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Neural Networks Based Modeling of Viscosity for Facilitating Transportation of Magnetite Orewater Slurry


Affiliations
1 Department of Chemical Engineering, National Institute of Technology, Durgapur- 713 209, West Bengal, India
2 Department of Mechanical Engineering, Kalyani Government Engineering College, Kalyani- 741 235, West Bengal, India
     

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Transportation of ores from mines to distant processing plants can be done in different ways. One method, which is found to be suitable in certain situations, is to carry magnetite through a pipeline in the form of solid-liquid slurry. For smooth flow of slurry, maintaining appropriate slurry viscosity along with flow pressure and flow velocity is essential. The Neural Networks (NN) algorithm can be used to find out the condition to guarantee smooth transportation process of magnetite-water slurry. In the present work, experiments are conducted to determine the effect of solids concentration, particle size and additive on the apparent viscosity of magnetite ore water slurry. The back propagation type Neural Networks (NN), constructed based on experimental results, is utilized to estimate apparent, or dynamic, viscosity of magnetite-water slurry under different parametric conditions. The NN model outputs show good prediction of apparent viscosity of the slurry, and hence, the applicability of the system adopted.

Keywords

Solid-Liquid Suspension, Slurry, Viscosity, Apparent Viscosity, Transportation, Back Propagation, Neural Networks, Prediction.
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  • Neural Networks Based Modeling of Viscosity for Facilitating Transportation of Magnetite Orewater Slurry

Abstract Views: 395  |  PDF Views: 0

Authors

Kartik Chandra Ghanta
Department of Chemical Engineering, National Institute of Technology, Durgapur- 713 209, West Bengal, India
Santanu Das
Department of Mechanical Engineering, Kalyani Government Engineering College, Kalyani- 741 235, West Bengal, India

Abstract


Transportation of ores from mines to distant processing plants can be done in different ways. One method, which is found to be suitable in certain situations, is to carry magnetite through a pipeline in the form of solid-liquid slurry. For smooth flow of slurry, maintaining appropriate slurry viscosity along with flow pressure and flow velocity is essential. The Neural Networks (NN) algorithm can be used to find out the condition to guarantee smooth transportation process of magnetite-water slurry. In the present work, experiments are conducted to determine the effect of solids concentration, particle size and additive on the apparent viscosity of magnetite ore water slurry. The back propagation type Neural Networks (NN), constructed based on experimental results, is utilized to estimate apparent, or dynamic, viscosity of magnetite-water slurry under different parametric conditions. The NN model outputs show good prediction of apparent viscosity of the slurry, and hence, the applicability of the system adopted.

Keywords


Solid-Liquid Suspension, Slurry, Viscosity, Apparent Viscosity, Transportation, Back Propagation, Neural Networks, Prediction.



DOI: https://doi.org/10.22485/jaei%2F2013%2Fv83%2Fi2%2F119899