Towards an online mitigation strategy for N2O emissions through principal components analysis and clustering techniques. (1st May 2020)

• Record Type: Journal Article
• Authors: Bellandi, Giacomo; Weijers, Stefan; Gori, Riccardo; Nopens, Ingmar
• Is Part Of: Journal of environmental management. Volume 261(2020)
• Publication Date: 2020-05-01
• Subjects: Greenhouse gas -- Nitrous oxide -- Wastewater treatment -- PCA -- Carbon footprint -- Control; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2020.110219
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: Emission of N2 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 N2 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 N2 O kinetic models. In this paper, a possible full-scale implementation of a data mining approach linking plant-specific dynamics to N2 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 N2 O emission mitigation. Results show the ability of the algorithm to isolate specific N2 O emission pathways, and highlight possible solutions towards emission control. Graphical abstract: Image 10535 Highlights: PCs could isolate the main known relations between N2 O production and plant operation. AOB denitrification and incomplete NH2 OH oxidation were clearly identifiable by PC. Agglomerative methods effectively discriminate the main N2 O production pathways. SCADA data contain important information to understand plant's N2 O production dynamics. Roadmap towards a new N2 O emissions monitoring and control system trained on historical plant data.