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Volume 26 Issue 5
May  2019
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文章导航 >  矿物冶金与材料学报（英文）  > 2019  >  26(5): 538-546
Jalil Pazhoohan, Hossein Beiki,  and Morteza Esfandyari, Experimental investigation and adaptive neural fuzzy inference system prediction of copper recovery from flotation tailings by acid leaching in a batch agitated tank, Int. J. Miner. Metall. Mater., 26(2019), No. 5, pp. 538-546. https://doi.org/10.1007/s12613-019-1762-4
Cite this article as:
Jalil Pazhoohan, Hossein Beiki,  and Morteza Esfandyari, Experimental investigation and adaptive neural fuzzy inference system prediction of copper recovery from flotation tailings by acid leaching in a batch agitated tank, Int. J. Miner. Metall. Mater., 26(2019), No. 5, pp. 538-546. https://doi.org/10.1007/s12613-019-1762-4
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研究论文

Experimental investigation and adaptive neural fuzzy inference system prediction of copper recovery from flotation tailings by acid leaching in a batch agitated tank

  • Department of Chemical Engineering, Quchan University of Technology, Quchan 94771-67335, Iran

  • Department of chemical engineering, University of Bojnord, Bojnord 94531-55111, Iran

  • 通讯作者:

    Hossein Beiki    E-mail: hbeiki@qiet.ac.ir

  • The potential of copper recovery from flotation tailings was experimentally investigated using a laboratory-mixing tank. The experiments were performed with solid weight percentages of 30wt%, 35wt%, 40wt% and 45wt% in water. The measurements revealed that adding sulfuric acid all at once to the tank rapidly increased the efficiency of the leaching process, which was attributed to the rapid change in the acid concentration. The rate of iron dissolution from tailings was less than when the acid was added gradually. The sample with 40wt% solid is recommended as an appropriate feed for the recovery of copper. The adaptive neural fuzzy system (ANFIS) was also used to predict the copper recovery from flotation tailings. The back-propagation algorithm and least squares method were applied for the training of ANFIS. The validation data was also applied to evaluate the performance of these models. Simulation results revealed that the testing results from these models were in good agreement with the experimental data.
    • Research Article

    Experimental investigation and adaptive neural fuzzy inference system prediction of copper recovery from flotation tailings by acid leaching in a batch agitated tank

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      Abstract
    • The potential of copper recovery from flotation tailings was experimentally investigated using a laboratory-mixing tank. The experiments were performed with solid weight percentages of 30wt%, 35wt%, 40wt% and 45wt% in water. The measurements revealed that adding sulfuric acid all at once to the tank rapidly increased the efficiency of the leaching process, which was attributed to the rapid change in the acid concentration. The rate of iron dissolution from tailings was less than when the acid was added gradually. The sample with 40wt% solid is recommended as an appropriate feed for the recovery of copper. The adaptive neural fuzzy system (ANFIS) was also used to predict the copper recovery from flotation tailings. The back-propagation algorithm and least squares method were applied for the training of ANFIS. The validation data was also applied to evaluate the performance of these models. Simulation results revealed that the testing results from these models were in good agreement with the experimental data.
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