Predictive modeling of Enterococcus sp. removal with limited data from different advanced oxidation processes: A machine learning approach

Abstract

The removal of contaminants through Advanced Oxidation Processes (AOPs) is a complex task that demands the simultaneous consideration of multiple operating parameters, such as type and concentration of oxidant and catalyst, type and intensity of radiation, composition of aqueous matrix, etc. Designing efficient AOPs often requires expensive and time-consuming laboratory experiments. To improve this process, this study proposes a Machine Learning approach based on a Random Forest (RF) model, to predict Enterococcus sp. concentration in wastewater treated with various AOPs, even when dealing with limited data. To assess our approach under diverse conditions, a data partitioning methodology is used to categorize the different AOPs into three distinct study cases of increasing complexity, from Case I to Case III. The evaluation of the RF model’s performance, combined with the data partitioning methodology, demonstrated its usefulness in predicting missing or additional disinfection values at any instant during the AOPs. Specifically, in Case I, the model excels at generalizing predictions across various AOP treatments, followed by Case II and III, which achieve Root Mean Squared Error (RMSE) values below or comparable to the average RMSE of Case I (0.72) in 8 out of 15 and 2 out of 4 treatments, respectively. Moreover, the effects of imbalanced data on model performance are discussed. This highlights the potential of our approach to assess AOPs performance and facilitate the design of new experiments of the same treatment type without the need for additional laboratory trials, even in challenging conditions.