Development of Al-Based Diagnostic Tool for Enhanced Operational Control of Energy Efficient Wastewater Treatment System /

Abstract

Biological wastewater treatment is an established technique to treat industrial and municipal wastewater, which degrades pollutants through the actions of microorganisms. The primary challenge with current biological wastewater treatment is the need for external aeration or supply of O₂, which is required for the oxidation of organic matter and nitrification processes. Oxygenic photogranulation (OPG) is an aeration-free biological wastewater treatment in which dense photogranules are formed and characterized by high settling velocities. However, the scale-up of OPG-based wastewater treatment systems poses significant issues due to dynamic and complex system variables, which have non-linear interactions, making troubleshooting an expensive endeavour. To solve these issues, machine learning models are effective in simulating the wastewater treatment process, as mechanistic models are computationally expensive and interactions between input and output features are not well understood because of non-linearity. This study investigates the two-stage feature selection method to enhance the prediction performance of SVI30, an operational parameter that ensures the settleability of biomass and minimizes the loss of photogranules. The two-stage feature selection method identifies the relevant subset of input features to predict SVI30, thus enhancing the accuracy and performance of machine learning models. The optimal feature subsets generated by two-stage features are evaluated by four regression models: decision tree, random forest, gradient boosting, and XGBoost. The performance efficiency of all regression models is evaluated by an evaluation matrix. The regression models with optimal subsets of features identified by two-stage feature selection demonstrate a prediction efficiency of 85%. This research provides a comprehens machine learning-based approach that can improve the predictability and control of operational parameters for an efficient OPG wastewater process. Advanced feature selection methods can significantly enhance the performance of machine learning models in OPG-based systems, leading to more sustainable wastewater management solutions.