## 3 Results
Regarding Figure 5, the maximum and minimum efficiencies equal respectively the 40 th and 21 th iterations number occurring at exergy efficiency of the model and the efficiency of the real system is very close to that of the model. Figure 4: Grossman flow diagram for the exergy analysis results of the Sinem GPP. To display dynamic behaviour of the model, the variation in system exergy efficiency with environmental (ambient) temperature is shown in Figure 6. As it can be seen from Figure 6, while environmental temperature increases, the exergy efficiency of the system falls. In conclusion, in the general literature, all studies and all engineers, operators and workers researching air-cooled organic Rankine cycles acknowledge that the beneficial exergy of the system is affected by environmental temperature and is known that system exergy efficiency decreases [56; 57; 58; 59]. After the modelling of the system mentioned above, the thermodynamic performance of the model for the Sinem GPP with real air-cooled geothermal binary organic Rankine cycle has been maximized with the ABC optimization method. To apply the ABC method to the developed model and thus find the best performance of the Sinem GPP, the main parameters presented in Table 2 are used. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **1\(\leq\)**_n\(\leq\)5** & **6\(\leq\)**_n\(\leq\)10** \\ \hline Number of colony size (NP) & 10 & 20 \\ Number of food sources & 10 & 20 \\ Number of cycles (stopping criteria) & 50 & 100 \\ Limit & 100 & 100 \\ Number of optimization parameter (D) & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main parameters and their values used in the ABC optimization. Figure 5: The comparison of the modeling system with the real system for system exergy efficiency. Figure 6: The change of exergy efficiency of the modelled system with ambient temperature. By optimizing the model as maximum exergy efficiency, the optimum values of the pressure, temperature and flow rate data given in Table 2 for the line numbers of Figure 1 are found. During the ABC optimization process, the variable parameters and constraints given in Table 3 are used. The constraints are variable parameters simultaneously. As the real system was modelled according to temperature data at the line numbers in Figure 1, the exergy efficiency of the developed model was not significantly affected by the changes of temperature data. Each temperature change on the system line numbers, especially changes in the source/well temperature and pressure data in the cycle, completely change the model and sometimes result in insolvability. So temperature and pressure data of the real system were accepted as being dependent or kept fixed some places. Because of this reason, all the data except temperature are used for optimization. Thus, some cases are created by grouping the variable/constraint parameters (see Table 3). To maximize the exergy efficiency of the model of the Sinem GPP, the cases accepted for optimizing the model in the best way is listed in the last column of Table 3. Here, the integers specify 12 cases (variable parameter groups). Each of these groups is called as \"case\". For example, seeing on the first line of Table 3 indicates that the variable parameter P\({}_{1}\) is included in case 1, case 3 and case 6. To reveal these cases, numerous calculations have been repeated. In conclusion, to evaluate the effects of variable parameters given in Table 3 on exergy efficiencies of the modelling components of the GPP components, 12 cases were determined as optimization conditions. The behaviour of the optimization process for maximizing the exergy efficiency of the model of the Sinem GPP is illustrated in Figure 7, as a change of exergy efficiency versus cycle number.