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Water Research|October 1, 2023
Machine learning for modeling N2O emissions from wastewater treatment plants: Aligning model performance, complexity, and interpretabilityMostafa Khalil, Ahmed AlSayed, Yang Liu, et al.Current Opinion in Biotechnology|February 25, 2025
Machine learning in wastewater: opportunities and challenges - "not everything is a nail!"Peter A Vanrolleghem, Mostafa Khalil, Marcello Serrao, et al.Water Research|July 18, 2003
Equilibrium temperature in aerated basins--comparison of two prediction modelsSylvie Gillot, Peter A VanrolleghemWater Research|May 15, 2007
Extensions to modeling aerobic carbon degradation using combined respirometric-titrimetric measurements in view of activated sludge model calibrationGürkan Sin, Peter A VanrolleghemWater Science and Technology : a Journal of the International Association on Water Pollution Research|March 15, 2022
An influent generator for WRRF design and operation based on a recurrent neural network with multi-objective optimization using a genetic algorithmFeiyi Li, Peter A VanrolleghemBiotechnology Progress|January 7, 2010
Modeling with a view to target identification in metabolic engineering: a critical evaluation of the available toolsJo Maertens, Peter A VanrolleghemBioprocess and Biosystems Engineering|June 4, 2013
Calibration and validation of an activated sludge model for greenhouse gases no. 1 (ASMG1): prediction of temperature-dependent N₂O emission dynamicsLisha Guo, Peter A VanrolleghemWater Science and Technology : a Journal of the International Association on Water Pollution Research|March 9, 2017
Chemically enhancing primary clarifiers: model-based development of a dosing controller and full-scale implementationSovanna Tik, Peter A VanrolleghemWater Science and Technology : a Journal of the International Association on Water Pollution Research|May 16, 2022
An essential tool for WRRF modelling: a realistic and complete influent generator for flow rate and water quality based on data-driven methodsFeiyi Li, Peter A VanrolleghemEnvironmental Monitoring and Assessment|March 25, 2004
Adaptive consensus principal component analysis for on-line batch process monitoringDae Sung Lee, Peter A VanrolleghemPageof 2,389