### Abstract

Prediction of the terminal velocity of solid spheres falling through Newtonian and non-Newtonian fluids is required in several applications like mineral processing, oil well drilling, geothermal drilling and transportation of non-Newtonian slurries. An artificial neural network (ANN) is proposed which predicts directly the terminal velocity of solid spheres falling through Newtonian and non-Newtonian power law liquids from the knowledge of the properties of the spherical particle (density and diameter) and of the surrounding liquid (density and rheological parameters). With a combination of non-Newtonian data with Newtonian data taken from published data giving a database of 88 sets, an artificial neural network is designed. Analysis of the predictions shows that the artificial neural network could be used with good engineering accuracy to directly predict the terminal velocity of solid spheres falling through Newtonian and non-Newtonian power law liquids covering an extended range of power law values from 1.0 down to 0.06.

Original language | English |
---|---|

Pages (from-to) | 53-61 |

Number of pages | 9 |

Journal | International Journal of Mineral Processing |

Volume | 110-111 |

DOIs | |

Publication status | Published - 18 Jul 2012 |

Externally published | Yes |

### Fingerprint

### Keywords

- Artificial neural network
- Drilling cuttings transport
- Mineral processing
- Newtonian and power law fluid
- Terminal velocity

### ASJC Scopus subject areas

- Geotechnical Engineering and Engineering Geology
- Geochemistry and Petrology

### Cite this

*International Journal of Mineral Processing*,

*110-111*, 53-61. https://doi.org/10.1016/j.minpro.2012.03.012

**Prediction of terminal velocity of solid spheres falling through Newtonian and non-Newtonian pseudoplastic power law fluid using artificial neural network.** / Rooki, R.; Doulati Ardejani, F.; Moradzadeh, A.; Kelessidis, V. C.; Nourozi, M.

Research output: Contribution to journal › Article

*International Journal of Mineral Processing*, vol. 110-111, pp. 53-61. https://doi.org/10.1016/j.minpro.2012.03.012

}

TY - JOUR

T1 - Prediction of terminal velocity of solid spheres falling through Newtonian and non-Newtonian pseudoplastic power law fluid using artificial neural network

AU - Rooki, R.

AU - Doulati Ardejani, F.

AU - Moradzadeh, A.

AU - Kelessidis, V. C.

AU - Nourozi, M.

PY - 2012/7/18

Y1 - 2012/7/18

N2 - Prediction of the terminal velocity of solid spheres falling through Newtonian and non-Newtonian fluids is required in several applications like mineral processing, oil well drilling, geothermal drilling and transportation of non-Newtonian slurries. An artificial neural network (ANN) is proposed which predicts directly the terminal velocity of solid spheres falling through Newtonian and non-Newtonian power law liquids from the knowledge of the properties of the spherical particle (density and diameter) and of the surrounding liquid (density and rheological parameters). With a combination of non-Newtonian data with Newtonian data taken from published data giving a database of 88 sets, an artificial neural network is designed. Analysis of the predictions shows that the artificial neural network could be used with good engineering accuracy to directly predict the terminal velocity of solid spheres falling through Newtonian and non-Newtonian power law liquids covering an extended range of power law values from 1.0 down to 0.06.

AB - Prediction of the terminal velocity of solid spheres falling through Newtonian and non-Newtonian fluids is required in several applications like mineral processing, oil well drilling, geothermal drilling and transportation of non-Newtonian slurries. An artificial neural network (ANN) is proposed which predicts directly the terminal velocity of solid spheres falling through Newtonian and non-Newtonian power law liquids from the knowledge of the properties of the spherical particle (density and diameter) and of the surrounding liquid (density and rheological parameters). With a combination of non-Newtonian data with Newtonian data taken from published data giving a database of 88 sets, an artificial neural network is designed. Analysis of the predictions shows that the artificial neural network could be used with good engineering accuracy to directly predict the terminal velocity of solid spheres falling through Newtonian and non-Newtonian power law liquids covering an extended range of power law values from 1.0 down to 0.06.

KW - Artificial neural network

KW - Drilling cuttings transport

KW - Mineral processing

KW - Newtonian and power law fluid

KW - Terminal velocity

UR - http://www.scopus.com/inward/record.url?scp=84859924370&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84859924370&partnerID=8YFLogxK

U2 - 10.1016/j.minpro.2012.03.012

DO - 10.1016/j.minpro.2012.03.012

M3 - Article

AN - SCOPUS:84859924370

VL - 110-111

SP - 53

EP - 61

JO - International Journal of Mineral Processing

JF - International Journal of Mineral Processing

SN - 0301-7516

ER -