Robustness and Accuracy Test of Particular Matter Prediction Based on Neural Networks

doi:10.4236/cn.2013.52B010

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Communications and Network, 2013, 5, 53-59

doi:10.4236/cn.2013.52B010 Published Online May 2013 (http://www.scirp.org/journal/cn)

Robustness and Accuracy Test of Particular Matter

Prediction Based on Neural Networks

Jiamei Deng1, Shaohua Zhong2, Andrew Ordys1

1School of Mechanical and Automotive Eng., Kingston University London, UK SW15 3DW

2School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China

Email: [email protected], [email protected],

Received 2013

ABSTRACT

The increasingly stringent emissions regulations require that engine manufacturers must reduce emissions of particulate

matter (PM). PM is made up mainly of carbon in the form of PM and complex compound that are absorbed into the PM

particles. Significant quantities PM poses a threat to health. Technologies available for PM reduction are heavily de-

pendent on after-treatment systems, which are respectively active and passive. An active trap system requires a control

unit to trigger and control the regeneration process (clean the trap system). The measurement of PM is crucial for the

trap system to enable the fine judgment as to when to initiate the process. If the regeneration is too infrequent, the filter

will block. The frequency of the regeneration and the life of the filter are compromised. Diesel engine particular matter

prediction has always been a major challenge to the industry. A simple way to handle the PM estimation is to use

black-box modelling as described in [J. Deng, B. Maass, R. Stobart, PM prediction in both steady state and transient

operation of diesel engines, Proc IMechE, Part D: Journal of Automobile Engineering, 2011, 225, in press, DOI:

10.1177/0954407011418029]. This method is used to estimate the PM successfully in both steady and transient engine

operation condition. The main question is how robust and accurate the neural networks are for a regeneration trigger

signal of diesel particular filters. In order to answer such a question, the robust test of PM estimation is carried out

based on different composition of bio-diesel. In this paper, regular diesel fuel will be blended with up to 20% bio-diesel

to test the effect of different fuel resources on particulate matter. Bio-diesel is often added to regular diesel fuel to im-

prove the burning properties and reduce carbon emissions, also is alternative source of fuels. The aim of this paper with

these tests is to ensure that with a realistic change in the fuel composition the estimation of PM is still accurate. There-

fore, neural networks could be used to produce a regeneration signal for diesel particular filter. In this paper a virtual

sensor is proposed to measure the PM. The purpose of the proposed virtual sensor is to estimate the accumulation of PM

and to trigger a regeneration cycle. The virtual sensor based on neural network is used to estimate the PM. In this pa-

per, the performance, robustness and accuracy of simulated the sensor are evaluated in measuring the particulate

amount for non-road transient cycle tests.

Keywords: Neural Networks; Particulate Matter; Smoke; Prediction; Robustness

1. Introduction

The increasingly stringent emissions regulations require

that engine manufacturers must reduce emissions of par-

ticulate matter (PM). PM is made up mainly of carbon in

the form of PM and complex compound that are ab-

sorbed into the PM particles. Significant quantities of PM

pose a threat to health. Technologies available for PM

reduction are heavily dependent on after-treatment sys-

tems, which are respectively active and passive. Diesel

Particulate Filters (DPFs) are kinds of after-treatment

system and can effectively reduce the level of PM emis-

sions to ambient background levels [1].

If PM accumulates on the DPF, the large amount of

heat release during regeneration cannot effectively be

dissipated. This could result in filter damage, for exam-

ple, the formation of cracks or regions which may be

locally melted. Extensive studies try to identify all

mechanisms related to DPF failures and to develop

strategies to avoid these failures [4]. On the other hand, if

the DPF regenerates too frequently, it will also cause

additional fuel penalties. Therefore, accurate knowledge

of the DPF PM loading state is important for improving

fuel economy and extending DPF service life. It is also

very critical for the upcoming on-board diagnostics

regulations [5].

A normal DPF system is very expensive (a heavy-duty

catalyzed DPF unit costs about $5000) and a special de-

sign one will cost much more. Therefore, controlling the

J. M. DENG ET AL.

DPF regeneration safely is highly critical [4]. The timing

for safely and efficiently regenerating the DPF has be-

come a major industrial challenge.

It can be seen that a control unit to trigger and control

the regeneration process is crucial for both DPF life and

fuel economy. The measurement of PM is crucial for the

trap system to enable the fine judgment as to when to

initiate the process. The frequency of the regeneration

and the life of the filter are compromised.

Diesel engine PM Prediction has always been a major

challenge to the industry [6, 7, 8]. The conventional

method to estimate the PM loading is pressure drop

measurements. But it is affected by exhaust flow varia-

tions and exhibits a low degree of sensitivity to DPF PM

loading and has bad dynamic response over transient

operating conditions. There is study investigating the use

of radio frequency (RF) to directly monitor measure DPF

PM accumulation levels. However, this technique is not

mature enough to be applied in commercial applications

as it is not easy to be calibrated. Hence, more reliable,

stable and accurate PM loading estimation or sensing

method should be studied. Computational fluid dynamics

(CFD) based PM models are computationally intensive

and are not suitable for control purpose and real time

measurement. Recently, neural networks have been used

in a wide variety of automotive applications. The advan-

tage of neural networks is their ability to be used as an

arbitrary function approximation mechanism which has

no requirement to represent the complex underlying

process and is an economic way to obtain the measure-

ment. Model based PM emission prediction method

could be a good alternative way [9]. Neural networks

have been successfully used for emissions prediction [10].

He et al. [11] built a model that considers several engine

parameters such as boost pressure and exhaust gas recir-

culation (EGR) and it generates several outputs including

PM emissions. Maass et al. presented a smoke prediction

neural network model using a three-layer autoregressive

model with exogenous inputs (NLARX) model to predict

PM [12]. Bose and Kumar [13] use fuzzy logic to predict

the engine emissions.

The simple way to handle the PM estimation is to use

black-box modeling as described in [14].This method is

used to estimate the PM successfully in both steady and

transient engine operation condition. The disadvantage is

the robustness and accuracy of the PM prediction based

on neural networks while different fuels are used in the

engines. The neural network model is built on training

data, which has no information on fuel types. Therefore,

the test of robustness and accuracy is important for the

PM estimation based on neural networks. In order to

carry out the robust and accurate tests, different compo-

sitions of Bio-diesel with standard diesel EN590 are

used.

In order to improve the engine performance and emis-

sions and ensure the sustainability of the fuel supplies,

bio-diesel is a promising alternative for diesels. Bio-

diesel leads to significant reductions in PM emissions,

although the percentage reduction varied with fuel com-

position and engine technology. The average PM reduc-

tions are 26% compared to conventional diesel fuel [15].

In this paper, standard diesel fuel (EN590) will be

blended with up to 20% bio-diesel fuel to test the effect

of different fuel resources on PM. The aim with these

tests is to ensure that with a realistic change in the fuel

composition the estimation of PM based on neural net-

works is still accurate enough to trig a regeneration cycle.

The virtual sensor of PM measurement based on neural

networks will be used to estimate the PM. With the

simulated sensor we will evaluate its performance, ro-

bustness and accuracy in predicting the PM.

In this paper a virtual sensor is proposed to measure

the PM. The purpose of the proposed virtual sensor is to

estimate the PM and to trigger a regeneration cycle for

diesel particulate filter.

In Section 1 - introduction, a brief background of the

research is introduced. Section 2 reviews the non-linear

autoregressive model with exogenous inputs (NLARX)

neural networks that can be used to predict PM. Section

3 described details of the test facility. Section 4 describes

the data collection and neural network training and ro-

bustness test. Section 5 provides conclusions on this

work.

2. Neural Networks

The field of virtual sensing has become more and more

popular with growing systems complexity such as in

combustion engine control. Its origin lies in the field of

estimators which are specified through physical and nu-

merical relations whereas virtual sensors are character-

ized through black-box approaches such as neural net-

works.

Neural networks can be split into the following three

categories:

1) single-layer feed forward networks (SLFN),

2) multi-layer feed forward networks (MLFN),

3) recurrent neural network (RNN).

The chosen network structure or architecture is crucial

for the output performance. Depending on the systems

characteristic: linear or non-linear, static or dynamic, the

network needs to be designed accordingly. Here, the pre-

diction of PM is recognised as highly dynamic and

non-linear that implies a recurrent network structure has

to be chosen to offer sufficient predictive capability. The

NLARX structure can accommodate the dynamics of the

system by feeding previous network outputs back into the

input layer. It also enables the user to define how many

J. M. DENG ET AL. 55

previous output and input time steps are required for

representing the systems dynamics best. In this paper a

NLARX model is applied as it is suitable for non-linear-

ity of the problem. Although an important result of ap-

proximation theory is that a three-layer feed-forward

neural network with sigmoid activation functions in the

hidden layer and linear activation functions in the output

layer has the ability to approximate any continuous map-

ping to arbitrary precision, provided that the number of

units in the hidden layer is sufficiently large [16]. How-

ever, the performance of feed-forward neural networks is

limited due to limitations to the number of units in the

hidden states. Performance is further limited by the

memory of personal computers. It is for this reason that,

SLFN and MLFN have not formed part of the work re-

ported in this paper.

A typical structure of an NLARX model is illustrated

in Figure 1. The inputs are represented by u(n) and the

outputs are described by y(n). The formulation of this

NLARX model can be described as:

( )((-1),...,(-),( ),...,(-1))ynFynyn nyunun nu (1)

where ny is number of past output terms used to predict

the current output, nu is number of input terms used to

predict the current output.

Each output of an NLARX model is a function of re-

gressors which are transformations of past inputs and

past outputs. Usually this function has a linear block and

a nonlinear block. The model output is the sum of the

outputs of the two blocks. Typical regressors are simply

delayed input or output variables. More advanced re-

gressors are in the form of arbitrary user-defined func-

tions of delayed input and output variables.

NLARX model training can be cast as a non-linear

unconstrained optimization problem:

min( ,)( )(|)

MMMk

FZ ykyk







 (2)

where 1,...,

[(),()]

Zykuk



is a training data set,

y(k) represents the measured output which is the meas-

ured PM in the training set, k |)



is the NLARX

output which is predicted PM, ||.||2 is 2-norm operation,



is a parameter vector, where 1

[, ,, ,]







and p is the number of parameters.

The training process is described as follows. Given a

neural network described by Equation (1), there is an

error metric, that we refer to as performance index of

Equation (2), which is to be minimized. This index is a

representation of the approximation of the network to

some given training patterns. The task will be to modify

the network parameters



to reduce the index

(, )



over the complete trajectory to achieve the

minimal value. In this paper the neural networks are

trained using gradient descent algorithms while the initial

value of



is perturbed several times in order to avoid

the local minimal solution. The gradient descent methods

will calculate the vector whose elements are

θM

F

δF

δθ

(1,, ,,)iip



 . The training algorithm will find

the parameters of the network for which the performance

index has reached a desirable value. Given a vectorising

trajectory for the network output and training patterns,

the performance index is the Euclidean norm of the error

matrix of the whole training batch for the output PM.

This model has predicted PM accurately with R-square

= 0.99 as shown in [14]. The inputs for this model are

torque, speed, the first deritatives of torque and speed,

and the second deritative of torque. This paper would

like to investigate how robust the PM sensors are and

whether it is suitable to give an alarm signal to trig the

regeneration cycle for the DPF when different composi-

tion of fuels are used.

NLARX could be used to predict the PM of diesel en-

gines and thus produce a trigger signal for the regenera-

tion cycles of DPFs. This is a virtual sensor (software)

that takes account of various engine parameters to calcu-

late particulate matters.

The advantage of neural networks is their ability to be

used as an arbitrary function approximation mechanism

which has no requirement to represent the complex un-

derlying process and is an economic way to obtain the

measurement. A potential disadvantage of the neural

network is that how robust and accurate it is for the pre-

diction of PM when different fuels are used on engines.

In order to answer these questions, bio-diesel blended

with EN590 will be used for the robust and accurate test

of neural networks bio-diesel is used as an alternative

diesel has become more attractive due to its low emis-

sions properties.

Figure 1. NLARX canonical structure.

J. M. DENG ET AL.

3. Test Facility

The engine employed in this study is a Peugeot 2.0 L

HDI Engine. This is a 4-cylinder engine with a Bosch

common rail fuel system and turbo-charger. The engine

calibration used in this work produces a peak torque of

149 Nm at 2002 rpm.

The engine is fully instrumented to measure air, fuel

and cooling system pressures, temperatures and flow

rates. Emissions data is gathered principally using an

AVL 415 smoke and 439 opacity meters (offering steady

state and transient measurement respectively) and a Ho-

riba Horiba MEXA 9100 exhaust gas analyser measuring

nitrous oxide, carbon dioxide, carbon monoxide, unburnt

hydrocarbons and oxygen..

Figure 2 shows the engine facility. The engine is situ-

ated in the lab of the Mechanical and Automotive Eng of

Kingston University. An AVL 439 opacity meter is inte-

grated into the engine exhaust and provides a fast meas-

urement of the exhaust particulate concentration. This

instrument is highly suited to the study of engine

speed-torque transient events during which the control of

exhaust PM is difficult; events such as these contribute

significantly to the total PM produced by the engine.

Accurately predicting the particulates produced during

these events is essential for any model. This is particu-

larly so for a model which has the potential to be de-

ployed as a virtual sensor for determining the optimum

point in time for diesel predicate filter regeneration.

Figure 2. Peugeot 2.0-litre diesel engine and particulate

measurement instruments.

4. Data Collection

All the engine parameters are recorded in a 100 HZ sam-

pling frequency under the test conditions. For an initial

model set-up NRTC (Nonroad Transient Cycle) are op-

erated on a Peugeot 2.0 L HDI Engine at the test facili-

ties shown in Figure 1. The ECM is set to standard cali-

bration mode. Peugeot 2.0 L HDI Engine runs on a tran-

sient NRTC in order to catch as much as dynamics of the

engine.

NRTC is an engine dynamometer transient driving

schedule of total duration of about 1200 seconds. The

speed and torque during the NRTC test is shown in Fig-

ure 3 and Figure 4. Motivation for this choice of cycle is

twofold. First, experience has shown that this is one of

the most challenging cycles in terms of emissions model-

ling. Secondly, meeting emission formation requirements

under the NRTC cycle is also a major concern to engine

manufacturers. The current trend is to design engines

which are marginally passing legislative emission test,

thereby the use of reliable and highly accurate emissions

models is of critical importance.

Figure 3. The torque information of the NRTC.

Figure 4. The speed information of the NRTC.

J. M. DENG ET AL. 57

The test is completed with 70% maximum load and

full speed range covering a wide range of engine tran-

sients in different frequencies and combinations. The test

facility is only allowed to run NRTC at 70% load, also

there are some points that the torque drops and this prob-

lem cannot be solved in the current engine setting-up.

Figure 5 the PM results of NRTC

In order to test the robustness of the PM virtual sensor

based on neural network model. Different bio-diesel

blends mixed with standard diesel have been tested for

PM in the engine test cell shown in Figure 2. The blend

is made up of the standard diesel fuel (EN590) and dif-

ferent portion of bio-diesel fuel in volume in the experi-

ment. Different fuel blends which are made of up to 20%

biodiesel-diesel will be used to test the effects of differ-

ent fuel sources on PM emission. The test results are

shown in the Figure 5. It can be seen that the PM with

different percentages of bio-diesel will be different.

Therefore it is necessary to test whether the PM predic-

tion based on neural networks is robust enough to make a

PM estimation for the different bio-diesel blends.

The test results of the standard diesel and the blend of

the standard diesel and 20% bio-diesel have been shown

in Figure 6. It can be seen clearly that the standard diesel

produce higher PM emissions than that of 20% bio-diesel

blend with the same engine and without modification

with hardware and software.

Figure 5. Comparisons of the result for different percentage

bio-diesel and diesel.

Figure 6. Comparison of opacity emission between standard

fuel and 20% bio-diesel during NRTC.

5. Robustness Test of PM Sensors

The PM virtual sensor which is an NLARX model iden-

tified from engine test data is implemented [14] and

tested against data that collected for the blends of differ-

ent percentages of bio-diesel and standard diesel. In order

to test the robustness of the virtual sensor, the data of the

blend of 10% bio-diesel and standard diesel is used to

train the neural networks. 160 seconds of NRTC data

between 240 - 400 seconds will be enough to train a neu-

ral network [17]. The data between 240 and 400 seconds

data for training purpose is used. The rest of NRTC data

will be used for validation purpose. Inputs for training

the neural network using torque, speed and their deriva-

tives as inputs.

The resulting NLARX model achieves a result of

Rtrain2 = 0.99and Rvalid2 = 0.99 as shown in Figure 7.

This result shows that even if other data, uncorrelated to

the used training data can be predicted to a highly suffi-

cient correlation standard. The inputs are torque, speed

and its derivatives. They provide sufficient feature detail

for the network to generalise unseen and uncorrelated

features.

The PM model based on neural networks has been ob-

tained by using the 10% bio-diesel testing data. The es-

timation of PM based on this model is very accurate for

5% bio-diesel blend (Rpredict2 = 0.99), 10% bio-diesel

blend (Rpredict2 = 0.99), 15% bio-diesel blend (Rpredict2

= 0.99), 20% bio-diesel blend (Rpredict2 = 0.99). This

accuracy can also be seen from the prediction figures in

Figure 7, Figure 9, Figure 10, Figure 11, which show

the PM comparison between the neural network model

and NRTC cycle tests for different percentage blends.

But it cannot predict the standard diesel (Rpredict2 = 0.66)

accurately, shown in Figure 8. It can be concluded that

PM models based on neural networks, which are trained

based on 10% bio-diesel, are quite robust to predict the

emissions for the blends of different percentage

bio-diesels and standard diesel. However, the prediction

for the PM of the standard diesel is not accurate.

Figure 7. NLARX correlation results for training with 160

seconds data and validation agains residual cycle data –

Network Topology: y(n+1) = F(y(n), …, y(n-4), u(n-1)).

J. M. DENG ET AL.

Figure 8. PM measured by opacity meter and estimated by

NN model for Standard diesel EN590.

Figure 9. PM measured by opacity meter and estimated by

NN model for 95% EN590 and 5% bio-diesel.

Figure 10. PM measured by opacity meter and estimated by

NN model for 85% EN590 and 15% bio-diesel.

Figure 11. PM measured by opacity meter and estimated by

NN model for 80% EN590 and 20% bio-diesel.

6. Conclusions and Discussion

Neural network model has been used to predict the PM

accurately for the particular blended bio-diesel. For other

percentage blended bio-diesel, the neural networks could

still predict PM accurately. But the prediction is not ac-

curate for the PM of the standard diesel based on 10%

bio-diesel data. For the tests, it could be seen that the

blend of bio-diesel with the standard diesel fuel could

reduce PM emission without any engine modification.

The PM model based on neural networks which is trained

by one of the 10% bio-diesel NRTC transient test is good

for PM estimation with other percentages of bio-diesel,

but has big error with EN590 standard diesel fuel in this

paper.

7. Acknowledgements

The authors would like to thank the financial support of

Royal Academy of Engineering for the research ex-

change scheme with China.

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