Abstract
Copper oxide ore is an important copper ore resource. For a certain copper oxide ore in Yunnan, China, experiments have been conducted on the grinding fineness, collector dosage, sodium sulfide dosage, inhibitor dosage, and activator dosage. The results showed that, by controlling the above conditions, better sulfide flotation indices of copper oxide ore are obtained. Additionally, ammonium bicarbonate and ethylenediamine phosphate enhanced the sulfide flotation of copper oxide ore, whereas the combined activator agent exhibited a better performance than either individual activator. In addition, to optimize all of the conditions in a more reasonable way, a combination of the 5-11-1 genetic algorithm and back propagation neural network (GA–BPNN) was used to set up a mathematical optimization model. The results of the back propagation neural network (BPNN) model showed that the R2 value was 0.998, and the results were in accordance with the requirement model. After 4169 iterations, the error in the objective function was 0.001, which met the convergence requirements for the final solution. The genetic algorithm (GA) model was used to optimize the BPNN model. After 100 generations, a copper recovery of 87.62% was achieved under the following conditions: grinding fineness of 0.074 mm, which accounted for 91.7%; collector agent dosage of 487.7 g/t; sodium sulfide dosage of 1157.2 g/t; combined activator agent dosage of 537.8 g/t; inhibitor dosage of 298.9 g/t. Using the combined amine and ammonium salt to enhance the sulfide activation efficiency, a GA–BPNN model was used to achieve the goal of global optimizations of copper oxide ore and good flotation indices were obtained.
Highlights
Copper oxide is an important mineral resource, and studying the recovery of low grade copper oxide is of great importance for solving the problems of the shortage of copper resources and promoting its efficient utilization [1,2,3]
Prediction optimization model of copper oxide flotation concentrate, which was based on the genetic algorithm and back propagation neural network (GA–BPNN), was established considering the single factor experimental data
The results showed that monia-ammonium activator exhibited better activation effects along with good synergistic were in accordance with those obtained in the present study
Summary
Copper oxide is an important mineral resource, and studying the recovery of low grade copper oxide is of great importance for solving the problems of the shortage of copper resources and promoting its efficient utilization [1,2,3]. Some studies have shown that the combination of amine and an ammonium salt activator exhibits a satisfactory effect on the flotation of sulfide in copper oxide minerals [11,12,13]. On the basis of the experimental study, an optimization model to reasonably predict the recovery of floatation concentrate was established to improve the flotation effect of copper oxide minerals and provide references for production and further research. Using the neural network and genetic algorithm, Allahkarami [23] established a mathematical model to improve the copper recovery and grade in the process of industrial flotation. A prediction optimization model of copper oxide flotation concentrate, which was based on the genetic algorithm and back propagation neural network (GA–BPNN), was established considering the single factor experimental data. A regression analysis model was established for comparing the results
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