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  • Modelling of the adsorption of Pb, Cu and Ni ions from single and multi-component aqueous solutions by date seed derived biochar: Comparison of six machine learning approaches

    Author(s)
    El Hanandeh, Ali
    Mahdi, Zainab
    Imtiaz, MS
    Griffith University Author(s)
    El Hanandeh, Ali
    Mahdi, Zainab A.
    Year published
    2021
    Metadata
    Show full item record
    Abstract
    Biochar is an effective material for the removal of heavy metals from wastewater. Operational conditions, such as metal initial concentration, temperature, contact time as well as the presence of competing ions can impact the effectiveness of the treatment process. While several models have been proposed for modelling the adsorption process, no model currently exists that accounts for the mutual interactions of key process parameters on the adsorption capacity in multi-solute systems. The aim of this study is to address this gap in knowledge by formulating a multi-input multi-output (MIMO) model, which takes into account the ...
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    Biochar is an effective material for the removal of heavy metals from wastewater. Operational conditions, such as metal initial concentration, temperature, contact time as well as the presence of competing ions can impact the effectiveness of the treatment process. While several models have been proposed for modelling the adsorption process, no model currently exists that accounts for the mutual interactions of key process parameters on the adsorption capacity in multi-solute systems. The aim of this study is to address this gap in knowledge by formulating a multi-input multi-output (MIMO) model, which takes into account the effect of mutual interactions of key factors while predicting heavy metals adsorption capacity of the biochar in single and multi-solute systems. In this study, we use machine learning models, specifically several ANN models, radial basis and gradient boosting algorithms to model the MIMO process. The results of our models provide highly accurate predictions (R2 > 0.99). The generalized regression network provided the best match to the experimental data. This approach can allow operators to predict how the adsorption system will respond to changes in the operations and hence provide them with a tool for process optimization.
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    Journal Title
    Environmental Research
    Volume
    192
    DOI
    https://doi.org/10.1016/j.envres.2020.110338
    Subject
    Chemical sciences
    Environmental sciences
    Biological sciences
    Science & Technology
    Life Sciences & Biomedicine
    Public, Environmental & Occupational Health
    Ecology
    Publication URI
    http://hdl.handle.net/10072/405083
    Collection
    • Journal articles

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