Development and performance of a machine learning‐based tool that predicts influent flow to a WRF 72 h in advance and integrates with existing wet weather nutrient management protocols

Abstract

AbstractInfluent flow to the 75 mgd Neuse River Resource Recovery Facility (NRRRF) was modeled using machine learning. The trained model can predict hourly flow 72 h in advance. This model was deployed in July 2020, and has been in operation over two and a half years. The model's mean absolute error in training was 2.6 mgd, and mean absolute error has ranged from 10 to 13 mgd in deployment for any point during the wet weather event when predicting 12 h in advance. As a result of this tool, plant staff have optimized the use of their 32 MG wet weather equalization basin, using it approximately 10 times and never exceeding its volume.Practitioner Points A machine learning model was developed to predict influent flow to a WRF 72 h in advance. Selecting the appropriate model, variables, and properly characterizing the system are important considerations in machine learning modeling. This model was developed using free open source software/code (Python) and deployed securely using an automated Cloud‐based data pipeline. This tool has been in operation for over 30 months and continues to make accurate predictions. Machine learning combined with subject matter expertise can greatly benefit the water industry.