# Innovative Approaches for Minimizing Disinfection
## 7 Conclusions and Perspectives  
The formation of toxic DBPs during water treatment has been comprehensively documented over the last five decades. However, the detailed mechanisms behind the formation of individual DBP compounds, the identification of emerging DBP categories, and the properties of their precursors remain critical topics of investigation. These challenges, combined with the exploration of various water treatment strategies, continue to push research forward. The ultimate goal is to deliver drinking water that is entirely \"free\" from DBPs, or at least to reduce their concentrations to levels that are both minimized and safe for public health.  
Recent research efforts are increasingly focused on the development and optimization of cutting-edge techniques, including the application of novel disinfectants, adsorbents, AOPs, and innovative modifications or combinations of treatment technologies. The objective is to improve efficiency in DBP removal while maintaining water safety. Simultaneously, the rise of AI and ML has opened new horizons in water treatment, offering powerful tools for predictive modeling, process optimization, and real-time monitoring. These advanced methods are becoming integral in fine-tuning treatment processes, predicting DBP formation, and improving overall water quality control, ensuring that public health is safeguarded with greater precision and efficiency than ever before. The synergy between novel treatment methods and AI-driven approaches holds tremendous potential  
\begin{table}
\begin{tabular}{l l} \hline \hline
**DBPs Minimization** & **Techniques** & **Reference** \\ \hline \multicolumn{3}{c}{**Alternative disinfectants/modifying treatment**} \\ \hline \hline  & Preformed monochloramine & [77] \\  & Optimizing Cl\({}_{2}\) contact time & [78] \\  & UV/chlorine and VUV/chlorine as ultrafiltration membrane pretreatment & [79] \\  & UV/NH\({}_{2}\)Cl & [80] \\  & Monochloramine & [81] \\  & O\({}_{3}\), Fe(VI), Mn(VII), and ClO\({}_{2}\) & [64] \\  & Cl\({}_{2}\)/ClO\({}_{2}\) & [82] \\  & Ozone & [83] \\ \hline \multicolumn{3}{c}{**AI/machine learning**} \\ \hline \hline  & Membrane design & [84] \\  & Ultrafiltration processes & [85] \\  & Real-time monitoring of Cl\({}_{2}\) dosage & [86] \\  & Adsorption on nanocomposite material & [87] \\  & Optimal coagulant dose by artificial neural network fuzzy inference system (ANFIS) & [88] \\  & Optimized coagulation process & [89] \\  & Nanofliltration membrane performance & [90] \\  & Prediction of physicochemical water quality characteristics & [91] \\  & Model chlorine, chloramine, and chlorine odor intensity & [92] \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._for revolutionizing water treatment and achieving a future where DBPs are effectively controlled or eliminated.  
Conceptualization, S.K.G. and A.D.N.; methodology, S.K.G. and A.D.N.; validation, S.K.G., A.D.N. and D.E.A.; formal analysis, S.K.G., A.D.N. and D.E.A.; investigation, S.K.G., A.D.N. and D.E.A.; resources, S.K.G., A.D.N. and D.E.A.; data curation, S.K.G., A.D.N. and D.E.A.; writing--original draft preparation, S.K.G., A.D.N. and D.E.A.; writing--review and editing, S.K.G., A.D.N. and D.E.A.; visualization, S.K.G., A.D.N. and D.E.A.; supervision, S.K.G., A.D.N. and D.E.A.; project administration, S.K.G. and A.D.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data availability StatementData are contained within the paper.  
The authors declare no conflicts of interest.