91
a b
c d
Fig. 8.9 Marine Diesel Nitrogen Oxide Emission Simulation: a ANN
Sequential Operating Conditions Plot, b ANN “1-to-1” Scatter Plot, c ANN Blind Try, d Phenomenological Modeling Blind Try
a
b
Fig. 8.10 Automotive NO Emissions: a Measurements, b ANN Simulation
N o
rm a
li z
e d
N O
x
E m
is si
o n
s [a
.u .]
60 80
100 120
140 160
Marine Operating Conditions [-] 5
10 15
20 25
Verification Training
Measurement Simulation
N o
rm a
li z
e d
N O
x
S im
u la
ti o
n [
a .u
.]
60 80
100 120
140 160
Normalized NO
x
Measurement [a. u. ] 60
80 100
120 140
160
Training Verificat ion
N o
rm a
li z
e d
N O
x
E m
is si
o n
s [a
.u .]
20 40
60 80
100 120
140 160
Marine Operating Conditions [-] 5
10 15
20 25
Measurement Simulat ion
N o
rm a
li z
e d
N O
x
E m
is si
o n
s [a
.u .]
20 40
60 80
100 120
140 160
Marine Operating Conditions [ -] 5
10 15
20 25
Measurement Simulat ion
1. 55 4. 65
7. 75 10. 85
13. 95 c
m
[m s]
5 10
15 20
BME P [b
ar] 2
4 6
8 10
12 14
16 18
N O
x [g
k W
h ]
B M
E P
[b a
r] 2
4 6
8 10
12 14
16 18
20
c
m
[ m s] 1. 55
3. 1 4. 65
6. 2 7. 75
9. 3 10. 85
12. 4 13. 95
8.0 8
.0 8. 0
8. 0 1
.0 1
.0 10.
12. 0 2. 0
2. 2.0
2. 0 4. 0
4.0 4.
4. 0 4. 0
6. 0 6. 0
6. 0 6.
1. 4
1. 55 4. 65
7. 75 10. 85
13. 95 c
m
[m s]
5 10
15 20
BME P [b
ar] 2
4 6
8 10
12 14
16 18
N O
x [g
k W
h ]
B M
E P
[b a
r] 2
4 6
8 10
12 14
16 18
20
c
m
[m s] 1. 55
3. 1 4. 65
6. 2 7. 75
9. 3 10. 85
12. 4 13. 95
12.0 10. 0
10. 0 1
.0 1
.0
10. 8. 0
8 .0
8. 0 8. 0
8. 0 6. 0
6. 0 6. 0
6. 6. 0
6. 0 2
.0 2
.0 2. 0
4. 4. 0
4.0 4. 0
2. 1
92
Given the difficulties with the blind try application of the heavy-duty calibrated nitrogen oxide emission model to marine diesel operating conditions Section 5.3.3,
the use of the heavy-duty nitrogen oxide ANN for marine diesel operating condi- tions was investigated. Except for the low injection pressure p
Inj
= 450 [bar] oper- ating conditions 3, 7, 10, 21, and 26, the blind try ANN simulated NO emissions
vary between 60 and 65 [a.u.] only c.f. Figure 8.9 c. Thus, neither the nitrogen oxide ANN nor the phenomenological NO model are capable to predict the emis-
sions of the marine diesel engine correctly without a recalibration.
8.3.3 “Automotive” Diesel
In order to demonstrate both the assets and drawbacks of the ANN approach, the 57 automotive calibration and verification operating conditions c.f. Table A.1 are
used to train a specific nitrogen oxide emissions ANN which subsequently is verified for the entire engine operating map with more than 300 operating conditions n
OC
. The measured and simulated NO emissions given in Figure 8.10 indicate a qualita-
tively correct prediction of the experimental values over the entire operating map. When considering only the ANN training operating conditions, a quantitatively cor-
rect prediction of the experimental values is possible c.f. Figure 8.11 a, as indi- cated by Pearson’s correlation coefficients of 0.9121 and 0.6309 for the training and
verification operating condition sets, respectively
1
. From the configuration of the specific nitrogen oxide emission residuals shown in
Figure 8.11 b, a significant dependence of the ANN prediction quality on the range of parameters used during network training is seen. Despite minor errors in the
absolute NO emission values, the ANN approach is able to accurately predict the major effects and trends for an entire engine operating map, given sufficient and
comprehensive training data.
1. Phenomenological Model: Calibartion r = 0.9593
n
OC
: 20, Verification r = 0.7428
n
OC
: 37 ANN : Training r = 0.9121
n
OC
: 57, Verification r = 0.6309
n
OC
: 256 operating conditions
a b
Fig. 8.11 Automotive NO Emissions: a “1-to-1” Plot of Training and
Verification Operating Conditions, b NO Emission Residuals
N O
x
S im
u la
ti o
n s
[ g
k W
h ]
2 4
6 8
10 12
14 16
18
NO
x
Measurements [ g kWh] 2
4 6
8 10
12 14
16 18
Training Verification
p _
m e
[b a
r]
2 4
6 8
10 12
14 16
18 20
n [rpm] 1. 55
3. 1 4. 65
6. 2 7. 75
9. 3 10. 85
12. 4 13. 95
10. 0 2. 5
2. 5
-2 .5
-2. 5 -2.5
-2.5 1. 0
1. 0 1. 0
1.0 1. 0
1. 1
.0 1. 0
-1. 0 -1. 0
-1 .0
-1. 0 -1. 0
Training Verificat ion
1. 0
Residuals
93
8.4 Conclusions
The comparison of the ANN and phenomenological model results for Common- Rail DI diesel engine combustion and emissions clearly demonstrates the advantages
and disadvantages of both approaches. While the ANN approach yields fast and accurate training results, it may implicate errors for verification operating conditions
outside of the training range
1
. Alternatively, the phenomenological modelknowl- edge based approach needs fundamental knowledge about the governing processes
and advanced calibration methods, but allows for accurate predictions for verifica- tion operating condition and engine setups even outside of the training range.
From the heavy-duty diesel ROHR, NO and soot emission model comparison, it is noted that the emission ANNs show better agreement with the measured values
than the ROHR ANN. This behavior is particularly evident for operating conditions outside of the training range, as well as for the verification operating conditions
although to a lesser degree. Inspite of this, neither the ROHR nor the NO emission ANNs allow for reliable predictions when switching from one engine to another,
such as from a heavy-duty diesel to a two-stroke marine diesel.
Given an appropriate number of experimental measurements for training, it is possible for the ANN to generate a rough estimate of the NO emissions for an
entire engine operating map within minutes. This is a major advantage of the ANN over other, for example phenomenological model based methods.
1.
Training range
- range of operating condition parameters used used for the model calibration
