## 4 Discussion
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & \multicolumn{1}{c}{**B**} & \multicolumn{3}{c}{**Kolmogorov–Smirnov Test \({}^{\mathbf{a}}\)**} & \multicolumn{3}{c}{**Shapiro–Wilk Test**} \\  & & \multicolumn{1}{c}{**Statistic**} & \multicolumn{1}{c}{**df**} & \multicolumn{1}{c}{**Sig.**} & \multicolumn{1}{c}{**Statistic**} & \multicolumn{1}{c}{**df**} & \multicolumn{1}{c}{**Sig.**} \\ \hline \multirow{4}{*}{J\({}_{\text{P}}\)} & \(-\mathbf{1}\) & 0.282 & 9 & 0.037 & 0.790 & 9 & 0.016 \\  & \(\mathbf{0}\) & 0.239 & 9 & 0.146 & 0.817 & 9 & 0.032 \\  & \(\mathbf{1}\) & 0.219 & 9 & 0.200 \({}^{\star}\) & 0.904 & 9 & 0.275 \\  & \(\mathbf{-1}\) & 0.162 & 9 & 0.200 \({}^{\star}\) & 0.932 & 9 & 0.498 \\ COD & \(\mathbf{0}\) & 0.186 & 9 & 0.200 \({}^{\star}\) & 0.922 & 9 & 0.413 \\  & \(\mathbf{1}\) & 0.162 & 9 & 0.200 \({}^{\star}\) & 0.906 & 9 & 0.291 \\  & \(\mathbf{-1}\) & 0.295 & 9 & 0.023 & 0.735 & 9 & 0.004 \\ SFD & \(\mathbf{0}\) & 0.258 & 9 & 0.085 & 0.792 & 9 & 0.017 \\  & \(\mathbf{1}\) & 0.169 & 9 & 0.200 \({}^{\star}\) & 0.918 & 9 & 0.377 \\ \hline \hline \end{tabular} \({}^{\star}\) This is a lower bound of the true significance. \({}^{\mathbf{a}}\) Lilliefors Significance Correction.  
\end{table} 
Table 12: Tests of normality (Kolmogorov–Smirnov test and Shapiro–Wilk test at \(\alpha=0.05\)) for data partitioned according to the three settings of factor B (CFV) for (A) average permeate flux (J\({}_{\text{P}}\)), (B) COD rejection rate (COD), and (C) cumulative flux decline (SFD) (IBM SPSS v.29). the effects, which is attributed to specific factorial settings (Figure 8). Using this screening approach, it emerges that factor A and factor D are the effects that might influence the average permeate flux the most; the strength differential is clearly formed by the adjustments of the first and third level. Violin lengths and the bulging motifs of the distributions also broadly vary. For example, for factor A, the data associated with the first level are rather symmetric, while the next two levels generate data with alternating skewed protrusions. Even more impressive are the effects owing to factor B, which is deemed, nevertheless, inactive. The first two levels produce data that are reverse images of each other, while the data created by the third level are shaped into an \"ink pot\". Consistent skewness in all three levels is exhibited only in connection to factor D.  
A strong non-uniform dispersion in the COD rejection rate response data is portrayed in Figure 9. The only strong effect is identified to factor D, which, through its first- and second-level adjustments, generated non-overlapping violin patterns, thus demonstrating the lowering effect of factor D. The conditional influence of the second level of the temperature is somehow unexpected, compared to the first and third levels. A great variety of motifs are produced in reference to the cumulative flux decline response data (Figure 10). The length, skewness, and bulging characteristics of the violins may not be considered predictable for any of the four controlling factors. What may be discerned, though, is that factors A and D may appear active when tuned and contrasted at their respective end levels.