Babakhani, P orcid.org/0000-0002-5318-4320, Bridge, J, Doong, R-A et al. (1 more author) (2017) Parameterization and prediction of nanoparticle transport in porous media: A reanalysis using artificial neural network. Water Resources Research, 53 (6). pp. 4564-4585. ISSN 0043-1397
Abstract
The continuing rapid expansion of industrial and consumer processes based on nanoparticles (NP) necessitates a robust model for delineating their fate and transport in groundwater. An ability to reliably specify the full parameter set for prediction of NP transport using continuum models is crucial. In this paper we report the reanalysis of a data set of 493 published column experiment outcomes together with their continuum modeling results. Experimental properties were parameterized into 20 factors which are commonly available. They were then used to predict five key continuum model parameters as well as the effluent concentration via artificial neural network (ANN)‐based correlations. The Partial Derivatives (PaD) technique and Monte Carlo method were used for the analysis of sensitivities and model‐produced uncertainties, respectively. The outcomes shed light on several controversial relationships between the parameters, e.g., it was revealed that the trend of Katt with average pore water velocity was positive. The resulting correlations, despite being developed based on a “black‐box” technique (ANN), were able to explain the effects of theoretical parameters such as critical deposition concentration (CDC), even though these parameters were not explicitly considered in the model. Porous media heterogeneity was considered as a parameter for the first time and showed sensitivities higher than those of dispersivity. The model performance was validated well against subsets of the experimental data and was compared with current models. The robustness of the correlation matrices was not completely satisfactory, since they failed to predict the experimental breakthrough curves (BTCs) at extreme values of ionic strengths.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 American Geophysical Union. Reproduced in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Earth Surface Science Institute (ESSI) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Jan 2019 13:27 |
Last Modified: | 15 Jan 2019 13:27 |
Status: | Published |
Publisher: | American Geophysical Union (AGU) |
Identification Number: | 10.1002/2016WR020358 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:141001 |