Abstract
Drinking water disinfection effectively prevents waterborne disease outbreaks but inevitably results in the formation of carcinogenic disinfection by-products (DBPs). Mitigating DBP formation without compromising microbial safety remains a critical challenge. Here we developed DISoptimizer, a water-quality-adaptive optimization framework designed to determine the optimal chlorine dose across heterogeneous water matrices, thereby maintaining a user-defined residual chlorine target as an operational surrogate for disinfection reliability while minimizing four trihalomethane (THM4) formation. Using machine learning models trained on experimental datasets covering diverse water quality conditions, DISoptimizer predicts residual chlorine and THM4 formation after 24 h, representing the point of delivery. Crucially, DISoptimizer incorporates a weighting factor to encode user-defined preferences, allowing utilities to adjust the operational emphasis on maintaining residual chlorine margins versus mitigating THM4 formation in response to fluctuating water quality and different management priorities. Computational simulations coupled with external validation demonstrated that DISoptimizer achieved a 5–35% reduction in THM4 formation compared to the empirical fixed-dosing strategies, while consistently maintaining residual chlorine margins. This work advances disinfection management from DBP concentration prediction towards proactive chlorine-dosing decisions, supporting the management of health risks associated with microbial contamination and DBP exposure in drinking water.
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Data availability
The dataset used for model development in this study is available via GitHub at https://github.com/pinfish94/DISoptimizer.git. Source data supporting the findings of this study are provided with this paper.
Code availability
All code created in this work is available via GitHub at https://github.com/pinfish94/DISoptimizer.git.
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Acknowledgements
We thank C. Ye, Z. Du, R. Zhang, R. Xiao, Y. Kong, F. Ao and Y. Li for assistance with water sample collection.
Funding
This work was supported by the National Natural Science Foundation of China (52325001 to W.C. and 52500010 to P.W.) and the Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China (JYB2025XDXM908 to W.C.). Jing-Jin-Ji Regional Integrated Environmental Improvement-National Science and Technology Major Project (2026ZD1211901 to W.C.).
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W.C. and P.W. conceived the work. P.W., Y.Y. and Z.W. conducted the experiments. P.W. and Y.Y. developed and tested the machine learning framework. C.W. and P.W. analysed the results. P.W. wrote and revised the paper. W.C. and S.D. provided constructive advice on result interpretation and paper preparation. All authors discussed and reviewed the final paper.
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Wang, P., Wu, Z., Yang, Y. et al. A machine learning-driven framework for optimizing disinfection in drinking water treatment.
Nat Water (2026). https://doi.org/10.1038/s44221-026-00702-0
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DOI: https://doi.org/10.1038/s44221-026-00702-0
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