Estimation of selectivity index and separation efficiency of copper flotation process using ANN model. Issue 1 (2nd January 2017)

• Record Type: Journal Article
• Authors: Salmani Nuri, Omid; Allahkarami, Ebrahim; Irannajad, Mehdi; Abdollahzadeh, Aliakbar
• Is Part Of: Geosystem engineering. Volume 20:Issue 1(2017)
• Page Start: 41
• Page End: 50
• Publication Date: 2017-01-02
• Subjects: Artificial neural network -- flotation -- separation efficiency -- selectivity index -- copper ore; Mining engineering -- Periodicals; Petroleum engineering -- Periodicals; Gas engineering -- Periodicals; Geology, Economic -- Periodicals; 620
• DOI: 10.1080/12269328.2016.1220334
• Languages: English
• ISSNs: 1226-9328

Abstract

Abstract: Artificial neural network was used to predict the copper ore flotation indices of Separation Efficiency (SE) and Selectivity Index (SI) within different operational conditions. The aim was to predict SECu and SIFe and SIMo as a function of chemical reagent dosages (collector, frother, modifier), feed rate, solid percentage, and the feed grade of Cu, Fe, and Mo. A three-layered back propagation neural network with the structure of 9-10-10-3 is selected and standard Bayesian regularization was used as a training function in which, it is unnecessary the validation data-set being apart from the training data-set. The advantage of this algorithm is the minimization of weights and linear combinations of squared errors of producing the appropriate network. In the training and testing stages, the quite satisfactory correlation coefficient of 1 for three training outputs and .93, .9, and .88 for testing outputs was achieved. The results show that the proposed approach models can be used to determine the most advantageous industrial conditions for the expected SE and SI in the froth flotation process.