Slurry-Flow Pressure Drop in Pipes With Modified Wasp Method

Over the last many decades, a significant amount of research has gone into the domain of slurry transport. However, design engineers still face many challenges with respect to prediction of pressure drop, critical velocity and other design parameters as a function of Solids % and Particle Size Distribution (PSD). The industry requirement is to transfer the slurry at the maximum concentration as possible (above 30% (volume %)) to make slurry transport more economically viable and to reduce water consumption. To facilitate the design and scale-up of slurry transport in pipelines and in process plants, there is a need for a correlation that can predict slurry pressure drops over a wide range of operating conditions and physical properties of different slurries. The objective of this study is to overcome the limited range of applicability and validity of existing correlations and to develop a generalized but more rigorous correlation applicable to a wider range of slurry sys-tems. The existing Wasp et al. (1977) method is based on multi-phase flow modeling approach. This study attempts to modify this approach by considering material-specific values of Durand?s equation co-efficient and by defining flow regimes based on particle Reynolds number. When compared with experimental data, the modified Wasp method proposed in this study predicts the pressure drop for slurry flows more accurately than other available correlations. Also, the pro-posed method requires minimal test/experimental data for a particular slurry system and can be extended over different input conditions. An iterative computer algorithm is developed to calculate the criti-cal settling velocity and pressure drop in a pipe as a function of Solids % and PSD. The solution method can easily be implemented in de-signing slurry pipes, design validation, and studying the different slurry transport scenarios. The modified method can also be extended to accurately predict pressure drops in dynamic pressure flow networks used in commercial process simulators.