Machine Learning prediction of ozone exposure for improving the ozonation process in a drinking water treatment plant

<p dir="ltr">Ozonation is a critical process in drinking water treatment plants for disinfection and oxidation of contaminants. Determining the optimal ozone dosage is challenging due to the complex interplay of various factors and the reliance on a low frequency of grab samples for ozone concentration measurements - and consequently estimation of the ozone exposure - as well as monitoring of pathogenic micro-organisms. This paper describes the development of a data-driven soft sensor aimed at providing daily predictions of ozone concentration and ozone exposure values. Three machine learning techniques - random forest, eXtreme Gradient Boosting, and Physics-Informed Neural Networks - and ensemble combinations of those, were tested and compared. Preliminary results indicate that an ensemble of 15 eXtreme Gradient Boosting models exhibited the best performance, capturing up to 82.6% of the variance in ozone exposure values on the testing dataset (R2=0.826). However, while the current model shows high reliability, it tends to underestimate high CT values, above 2.5mgO3/l *min, a limitation likely due to their underrepresentation in the training data. The results support the potential of soft sensors to enhance process monitoring and ozone dosage optimization in drinking water treatment.</p><p dir="ltr">This paper was presented at the 21st Computing and Control in the Water Industry Conference (CCWI 2025) at the University of Sheffield (1st - 3rd September 2025).</p>