Predicting screening/classification products via the pseudorandom number selection routine

Abstract

Screening/classification is performed for the separation of particles by their sizes. There are empirical, phenomenological, and numerical models for predicting the size distributions of screening/classification products. This paper introduces a new algorithm for the same purpose, which partially mimics phenomenological and numerical models. The algorithm iteratively selects the monosize fractions with pre-defined probabilities, then carries particle masses from the selected fractions either to the oversize or undersize product. The applicability of the algorithm was validated against the product size distributions from some industrial-scale screening/classification equipment - namely rake classifier, sieve bend (0.212 mm), vibrating screen (20 mm), and hydrocyclone - which are provided in the literature. The results show that the algorithm is predictive if each particle has a selection probability proportional to the mass of its monosize fraction and some power of its diameter. Results also suggest that vibrating screens can provide the sharpest size separation