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A real-world application of the Texaco gasfier is in its normal and stable working
condition, and its 18 variables are M*=1.16%, A* = 7.68%, Va* = 41.39%, C* =
49.77%, ST* = 1210
℃
, Cc* = 65.05%, Fc* = 68.69 m
3
/h, Pc* = 5.74MPa, Tc* =
42.91
℃
, Fo* = 28550.82 m
3
/h, Foc* = 4340.00m
3
/h, Po* = 7.40 MPa, To* = 33.55
℃
,
Fw* = 337.55 m
3
/h, Tw* = 229.45
℃
, Fg* = 210528.09 m
3
/h, CO* = 39.96%, H
2
* =
40.28%.[17]
MO-3LM-CDE and MO-DE algorithms with the 4 strategies in section 2 are used
to optimize the 3 control parameters (
x
Fo
,
x
Foc
, and
x
Fw
) of the gasfier. The simulation
results are shown in Fig.27-Fig.30. The Main Objective strategy achieved the best
results. They are shown in Table 2. From Table 2 we can know that the optimal
results of MO-3LM-CDE are more balanced and at the same time the effective gas
yield is better than the single objective calculations.
6
Conclusion
Optimizing operation parameters for Texaco coal-water slurry gasifier with the
consideration of multiple objectives was studied in this paper. 4 multi-objective
strategies were developed. From the simulation results we can suggest that the
strategies of finding the Pareto optimal sets have found better performances not only
in the 6 test functions, but also in engineering practice problems.
Acknowledgments.
Financial support from the National Natural Science Foundation
of China (No.61174040), Shanghai commission of Nature Science (No.
12ZR1408100) and the Fundamental Research Funds for the Central Universities was
grateful appreciated.
Appendix
Six test problems in reference [15] are listed below.
g01
:
Subject to 0
xx
xx
≤
≤
−≤ −≤
−−≤ −−≤
100
1
2
2
2
2
2
+4
100
0, 3
xx
+2
150
0
Mi ni mi ze [
fx x x
( )
=
+
,
f x
( )
= −
(
x
5) +(
x
−
5) ]
1
2
1
2
1
1
2
2
1
2
200
5
xx
3
0,75
2
xx
8
0
Subject to
−≤
5
xx
≤
10
1
2
1
2
12
g05
:
g02
:
2
Minimize [
fx
( )
=−
x x f x
+
,
( )
=
0.5
x x
+
+1]
Minimize [
fx
( )
=
x x
2
+
2
,
f x
( )
=
(
x
+2) +
2
x
2
]
1
1
2
2
1
2
1
1
2
2
1
2
Subject to 0
≤
xx
≤
7, (1/6)
xx
+
− ≤
6.5
0
Subject to
−≤≤
50
x
50
1
2
1
2
1
0.5
xx
+
−≤
7.5
0, 5
xx
+
−≤
30
0
g03
:
12
12
g06
:
Minimize [
fx
( )
=−
2
xx fx
+
,
( )
=
2
xx
+
]
1
1
2
2
1
2
Minimize [
fx
( )
=
x f x
,
( )
=
x
]
Subject to 0
≤≤ ≤≤
−
x
5, 0
x
3
1
1
2
2
1
2
Subject to 0
≤
xx
,
≤
1,
x x
2
+
2
≤
1
xx
+
− ≤
1
0,
xx
+
− ≤
7
0
12
1
2
12
12
g04
:
Minimize [
fx
( )
=
5
x
+3
x f x
;
( )
=
2
x
+8
x
]
1
1
2
2
1
2
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