Title: Flotation column performance optimisation based on imperialist competitive algorithm

Authors: Fardis Nakhaei; Mehdi Irannajad; Majid Yousefikhoshbakht

Addresses: Department of Mining and Metallurgical Engineering, Amirkabir University of Technology, Hafez Ave., Tehran 158754413, Iran ' Department of Mining and Metallurgical Engineering, Amirkabir University of Technology, Hafez Ave., Tehran 158754413, Iran ' Young Researchers and Elite Club, Hamedan Branch, Islamic Azad University, Hamedan 6518115743, Iran

Abstract: Optimisation in flotation columns is an important factor that influences the recovery and the grade of concentrate from the column. A column flotation process is a nonlinear, multi-variable problem with changeable parameters that traditional methods have difficulty optimising. Finding the optimum values of the column flotation variables is difficult due to the presence of many variables, large size of the search space, and many constraints. In this study, a novel optimisation method is presented based on a socio-politically motivated strategy, called imperialist competitive algorithm (ICA) which is paired with the multivariate non-linear regression (MNLR) model of the column flotation metallurgical performance as fitness function to optimise the operation parameters of flotation column in order to produce the optimum grade and recovery with respect to control parameter. The designed ICA system uses the practical data of pilot plant located in Sarcheshmeh copper complex. The results indicate that the proposed ICA finds accurately the best values of flotation column model parameters with error 1.36 × 10-16.

Keywords: flotation columns; simultaneous optimisation; ICA; imperialist competitive algorithm; concentrate grade; concentrate recovery; flotation column performance; performance optimisation; multivariate nonlinear regression; MNLR; metallurgical performance; copper; Iran.

DOI: 10.1504/IJMME.2016.074590

International Journal of Mining and Mineral Engineering, 2016 Vol.7 No.1, pp.1 - 17

Available online: 07 Feb 2016 *

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