Track 6: Mining Engineering and Mine Planning

comminution geometallurgical variables. Journal of Sustainable Mining. 24, 472-485. hiips://doi.org/10.46873/2300- 3960.1470. Mariz, J. L. V., and Soofastaei, A. (2022). Advanced Analytics for Surface Mine Planning. In: Advanced Analytics in Mining Engineering. Cham, Springer. hiips://doi.org/10.1007/978-3-03091589-6_9. Mariz, J. L. V., Badiozamani, M. M., Peroni, R. L., and Silva, R. M. A. (2024). A critical review of bench aggregation and mining cut clustering techniques based on optimization and artificial intelligence to enhance the open-pit mine planning. Engineering Applications of Artificial Intelligence. 133, 108334. hiips://doi.org/10.1016/j.engappai.2024.108334. Mazzinghy, D. B.; Varela, N. V. M.; Brickey, A.; Suazo, G. I. N.; Ortiz, J.; and Souza, M. J. F. (2025). Mineral processing plant capacity based on geometallurgical block model scheduling. In: Proceeding of the 42nd International APCOM Symposium, Perth. Meagher, C., Dimitrakopoulos, R., and Avis, D. (2014). Optimized open pit mine design, pushbacks, and the gap problem-A review. Journal of Mining Science. 50(3), 508-526. hiips://doi.org/10.1134/S1062739114030132. Minelib (2025). Instance: marvin. Available from: <hiips://mansciweb.uai.cl/minelib/marvin.xhtml>. Morales, N., Jélvez, E., Nancel-Penard, P., Marinho, A., and Guimarães, O. (2015). A Comparison of Conventional and Direct Block Scheduling Methods for Open Pit Mine Production Scheduling. In: Proceeding of the 37th International APCOM Symposium, Alaska. Morales, N., Seguel, S., Cáceres, A., and Jélvez, E. (2019). Alarcón, M. Incorporation of Geometallurgical Attributes and Geological Uncertainty into Long-Term Open-Pit Mine Planning. Minerals. 9, 108. hiips://doi.org/10.3390/min9020108. Moreno, E., Espinoza, D., and Goycoolea, M. (2010). Large-scale multi-period precedence constrained knapsack problem: A mining application. Electronic Notes in Discrete Mathematics. 36, 407-414. hiips://doi.org/10.1016/j.endm.2010.05.052. Niquini, F. G. F. and Costa, J. F. C. L. (2020a). Mass and Metallurgical Balance Forecast for a Zinc Processing Plant Using Artificial Neural Networks. Natural Resources Research, 29, 35693580. hiips://doi.org/10.1007/s11053-020-09678-4. Niquini, F. G. F. and Costa, J. F. C. L. (2020b). Forecasting mass and metallurgical balance at a gold processing plant using modern multivariate statistics. REM - International Engineering Journal, 73 (4), 571- 578. hiip://dx.doi.org/10.1590/0370-44672020730001. Niquini, F. G. F., Branches, A. M. B., Costa, J. F. C. L., Moreira, G. C., Schneider, C. L., Araújo, F. C., and Capponi, L. N. (2023). Recursive Feature Elimination and Neural Networks Applied to the Forecast of Mass and Metallurgical Recoveries in A Brazilian Phosphate Mine. Minerals, 13, 748. hiips://doi.org/10.3390/min13060748. Niquini, F. G. F., Andrade, I. A., Costa, J. F. C. L., Silva, V. M., and Marcelino, R. S. (2025). A workflow to create geometallurgical clusters without looking directly at geometallurgical

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