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    Combined fuzzy based feedforward and bubble size distribution based feedback control for reagent dosage in copper roughing process

    Access Status
    Fulltext not available
    Authors
    Zhu, J.
    Gui, W.
    Liu, J.
    Xu, Honglei
    Yang, C.
    Date
    2016
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Zhu, J. and Gui, W. and Liu, J. and Xu, H. and Yang, C. 2016. Combined fuzzy based feedforward and bubble size distribution based feedback control for reagent dosage in copper roughing process. Journal of Process Control. 39: pp. 50-63.
    Source Title
    Journal of Process Control
    DOI
    10.1016/j.jprocont.2015.12.003
    ISSN
    0959-1524
    School
    Department of Mathematics and Statistics
    URI
    http://hdl.handle.net/20.500.11937/34652
    Collection
    • Curtin Research Publications
    Abstract

    All rights reserved. A combined fuzzy based feedforward (FBF) and bubble size distribution (BSD) based feedback reagent dosage control strategy is proposed to implement the product indices in copper roughing process. A fuzzy theory based feedforward compensator will be used to calculated the reagent dosage in advance to eliminate the influence of large disturbances according to ore grade and handling capacity. Since the bubble size is believed to be closely related to flotation performance and responds to changes in the reagent dosage, using BSD based feedback predictive control calculates the reagent dosage to stabilize the flotation running. Instead of simple statistic feature, the bubble size with non-Gaussian feature is characterized to be probability density function (PDF) by using B-spline. A multi-output least square support vector machine (MLS-SVM) based is then applied to establish a dynamical relationship between the weights of B-spline and the reagent dosage since the weights are interrelated and related to the reagent dosage. A multiple step based optimization algorithm is finally proposed to determine the reagent dosage. Experimental results can show the effectiveness of the proposed method.

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