Kinetic Parameter Identification for a DOC Catalyst Using ... · 09-10-2017 Federico MILLO...

EU GT-Conference

Frankfurt-am-Mein, Germany

October 9th , 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

09-10-2017Federico MILLO – Kinetic Parameter Identification for a DOC Catalyst Using SGB test and Advanced Optimization Algorithms

EU 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



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 [-]



Synthetic Gas Bench (SGB) tests

• SGB test protocols are defined with the aim to decouple the effects of different mechanisms, by feeding the

catalyst sample with controlled species concentrations, flow rates and temperatures, thus facilitating the

model 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



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



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



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



Simulation model: reaction schemes

8

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



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



10

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 consumingDoE

•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:



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)

𝑶𝒃𝒋𝒆𝒄𝒕𝒊𝒗𝒆 𝑭𝒖𝒏𝒄𝒕𝒊𝒐𝒏 = න𝟎

𝒕𝒆𝒏𝒅

𝑪𝒎𝒆𝒂𝒔𝒖𝒓𝒆𝒅 − 𝑪𝒔𝒊𝒎𝒖𝒍𝒂𝒕𝒆𝒅 𝒅𝒕



Example: C10H22 and NOx reactions

Calibration of C10H22 and NOx reactions on the basis of SGB tests # 16 & 17

• 4 kinetic parameters + 5 inhibition

parameters 9 parameters

• The objective function is defined using

suitable weighting factors for each specie:

𝑶𝒃𝒋𝒆𝒄𝒕𝒊𝒗𝒆 𝑭𝒖𝒏𝒄𝒕𝒊𝒐𝒏 = න𝟎

𝒕𝒆𝒏𝒅

𝑾𝟏 𝑪𝒎𝒆𝒂𝒔𝒖𝒓𝒆𝒅 − 𝑪𝒔𝒊𝒎𝒖𝒍𝒂𝒕𝒆𝒅 𝑵𝑶𝒙 +𝑾𝟐 𝑪𝒎𝒆𝒂𝒔𝒖𝒓𝒆𝒅 − 𝑪𝒔𝒊𝒎𝒖𝒍𝒂𝒕𝒆𝒅 𝑵𝟐𝑶 +𝑾𝟑 𝑪𝒎𝒆𝒂𝒔𝒖𝒓𝒆𝒅 − 𝑪𝒔𝒊𝒎𝒖𝒍𝒂𝒕𝒆𝒅 𝑪𝟏𝟎𝑯𝟐𝟐𝒅𝒕



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



Results

An example of results for a validation point (not included in the calibration) for the rear core and the

front core samples.



Scaling from Reactor-Scale to Full-Size:

Limitations and Assumptions

The 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 porous

catalysts, 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 oxidation

catalysts, Ind. Eng. Chem. Res. 46 (2007) 7993–8003.

doi:10.1021/ie070642w.



A methodology for the kinetic parameter identification for a DOC catalyst using SGB tests and advanced

optimization 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) and

light-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, targeting

the 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



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

EU GT-Conference

Frankfurt-am-Mein, Germany

October 9th , 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

18



Example of full scale model results

19

Cumulative NOx mass for a LNT catalystover WLTC: dashed lines represent thecase with initial NOx storage and fulllines represent the empty initial NOxtrapping condition.

Example of full scale model results

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