Predictive Modeling of Polyphenol Concentration After Sequencing Batch Reactor Winery Wastewater Treatment

Winery wastewater contains recalcitrant pollutants, such as phenolic compounds, which can hinder biological treatment processes. While monitoring these systems is essential to prevent treatment failure, quantifying recalcitrant compounds through conventional methods can be time-consuming and costly due to complex analytical procedures and chemical disposal. In this study, machine learning (ML) was used to predict polyphenol concentration after the biological treatment of winery wastewater using a sequencing batch reactor (SBR). ML models, including ElasticNet (ENet), Multi-Layer Perceptron Regressor (MLPR), and Support Vector Regressor (SVR), were developed and tested using a small, high-dimensional dataset and leave-one-out cross-validation (LOOCV). Feature selection and hyperparameter tuning were applied to improve model performance. After optimization, the SVR model achieved the best performance, with MAE, MAPE, and R2 of 0.88 mg/L, 9.3%, and 0.75, respectively.