## 3 Results and Discussions
### Simulation Optimization of BTS Using Genetic Algorithm  
In this study, an air booster was installed to prevent clogging and equipment damage due to the pressure loss effect discussed in the previous section. The air booster minimized the pressure loss in the vertical pipe section where the pressure loss was high. A genetic algorithm was applied to optimize the number of boosters installed and the distance between them (Figures 11 and 12). The optimization process was carried out using MATLAB/Simulink (version 2019b). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Inlet Pressure** & **Horizontal Length** & **Vertical Length of** & **Bending Pipe** & **Outlet** \\
**(3.8 bar)** & **of Pipe (m)** & **Pipe (m)** & **Number (EA)** & **Number (EA)** & **Pressure (bar)** \\ \hline Case 1 & 127.94 & 26.17 & 13 & 161.57 & 2.66 \\ Case 2 & 137.94 & 16.17 & 13 & 161.57 & 2.99 \\ Case 3 & 117.94 & 36.17 & 13 & 161.57 & 2.32 \\ Case 4 & 131.67 & 29.9 & 26 & 161.57 & 2.65 \\ Case 5 & 124.21 & 29.90 & 26 & 161.57 & 2.41 \\ Case 6 & 120.48 & 33.63 & 26 & 161.57 & 2.78 \\ Case 7 & 135.40 & 18.71 & 26 & 161.57 & 2.28 \\ \hline \hline \end{tabular}  
* Inlet pressure: 3.8 bar; Case 1: Simulation model; Case 2: Length of horizontal pipe = 10 m, length of vertical pipe = 10 m; Case 3: Length of horizontal pipe = 10 m, length of vertical pipe = 10 m; Case 4: Bending pipe number = 13 EA, length of vertical pipe = 3.73 m, length of vertical pipe = 3.73 m, Case 5: Bending pipe number = 13 EA, length of vertical pipe = 3.73 m, length of vertical pipe = 3.73 m; Case 6: Bending pipe number = 13 EA, length of vertical pipe = 7.46 m, length of vertical pipe = 7.46 m; Case 7: Bending pipe number = 13 EA, length of vertical pipe = 7.46 m, length of vertical pipe = 7.46 m, length of vertical pipe = 7.46 m.  
\end{table} 
Table 6: Case simulation parameters with test scenario. Figure 10: Pressure drop comparison between horizontal and vertical pipes. The pressure loss of the BTS was set as the objective function. The number of air boosters and the distance between them, which were to be optimized, were configured as variables. The pressure was  
Figure 11: Optimal positions of air boosters for pressure loss minimization. Figure 12: Schematic of genetic algorithm. measured in bars, and the installation distance was measured in meters. Table 7 lists the set values for the parameters of the genetic algorithm. These values were set by focusing on the convergence of the fitness values. In this study, considering the convergence of the goodness of fit and the performance of the computer, the population size was set to 2000 individuals and the number of generations was 400, which is 100 times the number of variables. The convergence condition for the goodness of fit was set proportionally. Selection refers to the process of selecting parents from a population for mating according to the fitness value after all the individuals in the population have been evaluated for fitness. The selection types are roulette selection, ranking-based selection, and tournament selection. Roulette was selected in this case. Crossover is an operator that uses the advantages of two chromosomes to make a better chromosome, and crossover types include one-point crossover, two-point crossover, and uniform crossover. One-point crossover was selected. Reproduction (crossover ratio) is a variable that determines how often crossover occurs and is generally maintained between 80% and 90%. We selected the ratio as 0.85. The probability of mutation is a variable that determines how often a gene is altered and is set to a very small value in genetic algorithms, as the probability of mutation in the natural world is very rare. It was selected as 0.01 in our study. Figure 13a,b shows the results of the genetic algorithm. Figure 13a shows the best fitness, best function value, and number of iterations in each generation. Figure 13b shows the best individual and the vector item of the individual as the best-fit function value in each generation.