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    Probability density function of bubble size based reagent dosage predictive control for copper roughing flotation

    199714_199714.pdf (690.6Kb)
    Access Status
    Open access
    Authors
    Zhu, J.
    Gui, W.
    Yang, C.
    Xu, Honglei
    Wang, X.
    Date
    2014
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Zhu, J. and Gui, W. and Yang, C. and Xu, H. and Wang, X. 2014. Probability density function of bubble size based reagent dosage predictive control for copper roughing flotation. Control Engineering Practice. 29: pp. 1-12.
    Source Title
    Control Engineering Practice
    DOI
    10.1016/j.conengprac.2014.02.021
    ISSN
    09670661
    Remarks

    NOTICE: this is the author’s version of a work that was accepted for publication in Control Engineering Practice. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Control Engineering Practice, Vol. 29 (2014). DOI: 10.1016/j.conengprac.2014.02.021

    URI
    http://hdl.handle.net/20.500.11937/36046
    Collection
    • Curtin Research Publications
    Abstract

    As an effective measurement indicator of bubble stability, bubble size structure is believed to be closely related to flotation performance in copper roughing flotation. Moreover, reagent dosage has a very important influence on bubble size structure. In this paper, a novel reagent dosage predictive control method based on probability density function (PDF) of bubble size is proposed to implement the indices of roughing circuit. Firstly, the froth images captured in the copper roughing are segmented by using a two-pass watershed algorithm. In order to characterize bubble size structure with non-Gaussian feature, an entropy based B-spline estimator is hence investigated to depict the PDF of the bubble size. Since the weights of B-spline are interrelated and related to the reagent dosage, a multi-output least square support vector machine (MLS-SVM) is applied to depict a dynamic relationship between the weights and the reagent dosage. Finally, an entropy based optimization algorithm is proposed to determine reagent dosage in order to implement tracking control for the PDF of the output bubble size. Experimental results can show the effectiveness of the proposed method.

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