Performance prediction of sludge volume index of oxygenic photogranule based wastewater treatment system using machine learning algorithms

Abstract

Oxygenic Photogranulation is a novel biotechnology that treats wastewater without external aeration and produces biomass in dense photogranules with high settling velocities. Oxygenic photogranules (OPG) based wastewater treatment (WWT) faces challenges during scaleup due to its dynamic and complex system variables, making troubleshooting costly. The Machine Learning (ML) approach can address this issue by creating a WWT process simulation. Moreover, traditional mechanistic models do not capture the interaction between input and output features due to their high dimensionality and the non-linear relationship, making them computationally expensive. In this study, the two-stage feature selection (FS) method is studied to enhance the prediction performance of SVI30, a critical operational parameter, to ensure optimal settleability and minimal loss of active photogranules. The optimal feature subsets generated by the two-stage selection method were evaluated using four regression models: decision tree, random forest, gradient boosting, and extreme gradient boosting. Results indicate that, among all regression models, the decision tree performs well having a prediction efficiency of 85 % with the subset of features obtained after Recursive Feature Elimination (RFE) of decision tree features in the second stage. This indicates the effectiveness of the two-stage FS method in identifying the most relevant features for predicting SVI30. The structured approach of FS and model evaluation highlights the potential of ML in addressing complex operational challenges in OPG WWT operations.