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Optimization of Glass-beads Water Slurry flow Characteristics using Taguchi’s Decision-making Technique coupled with Genetic Algorithm

Shofique Uddin Ahmed, Rajesh Arora and Ranjana Arora

Pertanika Journal of Tropical Agricultural Science, Volume 27, Issue 4, October 2019

Keywords: ANOVA, concentration distribution, genetic algorithm, pressure drop, regression analysis, slurry flow, Taguchi’s method, velocity distribution

Published on: 21 October 2019

This current study represents a numerical computational fluid dynamics (CFD) study of Glass beads-water slurry flow and aims for the optimization of various flow parameters adopting a new approach combining Taguchi’s method and genetic algorithm (GA) for a smooth and better flow of the slurry. Three flow parameters viz. inlet mean velocity (Vm), volume fraction (Cvf) and particle size (Dp) with three levels were considered for the simulation and two responses vs. particle flow velocity (Vf) and pressure drop (ΔPL) were analysed to achieve a better understanding of the complex multiphase Glass-beads Water Slurry flow. Taguchi decision making technique and Genetic algorithm were exploited in order to optimize the particle flow velocity and pressure drop. A mathematical model was developed in order to forecast the responses analytically. Analysis of variance (ANOVA) was selected for finding the contribution of the selected flow parameters on particle flow velocity and pressure drop. The designed experiments were simulated numerically using analysis systems (ANSYS) software package by adopting Eulerian two-phase approach along with RNG K-ε model. Finally the optimized responses along with percentage reduction in pressure drop were reported and validated with the experimental data. This study optimized the pressure drop and particle flow velocity for three different input factors which would be helpful for the slurry-transporting industries, Oil and Gas industries and thermal power plants for optimal pipe design as well as to initiate a smooth slurry flow through horizontal pipeline.

ISSN 1511-3701

e-ISSN 2231-8542

Article ID

JST-1357-2018

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