4.2. Optimization operating conditions in multi-level refrigeration cycle

This section only explores the optimization of the evaporating temperatures in the lower refrigeration cycle presented in Figure 6(b). This is because the temperature differences between the heat source temperature profile of the process (i.e., the GCC) and the evaporation temperature of refrigeration levels in the upper cycle are small, as shown in Figure 6(a). This small temperature difference leads to slight improvement on the energy efficiency of the upper cycle [10].

In this work, the deterministic method (generalized reduced gradient (GRG)) will be used to search for the optimal solution of the objective function. In order to test the prediction accuracy of the proposed method, the results of the proposed method will also be compared with the published results of Oh et al. [10], in which genetic algorithm (GA) was employed to search for the optimal operating conditions of multi-level refrigeration cycle.

The decision variables manipulated for the optimization and the upper and lower bounds are listed in Table 9.

The optimal results for the three-level refrigeration cycle using ethylene as a refrigerant are summarized in Table 10. The results show that a significant decrease in the overall compression duty of the refrigeration cycle (9%) over the base case is obtained due to the reduction of the temperature lift (the temperature difference between the evaporator and condenser).


Table 8. Predicted power demand for cascade cycle using HYSYS and new model.


Table 9. Optimization variables and their constraints for case 2.

is no pressure drop in both heat exchangers and piping [10]. The partition temperature, which

Table 8 presents the results of the predicted power demand for complex cycle using HYSYS and the new refrigeration model. The error between the refrigeration model and HYSYS predictions for net power demand prediction is about 10% in the lower cycle and �2% in the upper cycle. As can be seen from the HYSYS simulation results for the three parallel cycles, this

This section only explores the optimization of the evaporating temperatures in the lower refrigeration cycle presented in Figure 6(b). This is because the temperature differences between the heat source temperature profile of the process (i.e., the GCC) and the evaporation temperature of refrigeration levels in the upper cycle are small, as shown in Figure 6(a). This small temperature difference leads to slight improvement on the energy efficiency of the upper

In this work, the deterministic method (generalized reduced gradient (GRG)) will be used to search for the optimal solution of the objective function. In order to test the prediction accuracy of the proposed method, the results of the proposed method will also be compared with the published results of Oh et al. [10], in which genetic algorithm (GA) was employed to search for

The decision variables manipulated for the optimization and the upper and lower bounds are

The optimal results for the three-level refrigeration cycle using ethylene as a refrigerant are summarized in Table 10. The results show that a significant decrease in the overall compression duty of the refrigeration cycle (9%) over the base case is obtained due to the reduction of the temperature lift (the temperature difference between the evaporator and condenser).

Modeling approach Power demand (kW) Error Complex cycle (HYSYS) 780 + 1794 + 1615 = 4189 � Three simple cycles (shortcut model) 2794 + 1620 + 230 = 4644 �10.8% Three simple cycles (HYSYS) 2393 + 1541 + 228 = 4161 0.67%

Complex cycle (HYSYS) 4218 + 21,810 = 26,028 � Two simple cycles (shortcut model) 17,863 + 8669 = 26,532 �2% Two simple cycles (HYSYS) 17,362 + 8656 = 26,018 0.04%

is the temperature of the evaporator of the upper cycle, was set at �44�C.

error arises mainly from the mixing effect at the inlet to the compressor.

4.2. Optimization operating conditions in multi-level refrigeration cycle

the optimal operating conditions of multi-level refrigeration cycle.

Table 8. Predicted power demand for cascade cycle using HYSYS and new model.

cycle [10].

14 Refrigeration

listed in Table 9.

Low temperature cycle

High temperature cycle


Table 10. Comparison between the optimization results of the proposed model and the published results [10].

It is clear from the results that GRG has good performance (in terms of getting closer to the optimal solution) and less computationally expensive compared to GA. The results in Table 10 show that insignificant difference between the results from the proposed model and those obtained from the published results of Oh et al. [10], where the deviation between the results is only 1% both for the evaporation temperature of cooling level 2 and for the evaporation temperature of cooling level 3. The differences between the results of the proposed model and those of Oh et al. [10] are mainly due to using the decomposition approach, which is used to predict the coefficient performance of the refrigeration cycle.
