Control of Industrial Copper Solvent Extraction Process

Tiina Komulainen, Ari Rantala, F. Doyle, Sirkka-Liisa Jämsä-Jounela

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    A two level control strategy that stabilizes and optimizes the production of an industrial copper solvent extraction process is presented. The stabilizing layer consists of a multi-input multi-output controller or two single-input single-output controllers with additional four feedforward compensators that regulate the flow rates in the copper solvent extraction process. The optimization layer consists of an optimizer that maximizes the production of the copper solvent extraction process and gives setpoints to the controllers at the stabilizing level. The mechanistic plant models, verified with industrial data, are linearized by identifying first and higher order transfer function models from simulated PRBS data. On the basis of the linear models, the interactions of the controlled variables, and the pairing of the controlled and manipulated variables are studied and the optimizer and the controllers designed. The control strategy employing two PI-control loops or a model predictive controller and additionally four feedforward control loops is successfully tested against simulated disturbances and setpoint changes. The control strategy is also compared to the data collected from the industrial plant under manual control. With this two level control strategy the production of the copper solvent extraction process is increased by 3-5% and the process variation is decreased by 70-90% compared to the manual operation of the case industrial plant. The results gained in simulation environment are successful and encouraging for further testing in an industrial plant. Copyright © 2006
    Original languageEnglish
    Pages (from-to)2-15
    JournalJournal of Process Control
    Issue number1
    Publication statusPublished - 2009
    MoE publication typeA1 Journal article-refereed


    • control
    • Copper solvent extraction
    • SX/EW
    • hydrometallurgy
    • extractive metallurgy
    • industrial control
    • PI control
    • model predictive control
    • control application


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