## 5 Conclusions  
This study examined the robustness of applying MLPNN to model the nonlinear relationship between various textural properties such as pore diameter, pore volume, specific surface area, crystallite size, and BPA degradation from wastewater. The parametric analysis and visualization show that there are nonlinear relationships between the textural properties of the photocatalysts and their tendency to photodegrade the BPA in the water. Data-driven models, namely, MLPNN, CSVM, and RQGPR, have been employed to model the relationship between textural properties and the degradation efficiency of BPA in industrial wastewater. Various configurations of the MLPNN, CSVM, and RQGPR were trained and tested to determine the one that best models the relationship between the input parameters and the targeted output. The MLPNN configuration with 4 input nodes, 13 artificial neurons in the hidden layer, and 3 output nodes in the output layer displayed the best performance, with R\({}^{2}\) of 0.902 for training and 0.992 for testing, while RMSE values of 4.21 and 4.66 were obtained for the training and the testing, respectively. The 4-13-3 MLPNN trained using the Levenberg-Marquardt algorithm was robust in predicting the BPA degradation, the rate of BPA degradation, and the initial concentration of BPA in the wastewater due to its inherent ability to model complex nonlinear processes. Neither the CSVM nor RQGPR showed impressive performance as indicated by the low R\({}^{2}\) values. Hence, the MLPNN algorithms could be subsequently employed to improve and optimize the performance of the photodegradation process.  
Conceptualization, M.A.A. and B.V.A.; methodology, B.V.A.; software, B.V.A.; validation, M.A.R.A.G., A.K.M. and A.A.A; formal analysis, J.M.A.; investigation, K.A.S.; resources, M.A.A.; data curation, A.K.M.; writing--original draft preparation, B.V.A.; writing--review and editing, M.A.A., A.K.M. and A.A.A.; visualization, J.M.A. and K.A.S.; supervision, M.A.A. and B.V.A.; project administration, M.A.A. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Process** & **Best Model** & **Performance** & **Reference** \\ \hline Photodegradation of BPA & MLPNN & R\({}^{2}\) = 0.992 & This study \\  & & RMSE = 4.21 & \\ \hline Photodegradation of malachite green & CatBoost & R\({}^{2}\) = 0.990, RMSE = 1.34 & [59] \\ \hline Photodegradation of organic pollutants & LightGBM & R\({}^{2}\) = 0.928, RMSE = 0.194 & [37] \\ \hline Photodegradation of organic pollutants & BPANN & R\({}^{2}\) = 0.999, RMSE = N. R & [60] \\ \hline Photodegradation of organic pollutants & CGCCN-MF-ANN & R\({}^{2}\) = 0.746 and RMSE of 0.293 & [61] \\ \hline Photodegradation of perfluorocatanoic acid & AdaBoost & R\({}^{2}\) = 0.878 and RMSE = 10.33 & [62] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the best model with literature.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were generated in this research.  
**Acknowledgments:** The authors acknowledge the support of Department of Chemical Engineering University of Technology Iraq.  
**Conflicts of Interest:** The authors declare no conflict of interest.