2134/23654
Faiz M. Mahdi
Faiz M.
Mahdi
Richard Holdich
Richard
Holdich
Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials
Loughborough University
2017
Loosely-packed granular materials
Multivariate regression
Artificial neural network and permeability
Prediction
Chemical Engineering not elsewhere classified
2017-01-10 11:41:04
Journal contribution
https://repository.lboro.ac.uk/articles/journal_contribution/Using_statistical_and_artificial_neural_networks_to_predict_the_permeability_of_loosely_packed_granular_materials/9244967
© 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.