Rheological Characteristics of Uni/Bi-Variant Particulate Iron Ore Slurry: Artificial Neural Network Approach
- Autores: Kumar S.1, Singh M.1, Singh J.1, Singh J.P.2, Kumar S.3
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Afiliações:
- Mechanical Engineering Department
- School of Mechanical Engineering
- Department of Mechanical Engineering
- Edição: Volume 55, Nº 2 (2019)
- Páginas: 201-212
- Seção: Geomechanics
- URL: https://ogarev-online.ru/1062-7391/article/view/184659
- DOI: https://doi.org/10.1134/S1062739119025468
- ID: 184659
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Resumo
A rigorous literature review has been carried out on rheological behavior of hard and soft particle slurries. The rheological characteristics of unimodal and bimodal suspension are presented. From experimentation, it was observed that mineral viscosity increases with solid concentration, while decreases with temperature. Addition of 30% (by weight) proportion of finer particles in coarse particles resulted in significant decrease in apparent viscosity of iron ore suspension. Artificial neural network approach was used for predicting the apparent viscosity of slurry.
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Sobre autores
S. Kumar
Mechanical Engineering Department
Autor responsável pela correspondência
Email: skumar_me16@thapar.edu
Índia, Patiala, 147004
M. Singh
Mechanical Engineering Department
Email: skumar_me16@thapar.edu
Índia, Patiala, 147004
J. Singh
Mechanical Engineering Department
Email: skumar_me16@thapar.edu
Índia, Patiala, 147004
J. Singh
School of Mechanical Engineering
Email: skumar_me16@thapar.edu
Índia, Jalandhar, 144411
S. Kumar
Department of Mechanical Engineering
Email: skumar_me16@thapar.edu
Índia, Jamshedpur, 831014
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