Global sensitivity analysis in wastewater treatment plant model applications: Prioritizing sources of uncertainty Gu ¨ rkan Sin a, *, Krist V. Gernaey b , Marc B. Neumann c,d , Mark C.M. van Loosdrecht e , Willi Gujer c,f a CAPEC, Department of Chemical and Biochemical Engineering, Technical University of Denmark, Building 229, DK-2800 Kgs. Lyngby, Denmark b Center for Process Engineering and Technology (PROCESS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Building 229, DK-2800 Kgs. Lyngby, Denmark c Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Du ¨ bendorf, Switzerland d modelEAU, De ´partement de Ge ´nie Civil, Universite ´ Laval, 1065 Avenue de la Me ´decine, Que ´bec, QC, G1V 0A6, Canada e Department of Biotechnology, Delft University of Technology, Julianalaan 67, 2628 BC Delft, The Netherlands f Institute of Environmental Engineering, ETH Zurich, 8093 Zurich, Switzerland article info Article history: Received 7 March 2010 Received in revised form 9 August 2010 Accepted 12 August 2010 Available online 20 August 2010 Keywords: BSM1 Design Influent fractions Sensitivity Uncertainty WWTP Plant performance Wastewater treatment abstract This study demonstrates the usefulness of global sensitivity analysis in wastewater treatment plant (WWTP) design to prioritize sources of uncertainty and quantify their impact on performance criteria. The study, which is performed with the Benchmark Simulation Model no. 1 plant design, complements a previous paper on input uncertainty characterisation and propagation (Sin et al., 2009). A sampling-based sensitivity analysis is conducted to compute standardized regression coefficients. It was found that this method is able to decompose satisfactorily the variance of plant performance criteria (with R 2 > 0.9) for effluent concentrations, sludge production and energy demand. This high extent of linearity means that the plant performance criteria can be described as linear functions of the model inputs under the defined plant conditions. In effect, the system of coupled ordinary differential equations can be replaced by multivariate linear models, which can be used as surrogate models. The importance ranking based on the sensitivity measures demonstrates that the most influential factors involve ash content and influent inert particulate COD among others, largely responsible for the uncertainty in predicting sludge production and effluent ammonium concentration. While these results were in agreement with process knowledge, the added value is that the global sensitivity methods can quantify the contribution of the variance of significant parameters, e.g., ash content explains 70% of the variance in sludge production. Further the importance of formulating appropriate sensitivity analysis scenarios that match the purpose of the model application needs to be highlighted. Overall, the global sensitivity analysis proved a powerful tool for explaining and quantifying uncertainties as well as providing insight into devising useful ways for reducing uncertainties in the plant performance. This information can help engineers design robust WWTP plants. ª 2010 Elsevier Ltd. All rights reserved. * Corresponding author. Tel.: þ45 45 25 2806; fax: þ45 45 93 2906. E-mail addresses: gsi@kt.dtu.dk (G. Sin), kvg@kt.dtu.dk (K.V. Gernaey), marc.neumann@gci.ulaval.ca (M.B. Neumann), M.C.M.van- Loosdrecht@tudelft.nl (M.C.M. van Loosdrecht), gujer@eawag.ch (W. Gujer). Available at www.sciencedirect.com journal homepage: www.elsevier.com/locate/watres water research 45 (2011) 639 e651 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.08.025