Many large slurry pipelines were built and operating around the world. Pipeline transport is considered economical and environment friendly as compared to rail and road transport. To design the pipelines and its associated facilities (pumps etc) designers need accurate information regarding pressure drop, hold up, critical velocity, flow regimes etc at the early design phase. Also the operating engineers need to know accurately the critical velocity so that he can adjust the slurry flow to have a minimum pressure drop to ensure minimum operating cost. Such flows are complex and presently very little known about the two-phase interaction of solid liquid behavior inside pipeline. The correlations presently available in open literatures for the above mentioned parameters have a prediction error of 25-35%. This much of error in design and slurry operation has serious cost implication and is totally unacceptable in present day competitive business scenario. This study was performed in order to develop model for flow of slurries through pipelines so that the error % can be reduced. This thesis can be considered as a step forward for better understanding of flow behavior in slurry pipelines. Attempt has been made in this thesis to utilize the computational capability of two recent advanced numerical technique namely artificial neural network (ANN) and support vector regression (SVR) in slurry flow modeling. This thesis has build some simple and superior correlations of pressure drop, hold up, critical velocity, flow regimes which can be readily used by design engineers to design slurry pipelines and pumps. There are some model parameters both in ANN and SVR that are to be tuned by the ‘expert’ user during model building time. A new approach was developed in this thesis to tune these parameters automatically using differential evolution (DE) and genetic algorithm (GA). The method employs a hybrid approach for minimizing the generalization error. The proposed hybrid technique relieves the non-expert users to choose the meta parameters of ANN or SVR algorithm for the used case study and find out the optimum value of these meta parameters on its own. In the present study existing Wasp model (1977) for pressure drop has been modified by alleviating some of the restrictive assumptions used in that model. A new method was also developed to calculate concentration profile using Wasp model as a starting point. The concentration profile and pressure drop data predicted by modified model were compared with the experimental one collected from literature. In this study the capability of computational fluid dynamics (CFD) is explored to model complex solid liquid slurry flow in pipeline. A comprehensive CFD model was developed to gain deeper insight of the solid liquid slurry flow in pipelines. The theoretical model developed in this work represents the synthesis of hydrodynamic and interparticle interaction effects within the framework of equation of conservation of momentum and mass. Two and three-dimensional model problems are developed using CFD to understand the influence of the particle drag coefficient on solid concentration profile. It is found that the commercial CFD software is capable to successfully model the solid liquid interactions in slurry flow and the predicted concentration profiles show reasonably good agreement with the experimental data.
sandip k. lahiri. "My PhD thesis : Study on slurry flow modelling in pipeline" 2010
Available at: http://works.bepress.com/sandip_lahiri/23
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