4.1. Minimization of SEGR
The influence of
on
and
of EOCP is shown in
Figure 4. As can be seen from the figure, with an increase in
,
gradually increased, the rate of increase slowed and the maximum value of
was obtained near 4.1 MPa before gradually decreasing;
increased monotonically. The compressor outlet temperature increased with the increase in
, and the inlet temperature of the reactor increased under the action of the heat exchanger. The inlet temperature increased with the increase in
, the reaction rates of exothermic reactions Ⅰ and Ⅱ were decreased, and the yield of C
10H
20 was decreased. When
was greater than 4.1 MPa,
decreased, as the influence of temperature was greater than that of pressure. From 2 MPa to 4.1 MPa, although
increased by 12.51%,
also increased by 16.62%. The influence law of
on the values of
and
of EOCP is shown in
Figure 5. It shows that with an increase in
,
first decreased and then increased. At about 2.58 MPa, the minimum value of 510.0549 was taken as the minimum point of the EGR per C
10H
20 yield. At this time,
was 0.0455 mol/s and
was 23.2147 W/K.
The influence law of
on
and
of EOCP is shown in
Figure 6. Both
and
increased with an increase in
. Among them,
increased by 14.36 times and
increased by 10.25 times; that is, the rate of increase for
was faster. The influence law of
on
and
of EOCP is shown in
Figure 7. It shows that
decreased first and then increased with the an increase in
; the minimum value was taken at about 0.119 mol/s. The minimum EGR per C
10H
20 yield is the most economical point. At this time,
was 0.0081 mol/s and
was 3.32623 W/K.
The optimal variable values for the minimum
are shown in
Table 5. Compared with the reference process, the optimal
increased by 0.9923 MPa and the optimal
decreased by 0.9 mol/s. It was calculated that
decreased by 27.7%,
decreased by 89.16% and
decreased by 85.01% compared to the reference process. This result indicates that the reduction in the value of
in EOCP is realized by sacrificing part of
.
4.2. Multi-Objective Optimization
Using the PlatEMO toolbox, we set the population number as 100, the optimization objective number as 2, the optimization variable number as 2 and the genetic algebra as 100, and the Pareto optimal frontier of EOCP based on the objectives of
minimum and
maximum was obtained. LINMAP [
28], TOPSIS [
29] and Shannon entropy [
30] decision-making methods were used, where the weight (
) of
and
was set to 0.5; the optimization results obtained using different decision-making methods were evaluated with the deviation index from Ref. [
31].
For the LINMAP decision-making method, the optimal solution i
opt is calculated as:
where
is the j-th target value, positive is the positive ideal point and
PEDi is the Euclidean distance between the i-th feasible solution and the positive ideal point. The LINMAP decision-making method calculates the closest point to the positive ideal point.
For the TOPSIS decision-making method, the optimal solution i
opt is calculated as:
where negative is the negative ideal point and
NEDi is the Euclidean distance between the i-th feasible solution and the negative ideal point. The TOPSIS decision-making method determines the point farthest from the negative ideal point.
For the Shannon entropy decision-making method,
Fi,j needs to be normalized first:
The Shannon entropy of the j-th target can be calculated by the following formula:
The optimal solution i
opt is calculated as:
where
wj is the weight of the j-th target.
The calculation formula for the deviation index is:
where opt denotes the decision point. The deviation index is the ratio of the distance between the decision point and the positive ideal point to the sum of the distance between the decision point and the positive and negative ideal point. The deviation index can be used to evaluate the quality of the decision point. The smaller the deviation index, the closer the decision point is to the positive ideal point, and the better the decision point is.
The Pareto optimal frontier of EOCP is shown in
Figure 8. The
and
corresponding to the point where
is at a minimum and
is at a maximum are the least ideal on the Pareto front, as can be seen from
Figure 8, which verifies that for the EOCP, the minimum
and the maximum
cannot be met at the same time, and the optimal solutions can only be obtained under the different degrees of importance of these two objectives. Both the
minimum point and
maximum point are the result of a single-objective optimization of one objective without considering the other objective. The reference point in the figure divides the Pareto front into two segments: A and B. The reference point (shown in the hexagon) was obtained by calculating the yield of C
10H
20 and the EGR of the reference process. Compared with the reference point, the multi-objective optimization solution on segment A had lower
and
values, while the multi-objective optimization solution on segment B had higher
and
values.
The distribution of the Pareto front within the variation range of
and
is shown in
Figure 9 and
Figure 10.
Figure 9 shows that in the Pareto front, the optimization range of
pin was 2–4.5 MPa; however, it was mainly distributed in the range of 2–3.5 MPa—that is, the selection of
in this range would be beneficial to the optimization of EOCP.
was evenly distributed in the whole optimization range, which shows that the selection of
was used to adjust the opposition between decreasing
and increasing
at the same time.
The results of the single-objective optimization, reference point and multi-objective optimization are shown in
Table 6. The deviation indexes corresponding to the results of all the optimizations were smaller than the reference point. In the results of the single-objective optimization, the
minimum point and the
minimum point were the same, and the deviation index of the
maximum point was the largest in the optimization results, indicating that the optimization effect of
was worse than
. The decrease in
was realized by sacrificing part of
to reduce
. Among the multi-objective optimization results, the values for
and
obtained by LINMAP were the largest, and the corresponding deviation index was also the largest. The values for
and
obtained with the Shannon entropy decision method were the smallest, and the corresponding deviation index was also the smallest. The values of
and
and the deviation index determined using the TOPSIS decision method were between the other two decision results. When using the deviation index to evaluate the results, the deviation index of the optimal solution under the Shannon enterprise decision result is the smallest; thus, it is the best optimization scheme. In the results obtained using the Shannon entropy decision method, the value of
was 2.0315 MPa and the value of
was 0.3909 mol/s. Compared with the reference process, the value of
was reduced by 32.28% and the value of
was decreased by 60.91%. The final value of
was reduced by 59.01%, and the value of
was decreased by 49.46%. The degree of this reaction was lowered by the Shannon entropy decision method, and the EGR was reduced by the decrease in the C
10H
20 yield of the process.