## 3 Results and Discussion
### Response Surface Modelling and Optimization
#### 3.6.2 Cod  
The fit summary for response 2, shown in Table 6, was generated, and the quadratic model was suggested in Equation (4). The results for the analysis of variance (ANOVA) for the quadratic model can be seen in Table 7, and they suggest that the Model F-value of 6.85 indicates the model is significant. It also suggests that an F-value this large only has a 1.06% chance of occurring due to noise. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Sequential** & **Lack of Fit** & \multirow{2}{*}{**Adjusted R2**} & \multirow{2}{*}{**Predicted R2**} & \\  & \(p\)**-Value** & & & & \\ \hline Linear & 0.1033 & 0.2772 & 0.2379 & \(-\)0.0206 & \\
2 FI & 0.6440 & 0.2311 & 0.1742 & \(-\)0.5861 & \\ Quadratic & 0.0522 & 0.5445 & 0.5432 & \(-\)0.0752 & Suggested \\ Cubic & 0.6532 & 0.2932 & 0.4607 & \(-\)6.2294 & Aliased \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Fit summary for COD response model. Figure 5: (**a**) Predicted vs. actual values of turbidity removal; (**b**) 3D surface plot of turbidity removal. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Sum of** & **Degree of** & **Mean** & \multirow{2}{*}{**F-Value**} & \multirow{2}{*}{_p-Value_} & \\  & **Squares** & **Freedom** & **Square** & & \\ \hline Model & 395.84 & 5 & 79.17 & 4.91 & 0.0301 & significant \\ A-Catalyst load & 1.71 & 1 & 1.71 & 0.1058 & 0.7545 & \\ B-Reaction time & 0.7004 & 1 & 0.7004 & 0.0434 & 0.8409 & \\ AB & 0.5329 & 1 & 0.5329 & 0.0330 & 0.8609 & \\ A\({}^{2}\) & 357.70 & 1 & 357.70 & 22.18 & 0.0022 & \\ B\({}^{2}\) & 2.96 & 1 & 2.96 & 0.1833 & 0.6814 & \\ Residual & 112.90 & 7 & 16.13 & & & \\ Lack of Fit & 6.86 & 3 & 2.29 & 0.0863 & 0.9639 & \\ Pure Error & 106.04 & 4 & 26.51 & & & \\ Cor Total & 508.74 & 12 & & & & not significant \\ \hline R\({}^{2}\) & Adjusted R\({}^{2}\) & C.V.\% & Predicted & Adeq. Pr & Mean & SD \\
0.7781 & 0.6196 & 4.39 & R\({}^{2}\) 0.5647 & 4.8711 & 91.57 & 4.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ANOVA for quadratic model for response 1: turbidity. The \(p\)-value generated is 0.0106 and indicates that the model term is significant as it is less than 0.0500. Any \(p\)-value that is greater than 0.1000 suggests the model terms are not significant; however, hierarchy terms are not included in the terms counted. Therefore, this model is significant and acceptable, as only model term B is above 0.100. Lastly, the value of 0.6632 for the lack of fit indicates that it is not significant relative to pure error, and a non-significant lack of fit value indicates that the model will fit. Table 7 indicates that the predicted R\({}^{2}\) value of 0.4378 is in reasonable agreement with the adjusted R\({}^{2}\) value of 0.5939, as the difference between the two values is less than 0.2. The Adeq. Pr of 6.0593 shows an adequate signal as the value is greater than 4, which is desirable. The predicted values were plotted against the experimental values using an experimental predicted data interactive plot to approximate the extent of the correlation. Figure 6a shows that only a few data points were recurrently scattered around the line of best fit, which could explain the inconsistent COD % removal (organic degradation) based on the specified experimental conditions. The standard error for the line of best fit showed an insignificant deviation with a \(p\)-value less than 0.05 at a 95% confidence level. The COD model regression is low (Table 7) despite the model's adjusted and predicted R\({}^{2}\) values being in reasonable agreement with a difference of less than 0.2. A curvature of considerable magnitude can be seen in Figure 6b. This curve also suggests that the correlation between the factors (AB) and the response (COD) was well fitted to a quadratic function (4).