Experimental investigation and adaptive neural fuzzy inference system prediction of copper recovery from flotation tailings by acid leaching in a batch agitated tank

论文摘要

The potential of copper recovery from flotation tailings was experimentally investigated using a laboratory-mixing tank. The experiments were performed with solid weight percentages of 30 wt%, 35 wt%, 40 wt% and 45 wt% in water. The measurements revealed that adding sulfuric acid all at once to the tank rapidly increased the efficiency of the leaching process, which was attributed to the rapid change in the acid concentration. The rate of iron dissolution from tailings was less than when the acid was added gradually. The sample with 40 wt% solid is recommended as an appropriate feed for the recovery of copper. The adaptive neural fuzzy system（ANFIS） was also used to predict the copper recovery from flotation tailings. The back-propagation algorithm and least squares method were applied for the training of ANFIS. The validation data was also applied to evaluate the performance of these models. Simulation results revealed that the testing results from these models were in good agreement with the experimental data.

论文目录

• 1. Introduction
• 2. ANFIS theory
• 3. Experimental setup and procedure
• 4. Results and discussion
•   4.1. Acid addition methods
•   4.2. Effect of the initial acid amount
•   4.3. ANFIS comparative analyses and discussion
• 5. Conclusions

文章来源

类型: 期刊论文

作者: Jalil Pazhoohan,Hossein Beiki,Morteza EsfANDyari

来源: International Journal of Minerals Metallurgy and Materials 2019年05期

年度: 2019

分类: 工程科技Ⅱ辑,工程科技Ⅰ辑

专业: 矿业工程,冶金工业

分类号: TD926.4;TF811

页码: 538-546

总页数: 9

文件大小: 445K

下载量: 22