Towards an online mitigation strategy for N 2 O emissions through principal components analysis and clustering techniques

Emission of N O represents an increasing concern in wastewater treatment, in particular for its large contribution to the plant's carbon footprint (CFP). In view of the potential introduction of more stringent regulations regarding wastewater treatment plants' CFP, there is a growing need for advanced monitoring with online implementation of mitigation strategies for N O emissions. Mechanistic kinetic modelling in full-scale applications, are often represented by a very detailed representation of the biological mechanisms resulting in an elevated uncertainty on the many parameters used while limited by a poor representation of hydrodynamics. This is particularly true for current N O kinetic models. In this paper, a possible full-scale implementation of a data mining approach linking plant-specific dynamics to N O production is proposed. A data mining approach was tested on full-scale data along with different clustering techniques to identify process criticalities. The algorithm was designed to provide an applicable solution for full-scale plants' control logics aimed at online N O emission mitigation. Results show the ability of the algorithm to isolate specific N O emission pathways, and highlight possible solutions towards emission control.