## 4 Conclusions  
The one-factor-at-a-time method revealed that the two operating parameters, reaction time and catalyst load, had a significant effect on two output parameters, turbidity and COD removal. Furthermore, the optimal ranges for catalyst load and reaction time were established to be 0.3-0.6 g/L and 20-40 min, respectively. These conditions were then applied to the optimization technique. The Central Composite Design (CCD) matrix of 13 experimental runs and 5 center points, with reaction time (20-40 min) and catalyst load (0.3-0.6 g/L) as the 2 input variables, were developed at their low, high, and middle points. RSM was used to model and optimize the input-output variable relationship. The analysis of variance (ANOVA) revealed that all models generated were significant as they possessed a \(p\)-value less than 0.05 at a 95% confidence level. Therefore, this signifies good predictability for the optimization of the process. A desirability of 74.7% was achieved at optimal conditions of 0.6 g/L for catalyst load and 40 min for reaction time. The predicted results from RSM were found to be in good agreement with the experimental results. The optimal responses achieved were 32.64% COD removal and 95.17% turbidity removal. The optimal conditions were also applied to the comparative experiment that utilized UV-Vis light instead of UV light. The optimal responses achieved for this were 25.58% COD removal and 66.88% turbidity removal. This is possibly due to TiO\({}_{2}\) not being photoexcited by visible light due to its large energy band gap. Due to the turbidity of the TiO\({}_{2}\), additional  
Figure 8: (**a**) Actual vs. predicted values of optimal turbidity removal; (**b**) Actual vs. predicted values of optimal COD removal.  
post-treatment separation methods are required. Further studies making use of TiO\({}_{2}\) as a catalyst for photocatalytic wastewater treatment should include additional post-treatment filtration steps to recover the TiO\({}_{2}\) to ensure optimal removal of the nanoparticles and to enhance turbidity and COD removal. The use of actual wastewater highlighted the need for better characterization of wastewater for better comparison and explanation of variation in removal efficiencies of different systems.  
Conceptualization, E.K.T., S.R., S.J., C.M. and T.G.; methodology, S.J., C.M. and T.G.; software, S.J., C.M. and T.G.; validation, S.J., C.M. and T.G.; formal analysis, C.M. and T.G.; investigation, C.M. and T.G.; resources, S.R. and E.K.T.; data curation, C.M. and T.G.; writing--original draft preparation, S.J., C.M. and T.G.; writing--review and editing, L.L.M., S.R. and E.K.T.; visualization, C.M.; supervision, S.R. and E.K.T.; project administration, S.R. and E.K.T. All authors have read and agreed to the published version of the manuscript.  
This project was supported by the South African Water Research Commission (WRC Project: C2021/2022-00958).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors are thankful to the Green Engineering Research Group, Department of Chemical Engineering, Durban University of Technology, and eThekwini municipality for their support.  
The authors declare no conflict of interest.