NA GT-Conference Plymouth, MI, United States November 6 th , 2017 Kinetic Parameter Identification for a DOC Catalyst Using SGB test and Advanced Optimization Algorithms Mahsa RAFIGH - Politecnico di Torino Federico MILLO - Politecnico di Torino Paolo FERRERI – General Motors Global Propulsion Systems Eduardo Jose BARRIENTOS BETANCOURT - General Motors Global Propulsion Systems Marcello RIMONDI - General Motors Global Propulsion Systems
Transcript
NA GT-Conference Plymouth, MI, United States
November 6th , 2017
Kinetic Parameter Identification for a DOC Catalyst Using SGB test and Advanced Optimization Algorithms
Mahsa RAFIGH - Politecnico di TorinoFederico MILLO - Politecnico di Torino
Paolo FERRERI – General Motors Global Propulsion SystemsEduardo Jose BARRIENTOS BETANCOURT - General Motors Global Propulsion SystemsMarcello RIMONDI - General Motors Global Propulsion Systems
09-10-2017Federico MILLO – Kinetic Parameter Identification for a DOC Catalyst Using SGB test and Advanced Optimization AlgorithmsEU GT Conference – 2017
In order to develop simulation models capable of reliably predicting performance and emissions of innovative diesel powertrain systems, the following steps are required for aftertreatment systems modelling:
Definition of suitable Synthetic Gas Bench (SGB) test protocols
Development and calibration of kinetic mechanisms based on SGB data using optimization tools
Validation of the model on full scale component data using engine-out emissions over driving cycles
Sample Extraction Reactor-scale Tests Simulation Model Model Calibration
Validation of the model from roller bench data
Introduction:Need for Aftertreatment Modelling
09-10-2017Federico MILLO – Kinetic Parameter Identification for a DOC Catalyst Using SGB test and Advanced Optimization AlgorithmsEU GT Conference – 2017 3
Test case:DOC with zone coating
Characteristic Unit Front Zone Rear Zone
Core size: diameter x length in x in 1 x 3 1 x 3
Washcoat loading - 1.2 x REF REF
PGM - Pt and Pd Pt
Cells density [cpsi] 400 400
Wall thickness [mil] 4.5 4.5
Substrate material [-] cordierite cordierite
Zeolite coating [-]
09-10-2017Federico MILLO – Kinetic Parameter Identification for a DOC Catalyst Using SGB test and Advanced Optimization AlgorithmsEU GT Conference – 2017
Synthetic Gas Bench (SGB) tests
• SGB test protocols are defined with the aim to decouple the effects of different mechanisms, by feeding thecatalyst sample with controlled species concentrations, flow rates and temperatures, thus facilitating themodel calibration process.
• Cylindrical reactor-size components are extracted from full-scale monolith maintaining the length of the sample.• Gas concentration were measured with a multicomponent FTIR, 1 Hz sampling frequency.
• Gas are sampled upstream and downstream of the sample.
• Temp probes at sample inlet and outlet
Scale: 1 inch
09-10-2017Federico MILLO – Kinetic Parameter Identification for a DOC Catalyst Using SGB test and Advanced Optimization AlgorithmsEU GT Conference – 2017
SGB test protocols include HC storage tests and light-off tests
Heavy HC Storage tests (TPD) (4 tests)
Base feed: 4.5% H2O, 4.5% CO2, Balanced N2
400 and 800 ppmC1 C10H22
Inlet T ramp 90/120 °C 400 °C, rate = 5 °C/min
Standard SV: 30 k/hr @ T = 273 K, p = 1 atm
Light-off tests (2x24 = 48 tests for each core)
Base feed: 12% O2, 4.5% H2O, 4.5% CO2, Balanced N2
Inlet T ramp 80 °C 400 °C, rate = 5 °C/min
SV: 30 and 60 k/hr @ T = 273 K, p = 1 atm
Synthetic Gas Bench (SGB) tests
09-10-2017Federico MILLO – Kinetic Parameter Identification for a DOC Catalyst Using SGB test and Advanced Optimization AlgorithmsEU GT Conference – 2017
1D Simulation Model Assumptions
A 1D-CFD model using GT-SUITE is built based on the following assumptions:
Neglect non-homogeneity and non-uniformity of flow and thermal field in a cross-section
Only variations along the catalyst length (x)
Governing equations: continuity, momentum, solid and gas energy balances
Quasi-steady approximation
Global kinetic mechanism
Reaction rates: Arrhenius form:
Objective function defined for the calibration of kinetic parameters to be minimized by means of suitable calibration method
09-10-2017Federico MILLO – Kinetic Parameter Identification for a DOC Catalyst Using SGB test and Advanced Optimization AlgorithmsEU GT Conference – 2017
Models for zone coating
Due to differences in formulation of each core (front and rear) in terms of washcoat, zeolite coating, PGM loading and PGM ratio, 2 separate kinetic models were built for each core with different calibration and optimization runs.
Full-scale Model
The model of the full-size component used for the engine-scale simulation is then built by combining
the models of the two catalyst zonesDOC front
DOC rear
09-10-2017Federico MILLO – Kinetic Parameter Identification for a DOC Catalyst Using SGB test and Advanced Optimization AlgorithmsEU GT Conference – 2017
Simulation model: reaction schemes
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The following 11 reactions are considered in a DOC model:
The following parameters have to be identified:• 21 pre-exponent multiplier and activ. energy• 2 site densities (zeolite and PGM)• 7 exponent of inhibition terms• 16 parameters for inhibition terms
46 parameters for front core and
42 parameters for rear core
09-10-2017Federico MILLO – Kinetic Parameter Identification for a DOC Catalyst Using SGB test and Advanced Optimization AlgorithmsEU GT Conference – 2017
Kinetic Scheme and Calibration Guideline
Overall 46 parameters are unknown for the front core with zeolite coating
Thanks to suitable definition of test protocols, a step-by-step guideline for the calibration of kinetic parameters is defined such that in each step a reduced number of unknowns are optimized:
1• HC storage reactions using TPD tests
2• Oxidation reactions using single species light-off tests
3• Inhibition terms for oxidation reactions using 2 species light-off tests
4• HC reactions with NOx
09-10-2017Federico MILLO – Kinetic Parameter Identification for a DOC Catalyst Using SGB test and Advanced Optimization AlgorithmsEU GT Conference – 2017
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Calibration Approaches
•Time consuming•May results in local minimum•Requires deep knowledge of kinetics
Manual/ Trial and Error
•Includes an initial exploration of the variables domain in their routines•Running full test matrix not smart•Time consuming
DoE
•When the analytical expression of the function to be optimized is known, numerical methods can be used.
•Linear or quadratic programming•Some examples: Brent method, Newton Method
Numerical Methods
•Based on iterative algorithms moving along a certain direction to reach minimum•Used for smooth and continuous objective functions•Possibility to be trapped in a local minimum•Some examples: Hooke-Jeeves Direct Search, Discrete-grid bisection, …
Direct Search Methods
•Implies a systematic exploration of the variables domain•Used for complex and non-linear systems•Reaching global minimum•Some examples: Genetic Algorithm, … selected for the DOC model
Explorative Methods
For the identification of the optimal values for the kinetics parameters the following approaches were evaluated:
09-10-2017Federico MILLO – Kinetic Parameter Identification for a DOC Catalyst Using SGB test and Advanced Optimization AlgorithmsEU GT Conference – 2017
Use of Genetic Algorithm for Model Calibration
An automatic and smart optimization procedure is adopted with the aim to find the optimized independent variables (unknowns) such that the objective function defined based on the error between simulated and measured concentration of each species, using suitable weighting factor, is minimized.
Genetic Algorithm (GA) embedded in GT-SUITE is an appropriate approach, since the final results do not depend on the initial guess and therefore global minimum can be achieved.
Depending on the number of independent variables optimization settings can be defined as follows:
Mutation rate: 1/(# independent variables)
Generations: starting from 20 and increasing up 35 (depending on the convergence)
09-10-2017Federico MILLO – Kinetic Parameter Identification for a DOC Catalyst Using SGB test and Advanced Optimization AlgorithmsEU GT Conference – 2017 12
Example: C10H22 and NOx reactions
Calibration of C10H22 and NOx reactions on the basis of SGB tests # 16 & 17
09-10-2017Federico MILLO – Kinetic Parameter Identification for a DOC Catalyst Using SGB test and Advanced Optimization AlgorithmsEU GT Conference – 2017 13
Example: C10H22 and NOx reactions
Optimization Settings Value
Mutation Rate 0.1
Population Size 80
Number of Generations 20 (increased up to 30)
Total Number of Iterations
80 x 20 =1600 (increased up to 2400)
Simulation Run Time ~ 26 hours * number of cases optimized/ number of licenses used at a time
on a processor: Intel (R)Core(TM) i7 – 4600U CPU @2.10GHz 2.70 GHz
Mutation rate: 1/(# independent variables)
Population size: > 50 for # ind var greater than 5
Generations: starting from 20 and increasing up 35
09-10-2017Federico MILLO – Kinetic Parameter Identification for a DOC Catalyst Using SGB test and Advanced Optimization AlgorithmsEU GT Conference – 2017
Results
An example of results for a validation point (not included in the calibration) for the rear core and the front core samples.
09-10-2017Federico MILLO – Kinetic Parameter Identification for a DOC Catalyst Using SGB test and Advanced Optimization AlgorithmsEU GT Conference – 2017
Scaling from Reactor-Scale to Full-Size:Limitations and AssumptionsThe calibrated model based on SGB data can be transferred to full-size component for validation over driving cycles, paying attention to the issues listed here below.
Possible sources of different results between reactor-size and full-scale model: Absence of pore diffusion model in washcoat layer may lead to higher conversions [1,2]
Non-uniformity of flow and temperature field in full-size component [3] affecting kinetics
The engine exhaust gas includes a mixture of different gas species, expecially for HC [4]
Presence of external heat transfer in the full-size component [4]
Different ageing status of the catalyst components [5]
References[1]P. Kočí, V. Novák, F. Štěpánek, M. Marek, M. Kubíček, Multi-scale modelling of reaction and transport in porouscatalysts, Chem. Eng. Sci. 65 (2010) 412–419. doi:10.1016/j.ces.2009.06.068.
[2] D. Kryl, P. Koc, Kryl - Catalytic converters for automobile diesel engines with adsorption of hydrocarbons on zeolites.pdf, (2005) 9524–9534.
[3] T. Gu, V. Balakotaiah, Impact of heat and mass dispersion and thermal effects on the scale-up of monolith reactors, Chem. Eng. J. 284 (2016) 513–535. doi:10.1016/j.cej.2015.09.005.
[4] J. Sjöblom, Bridging the gap between lab scale and full scale catalysis experimentation, Top. Catal. 56 (2013) 287–292. doi:10.1007/s11244-013-9968-6.
[5] C.S. Sampara, E.J. Bissett, M. Chmielewski, D. Assanis, Global kinetics for platinum diesel oxidationcatalysts, Ind. Eng. Chem. Res. 46 (2007) 7993–8003. doi:10.1021/ie070642w.
09-10-2017Federico MILLO – Kinetic Parameter Identification for a DOC Catalyst Using SGB test and Advanced Optimization AlgorithmsEU GT Conference – 2017
A methodology for the kinetic parameter identification for a DOC catalyst using SGB tests and advancedoptimization algorithms was developed and successfully applied to a zone coated DOC.
Two different SGB test protocols were used including HC storage tests (only for cores with zeolite coating) andlight-off tests for a total of about 50 tests for each catalyst.
From 42 to 46 kinetic parameters needed to be identified for the 11 reactions used in the model.
The kinetic parameters were identified in the following sequence, by means of GA optimization algorithms, targetingthe minimization of error functions comparing measured and simulated concentrations of the main chemical species:
1.HC storage reactions using TPD tests
2.Oxidation reactions using single species light-off tests
3.Exponents of inhibition terms for oxidation reactions using 2 species light-off tests
4.HC reactions with NOx
Finally, caveats & guidelines were provided for the up-scaling of the calibrated model based on SGB data to the full-size component for validation over driving cycles.
Conclusions
09-10-2017Federico MILLO – Kinetic Parameter Identification for a DOC Catalyst Using SGB test and Advanced Optimization AlgorithmsEU GT Conference – 2017
This work has been carried out as part of the PhD thesis “Exhaust Aftertreatment Modeling for
Efficient Calibration in Diesel Passenger Car Applications” defended at Politecnico di Torino on June,
27th 2017 by Mahsa Rafigh, and of the Research Project “GT-Power 1-D kinetics modeling
improvements of LNT systems”, both funded by General Motors Global Propulsion Systems, which is
gratefully acknowledged for the financial support and for providing the experimental data for models
calibration and validation.
The authors would also like to gratefully acknowledge Gamma Technologies for the valuable support
provided, and in particular Syed Wahiduzzaman and Ryan Dudgeon for their precious suggestions
and remarks.
Aknowledgments
NA GT-Conference Plymouth, MI, United States
November 6th , 2017
Kinetic Parameter Identification for a DOC Catalyst Using SGB test and Advanced Optimization Algorithms
Mahsa RAFIGH - Politecnico di TorinoFederico MILLO - Politecnico di Torino
Paolo FERRERI – General Motors Global Propulsion SystemsEduardo Jose BARRIENTOS BETANCOURT - General Motors Global Propulsion SystemsMarcello RIMONDI - General Motors Global Propulsion Systems
