284
Y. Xu et al.
Table 2. Selected optimization variables.
Unit
Optimization variable
Range
GSU Amine solution circulation rate, m 3 /h
11–15
Amine solution concentration, wt%
40–50
Regeneration tower bottom pressure, kPa 130–150
Regeneration tower reflux ratio, %
0.8–1.2
DU
Stripping gas flow, m 3 /h
8–13
SRU Claus furnace combustion air flow, m 3 /h 50–78
TGTU The combustion air flow, m 3 /h
30–63
Fuel gas flow, m 3 /h
16–22
Table 3. The operating parameters and unit energy consumption of on-site and optimized.
Optimization variable
On-site Optimum
Amine solution circulation rate, m 3 /h
13.8
11.8
Amine solution concentration, wt%
40.0
46.7
Regeneration tower bottom pressure, kPa 130
142
Regeneration tower reflux ratio, %
1.00
0.81
Stripping gas flow, m 3 /h
8.8
9.8
Claus furnace combustion air flow, m 3 /h 55
60
The combustion air flow, m 3 /h
35
34
Fuel gas flow, m 3 /h
19.2
17.3
Energy consumption, MJ/10 4 Nm3
1581
1441
From Fig. 4, it is found that the PSO algorithm converges at the 13th iteration when
the population size is 20. The algorithm has good convergence and versatility.