing a newly constructed reactor, along with experiments using reactors in series, we rigorously experimented with various factors, including the addition of hydrogen peroxide (H2O2) as an enhancer, seed activation methods, the integration of a hydrocyclone within the processing unit, the application of a magnetic field, and the injection of supersaturated liquor midway through the process. These diverse strategies were systematically assessed to decipher their individual and synergistic effects on precipitation extent. Our research aims to uncover the optimal conditions for maximizing alumina precipitation while maintaining product quality and seed particle stability. By offering new insights and practical solutions, this study contributes to the ongoing advancement of alumina production within the Bayer process.

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Exploring innovative strategies for precipitation extent enhancement in a downscaled Bayer process tank

Bakhtom,

Ghasemzade Bariki,

Movahedirad

2024

Can J Chem Eng

View full text Add to dashboard Cite

show abstract

Machine learning-aided tailoring of double-emulsions within double-T microchannel

Bariki,

Movahedirad,

layaei

2024

Microfluid Nanofluid

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Development of supervised machine learning model for prediction of TEG regeneration performance in natural gas dehydration

Moghaddam,

Sadeq

2023

Chem. Pap.

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Machine learning-guided prediction and optimization of precipitation efficiency in the Bayer process

Cited by 5 publications

References 39 publications

Exploring innovative strategies for precipitation extent enhancement in a downscaled Bayer process tank

Exploring innovative strategies for precipitation extent enhancement in a downscaled Bayer process tank

Machine learning-aided tailoring of double-emulsions within double-T microchannel

Development of supervised machine learning model for prediction of TEG regeneration performance in natural gas dehydration

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