A CFD-based surrogate model for predicting slurry pipe flow pressure drops

Abstract

Slurry pipelines are extensively employed in most mining operations to transport raw materials and tailings. The aim of the paper is twofold: on the one hand, to develop a generalized slurry flow model using computational fluid dynamics (CFD) to develop a better insight into the complexity of slurry pipe flows, and on the other hand, to develop a surrogate model for pressure drop predictions, which can be used in operations simulation and monitoring tools. A two-fluid model based on the Eulerian-Eulerian approach along with the SST k-ω turbulence model was used. The motion of the solid phase was modeled by the kinetic theory of granular flow (KTGF) to account for both particle-particle and particle-wall interactions. The model was validated by comparison with experimental data available in literature. A new two-phase pressure drop correlation is proposed based on the numerical computations of horizontal pipes featuring 525 data points.