1D Simulation Modeling of SCR Catalyst at Steady State Condition

Transcription

1 1D Simulation Modeling of SCR Catalyst at Steady State Condition Presented by Hitesh Chaudhari & Mohak Samant The Automotive Research Association of India, Pune 6 th February 2017

2 Overview Objective SCR Chemical Kinetics SCR Model Validation at Steady State Condition Vanadium Catalyst Fe-Zeolite Catalyst Cu-Zeolite Catalyst 1D Simulation Methodology for Cu-Zeolite Catalyst Ammonia Storage modelling NOX reduction reactions modelling Model Predictions for Supplier Data Summary 2

3 Objective SCR catalyst material and size selection for required performance Establish 1D simulation methodology for kinetic parameter tuning Ammonia storage modelling Prediction of NO X conversion efficiency Sensitivity study of parameters like, Exhaust temperature Space Velocity Species concentration such as NO/NO 2 ratio 3

4 Overview Objective SCR Chemical Kinetics SCR Model Validation at Steady State Condition Vanadium Catalyst Fe-Zeolite Catalyst Cu-Zeolite Catalyst 1D Simulation Methodology for Cu-Zeolite Catalyst Ammonia Storage modelling NOX reduction reactions modelling Model Predictions for Supplier Data Summary 4

5 SCR Chemical Kinetics SCR Reaction Mechanism: NH 3 + θ 4θNH 3 + 4NO + O 2 2θNH 3 + NO + NO 2 4θNH 3 + 3NO 2 2θNH 3 + 2NO 2 NO + 0.5O 2 θnh 3 (Adsorption/ Desorption) 4N 2 + 6H 2 O + 4θ (Standard) 2N 2 + 3H 2 O + 2θ (Faster) 3.5N 2 + 6H 2 O + 4θ (Slowest) N 2 + N 2 O + 3H 2 O + 2θ (N 2 O Formation) NO 2 (Oxidation) SCR Reaction rate expression (Modified Arrhenius form): R = A*T b *exp(-e a /RT)*{conc}*f(Gi)*g(θ) R = reaction rate (mole/sec) A = pre-exponent multiplier T = temperature (wall) b = temperature exponent E a = activation energy R = Universal gas constant {conc} = concentration expression f(gi) = general and inhibition function g(θ) = coverage expression θ = coverage Current study focusses on calibrating above mentioned reaction rate for relevant reactions pertaining to reactor data 5

6 Experimentation for Model Calibration NH3 Step Feed Experiment Typical Synthetic Gas Bench Reactor (SGB) Image source: Study of Urea-Water solution injection spray in De-Nox SCR system, ISSN Image source: Winkler, SAE NH 3 step feed data used for NH 3 storage modelling calibration TPR experimental data used for calibrating transient behaviour Temperature Programmed Reaction (TPR) Experiment Image source: J. Nicolas, GT Publication,2007 6

7 Assumptions for 1D SCR Model Building Standalone SCR Model Assumptions: Molar fraction basis Excludes DOC,DPF, Urea dozer Adiabatic substrate Intra-porous diffusion is excluded Uniform Urea decomposition Uniform mixing 7

8 Overview Objective SCR Chemical Kinetics SCR Model Validation at Steady State Condition Vanadium Catalyst Fe-Zeolite Catalyst Cu-Zeolite Catalyst 1D Simulation Methodology for Cu-Zeolite Catalyst Ammonia Storage modelling NOX reduction reactions modelling Model Predictions for Supplier Data Summary 8

9 Validation: Vanadium Based Catalyst Reaction Pre Exponent multiplier Activation energy, E a (kcal/kmol) NH 3 Adsorption NH 3 Desorption Standard NO reduction Adsorption/desorption reactions calibrated with Temperature programmed desorption (TPD) data NH 3 Step feed data at 220 deg C to calibrate NO X reduction reaction Feed is assumed free of NO 2 hence Fast and Slow reactions not considered Reference: L.Lietti, I. Nova, Transient Kinetic Study of SCR De-NOX system, Catalysis Today

10 Overview Objective SCR Chemical Kinetics SCR Model Validation at Steady State Condition Vanadium Catalyst Fe-Zeolite Catalyst Cu-Zeolite Catalyst 1D Simulation Methodology for Cu-Zeolite Catalyst Ammonia Storage modelling NOX reduction reactions modelling Model Predictions for Supplier Data Summary 10

11 Validation: Iron Exchanged Zeolite Catalyst Reaction Activation energy, E a (Kcal/Kmole) NH 3 adsorption Pre Exponent multiplier, K NH 3 desorption E6 Standard Reaction Fast Reaction E15 NO Oxidation NH 3 Oxidation E9 Adsorption/desorption reactions calibrated with Temperature Programmed Desorption (TPD) data NO X reduction reactions are calibrated with transient TPR data 11 Reference: D. Chatterjee, T. Burkhardt, Numerical Simulation of Zeolite and V-Based SCR Catalytic converters SAE

12 Overview Objective SCR Chemical Kinetics SCR Model Validation at Steady State Condition Vanadium Catalyst Fe-Zeolite Catalyst Cu-Zeolite Catalyst 1D Simulation Methodology for Cu-Zeolite Catalyst Ammonia Storage modelling NOX reduction reactions modelling Model Predictions for Supplier Data Summary 12

13 Validation: Copper Exchanged Zeolite Catalyst Reaction Activation energy, E a (J/mole) Pre Exponent multiplier, K NH 3 adsorption NH 3 desorption Standard Reaction E12 Fast Reaction E12 Slow Reaction E7 NH 3 Oxidation E10 NO Oxidation Adsorption/desorption reactions calibrated with TPD data Steady state experiments to validate NO X reduction reactions Reference: K. Narayanaswamy, Yongsheng He, Modelling of Copper Zeolite Selective Catalytic Reduction(SCR) catalysts at steady and transient conditions SAE

14 Overview Objective SCR Chemical Kinetics SCR Model Validation at Steady State Condition Vanadium Catalyst Fe-Zeolite Catalyst Cu-Zeolite Catalyst 1D Simulation Methodology for Cu-Zeolite Catalyst Ammonia Storage modelling NOX reduction reactions modelling Model Predictions for Supplier Data Summary 14

15 NH 3 Adsorption/Desorption Calibration Temperature Programmed Desorption Experiment (TPD) Inputs for Ammonia storage modelling: TPD Experiment data from SGB Inlet gas feed mass flow rate Inlet gas feed composition Catalyst sample volume Image source: Study of Urea-Water solution injection spray in De-Nox SCR system, ISSN Simulated TPD experiment with single site modelling approach Simulated TPD experiment with two site modelling approach 15

16 NH 3 Adsorption/Desorption Calibration- Two Site Approach Model is calibrated with TPD at 150 degc Calibrated rate constants are verified with remaining experiments Two site modelling approach offers more proximity to experimental data Reference: K. Narayanaswamy, Yongsheng He, Modelling of Copper Zeolite Selective Catalytic Reduction(SCR) catalysts at steady and transient conditions SAE

17 Overview Objective SCR Chemical Kinetics SCR Model Validation at Steady State Condition Vanadium Catalyst Fe-Zeolite Catalyst Cu-Zeolite Catalyst 1D Simulation Methodology for Cu-Zeolite Catalyst Ammonia Storage modelling NOX reduction reactions modelling Model Predictions for Supplier Data Summary 17

18 Steady State NO X Reduction Calibration Inputs Required: Steady state experiment data from SGB Calibrated reaction rate constants from TPD experiment Inlet gas feed mass flow rate Inlet gas feed composition Catalyst sample volume Typical steady state experiments used for calibration* *Ref.: K. Narayanaswamy, Yongsheng He, Modelling of Copper Zeolite Selective Catalytic Reduction(SCR) catalysts at steady and transient conditions SAE

19 Steady State NO X Reduction Calibration Total Error function Surface reaction template with two site approach Log space optimisation is faster and precise optimisation technique Genetic Algorithm with parameter sweep offers robust solution Minimised error function with Genetic Algorithm Calibrated log spaces for reaction rate constants 19

20 Steady State NO X Reduction Calibration Significant Under-predictions at higher NO 2 fraction in gas feed 20

21 1D Numerical Model Calibration Methodology Temperature, Mass flow rate, Composition, Catalyst properties SCR standalone model building Synthetic gas bench experiment data Simulate Ammonia step feed experiment Fine tune Model predictions using optimiser tool Adsorption/Desorption reaction rate constants Synthetic gas bench experiment data Simulate Steady state gas bench experiments for NOX reduction Fine tune Model predictions using robust optimiser tool NOX reduction reaction rate constants Opt for 2 site modelling approach for better prediction quality Synthetic gas bench experiment data Simulate transient gas bench experiments for further maturity of the model Calibrated 1D SCR model 21

22 Overview Objective SCR Chemical Kinetics SCR Model Validation at Steady State Condition Vanadium Catalyst Fe-Zeolite Catalyst Cu-Zeolite Catalyst 1D Simulation Methodology for Cu-Zeolite Catalyst Ammonia Storage modelling NOX reduction reactions modelling Model Predictions for Supplier Data Summary 22

23 Model Prediction for Synthetic Test Bench Data Test Conditions: Catalyst = Fe-Zeolite ANR = 1 NOX = 500PPM O2 = 5% CO2 = 5% H2O = 5% Case-1: Space velocity = 86000, NO2/NOX = 0 Case-2: Space velocity = 86000, NO2/NOX = 0.3 Case-3: Space velocity = 40000, NO2/NOX = 0.3 NO2 in the feed gas improves SCR performance at lower temperatures which is attributed to Fast conversion reaction 23

24 Model Prediction for Synthetic Test Bench Data Test Conditions: GHSV = 86K Alpha = 1 NOX = 500PPM NO2/NOX = 0.0 O2 = 5% CO2 = 5% H2O = 5% Cu-Zeolite catalysts are suitable for low temperature application whereas Fe-Zeolite catalysts are preferred at higher temperatures 24

25 Overview Objective SCR Chemical Kinetics SCR Model Validation at Steady State Condition Vanadium Catalyst Fe-Zeolite Catalyst Cu-Zeolite Catalyst 1D Simulation Methodology for Cu-Zeolite Catalyst Ammonia Storage modelling NOX reduction reactions modelling Model Predictions for Supplier Data Summary 25

26 Summary SCR 1D simulation model calibration methodology is developed Accurate Experimental data is a prerequisite for precise model calibration Synthetic gas bench experiment is more flexible approach for model calibration Robust optimisation algorithm is necessary for numerical model calibration Two site modelling approach improves model prediction quality Transient response can be calibrated by running transient engine test cycles on SGB Developed calibration methodology provides good initialization without requiring extensive catalyst data 26

27 Future work Transient response calibration Porous diffusion modelling across the substrate Urea dosing system Integrated exhaust after-treatment system modelling Engine plus after-treatment modelling Integrated 1D+3D system modelling 27

28 Acknowledgement We would like to thank Mr. N. V. Marathe (HoD PTE), Mr. N. H. Walke, Dr. S. Juttu and our colleagues for supporting us through this study. We specially thank Mr. Ryan Dudgeon from Gamma Technologies and Mr. Mangesh Dusane from ESI for their continual support and fruitful suggestions. THANK YOU!! 28