Machine learning-guided prediction of chlorinated/chloraminated disinfection by-product formation in drinking water treatment

Abstract

Chlorination and chloramination as common water disinfection methods are challenged by the unintended formations of hazardous disinfection by-products (DBPs). Accurately predicting DBP formation is essential for improving water treatment processes and protecting public health. However, existing models for predicting DBP levels in drinking water treatment, especially for unregulated DBPs, are insufficient. In this study, we developed machine learning (ML) models to predict the levels of five total DBPs (TDBPs) and their ten individual DBPs (IDBPs) resulting from chlorination and chloramination, covering both regulated and unregulated DBPs. To solve the challenge of redundant models, we adopted a data integration strategy to construct larger-scale unified models. The results suggested that the unified model performance outperformed individual models, whereas the individual models were more effective for predicting TDBPs. Moreover, the Shapley additivity interpretation and partial dependence plots provided valuable insights into the key factors influencing DBP formation, aligning with experimental findings. A web application, known as the ACAI platform, was deployed for the first time to predict DBP levels using an automated ML protocol. This user-friendly platform makes DBP prediction accessible to a wide range of users, including those without programming expertise. We expect that these ML models and web interface will support data-driven decision-making in disinfection.