Parametric Stochastic Modeling of Particle Descriptor Vectors for Studying the Influence of Ultrafine Particle Wettability and Morphology on Flotation-Based Separation Behavior

Practically all particle separation processes depend on more than one particulate property. In the case of industrially important froth flotation separation, these properties concern wettability, composition, size and shape. Therefore, it is useful to analyze different descriptors when studying influence wettability morphology behavior systems. A common tool for classifying are Tromp functions. Recently, multivariate functions, computed by means non-parametric kernel density estimation, have emerged which characterize with respect multidimensional vectors descriptors. present paper, an alternative parametric approach based copulas proposed in order compute functions and, this way, particular, bivariate area-equivalent diameter aspect ratio glass particles morphologies surface modification been computed, image characterization mineral liberation analysis (MLA). Comparing obtained another reveals combined multiple factors, size, introduces innovative evaluating separation. addition, we extend copula-based method computation processes, also measurements not available separated fractions.