Mahdi, FM orcid.org/0000-0002-3046-4389 and Holdich, RG (2017) Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials. Separation Science and Technology, 52 (1). pp. 1-12. ISSN 0149-6395
Abstract
Well-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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in Separation Science and Technology on 20 Sep 2016, available online: https://doi.org/10.1080/01496395.2016.1232735. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Loosely-packed granular materials, multivariate regression, artificial neural network and permeability prediction |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 29 Sep 2017 08:36 |
Last Modified: | 15 Jan 2018 17:54 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/01496395.2016.1232735 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:121827 |