900 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
Pu, Y., Szmigiel, A., Chen, J., Apel, D.B., FlotationNet: A
hierarchical deep learning network for froth flotation
recovery prediction. Powder Technology, 2020, 375,
317–326.
Quintanilla, P., Neethling, S.J., Brito-Parada, P.R.,
Modelling for froth flotation control: A review.
Minerals Engineering, 2021, 162, 106718.
Shahbazi, B., Chehreh Chelgani, S., Matin, S.S., Prediction
of froth flotation responses based on various condition-
ing parameters by Random Forest method. Colloids
and Surfaces A: Physicochemical and Engineering
Aspects, 2017, 529, 936–941.
Sverdrup, H.U., Ragnarsdottir, K.V., Koca, D., On mod-
elling the global copper mining rates, market supply,
copper price and the end of copper reserves. Resources,
Conservation and Recycling, 2014, 87, 158–174.
Pu, Y., Szmigiel, A., Chen, J., Apel, D.B., FlotationNet: A
hierarchical deep learning network for froth flotation
recovery prediction. Powder Technology, 2020, 375,
317–326.
Quintanilla, P., Neethling, S.J., Brito-Parada, P.R.,
Modelling for froth flotation control: A review.
Minerals Engineering, 2021, 162, 106718.
Shahbazi, B., Chehreh Chelgani, S., Matin, S.S., Prediction
of froth flotation responses based on various condition-
ing parameters by Random Forest method. Colloids
and Surfaces A: Physicochemical and Engineering
Aspects, 2017, 529, 936–941.
Sverdrup, H.U., Ragnarsdottir, K.V., Koca, D., On mod-
elling the global copper mining rates, market supply,
copper price and the end of copper reserves. Resources,
Conservation and Recycling, 2014, 87, 158–174.