Contents lists available at ScienceDirect Bioresource Technology journal homepage: www.elsevier.com/locate/biortech Optimizing the configuration of integrated nutrient and energy recovery treatment trains: A new application of global sensitivity analysis to the generic nutrient recovery model (NRM) library Céline Vaneeckhaute a,b,c, ⁎ , Enrico Remigi d , Filip M.G. Tack e , Erik Meers e , Evangelia Belia f , Peter A. Vanrolleghem b,c a BioEngine, Research Team on Green Process Engineering and Biorefineries, Chemical Engineering Department, Université Laval, 1065, avenue de la Médecine, Québec, QC G1V 0A6, Canada b modelEAU, Département de génie civil et de génie des eaux, Université Laval, 1065, avenue de la Médecine, Québec G1V 0A6, QC, Canada c CentrEau, Centre de recherche sur l'eau, Université Laval, 1065 Avenue de la Médecine, Québec, QC G1V 0A6, Canada d DHI, Agern Allé 5, 2970 Hørsholm, Denmark e Ecochem, Laboratory of Analytical and Applied Ecochemistry, Ghent University, Coupure Links 653, 9000 Ghent, Belgium f Primodal Inc., 145 Rue Aberdeen, Québec, QC G1R 2C9, Canada GRAPHICAL ABSTRACT ARTICLE INFO Keywords: Anaerobic digestion Mathematical modelling Monte Carlo Multivariate linear regression Resource recovery ABSTRACT This paper describes the use of global sensitivity analysis (GSA) for factor prioritization in nutrient recovery model (NRM) applications. The aim was to select the most important factors influencing important NRM model outputs such as biogas production, digestate composition and pH, ammonium sulfate recovery, struvite pro- duction, product purity, particle size and density, air and chemical requirements, scaling potential, among others. Factors considered for GSA involve: 1) input waste stream characteristics, 2) process operational factors, and 3) kinetic parameters incorporated in the NRMs. Linear regression analyses on Monte Carlo simulation outputs were performed, and the impact of the standardized regression coefficients on major performance in- dicators was evaluated. Finally, based on the results, the paper describes the original use of GSA to obtain insight https://doi.org/10.1016/j.biortech.2018.08.108 Received 12 June 2018; Received in revised form 24 August 2018; Accepted 25 August 2018 ⁎ Corresponding author at: BioEngine, Research Team on Green Process Engineering and Biorefineries, Chemical Engineering Department, Université Laval, 1065, avenue de la Médecine, Québec, QC G1V 0A6, Canada. E-mail address: celine.vaneeckhaute@gch.ulaval.ca (C. Vaneeckhaute). Bioresource Technology 269 (2018) 375–383 Available online 28 August 2018 0960-8524/ © 2018 Elsevier Ltd. All rights reserved. T