The effect of boost pressure on the performance characteristics of a diesel engine: A neuro-fuzzy approach

Applied Energy 86 (2009) 113–121

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Applied Energy journal homepage: www.elsevier.com/locate/apenergy

The effect of boost pressure on the performance characteristics of a diesel engine: A neuro-fuzzy approach I. Al-Hinti a,*, M. Samhouri b, A. Al-Ghandoor b, A. Sakhrieh a a b

Department of Mechanical Engineering, The Hashemite University, Zarqa 13115, Jordan Department of Industrial Engineering, The Hashemite University, Zarqa 13115, Jordan

a r t i c l e

i n f o

Article history: Received 6 November 2007 Received in revised form 12 April 2008 Accepted 13 April 2008 Available online 5 June 2008 Keywords: Boost pressure Diesel engine Neuro-fuzzy ANFIS

a b s t r a c t This paper uses a neuro-fuzzy interface system (ANFIS) to study the effect of boost pressure on the efficiency, brake mean effective pressure (BMEP), and the brake specific fuel consumption (BSFC) of a single cylinder diesel engine. Experimental data were used as inputs to ANFIS to simulate the engine performance characteristics. The experimental as well as the model results emphasize the role of boost pressure in improving the different engine characteristics. The results show that the ANFIS technique can be used adequately to identify the effect of boost pressure on the different engine characteristics. In addition, different data points that were not used for ANFIS training were used to validate the developed models. The results suggest that ANFIS can be used accurately to predict the effect of boost pressure on the different engine characteristics. Ó 2008 Elsevier Ltd. All rights reserved.

1. Introduction The improvement of the performance, fuel economy and emissions of automotive applications of internal combustion (IC) engines has become a prime priority in today’s world. This is mainly due to a combination of factors that include the continuously tightened emission standards, escalating fuel prices, and the drivers demand for increased power. Numerous strategies are either being explored or already implemented to achieve these requirements. They usually fall into two broad categories: Fuel modifications and engine-based technologies. Boosting the intake air pressure is one of the most common and effective strategies used today to improve the power and efficiency of IC engines. The effectiveness of this method has been demonstrated by a large number of studies [1–7]. Increasing the intake air pressure is achieved either through turbocharging, where the compressor is driven by a turbine driven by exhaust gases, or supercharging, where the compressor gets its power off the crankshaft [8]. Increasing the intake air pressure results in increased air density and consequently higher mass of air in the engine cylinders during each cycle. The pressure increase is generally between 0.2 and 0.8 bar, with most engines on the lower end of this range. Despite the proven success of pressure boosting in maximising the output power of IC engines, the effectiveness of this method varies depending on the value of pressure increase, and the engine geometrical and operational conditions. Moreover, adopting * Corresponding author. Tel.: +962 5 390 3333x4709; fax: +962 5 382 6348. E-mail addresses: [email protected], [email protected] (I. Al-Hinti). 0306-2619/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.apenergy.2008.04.015

pressure boosting on its own does not lead to significant improvements on exhaust gas emissions and can increase the tendency of knocking in spark-ignition engines. Therefore, careful design and control of turbochargers or superchargers are extremely essential to obtain optimum power, fuel economy and emissions. However, this is not an easy task using conventional analytical or numerical techniques. In fact, it is extremely difficult to precisely model and predict the effect of boost pressure on the performance or exhaust emissions of a given engine. This is due to the nature of the highly nonlinear gas dynamics, reaction kinetics and combustion phenomena encountered where the interaction between a large number of variables is complex and is not always fully understood. Artificial intelligence (AI) systems are widely accepted as a technology offering an alternative way to tackle complex and ill defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with nonlinear problems, and once trained can perform prediction and generalization at high computing speed. Many of the IC engines applications are exactly the types of problems and issues for which AI approach appear to be most applicable. In these models of computation, attempts are made to simulate the powerful cognitive and sensory functions of the human brain and to use this capability to represent and manipulate knowledge in the form of patterns. Based on these patterns, neural networks, for example, model input–output functional relationships, can make predictions about other combinations of unseen inputs. Many of the AI techniques have the potential for making better, quicker and more practical predictions than any of the traditional methods [9].

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Several studies are found in literature dealing with the application of the AI approaches in modelling and solving common IC engines problems. The usefulness of neural networks in engine control strategy optimization was demonstrated by Thompson et al. through a model for the prediction of output power and exhaust emissions of a heavy duty diesel engine [10]. Kilagiz et al. developed a fuzzy expert system for defining possible fuel system faults, ignition system faults, intake valve and exhaust valve faults depending on measurements of CO, HC, CO2, O2 and air fuel ratio [11]. Different AI approaches were implemented and compared by DeNicolao et al. for predicting the volumetric efficiency depending on different engine parameters such as speed and intake pressure [12]. Artificial neural networks (ANNs) have been extensively used in predicting the performance and emissions of IC engines with respect to different variables, such as fuel injection pressure, valve-timing and ignition timing. For example, Golcu et al. proposed the use of ANNs to determine the effects of intake valve-timing on the engine performance and fuel economy [13]. In a similar study, Atashkari et al. presented a group method of data handling type neural network and evolutionary algorithms for modelling the effects of intake valve-timing and engine speed of a spark-ignition engine on both engine torque and fuel consumption [14]. ANNs were also implemented by Arcaklioglu and Celikten to predict the exhaust emissions, torque, power, and other performance parameters of a diesel engine as function of the fuel injection pressure [15]. Yucesu et al. adopted ANNs to determine the engine torque and specific fuel consumption based on the ignition timing, air fuel ratio and compression ratio at a constant speed of 2000 rpm and at wide open throttle for different ethanol–gasoline fuel blends [16]. An ignition advance control strategy based on the location of peak pressure in spark-ignition engines was presented by Park et al. using ANNs [17]. Two other ignition timing control systems were also proposed by Wang et al. using fuzzy and neuro-fuzzy logic [18,19]. In this study, the effect of boost pressure on the efficiency, brake mean effective pressure (BMEP) and the brake specific fuel consumption (BSFC) of a single cylinder diesel engine is investigated. Experimental tests were carried out at four different intake air pressures (1.0, 1.4, 1.6, and 1.8 bar) over a speed range of 1600– 3000 rpm. The experimental data was used as input points to an adaptive neuro-fuzzy inference system (ANFIS), which in turn used these inputs to predict values for the engine performance characteristics. The main contribution of this study is to complement the literature cited above through investigating the suitability and effectiveness of ANFIS modelling techniques for predicting various engine performance parameters in general, and with respect to the intake pressure in particular. This paper is organized as follows: Section 2 gives details of the experimental work. Neuro-fuzzy modelling and its theoretical background are presented in Section 3. ANFIS-prediction model is described in Section 4, and the results are presented and discussed in Section 5. The paper ends up with the conclusions in Section 6.

Table 1 Engine specifications Type Bore Stroke Swept volume Maximum power Valves

Single cylinder, four stroke, water cooled diesel engine 75 mm 70 mm 0.31 l 5.1 kW at 3000 rpm 2 valves

through a three-phase electrical power source was used to supply the engine with the charged air. This was done in order to enable the study of the effect of boost pressure without the interference of other interacting variables such as the engine speed or exhaust temperature and pressure. This allowed effective control of the boost pressure at set values over a range of different engine operating conditions. The compressor was a 10 hp, stationary, belt-driven compressor which is equipped with a pressure and flow regulators. It was capable of providing the engine with charge air at high flow rates and relatively low gauge pressures, similar to those obtained from typical superchargers or turbochargers. The engine air intake pipe was modified to enable the connection with the charge compressor. It was also fitted with a pressure gauge, a K-type thermocouple and a flow meter to measure the inlet air pressure, temperature and flow rate. Fig. 1 shows a schematic diagram of the experimental setup. The experimental procedure was carried out first for naturally aspirated conditions (1 bar) and then repeated for charged air intake conditions at 1.4, 1.6 and 1.8 bar. In each test, the engine speed was varied between 1600 and 3000 rpm in steps of 200 rpm and the following data was recorded: torque (±0.1 N m), fuel consumption (± 0.01 g/s) and air flow rate (± 0.01 g/s). This data was used to calculate the power, efficiency, BMEP, and BSFC. The collected experimental data and results are given in Table 2. It is worth mentioning here that for each inlet pressure, the test procedure was repeated four times. Therefore, each data point shown in this table is the average of four independent data points. Fig. 2 illustrates the effect of the boost pressure on the power, efficiency, BMEP and BSFC over the engine speed range. As it can be seen from Fig. 2a, the power increases with engine speed and it is clearly enhanced by the increase of the boost pressure. The positive effect of boost pressure on the BMEP is also clearly shown in Fig. 2c. The trends of the efficiency and BSFC are not as clear as those of the power and the BMEP. This could be due to the influence of more experimental variables in the calculation of the efficiency and BSFC. However, it can be generally concluded from Fig. 2b that increasing the boost pressure can increase the efficiency by a percentage that varies depending on the speed and

4 9

10

5

3

2. Experimental work The engine used in this investigation is the GUNT CT 100.23 four stroke, single cylinder, diesel engine. The specifications of the engine are listed in Table 1. The engine is connected to asynchronous motor, which is operated as dynamometer. Both the engine and the motor are contained in a standard test bed that is available in the Internal Combustion Engines Lab at the Hashemite University. This test bed enables the measurement and control of engine speed, the measurement of the torque, the fuel flow rate and inlet air flow rate. Instead of connecting the engine to a supercharger or a turbocharger, a separate compressor that is driven independently

8 7 6

1 2

Fig. 1. Schematic diagram of the experimental setup: (1) dynamometer, (2) engine, (3) control unit, (4) fuel tank, (5) fuel flow meter, (6) compressor, (7) regulator, (8) air flow meter, (9) pressure gauge, (10) thermocouple.

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I. Al-Hinti et al. / Applied Energy 86 (2009) 113–121 Table 2 Experimental data and results used in the training and validation of the ANFIS models Intake pressure (bar)

Speed (rpm)

Torque (N m)

Fuel consumption (g/s)

Power (kW)

Efficiency (%)

BMEP (bar)

BSFC (g/kWh)

ANFIS application

1.0

1600 1800 2000 2200 2400 2600 2800 3000

17.8 16.9 17.0 16.9 16.8 16.9 16.5 15.5

0.28 0.30 0.35 0.39 0.48 0.55 0.55 0.55

2.98 3.19 3.56 3.90 4.22 4.60 4.84 4.87

25.4 25.3 24.2 23.8 21.0 19.9 21.0 21.1

7.24 6.88 6.92 6.88 6.83 6.88 6.71 6.31

337.8 338.9 353.7 360.5 409.1 430.1 409.1 406.5

Training Training Training Training Validation Training Training Training

1.4

1600 1800 2000 2200 2400 2600 2800 3000

17.7 17.3 17.3 17.3 17.2 16.9 16.5 16.0

0.28 0.30 0.32 0.39 0.43 0.55 0.55 0.55

2.96 3.26 3.61 3.99 4.32 4.61 4.85 5.03

25.5 26.1 26.7 24.5 23.9 19.8 20.9 21.6

7.18 7.04 7.02 7.04 7.00 6.88 6.73 6.51

335.8 328.8 320.7 349.4 358.0 432.2 410.6 396.1

Training Validation Training Training Training Training Training Validation

1.6

1600 1800 2000 2200 2400 2600 2800 3000

18.6 18.1 17.9 17.8 17.7 17.5 17.4 16.8

0.30 0.30 0.35 0.39 0.43 0.55 0.55 0.55

3.12 3.41 3.75 4.10 4.45 4.77 5.10 5.28

24.7 27.1 25.5 25.0 24.6 20.6 22.1 22.9

7.57 7.36 7.28 7.24 7.20 7.12 7.08 6.83

346.4 316.4 336.0 342.2 347.8 415.4 387.9 375.0

Training Training Training Training Training Training Validation Training

1.8

1600 1800 2000 2200 2400 2600 2800 3000

18.9 18.2 18.1 18.1 17.9 17.7 17.8 17.1

0.30 0.32 0.32 0.39 0.43 0.48 0.55 0.55

3.17 3.43 3.79 4.17 4.50 4.82 5.22 5.37

25.1 25.5 28.2 25.5 24.9 23.9 22.6 23.3

7.69 7.40 7.36 7.36 7.28 7.20 7.24 6.96

340.9 335.7 303.8 336.6 344.0 358.4 379.2 368.4

Validation Training Training Training Training Validation Training Training

a

6.0

b

30 28

5.5

26

4.5 4.0

2.5

22 20 1 bar

16

1.4 bar

1.6 bar

14

1.6 bar

1.8 bar

12

1.4 bar

3.0

24

18

1 bar

3.5

Efficiency (%)

Power (kW)

5.0

1.8 bar

10 1400 1600 1800 2000 2200 2400 2600 2800 3000 3200

2.0 1400 1600 1800 2000 2200 2400 2600 2800 3000 3200

Speed (rpm)

d

8.0

500

7.5

450

7.0

400

6.5 6.0

1 bar 1.4 bar

BSFC (g/wh)

BMEP (bar)

c

Speed (rpm)

350 1 bar

300

1.6 bar 1.8 bar

1.4 bar

5.5

250

5.0 1400 1600 1800 2000 2200 2400 2600 2800 3000 3200 Speed (rpm)

200

1.6 bar 1.8 bar

1400 1600 1800 2000 2200 2400 2600 2800 3000 3200

Speed (rpm)

Fig. 2. Effect of boost pressure on the performance parameters over the speed range: (a) power, (b) efficiency, (c) BMEP, (d) BSFC.

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the boost pressure. Almost the opposite can be said about the effect on the BSFC, as shown in Fig. 2d.

Start

3. Neuro-fuzzy modelling

Load training data

One way to represent data and knowledge, closer to human-like thinking, is to use fuzzy rules instead of exact rules [20]. Fuzzy systems are rule-based expert systems based on fuzzy rules and fuzzy inference. A fuzzy inference system can be viewed as a real-time expert system used to model and utilize a human operator’s experience or process engineer’s knowledge [21]. Fuzzy logic can model nonlinear functions of arbitrary complexity. It provides an alternative solution to nonlinear modelling because it is closer to the real world. Nonlinearity and complexity are handled by rules, membership functions, and the inference process which results in improved performance, simpler implementation, and reduced design costs [22]. Neuro-fuzzy is an associative memory system that consists of fuzzy nodes instead of simple input and output nodes. It uses neural network learning functions to refine each part of the fuzzy knowledge separately. Learning in a separated network is faster than learning in a whole network [23]. One approach to the derivation of a fuzzy rule base is to use the self learning features of artificial neural networks, to define the membership function based on input–output data. A fuzzy inference system is mapped onto a neural network-like architecture. Adaptive neuro-fuzzy inference system (ANFIS) is a fuzzy inference system implemented in the framework of an adaptive neural network. By using a hybrid learning procedure, ANFIS can construct an input–output mapping based on both human-knowledge as fuzzy If-Then rules and stipulated input–output data pairs for neural networks training. ANFIS is more powerful than the simple fuzzy logic algorithm and neural networks, since it provides a method for fuzzy modelling to learn information about the data set, in order to compute the membership function parameters that best allow the associated fuzzy inference system to track the given input/ output data [21]. The architecture of ANFIS is illustrated in Fig. 3, where x and y are the inputs, f is the output, Ai and A2n are the input membership functions, wi and w2n are the rules firing strengths. It has five layers to accomplish the tuning process of the fuzzy modelling system. Given the values of premise parameters, the overall output can be expressed as a linear combination of the consequent parameters. ANFIS applies two techniques in updating parameters. For premise parameters that define membership functions, ANFIS employs gradient descent back-propagation neural networks to fine-

Set input membership function

No

Input training data into ANFIS

Training Finished? Yes

Get results from trained ANFIS

Input prediction parameters

Predicted Engine Characteristics

End Fig. 4. ANFIS training and modelling process.

tune them. For consequent parameters that define the coefficient of each output equation, ANFIS uses the least squares method to identify them. This approach is called the hybrid learning method. More specifically, in the forward pass of the hybrid learning method, functional signals go forward until layer 4 and the consequent parameters are identified by the least square estimate. In the back-

Fig. 3. ANFIS architecture [21].

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Fig. 5. Training curve of ANFIS-prediction system for efficiency.

Degree of membership

Degree of membership

Degree of membership

Fig. 6. ANFIS-predicted results against actual efficiency values.

Low

1

High

0.5

0

1

1.1

1.2

1.3

1.4 IntakePressure (bar)

1.5

1.6

1.7

Slow

1

1.8

Fast

0.5

0 1600

1800

2000

2200

2400 Speed (rev/min)

2600

2800

High

Low

1 0.8 0.6 0.4 0.2 0

3

3000

3.5

4

4.5

Power (kW) Fig. 7. Final membership functions for the efficiency after ANFIS training.

5

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ward pass, the error rates propagate backward and the premise parameters are updated by the gradient descent. 4. ANFIS model description ANFIS modelling and prediction of performance characteristics of the diesel engine start with obtaining a data set (input–output data points) and dividing it into training and validating data sets. Table 2 gives the training data points (i.e., data base) used in this work to train ANFIS to identify or model the efficiency, BMEP, and BSFC characteristics of the diesel engine. Each input/output pair contains three inputs (intake pressure, speed, and power) and one output (an engine performance characteristic). The training data set is used to find the initial premise parameters for the membership functions by equally spacing each of the membership functions. A threshold value for error between the actual and desired output is determined. The consequent parameters are com-

puted using the least squares method. Then, an error for each data pairs is found. If this error is larger than the threshold value, the premise parameters are updated using the back-propagation neural networks. This process is terminated when the error becomes less than the threshold value. Then, the testing data points are used to compare the model with actual system for validating purposes. Fig. 4 shows the ANFIS training and modelling process. The overall property output (f) of ANFIS given in Fig. 3, can be written as Y ¼ ðw1 IPÞ P11 þ ðw1 SÞ P 12 þ ðw1 PtÞP13 þ ðw1 Þ P10 þ ::: 2

2

þ ðwn2 IPÞ Pn1 þ ðwn2 SÞ P n2 þ ðwn2 PtÞP13 þ ðwn2 Þ Pn0

Degree of membership Degree of membership Degree of membership

High

Low

0.5

0

1

1.1

1.2

1.3

1.4 Intake Pressure (bar)

1.5

1.6

1.7

Slow

1

1.8

Fast

0.5

0 1600

1800

2000

2200

2400 Speed (rev/min)

2600

2800

0.8 0.6 0.4 0.2 0

3

3000

High

Low

1

3.5

ð1Þ

The full equation has (5n2) terms, where n2 is the number of input implications. In this model of engine characteristics of Eq. (1), IP, S, and Pt are the intake pressure, speed, and power, respectively. w1 to wn2 are the normalized firing strengths of fuzzy rules. The

Fig. 8. ANFIS-predicted results against actual BMEP values.

1

2

4

4.5

Power (kW) Fig. 9. Final membership functions for the BMEP after ANFIS training.

5

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consequent parameters of the fuzzy membership functions 2 parameters, P 11 ; . . . ; P n0 are tuned off-line using linear least square method, and then updated on-line by a gradient descent back-propagation neural networks. 5. Results and discussion 5.1. Fuzzy model for efficiency The fuzzy logic toolbox of Matlab 7 was used to obtain the results. A total of 34 nodes and 8 fuzzy rules were used to build the fuzzy systems for modelling the engine’s efficiency. The neural network training for building the fuzzy model for efficiency used 26 selected data point, as designated in Table 2, and 15 learning epochs. Fig. 5 shows the training curve of ANFIS with root mean square error (RMSE) of 0.843 corresponding to almost 2%. A

comparison between the actual and ANFIS-predicted efficiency after training is shown in Fig. 6, which shows that the system is well-trained to model the actual efficiency. Different types of membership functions (MF) of the inputs and output were tested to train the ANFIS-prediction system. Two Gaussian-type MF for each input resulted in high accurate modelling results and minimum training and validation errors. The final MF, shown in Fig. 7, were tuned and updated by the ANFIS model to achieve a good mapping of the input variables to the efficiency output. The shape of MF is considered a key parameter in tuning the ANFIS. It determines the location of MF parameters, and consequently determines the degree of membership for the input values (i.e., fuzzified inputs). Also, referring to the previous discussion in Section 3, the fuzzy rules premise and antecedent parameters are directly affected by the MF shape and parameters. Therefore, the

Degree of membership

Degree of membership

Degree of membership

Fig. 10. ANFIS-predicted results against actual BSFC values.

1

Low

High

0.5

0

1

1.1

1.2

1.3

1.4 Intake Pressure (bar)

1.5

1.6

1.7

Fast

Slow

1

1.8

0.5

0 1600

1800

2000

2200

2400 Speed (rev/min)

2600

2800

High

Low

1 0.8 0.6 0.4 0.2 0

3

3000

3.5

4

4.5

Power (kW) Fig. 11. Final membership functions for the BSFC after ANFIS training.

5

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Table 3 Validation data Data point

Actual efficiency (%)

Predicted efficiency (%)

Error (%)

Actual BMEP (bar)

Predicted BMEP (bar)

Error (%)

Actual BSFC (g/ kW h)

Predicted BSFC (g/ kW h)

Error (%)

1 2 3 4 5 6

21.0 26.1 21.6 22.1 25.1 23.9

22.0 25.9 21.7 21.8 25.3 23.5

4.75 0.77 0.46 1.36 0.80 1.70

6.83 7.04 6.51 7.08 7.69 7.20

6.82 7.04 6.54 7.07 7.70 7.20

0.15 0.00 0.46 0.14 0.13 0.00

409.1 328.8 396.1 387.9 340.9 358.4

387.0 324.0 393.0 400.0 333.0 353.0

5.40 1.50 0.78 3.10 2.30 1.50

Average Error (%)

1.64

effect of changing the MF shape will propagate to reach all the ANFIS layers, and will have a considerable effect on the final output of the ANFIS network. 5.2. Fuzzy model for BMEP The same 26 training data points used in the previous section were used for the fuzzy modelling for BMEP using neural networks with 15 training epochs. This resulted in a RMSE of 0.0003, which corresponds to almost only 0.15%. The actual and ANFIS-predicted BMEP values are plotted in Fig. 8. It can be seen that the matching between the actual and the predicted values is excellent. Two triangular-type MF, shown in Fig. 9, for each input resulted in highly accurate prediction results and minimum training and validation errors.

0.15

2.43

The present study shows that ANFIS is a technique that can be efficiently used to predict the internal combustion engines performance characteristics. It is believed that this approach can be extended to either include additional performance and emissions characteristics (such as the volumetric efficiency, NOx and particulates emissions), or to examine the effect of other parameters such as the Air/Fuel ratio or injection timing and pressure on these characteristics, depending on the availability of reliable experimental data. Acknowledgements The authors deeply appreciate the efforts of the following engineers: Tariq Salameh, Ola Samara, Ruba Daoud and Amal Farhan, during the experimental work which was carried out at the Internal Combustion Lab in the Hashemite University.

5.3. Fuzzy model for BSFC A third ANFIS-based fuzzy model was built for BSFC based on the same training data and 30 training epochs. This resulted in a value of the RMSE equal to 16.17, which is equivalent to around 2.5%. Fig. 10 shows the ANFIS-predicted BSFC values against the experimental values. Although the agreement is not as pronounced as it is for the two previous cases, it still indicates that the system is well-trained and can be used for the prediction of BSFC. Fig. 11 illustrates the two triangular-type MF used for each input. 5.4. Models validation The three ANFIS-prediction models for diesel engine performance characteristics were validated with six data points representing the remaining experimental data shown in Table 2 that was not used for ANFIS training. The input parameters (IP, S, and Pt) of each validation data point were fed into the system, and then the output characteristics (efficiency, BMEP, or BSFC) were predicted. The validation results are summarized in Table 3 which shows the percentage error of each prediction in addition to the average percentage error for each performance characteristic. The percentage error associated with the efficiency model ranges between 0.46% and 4.75% with an average of 1.64%. The results of the BMEP were even better where the percentage error was less than 0.46% for all data points. Finally, the BSFC model predictions resulted in an average percentage error equal to 2.43%. 6. Conclusions In this paper, ANFIS fuzzy models for predicting the performance characteristics of a diesel engine were constructed and validated. The matching between the experimental and modelspredicted results was excellent. For the validated data, the average percentage error for the predicted efficiency, BMEP, and BSFC was 1.64%, 0.15%, and 2.43%, respectively.

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