prediction of emission and performance for a heavy duty diesel engine using artificial neural...

7th International Ege Energy Symposium & Exhibition June 18-20, 2014

Usak, Turkey

Prediction of emission and performance for a heavy duty diesel engine using articial neural network

JAVAD MARZBANRAD*, POURYA RAHNAMA

Iran University of Science and Technology Tehran - Iran

*[email protected]

Abstract: There is an environmental need both to lower emissions and to predict or model emissions more quickly and accurately. An artificial neural network was developed to predict the heavy duty diesel engine emissions and performance. The model was trained with data acquired via engine dynamometer testing. Once the artificial neural network was finalized, it was employed to predict eight different engine-out responses, namely unburned hydrocarbon, carbon monoxide, carbon dioxide, nitrogen oxide, smoke, power, brake specific fuel consumption and peak pressure with the inputs of injection timing, engine speed and engine load. It was demonstrated that the artificial neural network was able to predict emissions and performance that are associated with random test data that differ from those by which it is trained.

Keywords: Exhaust emission, Engine performance, Heavy duty diesel engine, Articial neural network

1. INTRODUCTION

The ability of the internal combustion engine to provide reliable power has resulted in mass production of the internal combustion engine in a variety of applications. However engine related emissions have become of environmental concern. The EPA has estimated that heavy-duty diesel vehicles contribute sixty percent of the on-road particulate matter emissions and twenty-seven percent of the on-road NOx emissions [1]. Therefore, regulations have been implemented concerning exhaust emissions from heavy-duty diesel engines, In order to reduce the health and environmental impact of heavy-duty diesel engine emissions. In addition, how to control the exhaust emissions from engines has become an important subject for researchers of the automotive engine.

Numerous approaches exist for modeling and predicting emissions and performance data. Articial neural network (ANN) is one such effort, and is now progressively utilized as a prognostic tool in the automotive sector to afford rapid predictions of various engine-out parameters when new strategies in engine operating conditions are tested. ANN is more attractive as an engine optimisation tool because it is robust and less expensive in terms of

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required time and resources. Other methods such as computational uid dynamics (CFD) and chemical kinetic modelling require high computing resources in order to produce accurate representation of the in-cylinder processes [2].

ANN is a collection of simple processors, referred to as neurons, connected together. Each neuron is connected to other neurons by means of directed communication links, each with an associated weight [3]. Each processor can only perform a very straightforward mathematical task, but a large network of them has much greater capabilities and can do many things which one on its own cant. Testing the engine under all possible operating conditions and fuel cases is both time consuming and expensive. As an alternative, the performance and exhaust emissions of an engine can be modelled using ANNs [2, 4, 5, 6].

There are a lot of studies which have used the ANN approach in engine performance and emissions [2, 4, 5, 6]. One major benefit of using an ANN is its ability to understand and simulate complex functions. One of the current drawbacks to such use is that as the complexity of the function increases, more training data is necessary if the ANN is to be robust enough to produce good results. Ismail et al. [2] used the neural networks to predict engine-out performance and emissions of a light-duty diesel engine using back-propagation ANN with R Values equal to 0.984, 0.987, 0.981, 0.985, 0.942, 0.977, 0.939, 0.542 and 0.567 for carbon monoxide (CO), carbon dioxide (CO2), nitrogen oxide (NO), maximum pressure (Pmax), location of maximum pressure (CAD Pmax), maximum heat release rate (HRRmax), cumulative HRR (CuHRR), unburned hydrocarbon (UHC) and location of maximum HRR (CAD HRRmax), respectively for 11 random unseen test data points. engine speed, output torque, fuel mass ow rate and four biodiesel blends types were used as the input parameters for this modelling work [2]. Naja et al. [4] developed an ANN model to nd the correlation between brake power, torque, brake specic fuel consumption, brake thermal efciency, volumetric efficiency and emission components by using different gasolineethanol blends and speeds as inputs data using back-propagation ANN with R Values in the range of 0.971. To train the ANN, 70% of the total experimental data (405 values) was selected at random and was used for training purpose, while the 30% was reserved for testing. The experimental data set for every output parameter includes 45 values, of which 30 values were used for training the network and 15 values were selected randomly to test the performance of the trained network experiment, it was found that when ethanol content increased, NOx increased and HC and CO emission were decreased [4]. Hashemi and Clark used an ANN to predict the oxides of nitrogen (NOx), carbon dioxide (CO2), hydrocarbons (HC), and carbon monoxide (CO) emitted from heavy-duty diesel vehicles. Axle speed, torque, their derivatives in dierent time steps, and two novel variables that dened speed variability over 150 seconds were dened as the inputs for the ANN [8].

In this work, the objective was to develop a model that can accurately predict in-use heavy duty diesel engine emissions by employing engine dynamometer data available through the previous work of the Iran University of Science and Technology, Automotive Research Center (IUST, ARC). Engine speed, engine load and injection timing were used as the input parameters where as Power, peak pressure, BSFC, and exhaust emissions (CO, CO2, NOx, HC, smoke) were used as the output parameters. The ANN output results are compared to actual in-use data in order to determine the models accuracy.

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2. ENGINE SPECIFICATION

In this study, the ANN model is performed on an agricultural engine (MT4.244) produced by Motorsazan. Details of the engines specications are given in Table 1 [7, 8, 9-11]. The experiment had been performed at four various injection timing (8,4,2 CA BTDC and 1 CA ATDC) and load (25%, 50%, 75% and 100%) and at three different engine speed, namely 1400 rpm (maximum torque speed), 1700 rpm and 2000 rpm (maximum power speed). engine-out responses, namely the power, BSFC, in-cylinder peak pressure and exhaust emissions of CO2, CO, HC, NOx and Smoke for these input parameters had been recorded. A total of 384 experimental data points had been collected.

In this work, 80% of the total experimental data was selected at random and was used for ANN training purpose, while the 20% was reserved for testing [4]. These data points were never introduced to the ANN during its developmental stage and were therefore regarded as unseen data points [2]. The experimental data set for every output parameter includes 48 values, of which 38 values were used for training the network and 10 values (unseen data) were selected randomly to test the performance of the trained network.

Table 1 Engine Specification

Bore Stroke 100 mm 127 mm Number of Cylinders 4 Volume Capacity 3.99 Liter Cycle 4 Stroke Maximum power output 61.5 kW @2000 rpm Maximum torque output 340 N m@1400 rpm Aspiration Wastegated Turbocharger Combustion System Fast ram direct injection Compression Ratio 17.5:1 Fuel Pump Bosch Rotary with Boost control Cooling Water, Belt Driven water pump Weight 265 Kg Length Width Height 678.7mm 655mm 748.5mm

3. ARTIFICIAL NEURAL NETWORKS

ANNs are used to solve a wide variety of problems in science and engineering. A well-trained neural network model can be used for fast prediction of complex and nonlinear problems, making it an easy to use tool in preliminary studies for such problems. Neural nets are of interest to researchers in many areas for different reasons. One of that reasons is the artificial neural network model does not require details about the exact construction of the system being modeled. Electrical engineers find numerous applications for ANNs in signal processing

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and control theory. Computer engineers are intrigued by the potential for hardware to implement neural nets efficiently and by applications of neural nets to robotics.

Computer scientists find that neural nets show promise for difficult problems in areas such as artificial intelligence and pattern recognition. For applied mathematicians, neural nets are a powerful tool for modeling problems for which the explicit form of the relationships among certain variables is not known [3].

A commonly used ANN model is a feed forward network which contains an input layer, some hidden layers and an output layer [2, 4, 5]. Each neuron in the network accepts a weighted set of inputs and responds with an output. The output is given by:

1 1

1

p

i i

i

N w x b

(1)

Where p is the number of inputs, 1 iw is the interconnecting weights, 1ix is the input and b is

the bias for the neuron. The error between the network output and the actual output is minimized by modifying the network weights and biases. This process is called learning process. The goal is to minimize the average of sum of these errors which is called as Mean Square Error of the output [5]. Mean Square Error is given by:

2

1

1

( ( ) ( ))Q

k

MSE

Q t k a k

(2)

Where t (k) is the actual value, a (k) is the network value and Q is the number of epochs. When the MSE or gradient of MSE falls below a predetermined value or the maximum number of epochs have been reached, the training process stops. Then this trained network can be used for simulating the system outputs for the inputs which have not been introduced before.

3. ANN Modeling

A multi layer perception network (MLP) was used for the ANN model and the back propagation algorithm was utilized as training algorithm.

The accuracy of a neural network is impacted by the activation or transfer function and the number of hidden neurons. It is important for the programmer to select an appropriate transfer function and number of neurons. The neural network setting that was found to have minimum error in this study is summarized in table 2. Transfer functions of hidden and output layer is selected to be logsig given by:

1log ( )

1 nsig n

e

(3)

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Table 2 ANN Setting

Network Type MLP Training function LevenbergMarquardt Transfer functions logsig/logsig Number of neurons 14 Performance function MSE

To ensure that each input provides an equal contribution in the ANN, initially total of 384 experimental data was normalized using following equation:

min

max min

N

a aa

a a

(4)

Where a is the actual value, mina is the minimum value of a , maxa is the maximum value of

a and Na is the normalized value of a which will be within the range from 0 to 1. Then 80% of the total experimental data employed to train the ANN. The performance of the ANN predictions was evaluated by regression analysis of the network outputs and the experimental values and the root-mean-squared-error (RMSE) that is dened as follows [4]:

0.5

2

1

( ( ) ( ))n

i

t i a i

RMSEn

(5)

Where n is the number of the points in the data set.

Engine speed, engine load and injection timing were selected as input neurons. Power, peak pressure, BSFC, and exhaust emissions (CO, CO2, NOx, HC, smoke) were identied as output neurons. Schematic representations of the ANN model are shown in Fig. 1

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Fig. 1 Schematic diagram for the ANN

4. RESULTS and DISCUSSION

After the ANN was trained, unseen data points were introduced to the ANN to determine the performance of the developed ANN model in predicting the engine-out responses. The R and RSME Values are reported to show the prediction accuracy. R is the proportionality value between predicted and experimental data, a value closer to unity implies higher accuracy of the ANN. In addition, smaller value for RMSE indicates higher accuracy of the prediction.

Fig. 2-9 shows the network outputs versus experimental values for unseen data points for eight different engine-out responses. The R values for the power, HC, CO, CO2, NOx, smoke, BSFC and Peak Pressure are 0.97979, 0.98388, 0.9291, 0.99508, 0.99532, 0.99646, 0.97585 and 0.99037, respectively. The RMSE values for the power, HC, CO, CO2, NOx, smoke, BSFC and Peak Pressure are 3.5445, 3.2781, 74.6681, 0.35355, 44.8955, 7.443, 66.9071 and 2.547, respectively.

The results showed that a single MLP ANN using Back Propagation training algorithm ,LevenbergMarquardt as training function and logsig as transfer function, is able to predict eight different heavy duty diesel engine-out responses when Engine speed, engine load and injection timing are introduced as input parameters. However these predictions can be further improved if higher number of experimental data can be provided. Furthermore, physical models can be incorporated within the transfer function of the network to improve the network outputs accuracy [12].

Investigation of the results shows that there is a good correlation between the network outputs and measured data. In addition, experimental tests over a wide range of control parameters such as speed, load and injection timing are both time-consuming and expensive.

Engine Speed

Engine Load

Injection

Timing

Power

NOx

Peak Pressure

BSFC

HC

CO

CO2

Smoke

Input Layer Hidden Layer

Output Layer

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Therefore, a well trained ANN can be used as a predicting tool for for simulating engine responses.

1 2 3 4 5 6 7 8 9 100

10

20

30

40

50

60

Test Points

Pow

er

(hp)

RMSE = 3.5445

Measured

Predicted

Fig. 2 Comparisons of the ANN outputs and experimental data for the power

1 2 3 4 5 6 7 8 9 100

10

20

30

40

50

60

70

Test Points

HC

(ppm

)

RMSE = 3.2781

Measured

Predicted

Fig. 3 Comparisons of the ANN outputs and experimental data for the HC

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1 2 3 4 5 6 7 8 9 10100

200

300

400

500

600

700

800

900

1000

Test Points

CO

(ppm

)

RMSE = 74.6681

Measured

Predicted

Fig. 4 Comparisons of the ANN outputs and experimental data for the CO

1 2 3 4 5 6 7 8 9 102

4

6

8

10

12

14

Test Points

CO

2 (

%)

RMSE = 0.35355

Measured

Predicted

Fig. 5 Comparisons of the ANN outputs and experimental data for the CO2

1 2 3 4 5 6 7 8 9 100

200

400

600

800

1000

1200

1400

Test Points

NO

x (

ppm

)

RMSE = 44.8955

Measured

Predicted

Fig. 6 Comparisons of the ANN outputs and experimental data for the NOx

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1 2 3 4 5 6 7 8 9 100

50

100

150

200

250

Test Points

Sm

oke (

ppm

)

RMSE = 7.443

Measured

Predicted

Fig. 7 Comparisons of the ANN outputs and experimental data for the smoke

1 2 3 4 5 6 7 8 9 10200

300

400

500

600

700

800

900

1000

Test Points

BS

FC

(g/K

W.h

)

RMSE = 66.9071

Measured

Predicted

Fig. 8 Comparisons of the ANN outputs and experimental data for the BSFC

1 2 3 4 5 6 7 8 9 1055

60

65

70

75

80

85

90

95

100

105

Test Points

Peak P

ressure

(bar)

RMSE = 2.547

Measured

Predicted

Fig. 9 Comparisons of the ANN outputs and experimental data for the Peak Pressure

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5. CONCLUSION

Experimental tests over a wide range of control parameters such as speed, load and injection timing are both time-consuming and expensive. Therefore, a single ANN can be utilized for a heavy duty diesel engine to predict engine-out responses.

In this study, an ANN was developed to model a heavy duty diesel engine performance and emissions with R Values in the range of 0.92-0.996. This reduces the experimental efforts and hence can serve as an effective tool for predicting the performance of the engine and emission characteristics under various operating conditions.

Acknowledgements

It should be noted that the measured data employed in this research was acquired through previous works efforts at IUST, ARC. Acknowledgement is given to the engineers, staff, and graduate students that were associated with the performance and emissions measurement system, and data acquisition which occurred at the engine and emissions research laboratory.

References

[1] Tatur, M., Laermann, M., Koehler, E., Tomazic, D., Holland, T., Robinson, D., Dowell, J. and Price, K., Development of an Emissions Control Concept for an IDI Heavy-Duty Diesel Engine Meeting 2007 Phase-In Emissions Standards, SAE Paper. 2007-01-0235.

[2] Harun Mohamed Ismail, Hoon Kiat Ng, Cheen Wei Queck, Suyin Gan, Articial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends, Applied Energy 2012; 92:769-777.

[3] Fausett, Laurene V., Fundamentals of Neural Network, Prentice Hall, 1993.

[4] G. Naja, B. Ghobadian, T. Tavakoli, D.R. Buttsworth, T.F. Yusaf, M. Faizollahnejad, Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using articial neural network, Applied Energy, 2009; 86:630-639

[5] Shivakumar, P. Srinivasa Pai, B.R. Shrinivasa Rao, Articial Neural Network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings, Applied Energy, 2011;88:2344-2354

[6] Talal F. Yusaf, D.R. Buttsworth, Khalid H. Saleh, B.F. Yousif, CNG-diesel engine performance and exhaust emission analysis with the aid of articial neural network, Applied Energy, 2010;87:1661-1669

[7] S. Emami, Effect of injection timing and pressure on the performance, emission and combustion characteristics of a diesel engine (advanced CFD modeling), Wulfenia Journal 19 (9) (2012).

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[8] N. Hashemi and N. N. Clark, Articial neural network as a predictive tool for emissions from heavy-duty diesel vehicles in Southern California, Int. J. Engine Res, 2007; 8:321-336

[9] M. M. Etghani, Sensitivity analysis and optimization of diesel engine MT4.244 performance and emissions using biodiesel fuels, PhD thesis, Iran University of Science and Technology, 2013

[10] S. A. Khabbaz, R. Mobasheri, Experimental investigation of the effects of Tri-aromatic utilization on combustion process, emission characteristics and engine performance of a DI diesel engine, Fuel, 2014; 123:26-32

[11] M. T. Boldaji, R. Ebrahimzadeh, K. Kheiralipour, A. M. Borghei, Effect of some BED blends on the equivalence ratio, exhaust oxygen fraction and water and oil temperature of a diesel engine, biomass and bioenergy, 2011; 35:4099-4106

[12] I. Brahma, Y. He, C. J. Rutland, Improvement of Neural Network Accuracy for Engine Simulations, SAE Paper, 2003-01-3227.

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