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    Hybrid intelligence model based on image features for the prediction of flotation concentrate grade

    200228_130280_401380.pdf (3.084Mb)
    Access Status
    Open access
    Authors
    Wang, Y.
    Chen, X.
    Zhou, X.
    Gui, W.
    Caccetta, Louis
    Xu, Honglei
    Date
    2014
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Wang, Y. and Chen, X. and Zhou, X. and Gui, W. and Caccetta, L. and Xu, H. 2014. Hybrid intelligence model based on image features for the prediction of flotation concentrate grade. Abstract and Applied Analysis. 2014: Article ID 401380.
    Source Title
    Abstract and Applied Analysis
    DOI
    10.1155/2014/401380
    ISSN
    1085-3375
    School
    Department of Mathematics and Statistics
    Remarks

    This article is published under the Open Access publishing model and distributed under the terms of the Creative Commons License http://creativecommons.org/licenses/by/3.0/ Please refer to the licence to obtain terms for any further reuse or distribution of this work

    URI
    http://hdl.handle.net/20.500.11937/47835
    Collection
    • Curtin Research Publications
    Abstract

    In flotation processes, concentrate grade is the key production index but is difficult to be measured online. The mechanism models reflect the basic tendency of concentrate grade changes but cannot provide adequate prediction precision. The data-driven models based on froth image features provide accurate prediction within well-sampled space but rely heavily on sample data with less generalization capability. So, a hybrid intelligent model combining the two kinds of model is proposed in this paper. Since the information of image features is enormous, and the relationship between image features and concentrate grade is nonlinear, a B-spline partial least squares (BS-PLS) method is adopted to construct the data-driven model for concentrate grade prediction. In order to gain better generalization capability and prediction accuracy, information entropy is introduced to integrate the mechanism model and the BS-PLS model together and modify the model output online through an output deviation compensation strategy. Moreover, a slide window scheme is employed to update the hybrid model in order to improve its adaptability. The industrial practical data testing results show that the performance of the hybrid model is better than either of the two single models and it satisfies the accuracy and stability requirements in industrial applications.

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