Wastewater Aeration Process Dynamic Modelling: Combined Mechanistic and Machine Learning Approach
Abstract
The aeration process is the largest energy consumer in wastewater treatment plants (WWTPs), and the optimization of the process based on computational models can offer significant savings for the plant. Recent theoretical developments have revealed that many of the parameters commonly assumed as constants in aeration modelling, in fact, have a dynamic nature; however, there still lacks a universal way to model these factors in an easy, accurate and timely manner. This work proposed a machine learning-based modelling approach to offer real-time estimations of the oxygen transfer rate, airflow demand, and energy consumption.
Utilizing the field data collected from Adelaide WWTP (London, Ontario, Canada), the study developed and screened a combination of modelling approaches and input parameters for optimum predictive power under different data availability conditions.
The results demonstrated that the machine learning models provided significantly higher predictive power than the traditional mechanism-based models. These models can provide informative predictions of the aeration parameters with only operational parameters and limited knowledge about the underlying mechanisms of the system. When integrated with the theoretical equations, the models still produce reasonable estimations without losing interpretability. The present finding confirms using the machine learning modelling approach on dynamic factors involved in the aeration process to be feasible and effective. It calls for further investigation into such methods to explore more in the field of wastewater modelling.