7
[4] Nasiri, M., Iqbal, S., &Särkkä, S. (2024). Physics-
informed machine learning for grade prediction in
froth flotation. arXiv preprint, arXiv:2408.15267.
[5] Alsafasfeh, A., Alagha, L., Alzidaneen, A., &
Nadendla, V. S. S. (2022). Optimization of flota-
tion efficiency of phosphate minerals in mine tail-
ings using polymeric depressants: Experiments and
machine learning. Physicochemical Problems of
Mineral Processing, 58(4), 150477.
[6] Szmigiel, A., Apel, D. B., Skrzypkowski, K., Wojtecki,
L., &Pu, Y. (2024). Advancements in machine
learning for optimal performance in flotation pro-
cesses: A review. Minerals, 14(4), 331.
[7] Ortiz, F., Villalobos, S., Opazo, J., &Zúñiga, P.
(2022). Optimization of Ore Flotation Process using
Artificial Intelligence algorithms. Proceedings of the
International Mineral Processing Conference.
[8] Gomez-Flores, A., Heyes, G. W., Ilyas, S., &Kim, H.
(2022). Prediction of grade and recovery in flotation
from physicochemical and operational aspects using
machine learning models. Minerals Engineering,
183, 107627.
[4] Nasiri, M., Iqbal, S., &Särkkä, S. (2024). Physics-
informed machine learning for grade prediction in
froth flotation. arXiv preprint, arXiv:2408.15267.
[5] Alsafasfeh, A., Alagha, L., Alzidaneen, A., &
Nadendla, V. S. S. (2022). Optimization of flota-
tion efficiency of phosphate minerals in mine tail-
ings using polymeric depressants: Experiments and
machine learning. Physicochemical Problems of
Mineral Processing, 58(4), 150477.
[6] Szmigiel, A., Apel, D. B., Skrzypkowski, K., Wojtecki,
L., &Pu, Y. (2024). Advancements in machine
learning for optimal performance in flotation pro-
cesses: A review. Minerals, 14(4), 331.
[7] Ortiz, F., Villalobos, S., Opazo, J., &Zúñiga, P.
(2022). Optimization of Ore Flotation Process using
Artificial Intelligence algorithms. Proceedings of the
International Mineral Processing Conference.
[8] Gomez-Flores, A., Heyes, G. W., Ilyas, S., &Kim, H.
(2022). Prediction of grade and recovery in flotation
from physicochemical and operational aspects using
machine learning models. Minerals Engineering,
183, 107627.