74129 92054v00 Modeling and Simulating Advanced Catalysts

7/27/2019 74129 92054v00 Modeling and Simulating Advanced Catalysts

1/6

Modeling and Simulating Advanced Catalysts to ReduceNon-Road Vehicle EmissionsBy Tim Watling, Johnson Matthey

Regulatory agencies worldwide are enforcing increasingly stringent emissions requirements for tractors, excavators, and other non-road,

diesel-powered machinery. To help meet these requirements, manufacturers use sophisticated aftertreatment systems that include

catalysts designed specically for non-road vehicles. Designing these systems using physical prototypes is both costly and

time-consuming not only because all the parts need to be manufactured, but also because each catalyst must be operated for a lengthy

period, or at least articially aged to represent this period, before it can be evaluated. The vehicle or machine must meet the emissions

targets at end of useful life, which is dened in the legislation as being between 3000 and 10,000 hours of operation, depending on engine

power and application. While laboratory-based accelerated aging methods can reduce the time required to, say 200 hours, this still

represents a considerable period of time.

At Johnson Matthey, we use MATLAB and Simulink to identify the most promising designs before committing to a prototype. We have

developed a complete aftertreatment system model in Simulink that incorporates optimized MATLAB models of individual catalystcomponents.

Simulations in MATLAB and Simulink enable us to understand the complex interactions that take place within a catalyst, perform

sensitivity analysis to see which parameters have the greatest effect on the output, and make design tradeoffs based on the results. By

simulating the Simulink model for various drive cycles, we can quickly and inexpensively evaluate multiple design options. We also use

the model to systematically check congurations and parameter ranges to nd the optimal design. As a result, we need far fewer

aftertreatment system prototypes.

Non-Road Catalyst Design Challenges

Catalysts are used in engines in a wide array of applications, including generators, passenger cars, and mining, agricultural, and

construction equipment. While the basic principles of catalyst design remain consistent across applications, optimizing catalyst designsfor non-road vehicles introduces some unique challenges.

Catalysts for non-road vehicles are produced in much smaller numbers than catalysts for passenger vehicles, which means building fewer

prototypes to minimize development costs. In addition, passenger vehicle catalysts (Figure 1) can be designed and optimized for a

specic vehicle, which means that many aspects of the design, including the distance of the catalyst from the engine, are known well in

advance. This is not the case for non-road engines.

Figure 1. An automotive emissions control catalyst with the outer shell cut to show the inner construction.

See more articles and subscribe at mathworks.com/newsletters

1
http://www.mathworks.com/newslettershttp://www.mathworks.com/newsletters


2/6

An obvious solution to the challenge is simulation. However, simulating catalysts for non-road vehicles presents its own difculties.

Unlike their stationary counterpartsincluding engines for backup power generatorsengines in non-road vehicles have a wide range

of operating conditions. A tractor pulling a plow, for example, could be tilling a eld or merely driving down the road. Simulations must

take into account changes in ow rate, variations in temperature, and other transients to maintain accuracy across the full range of

conditions under which the catalyst will operate.

Modeling Catalyst Components in MATLAB

To satisfy emissions regulations, a complete aftertreatment system for a diesel engine must remove carbon monoxide, unreacted

hydrocarbons, nitrogen oxides (NOX), and particulate matter. As a result, a complete Johnson Matthey aftertreatment system comprises

a diesel oxidation catalyst (DOC), a diesel particulate lter (DPF), an ammonia selective catalytic reduction (NH3 SCR) catalyst, and an

ammonia slip catalyst (ASC) (Figure 2).

Figure 2. Schematic of an aftertreatment system consisting of a DOC, a DPF, an NH3 SCR catalyst, and an ASC.

We created MATLAB models for each of these components. The models capture a complex combination of interrelated physical

processes and kinetics. The physical processes include gas ows, as well as heat and mass transfer within the catalyst. The kinetics

describe the rate at which chemical reactions take place, and show how the rate varies according to temperature and gas composition.

To develop a catalyst model we start with equations that describe the physics of the system, including energy and mass balances for the

gas and solid (catalyst) phases, together with equations describing the heat and mass transport between these phases. We then run

experiments in the lab that enable us to accurately measure the catalysts output while precisely controlling input and catalyst parameters.

For example, we measure carbon monoxide conversion as a function of temperature for various gas mixtures (Figure 3).

2


3/6

Figure 3. Plot showing how carbon monoxide oxidation varies with temperature for various gas mixtures. The points represent measured data;the lines, simulated data.

To optimize model accuracy, we t the parameters of our rate equations to the measured data using a genetic algorithm solver from

Global Optimization Toolbox. After building prototypes of the catalyst components, we verify the output of the model against

measurements taken from the actual component, and adjust the model as necessary.

Each MATLAB component is implemented as an S-Function block in a Simulink library (Figure 4). In addition to the catalyst

component models, the library includes a heat loss model for an exhaust pipe, a heat loss model for a dual skin pipe, and a feed block.The feed block provides gas ow, temperature, and other input to the Simulink aftertreatment system model based on drive cycles used

by regulatory agencies, including the Non-Road Transient Cycle (NRTC). We obtain data for the feed block by capturing engine exhaust

data from a real diesel engine as it executes the drive cycle.

Figure 4. Simulink library of catalyst components for diesel engines.

Simulating Complete Aftertreatment Systems

We rapidly assemble models of complete aftertreatment systems from the catalyst library blocks (Figure 5). This takes just a few minutes,

far less time than it would take to build the real system. We can congure any block by setting its length, diameter, initial temperature,

initial soot loading (for a lter model), precious metal loading, and other parameters.

3


4/6

Figure 5. Simulink model of the aftertreatment system shown in Figure 2.

We run simulations in Simulink to evaluate the effectiveness of various system congurations and parameters for any given drive cycle.

We can examine the intermediate output at any point in the chain. For example, we may plot simulated carbon monoxide and total

hydrocarbon (THC) emissions for the rst stage and compare the results with measured data to verify that stage of the model (Figure 6).

Figure 6. Plots comparing measured catalyst output for a DOC with model predictions for CO (top) and THC (bottom). Emissions are presentedas cumulative emissions over the Non-Road Transient Cycle.

In some cases, our customers design requirements are exiblefor example, they can raise the inlet temperature of the catalyst by

moving it closer to the engine or by changing the calibration of the engine. To evaluate design alternatives, we run multiple simulations

of the drive cycle, varying the inlet temperature for each simulation, and plot the results (Figure 7). The customer can then make an

informed decision about where to place the catalyst. Often, we automate the multiple simulation runs with a MATLAB script that

programmatically adjusts the key parameters in the Simulink model for each run, initiates the simulation, and captures the results for

analysis.

4


5/6

Figure 7. Plot showing the effect of changing the catalyst inlet temperature on NOX emissions for an ammonia SCR catalyst.

Intermediate results are useful for validation, but we are most interested in the output at the tailpipe, which for the model shown in

Figure 5 is the output of the ASC. Via simulation, we measure the cumulative CO, THC, and NOX emissions, as well as NH3 slip, at the

tailpipe to assess the overall effectiveness of the aftertreatment system (Figure 8).

Figure 8. Emission results for a complete aftertreatment system consisting of DOC + DPF + SCR: cumulative NOX (top) and NH3 slip (bottom).

When we do build a prototype, we compare its measured output with simulation output to verify the model. We can then use the model

and simulations to ne-tune the prototype before it goes into production.

Why We Chose MATLAB over Custom Process Modeling Packages

Before using MATLAB and Simulink to model catalysts, engineers at Johnson Matthey tried using a commercial software package for

developing custom process models. The models that we developed with this package were not exible enough to handle scenarios we

encountered regularly. The solvers, for example, were generally sufcient for steady-state conditions and constant temperatures but

could not handle the transient nature of the inputs that we deal with, including temperature ranges and variation in the gas mixtures

coming into the catalyst. With this package, we could not change the source code to make it more accurate or to overcome problems

such as the simulation failing to converge.

5


6/6

With MATLAB, in contrast, we write our own equations and algorithms, giving us full control over the entire model. We know exactly

how the model works and can easily identify the source of any discrepancy between the model output and measured data from a real

catalyst. The ability to integrate components in a Simulink system-level model and run timebased simulations saves time and cost.

Another advantage of developing our own system in MATLAB and Simulink is that we can capture the organizational knowledge and

expertise accumulated by Johnson Matthey engineers rather than relying on another companys one-size-ts-all solution.

Products Used

MATLAB

Simulink

Global Optimization Toolbox

Optimization Toolbox

Learn More

Model-Based Design for Off-Highway Machine Systems

Development

INCOVA Designs Intelligent Valve-Control System for a

20-Ton Excavator

See more articles and subscribe at mathworks.com/newsletters.

Published 201292054v0

mathworks.com

2012 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarksfor a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.

6
http://www.mathworks.com/products/matlab/http://www.mathworks.com/products/simulink/http://www.mathworks.com/products/global-optimization/http://www.mathworks.com/products/optimization/http://www.mathworks.com/tagteam/44719_Prabhu_2007-01-4248_Final.pdfhttp://www.mathworks.com/tagteam/44719_Prabhu_2007-01-4248_Final.pdfhttp://www.mathworks.com/company/user_stories/INCOVA-Designs-Intelligent-Valve-Control-System-for-a-20-Ton-Excavator.html?by=industryhttp://www.mathworks.com/company/user_stories/INCOVA-Designs-Intelligent-Valve-Control-System-for-a-20-Ton-Excavator.html?by=industryhttp://www.mathworks.com/newslettershttp://www.mathworks.com/http://www.mathworks.com/trademarkshttp://www.mathworks.com/trademarkshttp://www.mathworks.com/http://www.mathworks.com/newslettershttp://www.mathworks.com/company/user_stories/INCOVA-Designs-Intelligent-Valve-Control-System-for-a-20-Ton-Excavator.html?by=industryhttp://www.mathworks.com/company/user_stories/INCOVA-Designs-Intelligent-Valve-Control-System-for-a-20-Ton-Excavator.html?by=industryhttp://www.mathworks.com/tagteam/44719_Prabhu_2007-01-4248_Final.pdfhttp://www.mathworks.com/tagteam/44719_Prabhu_2007-01-4248_Final.pdfhttp://www.mathworks.com/products/optimization/http://www.mathworks.com/products/global-optimization/http://www.mathworks.com/products/simulink/http://www.mathworks.com/products/matlab/

Documents

74129 92054v00 Modeling and Simulating Advanced Catalysts