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Modeling and Optimization of SE and SI of Copper Flotation via Hybrid GA–ANN

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Abstract

In the present paper, a new attitude has been proposed for optimization of the separation efficiency (SE) and the Gaudin’s selectivity index (SI) in a flotation process by Hybrid artificial neural network (ANN) and genetic algorithm (GA). The chemical reagent’s dosage (collector, frother and fuel oil), pH, solid percentage, feed rate, Cu, Mo, and Fe grades in the flotation feed were selected as input variables and the SE-Cu and SI-Mo and SI-Fe were selected as output ones. Multilayer NN with back propagation (BP) algorithm was trained by the standard Bayesian regulation algorithm in which the validation data set did not required to be apart from its training. This algorithm with four-layer was used to relate output and input variables. Employment of Hybrid GA–ANN method resulted in significant improvement on GA fitnesses, as SE-Cu = 88, SI-Mo = 4.47 and SI of Fe = 12.85 were achieved. The input parameters corresponding to the fitnesses were as follows: pH = 12.25; the grade of Cu = 0.55%, Mo = 0.04% and Fe = 5.53%; the collector, frother and fuel–oil concentrations being 16.55, 15.54 and 2.71 (g/ton), respectively; the solid percentage was 25.84% and feed rate was 38,380 ton/day. The best fitness of GA was obtained after 10 generations by MSE value of 2.23.

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Authors and Affiliations

  1. Department of Mining and Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran

    Omid Salmani Nuri, Ebrahim Allahkarami & Aliakbar Abdollahzadeh

  2. University of Kashan, Kashan, Iran

    Aliakbar Abdollahzadeh

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  1. Omid Salmani Nuri
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  2. Ebrahim Allahkarami
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  3. Aliakbar Abdollahzadeh
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Correspondence to Omid Salmani Nuri.

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Salmani Nuri, O., Allahkarami, E. & Abdollahzadeh, A. Modeling and Optimization of SE and SI of Copper Flotation via Hybrid GA–ANN. Trans Indian Inst Met 70, 2255–2263 (2017). https://doi.org/10.1007/s12666-017-1078-x

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