Integrating Bio-Electrochemical Sensors and Machine Learning to Predict the Efficacy of Biological Nutrient Removal Processes at Water Resource Recovery Facilities.

Abstract

Monitoring biological nutrient removal (BNR) processes at water resource recovery facilities (WRRFs) with data-driven models is currently limited by the data limitations associated with the variability of bioavailable carbon (C) in wastewater. This study focuses on leveraging the amperometric response of a bio-electrochemical sensor (BES) to wastewater C variability, to predict influent shock loading events and NO3- removal in the first-stage anoxic zone (ANX1) of a five-stage Bardenpho BNR process using machine learning (ML) methods. Shock loading prediction with BES signal processing successfully detected 86.9% of the influent industrial slug and rain events of the plant during the study period. Extreme gradient boosting (XGBoost) and artificial neural network (ANN) models developed using the BES signal and other recorded variables provided a good prediction performance for NO3- removal in the ANX1, particularly within the normal operating range of WRRFs. A sensitivity analysis of the XGBoost model using SHapley Additive exPlanations indicated that the BES signal had the strongest impact on the model output and current approaches to methanol dosing that neglect C availability can negatively impact nitrogen (N) removal due to cascading impacts of overdosing on nitrification efficacy.