Parameterization and prediction of nanoparticle transport in porous media: A reanalysis using artificial neural network

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 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.

Inkjet technology for ceramic products. Influence of some process variables on ink penetration

“…Nanoparticle penetration into unsaturated porous media by capillary suction has drawn little attention , unlike nanoparticle transport in saturated porous media, as compiled by Babakhani et al They reviewed the experimental data obtained by numerous researchers (compiling more than 160 references), highlighting the influence that the properties of the liquid and porous medium, including the interactions of these media, had on nanoparticle retention. Despite the notable importance of having a nanoparticle concentration profile inside a porous medium in which a suspension has penetrated by capillary suction, no paper was found on this topic in inkjet printing technology (on paper, fabric, or ceramic substrate).…”

“…According to this model, the gradient of the Fe 2 O 3 mole fraction, which also expressed the point pigment particle retention in the glaze, d X /d z , at depth z , was directly proportional to the applied amount of ink, dpi, to the Fe 2 O 3 mole fraction, X , at depth z , and to a retention coefficient, k , dependent on unfired glaze porous texture and pigment particle size . The z − n function assumes that some pores become dead‐end near the surface, thus restricting mobile pigment particles to continuous larger‐pore networks.…”

Significance of Early and Late Stages of Coupled Aggregation and Sedimentation in the Fate of Nanoparticles: Measurement and Modeling

“…[1][2][3] Effective in situ field-scale management of NP is hindered by the lack of adequate models to simulate NP fate and transport in realistic environmental compartments. 4,5 Considering the long-established potential for colloids to enhance the mobility of contaminants in groundwater environments 6,7 or facilitate their redistribution in surface water bodies, [8][9][10] this lack of predictive capability is a critical concern. Aggregation of NP is important because progressive increase in particle size can substantially affect NP mobility, reactivity and hence potential contribution to mobilization of other solute contaminants.…”

Aggregation and sedimentation of shattered graphene oxide nanoparticles in dynamic environments: a solid-body rotational approach

“…13,14 Once they have entered aquatic environments, aggregation of such particles can significantly affect their functionality and transport behaviour, particularly in aqueous and porous media. 4,5,15 Despite the existence of many studies on homo-and hetero-aggregation of various nanoparticles (NP), 14,[16][17][18][19][20][21] and abundant reports on the impacts of various factors on the aggregation behaviour of these NP, [22][23][24][25][26] it still remains a problem of how system dynamics modify aggregation. It is particularly of paramount importance in groundwater (GW) transport to understand how complex multi-cascade processes of advective and diffusive transport, [27][28][29][30] tortuosity in porous media, 31 and the arrival of aggregates from upgradient pores 32,33 impact the aggregation behaviour of NP.…”