Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials
journal contributionposted on 10.01.2017 by Faiz M. Mahdi, Richard Holdich
Any type of content formally published in an academic journal, usually following a peer-review process.
© 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.
- Aeronautical, Automotive, Chemical and Materials Engineering
- Chemical Engineering