No abstract

No abstract

An attempt has been made in the present study to develop generalized slurry flow model using CFD and utilize the model to predict concentration profile. The purpose of CFD model is to gain better insight into the solid liquid slurry flow in pipelines. Initially a three-dimensional model problem was developed to understand the influence of the particle drag coefficient on solid concentration profile. The preliminary simulations highlighted the need for the correct modelling of the inter phase drag force. The various drag correlations available in literature was incorporated in a two-fluid model (Euler-Euler) along with the standard k-? turbulence model with mixture properties to simulate the turbulent solid-liquid flow in a pipeline. The computational model was mapped on to a commercial CFD solver FLUENT6.2 (of Fluent Inc., USA). To push the envelope of applicability of simulation, the recent data of Kaushal (2005) (with solid concentration up to 50%) was selected to validate the three dimensional simulations. The experimental data consists of water-glass bead slurry at 125& 440 micron particle with different flow velocity (from 1 to 5 m/s) and overall concentration up to 10 to 50% by volume. The predicted pressure drop and concentration profile was validated by experimental data and shows excellent agreement. Interesting findings were come out from the parametric study of velocity and concentration profiles. The computational model and results discussed in this work would be useful for extending the applications of CFD models for simulating large slurry pipelines.

This paper describes a robust hybrid artificial neural network (ANN) methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta-parameters. The algorithm has been applied for prediction of critical velocity of solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved prediction of critical velocity over a wide range of operating conditions, physical properties, and pipe diameters. 

This paper describes a robust hybrid artificial neural network (ANN) methodology which can offer a superior performance for the important process engineering problems. The method incorporates a hybrid artificial neural network and differential evolution technique (ANN-DE) for the efficient tuning of ANN meta parameters. The algorithm has been applied for the prediction of the hold up of the solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved the prediction of hold up over a wide range of operating conditions, physical properties, and pipe diameters.

No abstract

No abstract

= particle diameter, (m) Kartik Chandra Ghanta gratefully acknowledges the sincere help = coefficient of variance, dimensionless = volume fraction of solids, dimensionless I30