## 3 Results
### Storage Tank Volume Optimization
According to this figure, m\({}_{\text{Average}}\) is approximately 9.511 kg when \(\overline{\
u}_{\text{low}}\) is set to 0.25 and \(\overline{\
u}_{\text{m/h}}\) is set to 0.5. Figure 15 illustrates the graphical representation of the S/N ratios for the control factors calculated for the work performed by the compressor per mass filled (W\({}_{\text{Comp}}\)). According to this figure, the minimum W\({}_{\text{Comp}}\) is found to be 0.17 kWh/kg when \(\overline{\
u}_{\text{low}}\) is set to 0.75 and \(\overline{\
u}_{\text{m/h}}\) is set to 2. Table 11 presents the calculated S/N ratios of the control factors at the levels determined through the Taguchi analysis. For the number of vehicle tanks that can be filled in 24 h, the calculated weighting factor for n\({}_{\text{Vehicle}}\) is 10.31%. The effects of the control parameters on the average mass of gas filled are shown in the m\({}_{\text{Average}}\) row, with a weighting factor of 24.22% in the multi-objective optimization process. The work performed by the compressor per filled mass (W\({}_{\text{Comp}}\)) has the highest impact on the objective function, with a weighting factor of 65.47%. \begin{table}
\begin{tabular}{c c c} \hline
**Level** & – & – \\  & \(\mathbf{\
abla_{\text{low}}}\) & \(\mathbf{\
abla_{\text{m/h}}}\) \\ \hline n\({}_{\text{Vehicle}}\) & & \\ \hline
1 & 47.2 & 47.21 \\
2 & 47.09 & 47.11 \\
3 & 47.22 & 47.2 \\ Delta & 0.13 & 0.1 \\ Rank & 1 & 2 \\ \(\sum\) Delta & 0.23 & \\ w (\%) & 10.31\% & \\ \hline m\({}_{\text{Average}}\) [kg] & & \\ \hline
1 & 19.5 & 19.36 \\
2 & 19.35 & 19.31 \\
3 & 19.07 & 19.25 \\ Delta & 0.43 & 0.11 \\ Rank & 1 & 2 \\ \(\sum\) Delta & 0.54 & \\ w (\%) & 24.22\% & \\ \hline \end{tabular}
\end{table} 
Table 11: The S/N ratios and the weight factors calculated for the n\({}_{\text{Vehicle}}\), m\({}_{\text{Average}}\), and W\({}_{\text{Comp}}\). Figure 15: S/N ratios of control factors for the work performed by the compressor per unit mass filled (W\({}_{\text{Comp}}\)). As a result, the optimal simulation design is achieved with parameters of 0.75 for \(\overline{\
u}_{\text{low}}\) and 2 for \(\overline{\
u}_{\text{m/h}}\), corresponding to simulation 9. The selected simulation 9 results are: \(\text{n}_{\text{Vehicle}}\) = 229, \(\text{m}_{\text{Average}}\) = 8.941 kg, and \(\text{W}_{\text{Comp}}\) = 0.17 kWh/kg. With this configuration, \(\text{n}_{\text{Vehicle}}\) increases while \(\text{W}_{\text{Comp}}\) and \(\text{m}_{\text{Average}}\) decrease. \[\text{GRG}=0.1031\:\text{GRC}_{\text{n}_{\text{Vehicle}}}+0.2422\:\text{GRC}_{ \text{m}_{\text{Average}}}+0.6547\:\text{GRC}_{\text{W}_{\text{Comp}}} \tag{16}\]  
Following the pressure and volume optimizations, the pressure and temperature variations of the storage tanks during a 24 h operation are presented in Figures 16 and 17. The pressure of the high-pressure storage tanks, indicated by the black line, reaches up to 250 bar as a result of the optimization. Since the vehicle tanks are intended to be filled up to 200 bar, the minimum pressure level of the high-pressure storage tanks is set to 205 bar. According to the volume optimization, only 4 out of the total 48 storage tanks are designated as high-pressure tanks. This results in a rapid pressure drop to the minimum level in these tanks, causing the compressor to frequently engage and restore the pressure to its maximum level. In this context, it requires compressor activation roughly every 12.5 min. Due to the frequent and short-term refilling by the compressor, the temperature in the high-pressure storage tanks remains consistently high. During the first filling process, the low-, medium-, and finally high-pressure storage tanks are filled in sequence, leading to the highest temperature being observed during this initial filling phase.