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7.7 Conclusion
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network model and optimize the solution of non-equilibrium model to these
BTCs. Our results (Figure 7.9) show that the chemical non-equilibrium model
accurately fits the BTCs obtained from the pore network model, resulting in a
better fit compared to that obtained via the ADE model. Figure (7.9) shows
an example of fitting the analytical solution of the nonequilibrium model to the
BTC obtained from the pore network. In this case, we estimated the dispersion
coefficient independently using the BTC of a conservative (non adsorptive)
solute. This value for the dispersion coefficient was then used for adsorptive
solute (under the same saturation) and only the adsorptive parameters were
estimated.
Co oncentration [‐]
1
0.1
0.01
Network model
0.001
Nonequilibrium model
0.0 50.0 100.0
Time [min]
150.0
Figure 7.9: The resulting BTC from the network together with the fitted
BTC using the Nonequilibrium model. The BTC belong to a pore network with
a pore scale adsorption distribution coefficient of 1.0 and saturation of 0.53.
Compared with the ADE model, the non-equilibrium model provides a more
appropriate model for the tailing behavior of the BTCs under different saturations.
7.7 Conclusion
Multi-phase and (partially-) saturated reactive/adsorptive transport in porous
media is characterized by means of several macroscopic transport properties
including: relative permeability; capillary pressure; unsaturated dispersivity;
and upscaled reactive/adsorptive transport parameters. These properties have
been found to depend on macro-scale parameters such as average saturation
but also on pore-scale properties. Indeed, the state of fluid distribution within
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