This supplementary material supports the Ph.D. dissertation of Lucas Pereira, submitted to the Faculty 3 of the TU Bergakademie Freiberg.

C2.SM1.Percentiles.xlsx: Mentioned in the chapter 2 of the dissertation, this file contains, in terms of percentiles, the distribution of every particle descriptive variable in the different samples used to train the logistic regression models of the case study presented in this chapter.

C2.SM2.Coefficients.xlsx: Mentioned in the chapter 2 of the dissertation, this file contains the complete list of coefficients assigned to each variable, in each separation unit, of the case study presented in this chapter.

C4.SM1.StatWeight.xlsx: Mentioned in the chapter 4 of the dissertation, this file contains a detailed explanation of the statistical weights of particles and how they can be used to integrate a set of particle datasets from different streams and size fractions into a single and balanced training dataset.

{"references": ["Pereira, L., Frenzel, M., Khodadadzadeh, M., Tolosana-Delgado, R., Gutzmer, J., 2021. A self-adaptive particle-tracking method for minerals processing. Journal of Cleaner Production vol. 279. doi: 10.1016/j.jclepro.2020.123711", "Pereira, L., Frenzel, M., Hoang, D.H., Tolosana-Delgado, R., Rudolph, M., Gutzmer, J., 2021. Computing single-particle flotation kinetics using automated mineralogy data and machine learning. Minerals Engineering vol. 170. doi: 10.1016/j.mineng.2021.107054"]}