Multivariate stochastic modeling of the influence of particle descriptors on flotation-based separation behavior

Many separation processes are based on particle properties like wettability, composition, size and shape. Therefore, it is necessary to analyze the influence of different particle properties on the particle separation behavior. A common tool for classifying particle separation processes are Tromp functions. Recently, multivariate Tromp functions, computed by means of non-parametric kernel density estimation, have emerged which describe the separation behavior with respect to multidimensional particle properties. In this paper, an alternative flexible parametric modeling approach is proposed to model the separation behavior of particle systems with multivariate Tromp functions observed by mineral liberation analyzer (MLA) image measurements. However, different particle properties such as particle wettability cannot be observed in MLA data, although many separation processes such as flotation-based particle separation processes rely on differences in wettabilities. In order to analyze the influence of wettability on particle separation behavior, bivariate Tromp functions for area-equivalent diameter and aspect ratio of differently shaped glass particle systems with differently modified wettabilities are computed in a case study. Comparing the computed Tromp functions reveals
the influence of particle wettability on the separation behavior. In addition, we extend the parametric approach to model multivariate Tromp functions to handle separation processes when image measurements are not available for all separation streams.