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Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials

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journal contribution
posted on 10.01.2017 by Faiz M. Mahdi, Richard Holdich
© 2016 Taylor & FrancisWell-known analytical equations for predicting permeability are generally reported to overestimate this important property of porous media. In this work, more robust models developed from statistical (multivariable regression) and Artificial Neural Network (ANN) methods utilised additional particle characteristics [‘fines ratio’ (x50/x10) and particle shape] that are not found in traditional analytical equations. Using data from experiments and literature, model performance analyses with average absolute error (AAE) showed error of ~40% for the analytical models (Kozeny–Carman and Happel–Brenner). This error reduces to 9% with ANN model. This work establishes superiority of the new models, using experiments and mathematical techniques.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Chemical Engineering

Published in

Separation Science and Technology (Philadelphia)

Pages

1 - 12

Citation

MAHDI, F.M. and HOLDICH, R.G., 2016. Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials. Separation Science and Technology, 52(1), pp. 1-12.

Publisher

© Taylor & Francis

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

01/09/2016

Publication date

2016

Notes

This is an Accepted Manuscript of an article published by Taylor & Francis in Separation Science and Technology on 20 September 2016, available online: http://www.tandfonline.com/10.1080/01496395.2016.1232735.

ISSN

0149-6395

eISSN

1520-5754

Language

en

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