Sensitivity Analysis and Comparison of the Results from Analytical Model with the
Real ASR Performance Data
Figure 3-13 shows the results from the FORTRAN code based on the ASR data
from a site in Boynton Beach, Florida. It is assumed that aquifer is made of 100 layers,
with different hydraulic conductivities which are lognormally distributed through the
depth of the aquifer. Mean value of hydraulic conductivity (m) is equal to transmissivity
of the aquifer divided by thickness. The value of the standard deviation (o) is adjusted to
simulate the apparent dispersion in the aquifer, based on ASR results. Higher values of
the standard deviation will result in greater apparent dispersion at the ASR well during
the recovery phase. As a result, target solutes concentrations will appear to increase
sooner during the recovery phase than with a smaller value of (o).
Altering the value of natural gradient can also bring about a change in the solute
breakthrough that is similar to changes effected by adjusting (o). In general, an increase
in the hydraulic gradient produces premature solute breakthrough.
2000 Sigma=300 Gr=0.05 T=9406 (ft^
1800 x Sigma=300 Gr=0.06 T=9406 (ft'
0 Sigma=500 Gr=0.05 T=9422 (ftA
S1600 x Sigma=500 Gr=0.06 T=9422 (ft'
S1400 + Sigma=700 Gr=0.05T=9404(ftA
1200 Sigma=700 Gr=0.06 T=9404 (ft^
- 1200 Data from Boynton Beach, FL
5 1000
S800
u 600
400
200
n
0 5 10 15 20 25 30 35
Time(day)
Figure 3-13. Sensitivity analysis for the analytical results from the FORTRAN code
Figure 3-14 shows the results from the model with the real ASR performance data
during two cycles of ASR operation. By calibrating the model to the first cycle data we
2/day)
2/day)
2/day)
2/day)
2/day)
2/day)
ASR well
w