Application of Data Driven techniques to Predict N2O Emission in Full-scale WWTPs

Abstract

A number of data analytics techniques are deployed to measure the influence of various waste water treatment operational parameters against the nitrous oxide ($\text{N}_{\mathbf {2}}$O) emission. N2O is a major threat to the ozone layer and constitutes 80% of total Greenhouse Gas emissions of Waste Water Treatment Plants (WWTPs). The measurement and prediction of N2O emission from WWTP is challenging and costly. Thus, it is important to identify key control parameters that allows for accurately predicting and reducing N2O generation and emission. The current work compares various data driven techniques that identify key parameters and methods of predicting N2O emission. It provides insight to the suitability of each technique for control and optimisation of the target process. The main contribution of this research is introducing two new techniques that applied first time in WWTPs and could cover some current techniques shortcomings in real-time.