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.

Slurry transportation via pipelines has garnered growing attention across various industries worldwide, thanks to its efficiency and environmental friendliness. It has emerged as a vital tool for conveying significant volumes of raw phosphate material from extraction points to industrial plants, where it is processed into fertilizers. Yet, optimal and secure pipeline operations necessitate the careful calibration of several physical parameters and their interplay to minimize energy losses. A thorough exploration of the flow pressure drop and the various factors that influence it constitutes a crucial step in attaining this goal. The computational fluid dynamics techniques required to simulate three-dimensional slurry pipe flows pose formidable challenges, primarily due to their high computational costs. Furthermore, numerical solutions for slurry flows are frequently subject to uncertainties arising from the initial and boundary conditions in the mathematical models employed. In this study, we propose the use of polynomial chaos expansions to estimate the uncertainty inherent in the desired slurry flow and perform a sensitivity analysis of flow energy efficiency. In this framework, five parameters are considered as random variables with a given probability distribution over a prescribed range of investigation. The uncertainty is then propagated through the two-phase flow model to statistically quantify their effect on the results. Our findings reveal that variations in slurry velocity and particle size play a pivotal role in determining energy efficiency. Therefore, controlling these factors represents a critical step in ensuring the efficient and safe transportation of slurry through pipelines.