Biomedical Engineering Reference
In-Depth Information
TABLE E12-1.2 Parametric Estimation ResultsdCont'd
Error 2 or
( SLS C ) 2
S C calculated
from Eqn
(E12-1.5) , g/L
X c calculated
from Eqn
(E12-1.8) , g/L
Error
weighting
factor, u
2
D ( XLX c ) 2
S ,
mg/L
X ,
mg/L
u
D, h L 1
0.55
211.9521
341.5247
0.575
246.5724
322.1526
0.6
298
299
290.2169
297.37
63.23377
1
0.625
346.9428
264.7917
0.65
423.668
220.3428
0.675
533.2326
156.4519
0.7
702
59
702.4604
57.29264
3.12706
1
0.710082
3.4E-07
P ðS S C Þ 2 u 2 þðX X c Þ 2
800
q
¼ 16:43404
The strategy here is to make the error between Eqns (E12-1.5) and (E12-1.8) and the exper-
imental data minimum. Before we look at the error between the model prediction and the data,
we should examine the potential errors in the data set. Examining the data set, we can see that
the errors of S and X are not uniform. While the errors for all the data on biomass concentration
X may be regarded uniform (e.g.
1), however, the errors of S may be
1 for S
>
100 and
0.1
for S
100. Therefore, when we calculate the error we need to scale the errors to the same level
so that the final parameters are the best estimates from the data. A scaling factor
<
is thus
needed to include the errors of the substrate concentrations. Table E12-1.2 shows the results.
When minimizing
u
q
P ðS S C Þ 2 u 2 þðX X c Þ 2
by altering
m max , K S ,YF X/S , and k d , we obtain
0.8223 h 1 ,
0.0304 h 1 , d
m max ¼
K S ¼
88.30 mg/L,
YF X/S ¼
0.6129 g-cells/g-S,
k d ¼
q
P ðS S C Þ 2 u 2 þðX X c Þ 2
.
Now we are still missing the maintenance coefficient m S . To compute the maintenance
coefficient, we need to go back to definition Eqn (11.27) or
¼ 16:434
YF S = S m net þ m s
r S ¼
X
(E12-1.9)
When carrying out mass balance, we have used
YF S = S m net þ m s
r S ¼ YF S = S m G X ¼
X
(E12-1.10)
Therefore,
h
YF S = S
m G k d
þ m s i
r S ¼ YF S = S m G X ¼
X
(E12-1.11)
That is
m
¼ YF S = S k d ¼ k d =YF X = S
(E12-1.12)
s
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