Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2016WR020358 |
Parameterization and prediction of nanoparticle transport in porous media: A reanalysis using artificial neural network | |
Babakhani, Peyman1,2; Bridge, Jonathan2,3; Doong, Ruey-an1,4; Phenrat, Tanapon5,6 | |
2017-06-01 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2017 |
卷号 | 53期号:6 |
文章类型 | Article |
语种 | 英语 |
国家 | Taiwan; England; Thailand |
英文摘要 | 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 K-att 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. Plain Language Summary Models based on advection-dispersion-equation (ADE), have succeeded in describing a variety of nanoparticle (NP) transport mechanisms within subsurface porous media. These models are usually fitted against known observation data to obtain the unknown parameters. Nevertheless, the parameters determined in this way cannot be used for a new problem of the same type and again there exists the need for the data to calibrate the model parameters for a new problem. Black box models, such as artificial neural network (ANN), have been mostly, if not all the times, used in the same way of ADE models, i.e., single problem solver. In this paper we use the ability of ANN to develop a series of simple correlation matrices that can be easily used for the prediction of ADE parameters in the new problem without the need for additional calibration. Although comparisons between ANN model predictions and experimental data show that there is still further work to be done, our approach out-performs other comparable models and offers new insight into the complex interactions among the factors determining NP transport and fate in the environment. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000405997000005 |
WOS关键词 | GRAPHENE OXIDE NANOPARTICLES ; LABELED HYDROXYAPATITE NANOPARTICLES ; MODIFIED FE-0 NANOPARTICLES ; ZERO-VALENT IRON ; DEPOSITION RATE COEFFICIENTS ; PARTICLE-SIZE DISTRIBUTION ; QUANTUM-DOT NANOPARTICLES ; COLLOID FILTRATION THEORY ; SATURATED GRANULAR MEDIA ; SOLUTION IONIC-STRENGTH |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/22037 |
专题 | 资源环境科学 |
作者单位 | 1.Natl Tsing Hua Univ, Dept Biomed Engn & Environm Sci, Hsinchu, Taiwan; 2.Univ Liverpool, Dept Civil Engn & Ind Design, Liverpool, Merseyside, England; 3.Sheffield Hallam Univ, Dept Nat & Built Environm, Sheffield, S Yorkshire, England; 4.Natl Chiao Tung Univ, Inst Environm Engn, Hsinchu, Taiwan; 5.Naresuan Univ, Fac Engn, Dept Civil Engn, Res Unit Integrated Nat Resources Remediat & Recl, Phitsanulok, Thailand; 6.Naresuan Univ, Fac Engn, Ctr Excellence Sustainabil Hlth Environm & Ind, Phitsanulok, Thailand |
推荐引用方式 GB/T 7714 | Babakhani, Peyman,Bridge, Jonathan,Doong, Ruey-an,et al. Parameterization and prediction of nanoparticle transport in porous media: A reanalysis using artificial neural network[J]. WATER RESOURCES RESEARCH,2017,53(6). |
APA | Babakhani, Peyman,Bridge, Jonathan,Doong, Ruey-an,&Phenrat, Tanapon.(2017).Parameterization and prediction of nanoparticle transport in porous media: A reanalysis using artificial neural network.WATER RESOURCES RESEARCH,53(6). |
MLA | Babakhani, Peyman,et al."Parameterization and prediction of nanoparticle transport in porous media: A reanalysis using artificial neural network".WATER RESOURCES RESEARCH 53.6(2017). |
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