# Design and Optimization of Piezoelectric-Powered Portable UV-LED Water Disinfection System
## 5 Results and Discussion  
Firstly, single-objective optimization is performed using GA. In this case, the cost of the system is ignored, and the shake duration is taken as the OF. The minimum shake duration along with the optimal set of the parameters are determined based on the range of the constraints of the input parameters. Shown in Figure 6 is the variation of fitness value in GA optimization.  
Figure 5: GA optimization flowchart.  
Figure 6: GA optimization process.  
The best fitness value of \(T_{S}\) is found to be 15.34 s in the 375 th generation. Then, GA optimization helps to generate the optimal values of the design parameters (the first nine parameters in the table) along with the derived parameters (the last five parameters in the table), as shown in Table 5.  
It should be noted that the piezoelectric harvester generates 42.19 J of electrical energy within a shaking duration of 15.34 s. Then, the stored energy is dissipated by the UV-LEDs within 27.9 s to achieve the target disinfection performance. This optimal set of parameters may give an idea of the cost of the system. However, it would be better to optimize the cost and shake duration together. Next, multiple-objective optimization is applied by including the cost of the system. In this case, both objective functions (the shake duration and the cost) need to be solved together. Therefore, the shake duration and the cost are considered as the main criteria for the cost scenarios. Having considered various user expectations as well as the variability of the component cost in the market, there may be three different cost scenarios (low, typical or moderate, high). By inspecting the range of the cost and the shake duration, it is found that there are 175 different pairs of cost and shake duration. Among these pairs, a threshold value for the cost is determined for the low, moderate and high scenarios, respectively. More specifically, the low-cost scenario corresponds to the pairs with the lowest cost and the highest shake duration, whereas the high-cost scenario corresponds to the pairs with the lowest cost and the lowest shake duration. Besides, the moderate cost scenario corresponds to the mean value of the costs. The multi-objective GA optimization is shown in Figure 7 where all these pairs are considered. Then, the optimal values of the design parameters are derived as listed in Table 6. It should be noted that both the shake duration and the cost ranges present variability, and extreme values might indicate lower and upper parametric bounds. For more applicable values in between the extreme values, some more parameters need to be considered, and further fine-tuning would be possible.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Power dissipation of UV-LEDs (\(P_{d}\)) & 1.5 W. \\ Number of \(PZT\) ceramic (\(N\)) & 10 \\  Fx power of UV-LEDs (\(\phi\)) & 0.08 W \\  Bottle shake frequency (\(f_{s}\)) & 5.5 Hz \\  Bottle shake acceleration (\(\alpha\)) & 35 m/s\({}^{2}\) \\  Height of the moving block (\(h_{1}\)) & 0.1 m \\  Piezoelectric ceramics thickness (\(h_{3}\)) & 0.003 m \\  Inner radius of the moving block (\(r_{2}\)) & 0.031 m \\  Piezoelectric ceramic diameter (\(R_{PZT}\)) & 0.01 m \\  Mass of the moving block (\(m\)) & 0.42 kg \\  Applied force on \(PZT\)s (\(F\)) & 14.74 N \\  Generated voltage (\(V\)) & 13.96 V \\  Total harvested energy (\(E_{h}\)) & 42.19 J \\  Supercapacitor min. voltage (\(V_{S}\)) & 13.96 V \\  Supercapacitor min. energy capacity (\(E_{S}\)) & 42.19 J \\  Disinfection duration (\(T_{d}\)) & 27.9 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimal System Parameters (Objective Function (OF): Shake Duration).