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    Specification and prediction of nickel mobilization using artificial intelligence methods

    Access Status
    Open access via publisher
    Authors
    Gholami, Raoof
    Ziaii, M.
    Doulati Ardejani, F.
    Maleki, S.
    Date
    2011
    Type
    Journal Article
    
    Metadata
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    Citation
    Gholami, R. and Ziaii, M. and Doulati Ardejani, F. and Maleki, S. 2011. Specification and prediction of nickel mobilization using artificial intelligence methods. Central European Journal of Geosciences. 3 (4): pp. 375-384.
    Source Title
    Central European Journal of Geosciences
    DOI
    10.2478/s13533-011-0039-x
    ISSN
    2081-9900
    School
    Curtin Sarawak
    URI
    http://hdl.handle.net/20.500.11937/10308
    Collection
    • Curtin Research Publications
    Abstract

    Groundwater and soil pollution from pyrite oxidation, acid mine drainage generation, and release and transport of toxic metals are common environmental problems associated with the mining industry. Nickel is one toxic metal considered to be a key pollutant in some mining setting; to date, its formation mechanism has not yet been fully evaluated. The goals of this study are 1) to describe the process of nickel mobilization in waste dumps by introducing a novel conceptual model, and 2) to predict nickel concentration using two algorithms, namely the support vector machine (SVM) and the general regression neural network (GRNN). The results obtained from this study have shown that considerable amount of nickel concentration can be arrived into the water flow system during the oxidation of pyrite and subsequent Acid Drainage (AMD) generation. It was concluded that pyrite, water, and oxygen are the most important factors for nickel pollution generation while pH condition, SO 4, HCO3, TDS, EC, Mg, Fe, Zn, and Cu are measured quantities playing significant role in nickel mobilization. SVM and GRNN have predicted nickel concentration with a high degree of accuracy. Hence, SVM and GRNN can be considered as appropriate tools for environmental risk assessment. © Versita Sp. z o.o.

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