Representation of classifier distributions in terms of hypergeometric functions

This paper derives alternative analytical expressions for classifier product distributions in terms of Gauss hypergeometric function, ₂F₁, by considering feed distribution defined in terms of Gates–Gaudin–Schumann function and efficiency curve defined in terms of a logistic function. It is shown that classifier distributions under dispersed conditions of classification pivot at a common size and the distributions are difference similar. The paper also addresses an inverse problem of classifier distributions wherein the feed distribution and efficiency curve are identified from the measured product distributions without needing to know the solid flow split of particles to any of the product streams.