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Application of Artificial Neural Networks to Predict Dry Magnetic Separation of Low-Grade Hematite Fines

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Abstract

An artificial neural network model to predict the performance of induced roll high-intensity magnetic separator while upgrading low-grade iron ore fines is presented in the present investigation. Key process parameters of induced roll magnetic separator operation, i.e., applied current, speed of rotor, splitter position, particle size, and feed rate, are used as the network input. A feedforward ANN is designed with one hidden layer, with units varying from 9 to 40 and capable of estimating the separation performance (iron content, silica content, iron recovery, and silica rejection of the magnetic product) in an induced roll magnetic separator. Simulated results obtained using the neural network modeling correspond closely to the actual results with a correlation of coefficient (R2) > 0.95 in training and testing stages. The developed models are further used to understand the sensitivity of the studied variables. The effects of speed of the rotor and splitter position are found to be more sensitive to the separation. Furthermore, an optimized solution is designed to predict the best achievable grade-recovery curve for this pre-concentration.

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Acknowledgement

The authors are thankful to the management of Tata Steel Ltd. for the support and permission to publish this study. SKT would like to acknowledge Labex Resources 21 supported by the French National Research Agency through the national program “Investissements d’Avenir” [reference ANR–10–LABX–21] for his fellowship.

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Authors and Affiliations

  1. Research and Development Division, Tata Steel Ltd., Jamshedpur, India

    Sunil Kumar Tripathy & Itishree Mohanty

  2. GeoRessources, Université de Lorraine, CNRS, F54505, Nancy, France

    Sunil Kumar Tripathy & Lev O. Filippov

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  1. Sunil Kumar Tripathy
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  2. Itishree Mohanty
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  3. Lev O. Filippov
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Correspondence to Sunil Kumar Tripathy.

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Tripathy, S.K., Mohanty, I. & Filippov, L.O. Application of Artificial Neural Networks to Predict Dry Magnetic Separation of Low-Grade Hematite Fines. Trans Indian Inst Met 73, 1797–1807 (2020). https://doi.org/10.1007/s12666-020-01985-x

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  • DOI: https://doi.org/10.1007/s12666-020-01985-x

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