## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Variable Parameters**} & \multicolumn{1}{c}{**Real Values**} & \multicolumn{1}{c}{**Constraint Range**} & \multicolumn{1}{c}{**Cases Arising from the Grouping of**} \\  & & & & **Variable Parameters** \\ \hline P\({}_{1}\) & kPa & 1040 & 950–1150 & \(1,3,6\) \\ P\({}_{1,\text{steam}}\) & kPa & 1040 & 950–1150 & \(1,3,7,11\) \\ P\({}_{13}\) & kPa & 150 & 115–185 & \(2,4\) \\ P\({}_{20}\) & kPa & 119 & 90–150 & \(2,5,11\) \\ \(\Delta\text{PV}_{\text{AP}}\)\({}_{1}\) & kPa & 310 & 0–440 & \(1,3,6,7\) \\ \(\Delta\text{PV}_{\text{APAP}}\)\({}_{2}\) & kPa & 40 & 0–230 & \(1,3,6,7,11\) \\ \(\Delta\text{PV}_{\text{PREHE}}\)\({}_{1}\) & kPa & 100 & 0–200 & \(1,3,6\) \\ \(\Delta\text{PV}_{\text{PREHE}}\)\({}_{2}\) & kPa & 120 & 0–200 & \(1,3,7\) \\ \(\hat{m}_{1}\) & kg/s & 445 & 442–446 & \(1,6,11\) \\ \(\hat{m}_{1,\text{steam}}\) & kg/s & 8.33 & 8–10 & \(1,7,11\) \\ \(\hat{m}_{12}\) & kg/s & 160 & 159.6–161.6 & \(2,4,12\) \\ \(\hat{m}_{19}\) & kg/s & 169 & 167–172 & \(2,5,12\) \\ NCG & \% & 30 & 20–40 & \(1,7,11\) \\ \(\hat{m}_{\text{a}}\) & kg/s & 2000 & 1600–2400 & \(8,9,11,12\) \\ \(\hat{m}_{\text{c}}\) & kg/s & 2000 & 1600–2400 & \(8,10,12\) \\ Number of Fan 1 & unit & 33 & 21–45 & \(8,9,12\) \\ Number of Fan 2 & unit & 36 & 21–45 & \(8,10,12\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The conditions used for the system optimization. Figure 7: The change of exergy efficiency with cycle number during the optimization. Regarding Figure 7, the system exergy efficiency is taken as the objective function and the function has been maximized. As observed from the figure, although the ABC method may not select a suitable random solution (10.16%) at the initializing stage of finding the best exergy efficiency, it however can rapidly converge to the optimum (19.76% as maximum) exergy efficiency in 4 th cycle. It remains at this value until 10 th cycle. There are declines in exergy efficiency between 10 th and 48 th cycles due to random change in the constraint range of variable parameters. Still they are above the real value (14.52%). After 48 th cycle, the exergy efficiency has again reached its maximum value. As a result, the ABC optimization process is completed on the model of the GPP. The variation in system exergy efficiency for 12 cases as optimization conditions of the ABC optimization process is shown in Figure 8. Here Case 0 is the exergy efficiency of the real operating system. Its value is 14.52%. As observed on the figure, for all optimization conditions, their exergy efficiencies are higher than that of Case 0. The exergy efficiency (14.59%) obtained in Case 5 is the closest to that of the real system. This is followed by Cases 7 and 9. Cases 5 and 7 are related to optimization of the pressure of the geofluid along the lines shown in Figure 1 while Case 9 is related to that of TURB 1-related parameters. Especially in the system, Case 12 has the highest value at exergy efficiency as 23.92%. Case 8 as 22.82%, Case 10 as 20.52% and Case 9 as 20.51% come later. In Cases 8, 9, 10 and 12, it is optimized for the condensers-related parameters in the GPP. According to exergy analysis, the highest exergy destruction rate occured in condenser CON 2. This was followed by VAP 2, CON 1 and the fans. It is observed that, these optimization results show similar results with those of the exergy analysis. Figure 9 shows the variation in net power generated from the model according to optimization conditions. In the figure, Case 0 states the net power of the real system and its value is 14.21 MW. As seen in the figure, at the end of the optimization process, Cases 3, 8, 9 and 10 have a net power below the value of the real system. When optimized it for n-pentane line-related parameters of TURB 1 (Case 3) and condensers-related parameters (Cases 8-10), they have no much effect on maximum system exergy efficiency.