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In this paper, prediction of copper and molybdenum grades and their recoveries of an industrial flotation plant are investigated using the Artificial Neural Networks (ANN) model. Process modeling has done based on 92 datasets collected at different operational conditions and feed characteristics. The prominent parameters investigated in this network were pH, collector, frother and FOil concentration, size percentage of feed passing 75 microns, moisture content in feed, solid percentage, and grade of copper, molybdenum, and iron in feed. A multilayer perceptron neural network, with 10:10:10:4 structure (two hidden layers), was used to estimate metallurgical performance. To obtain the optimal hidden layers and nodes in a layer, a trial and error procedure was done. In training and testing phases, it achieved quite correlations of 0.98 and 0.93 for Copper grade, of 0.99 and 0.92 for Copper recovery, of 0.99 and 0.92 for Molybdenum grade and of 0.99 and 0.94 for Molybdenum recovery prediction, respectively. The proposed neural network model can be applied to determine the most beneficial operational conditions for the expected Copper and Molybdenum grades and their recovery in final concentration of the industrial copper flotation process.

Owing to the high toxicity of cerium toward living organisms, it is necessary to remove cerium from aqueous solutions. In this regard, the extraction of cerium (Ce (III)) from nitrate media by Cyanex 572 under different operating conditions was examined in this study. The effect of contact time, pH, extractant concentration, and nitrate ion concentration were investigated to characterize the extraction behavior of cerium and based on these outcomes, an extraction mechanism was suggested. The analysis of infrared spectra of Cyanex 572 before and after the extraction of cerium indicated that cerium extraction was performed via a cation-exchange mechanism. Then, the predictive models based on intelligent techniques [artificial neural network (ANN) and hybrid neural-genetic algorithm (GA-ANN)] were developed to predict the cerium extraction efficiency. The GA-ANN model provided better predictions that resulted higher R2 and lower MSE compared to ANN model for predicting the extraction efficiency of cerium by Cyanex 572. The interactive effects of each process variable on cerium extraction were also investigated systematically. pH was the most influential parameter on cerium extraction, followed by extractant concentration, nitrate ion concentration and contact time. Finally, the separation of cerium from other rare earth elements like La (III), Nd (III), Pr (III), and Y (III) was conducted and observed that the present system provides a better separation of cerium from rare heavy earth than light rare earths.

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