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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/23654

Title: Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials
Authors: Mahdi, Faiz M.
Holdich, R.G.
Keywords: Loosely-packed granular materials
Multivariate regression
Artificial neural network and permeability
Issue Date: 2016
Publisher: © Taylor & Francis
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.
Abstract: © 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.
Description: 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.
Version: Accepted for publication
DOI: 10.1080/01496395.2016.1232735
URI: https://dspace.lboro.ac.uk/2134/23654
Publisher Link: http://dx.doi.org/10.1080/01496395.2016.1232735
ISSN: 0149-6395
Appears in Collections:Published Articles (Chemical Engineering)

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