Flotation Recovery Prediction of Zijinshan Copper Ore Based on I−GWO−BP Neural Network

XIA Yongtao; MA Yingqiang; YIN Wanzhong; ZHONG Shuiping; LU Jun; ZHANG Dewen; ZHAN Yinquan

doi:10.13779/j.cnki.issn1001-0076.2023.03.005

2023 Vol. 43, No. 3

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Article navigation > Conservation and Utilization of Mineral Resources > 2023 > 43(3): 51-59

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XIA Yongtao, MA Yingqiang, YIN Wanzhong, ZHONG Shuiping, LU Jun, ZHANG Dewen, ZHAN Yinquan. Flotation Recovery Prediction of Zijinshan Copper Ore Based on I−GWO−BP Neural Network[J]. Conservation and Utilization of Mineral Resources, 2023, 43(3): 51-59. doi: 10.13779/j.cnki.issn1001-0076.2023.03.005

Citation:

XIA Yongtao, MA Yingqiang, YIN Wanzhong, ZHONG Shuiping, LU Jun, ZHANG Dewen, ZHAN Yinquan. Flotation Recovery Prediction of Zijinshan Copper Ore Based on I−GWO−BP Neural Network[J]. Conservation and Utilization of Mineral Resources, 2023, 43(3): 51-59. doi: 10.13779/j.cnki.issn1001-0076.2023.03.005

Flotation Recovery Prediction of Zijinshan Copper Ore Based on I−GWO−BP Neural Network

1.
Zijin School of Geology and Mining, Fuzhou University, Fuzhou 350108, China
2.
FujianKey Laboratory of Green Extraction and High−value Utilization of New Energy Metals, Fuzhou 350108, China
3.
Zijin Mining Group Co., LTD., Shanghang 364200, China

More Information

Corresponding author: MA Yingqiang, myq90426@qq.com

Received Date: 11 February 2023

Publish Date: 15 June 2023

Abstract

Abstract

The traditional method of measuring flotation recovery has some problems, such as low efficiency and hysteresis. Combined with the flotation plant production of Zijinshan sulfide copper ore, characteristics selection of flotation condition factors such as raw ore grade and dosage of ammonium dibutyl dithiophosphate was carried out based on MI (Mutual Information) method. On this basis, three prediction models of flotation recovery were established based on BP (Back Propagation) GWO−BP (Grey Wolf Optimizer−Back Propagation) and I−GWO−BP (Improved−Grey Wolf Optimizer−Back Propagation) . The flotation workshop production data of Zijinshan sulfide copper ore were selected for neural network training and verification test, and the accuracy of the flotation recovery prediction model was analyzed. The results showed that compared with BP and GGO−BP, the flotation recovery prediction model based on I−GWO−BP had a root mean squared error and a correlation coefficient and the predicted value of flotation recovery was the closest to the true value, and the generalization ability of the network was significantly stronger. The results of this study can support the development of efficient, accurate and automatic online prediction techniques for flotation recovery.
- flotation recovery /
- mutual information method /
- BP neural network /
- improved gray wolf algorithm

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References

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