Domingo Felez, Carlos

Plósz, Benedek G.

Sin, Gürkan

Smets, Barth F.

Valverde Pérez, Borja

Online Research Database In Technology

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Downloaded from orbit.dtu.dk on: Dec 18, 2017Sources and propagation of uncertainty in N2O model predictionsDomingo-Felez, Carlos; Valverde Pérez, Borja; Plósz, Benedek G.; Sin, Gürkan; Smets, Barth F.Publication date:2016Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Domingo Felez, C., Valverde Pérez, B., Plósz, B. G., Sin, G., & Smets, B. F. (2016). Sources and propagation ofuncertainty in N2O model predictions. Poster session presented at 5 th IWA/WEF Wastewater TreatmentModelling Seminar 2016, Annecy, France. 1. Introduction and research questions 1 Department of Environmental Engineering, Technical University of Denmark;      E-mail: cadf@env.dtu.dk  2. Computational work performed Nitrous oxide emissions during nitrogen removal in wastewater treatment operations  can compromise the environmental impact of the process. The carbon footprint of a WWTP is highly sensitive to N2O emissions. Model predictions carry uncertainty from the calibration process.  Q2. Are calibration results satisfiable for mitigation strategies?  6. Conclusions – Outlook  5. Total vs Individual pathway contribution  3. Uncertainty in model predictions  4. Identifying sources of uncertainty Strategies to mitigate N2O emissions are pathway-dependent. Are individual N2O production pathways sensitive to the same parameters as total N2O?  Uncertainty in non-sensitive parameters to total N2O revealed poor identifiability of individual pathway contributions.   This research was supported by a grant from the Danish council for strategic research     Sources and propagation of uncertainty in N2O model predictions Carlos Domingo-Félez1, Borja Valverde-Pérez1, Benedek Gy. Plósz1, Gürkan Sin2, Barth F. Smets1. ,  Q3. How can we reduce the uncertainty of model predictions?  EffluentInfluentSolids recirculationNH4+NO3-N2C AerobicAnaerobicCO2CO2NO2-N2ON2OHigh NO2-Low COD/N DO limitation High NO2-High DOCOD limitationNO2- carryover from aerobic tankInfluent characteristicsToo much COD removal at primary clarifierDO carryover from aeration tankInsufficient aerationCOD overloadToxic compoundsInsufficient aerationLow SRTHigh temperatureModel evaluation with reported parameter values,  scenario SND • Uncertainty of N2O emissions is related to both explicit and non-explicit N2O model parameters. • N2O  model calibrations should systematically address sensitivity and identifiability problems due to uncertainty propagation from previous processes. • Adequate experimental design for model calibration can significantly reduce uncertainty of parameter estimates and therefore prediction uncertainty. • Precise N2O predictions might underestimate uncertainty of individual pathway contributions. Q1. How precise are N2O model predictions? Model predictions if uncertainty is considered for Model_A, Batch: All parameters Parameters specific to N2O models Parameters not  specific to N2O models SND SBR cycle – Nitrification-Denitrification Model predictions for N2O carry higher uncertainty associated to previous processes.  Higher coefficient of variation for N2O compared to DO, NH4+, NO2- along the experiment. What parameters carry most of the uncertainty? Matlab  Batch SND Ranking of the most sensitive parameters for each model/scenario Propagation of uncertainty for: reported parameters (bottom left), and reported + sensitive non-calibrated parameters (bottom right) Model_A Model_B Model_CDNH4 (mgN/L) 29 26 25N2O_emitted/removed 2.1% 2.3% 4.8%NN - 95% 1%ND 58% - 91%HD 42% 5% 8%DO = 0.5 mg/LN2O_prodModel_A Model_B Model_CDNH4 (mgN/L) 35 38 39N2O_emitted/removed 0.4% 3.6% 0.5%NN - 99% 4%ND 67% - 82%HD 33% 1% 14%DO = 2.0mg/LN2O_prodModel_A Model_B Model_C [1] 10.1021/es201489n [2] 10.1021/es4005398 [3] 10.1016/j.watres.2015.11.029 [4] 10.1016/j.watres.2010.08.025 Effect of varying non-sensitive parameters to N2O predictions (black) and to individual pathway contributions (red, blue) (95% CI dashed lines. KHB,NO, KAOB,NO: 0.02 mgN/L ± 90%)  Model_A[1]AOB/HB   Model_B[2]AOBHB   Model_C[3]AOB(x2)HB Batch   Spikes NH3 / NO2- , Qair = constant Mixed liquor biomass NH3 followed by COD load Constant DO: 0.5 and 2.0 mg/L Mixed liquor biomass Uncertainty propagation[4]  Classification of parameter uncertainty MC simulations (LHS, n = 500) N2O model parameters:  Explicit (hAOB, KAOB,I,O2…) Non-explicit (mAOB, KNH4…) Sensitivity analysis Standardised regression coefficients (SRC, bi) (convergence for n = 1000, R2 > 0.7, bi > 0.1) For the 3 models considered N2O emissions were sensitive to non-explicit N2O parameters from AOB and HB. Calibration efforts for sensitive parameters can reduce the prediction uncertainty (Calibrated parameters: Model_C = 11, Model_A = 5).  Batch SND 

Sources and propagation of uncertainty in N2O model predictions

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Sources and propagation of uncertainty in N2O model predictions

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