Data-driven versus conventional N2O EF quantification methods in wastewater; how can we quantify reliable annual EFs?
Authors:
- V. VasilakiVVV. Vasilaki
- S. DanishvarSDS. Danishvar
- A. MousaviAMA. Mousavi
- E. KatsouEKE. Katsou
Open Access
- 1 October 2020
- journal article
- research article
- Published by Elsevier in Computers & Chemical Engineering
- Vol. 141
Abstract
No abstract availableKeywords
Funding Information
- Brunel University London
This publication has 30 references indexed in Scilit:
- Seasonal and diurnal variability of N 2 O emissions from a full-scale municipal wastewater treatment plantScience of The Total Environment, 2015
- Exploring change of internal nutrients cycling in a shallow lake: A dynamic nutrient driven phytoplankton modelEcological Modelling, 2015
- Instrumentation, control and automation in wastewater – from London 1973 to Narbonne 2013Water Science & Technology, 2014
- Analysis of hydroclimatic variability and trends using a novel empirical mode decomposition: Application to the Paraná River BasinJournal of Geophysical Research: Atmospheres, 2014
- Data-derived soft-sensors for biological wastewater treatment plants: An overviewEnvironmental Modelling & Software, 2013
- Influence of sampling strategies on the estimated nitrous oxide emission from wastewater treatment plantsWater Research, 2013
- ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHODAdvances in Adaptive Data Analysis, 2009
- On the trend, detrending, and variability of nonlinear and nonstationary time seriesProceedings of the National Academy of Sciences, 2007
- Stability of feature selection algorithms: a study on high-dimensional spacesKnowledge and Information Systems, 2006
- The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysisProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 1998