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Modeling and Analysis of Copper, Iron, and Cobalt Recovery in a Hybrid Sulfuric Acid–Sodium Chloride Media Using Artificial Neural Network

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Abstract

The effects of operating conditions on copper, iron, and cobalt dissolution from sulfide ores in sulfuric acid–sodium chloride media are predicted using an artificial neural network (ANN) model. The artificial neural network model was developed, trained, and predicted using the feed-forward back-propagation (BP) algorithm. The sulfuric acid concentration, sodium chloride concentration, temperature, leaching time, and particle size were used as input variables to the model. A total of 204 sets of data generated from the leaching experiments were used to develop and train the model. To reach the network with a good agreement and highest generalizability and to reduce the error between the measured and predicted values, the neural networks with a various number of hidden layers (one to ten hidden layers) were investigated. In the regression analysis of the {5–10–3} architecture, the R2 values were 0.998, 0.997, and 0.997, while the MSE values were 0.111, 0,148, and 0.106 for the training, validation, and testing sets, respectively. The results showed that ANN has a high potential for predicting copper, cobalt, and iron recoveries. The increase in the number of hidden layers was found to improve the performance of the ANN model.

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Authors and Affiliations

  1. Department of Metallurgy, Faculty of Engineering and the Built Environment, University of Johannesburg, Doornfontein Campus, P.O. Box 17911, Johannesburg, 2028, South Africa

    Kubangala Brest Kasongo, Ntumba Edward Malenga & Mamookho Elizabeth Makhatha

  2. Department of Metallurgy and Materials, Faculty of Engineering, University of Lubumbashi, Kassapa Campus, P.O. Box 1825, Lubumbashi, Democratic Republic of Congo

    Mbal Henock-Michel Mwanat

  3. Department of Mechanical and Industrial Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Doornfontein Campus, P.O. Box 17911, Johannesburg, 2028, South Africa

    Lagouge Kwanda Tartibu

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  1. Kubangala Brest Kasongo
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Correspondence to Mbal Henock-Michel Mwanat or Mamookho Elizabeth Makhatha.

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Kasongo, K.B., Mwanat, M.HM., Malenga, N.E. et al. Modeling and Analysis of Copper, Iron, and Cobalt Recovery in a Hybrid Sulfuric Acid–Sodium Chloride Media Using Artificial Neural Network. J. Sustain. Metall. 8, 2001–2014 (2022). https://doi.org/10.1007/s40831-022-00622-9

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