Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network

Energy 50 (2013) 177e186

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Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network Yusuf Çay a, *, Ibrahim Korkmaz b, Adem Çiçek c, Fuat Kara d a

University of Karabük, Faculty of Engineering, Department of Mechanical Engineering, 78050 Karabük, Turkey University of Düzce, Düzce Vocational School of Higher Education, 81500 Düzce, Turkey c Yıldırım Beyazıt University, Faculty of Engineering and Natural Sciences, Department of Mechanical Engineering, 06050 Ankara, Turkey d University of Düzce, Faculty of Technology, Department of Manufacturing Engineering, 81620 Düzce, Turkey b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 26 June 2012 Received in revised form 17 October 2012 Accepted 27 October 2012 Available online 1 December 2012

This study investigates the use of ANN (artificial neural networks) modelling to predict BSFC (break specific fuel consumption), exhaust emissions that are CO (carbon monoxide) and HC (unburned hydrocarbon), and AFR (airefuel ratio) of a spark ignition engine which operates with methanol and gasoline. To obtain training and testing data, a number of experiments were performed with a fourcylinder, four-stroke test engine operated at different engine speeds and torques. The experimental results reveal that the methanol improved the emission characteristics compared with the gasoline. For the ANN modelling, the standard back-propagation algorithm was found to be the optimum choice for training the model. In the building of the network structure, four different learning algorithms were used such as BFGS (Quasi-Newton back propagation), LM (LevenbergeMarquardt learning algorithm). It was found that the ANN model is able to predict the engine performance and exhaust emissions with a correlation coefficient of 0.998621, 0.977654, 0.998382 and 0.996075 for the BSFC, CO, HC and AFR for testing data, respectively. It was obvious that the developed ANN model is fairly powerful for predicting the brake specific fuel consumption and exhaust emissions of internal combustion engines. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: Gasoline Methanol ANN Engine performance Exhaust emissions

1. Introduction It is well known that all vehicles in the world are dependent on fossil fuels such as gasoline, diesel fuel, LPG (liquefied petroleum gas), LNG (liquefied natural gas) and CNG (compressed natural gas). The fossil fuel used in vehicles causes factors threatening human health such as air pollution, acid rains, smog, built up of carbon dioxide, changes in the heat balance of the earth, and so on. In addition, world fossil fuel reserves are also limited and will be consumed away in the near future [1]. Therefore, it seems that the use of alternative fuels is inevitable. These fuels include alcohols (such as ethanol and methanol), ethers, vegetable oils, animal fats, gaseous fuels and bio-diesel [2]. Among the alternative fuels, methanol is considered to be one of the most favourable fuels for engines. The reason is that methanol is liquid fuel and similar to gasoline and diesel in aspects of usage, storage and transportation. In addition, it can be produced from widely available raw materials including coal, natural gas and bio-substances. Methanol is a good

* Corresponding author. Tel.: þ90 370 4332021; fax: þ90 380 5421134. E-mail address: [email protected] (Y. Çay). 0360-5442/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.energy.2012.10.052

fuel for the spark-ignition engine. Benefits such as higher efficiency, specific power and lower emissions can be realised with methanol. Methanol molecule contains 50% oxygen which leads to combustion speed faster, also its higher laminar flame speed allows it to be run with rarefaction or more dilute air/fuel mixtures [3]. Methanol fuelled buses had once been introduced to reduce pollutant emission. However, operational problems have slowed down the development of methanol-fuelled vehicles, leading to the phasing out of the methanol-fuelled buses from the market [4]. Due to limitations on the development of methanol-fuelled engines, lots of studies have been done to improve the engine performance and to reduce exhaust emissions [5e11]. Yilmaz [12] performed comparative analysis of biodieseleethanolediesel and biodieselemethanolediesel blends in a diesel engine. Performance and emission characteristics of the engine fuelled with biodiesele methanolediesel and biodieseleethanolediesel were compared to standard diesel fuel as the baseline. Overall biodieselealcohole diesel blends showed higher brake specific fuel consumption than diesel. As alcohol concentrations in blends increase, CO (carbon monoxide) and HC (unburned hydrocarbon) emissions increase, while NO (nitrogen oxides) emissions are reduced. Also, methanol blends were more effective than ethanol blends for

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Nomenclature AFR ANN BFGS BSFC BTE CNG CO f Ft HC i.j LHV LM LNG LPG m MEP MRE

airefuel ratio artificial neural network Quasi-Newton back propagation break specific fuel consumption brake thermal efficiency compressed natural gas carbon monoxide transfer function fuel type unburned hydrocarbon processing elements lower heating value LevenbergeMarquardt learning algorithm liquefied natural gas liquefied petroleum gas fuel flow mean error percentage mean relative errors

reducing CO and HC emissions, while NO reduction was achieved by ethanol blends. Yilmaz and Sanchez [13] carried out analysis of operating a diesel engine on biodieseleethanol and biodiesele methanol blends. Performance and emission characteristics of the engine fuelled with biodieseleethanol and biodiesele methanol blends were compared to neat biodiesel and standard diesel fuel as the baseline fuels. Overall, biodieselealcohol blends, as compared to diesel, reduce NO emissions while increasing CO and HC emissions, at below 70% loads. It was also shown that biodieseleethanol blend was more effective than biodiesele methanol for emission reduction and overall engine performance. Sayin [14] experimentally investigated the effects of methanole diesel (M5, M10) and ethanolediesel (E5, E10) fuel blends on the performance and exhaust emissions. The use of methanolediesel and ethanolediesel blends caused a decrease in the emissions of smoke opacity, CO and THC (total hydrocarbon). However, NOx emissions increased with the use of blends. The BSFC (break specific fuel consumption) with the all fuel blends increased mainly due to the lower LHV (lower heating value) of methanol and ethanol. The increase in BSFC with the blend M10 was higher than that of E10 and M5 was higher than that of E5. All fuel blends yielded a decrease in BTE (brake thermal efficiency) because the lower LHV of methanol and ethanol. Among the blends, the lowest BTE was obtained from M10. Anand et al. [15] performed experimental investigations on combustion, performance and emissions characteristics of neat karanji biodiesel and its methanol blend in a diesel engine. It was observed that the HC emissions are slightly higher for biodieselemethanol blend at low load conditions and with increase in load the differences in HC emissions were insignificant between the two fuels. The differences in CO emissions were insignificant for the two fuels at lower load conditions whereas it was significantly reduced at higher load conditions with biodieselemethanol blend compared to neat biodiesel. The NO emissions of biodieselemethanol blend were significantly lower compared to neat biodiesel fuel operation at all the tested conditions. Zhu et al. [16], in their study, aimed to investigate the effects of the blended fuels on reducing NOx and particulate. On the whole, compared with Euro V diesel fuel, the blended fuels could lead to reduction of both NOx and PM (particulate matter) of a diesel engine, with the biodieselemethanol blends being more effective than the biodieseleethanol blends. The effectiveness of NOx and particulate reductions was more effective with increase of

N NETi n NOx o p PSI R2 RMSE RP SCG t T THC wij wbi Xj

engine speed the weighted sum of the input to the ith processing element number of processing elements in the previous layer nitrogen oxides output value of ANN number of pattern pounds per square inch determination coefficient root mean square error resilient back propagation scaled conjugate gradient learning algorithm experimental data torque total hydrocarbon the weights of the connections between ith and jth processing elements the weights of the biases between layers the output of the jth processing element

alcohol in the blends. With high percentage of alcohol in the blends, the HC, CO emissions could increase and the BTE might be slightly reduced but the use of 5% blends could reduce the HC and CO emissions as well. Ozsezen and Canakci [17] discussed the performance and exhaust emissions of a vehicle fuelled with low content alcohol (ethanol and methanol) blends and pure gasoline. The test results indicated that when the vehicle was fuelled with alcoholegasoline blends, the peak wheel power and fuel consumption slightly increased. And also, in general, alcohole gasoline blends provided higher combustion efficiency compared to pure gasoline use. Generally, the alcoholegasoline blends at all vehicle speeds provided slightly higher combustion efficiency relative to pure gasoline. The best combustion efficiency was obtained with the use of M5 and E10. In exhaust emission results, a stable trend was not seen, especially for CO emission. But, on average, alcoholegasoline blends exhibited decreasing HC emissions. Artificial neural network is fairly simple and small in size when compared to the human brain, and has some powerful knowledge and information processing characteristics due to its similarity to the human brain [18]. ANN (artificial neural network) is one such effort, and is now progressively utilised 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 required time and resources [19]. In recent years, with the developments in computer technology, ANN has been applied to many automotive engineering problems with some degree of success. In automotive engineering, neural networks have been applied to different engine investigations such as prediction of exhaust emissions and modelling of engine performance [20e23]. Najafi et al. [24] developed an ANN model to predict a correlation between brake power, torque, brake specific fuel consumption, brake thermal efficiency, volumetric efficiency, and emission components using different gasolineeethanol blends and speeds as inputs data. A standard Back-Propagation algorithm for the engine was used in this model. It was observed that the ANN model can predict engine performance and exhaust emissions with R2 (determination coefficient) in the range of 0.97e1. MRE (mean relative errors) values were in the range of 0.46e5.57%, while RMSE (root mean square error) were found to be very low. Ghobadian et al. [25] developed

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2. Material and method

Table 1 Technical specifications of the test engine.

2.1. Experimental setup and measurements

Item description

Stery

Engine type Work type Cylinder number Total cylinder volume Idling speed Cylinder diameter Piston stroke length Compression ratio Maximum torque Maximum power Cooling type

Ford-Escort 4-Stroke 4-Cylinder, sequence type 1297 cc 900 rpm 80.978 mm 62.99 mm 8:1 80 Nm (3000 rpm) 40 kW (5500 rpm) Water cooled

an ANN model to estimate diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel. It was observed that the ANN model can predict the engine performance and exhaust emissions quite well with correlation coefficients of 0.948, 0.999, 0.929 and 0.999 for the engine torque, BSFC, CO and HC emissions, respectively. Another study conducted by Deh Kiani et al. [26] dealt with an ANN modelling of a spark ignition engine to predict the engine brake power, output torque and exhaust emissions (CO, CO2, NOx and HC) of the engine. Results showed that the ANN provided the best accuracy in modelling the emission indices with correlation coefficient equal to 0.98, 0.96, 0.90 and 0.71 for CO, CO2, HC and NOx, and 0.99 and 0.96 for torque and brake power respectively. In this study, the changes in exhaust emissions and engine performance have been observed by using methanol and gasoline as fuel without any modifications on a spark ignition engine; and the impact of the fuel on exhaust emissions and break specific fuel consumption have been examined. An ANN model was also developed by considering the fuel type, torque, engine speed and fuel flow in the input layer. By this way, prediction of some parameters such as CO, HC, BSFC and AFR (airefuel ratio) was aimed.

The engine used in this experiment was a 4 cylinder, 4 strokes, and 1.3 L volume Ford-Escort automobile engine. In the hydraulic brakes, a DPX1A type braking system which has 100 kW power and 750 rpm rates that can rise up to 200 Nm torque maximum was used. The technical specifications of the test engine are given in Table 1. Electronic mean rotation tachometer was used for measuring engine rotation. The sensitivity of the device for measuring number of rotation is 0.04 rpm. During experiments, the engine inlet and outlet temperatures of engine cooling water, the temperature of the motor oil in the pan, the insert temperature for every cylinder, temperature of exhaust gases, the temperature of fuel at the inlet of carburettor, and the temperature of the testing environment were measured by three different thermometers, namely digital, mechanical and mercurial. The measurement ranges of digital, mechanical and mercurial thermometers were 230  C O 127  C, 0  C O 100  C and 0  C O 250  C respectively. The measurement sensitivities of the digital, mechanical and mercurial thermometers were 0.01  C, 1  C and 1  C, respectively. SUN SGA-900 Model digital exhaust analysis device was used for measuring exhaust gas emissions. The technical characteristic of the exhaust emission device is given in Table 1. Valve adjustments, point gapping, ignition advance adjustment, plug gapping, measurements of compression pressures of each cylinder, and measurement of their ignition voltages were conducted according to the catalogue values of the engine and tests were initiated. In the experiments, the rotation value of the engine was kept at 1100 rpm, and the engine was loaded at 5e70 Nm range by means of hydraulic dynamometer. Loading was continued at intervals until the engine reached vibration running limits. The engine rotation was increased in equal intervals of 300 rpm at 1100e4300 rpm range and loading operations were repeated at each rotation. Engine test mechanism can be seen in Fig. 1.

Fig. 1. The schematic layout of the experimental setup.

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The intervals within which the engine showed regular operation behaviour, environment, fresh insert, exhaust, motor oil, water inlet, outlet temperatures, and atmospheric pressure, oil pressure, mean relative humidity, skewed manometer deviation installed at the air tank, fuel flow rate, torque value shown by dynamometer, motor rotation value and exhaust emission values were observed as test values. All the measurements were collected and recorded. The test results obtained from the experimental study were used to train and test the ANN. 2.2. Artificial neural networks ANNs are a logic programming technique developed with the purpose of automatically performing skills such as learning, remembering, deciding, and inference, which are features of the human brain, without receiving any aid. By simply imitating the operation of the human brain, ANNs have various important features, such as learning from data, generalisation, working with an infinite number of variables, etc. The smallest units that form the basis of the operation of ANNs are called artificial neural cells. The artificial neural cells consist of mainly five elements; namely, inputs, weights, summation functions, activation functions and outputs (Fig. 2). ANN has three main layers; namely, the input, hidden and output layers. The inputs are data from the external world. Neurons (processing elements) in the input layer transfer data from the external world to the hidden layer. The data in the input layer is not processed in the same way as the data in the other layers. The weights are the values of connections between cells. The outputs are produced using data from neurons in the input and hidden layers, and the bias, summation and activation functions. The summation function is a function which calculates the net input of the cell. The summation function used in this study is given in Eq. (1).

NETi ¼

n X

wij xj þ wbi

(1)

j¼1

The activation function provides a curvilinear match between the input and output layers. In addition, it determines the output of the cell by processing the net input to the cell. The selection of an appropriate activation function significantly affects network performance. There are many ways to define the activation function, such as the threshold function, step activation function, sigmoid function, and hyperbolic tangent function. The type of activation function depends on the type of neural network to be designed. A

sigmoid function is widely used for the transfer function. Logistic transfer function of the ANN model in this study is given in Eq. (2). In the output layer, the output of network is produced by processing data from hidden layer and sent to external world.

f ðNETi Þ ¼

1 1 þ eNETi

(2)

The significant advantages of artificial neural networks are learning ability and the use of different learning algorithms. The most important factor which determines its success in practise, after the selection of ANN architecture, is the learning algorithm. In order to obtain the output values closest to the numerical values, the best learning algorithm and the number of optimum neurons in the hidden layer must be determined. In the training stage, to obtain the output precisely, the number of neurons in the hidden layer was increased step by step (i.e. 5e15). For this purpose, BFGS (Quasi-Newton back propagation), LM (LevenbergeMarquardt learning algorithm), RP (resilient back propagation) and SCG (scaled conjugate gradient learning algorithm) learning algorithms were used in the building of the network structure. As a result of the conducted trials, the best learning algorithms for CO, HC, BSFC and AFR were found to be the LM, RP, SCG and BFGS learning algorithms respectively. The best network structures for CO, HC, BSFC and AFR were also found to be 4-7-1, 4-14-1, 4-7-1 and 4-11-1 respectively (Table 2). The best ANN architecture built for the prediction of carbon monoxide is shown in Fig. 3. The mean prediction accuracy represents validity of prediction. It is calculated by the formula in Eq. (3). Also, the mean prediction accuracies of CO, HC, BSFC and AFR for four learning algorithms are given in Table 2.

MPA ¼ 100  MEP

(3)

In this study, 70 experimental data sets were prepared for the training and testing data for the ANN. The ratio for training and testing data was selected as 85%:15%, i.e. 10 and 60 sets of the experimental data were randomly selected for the testing data and training data, respectively. In the back propagation model, the scaling of inputs and outputs dramatically affects the performance of an ANN. As mentioned above, the logistic sigmoid transfer function was used in this study. One of the characteristics of this function is that only a value between 0 and 1 can be produced. The input and output data sets were normalised before the training and testing process. In this study, the input and output values were normalised between 0 and 1 to obtain the optimal predictions. Fuel type, engine speed, torque, fuel flow, carbon

Fig. 2. The structure of an artificial neural cell.

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Table 2 Prediction accuracy values for CO, HC, BSFC and AFR using four different learning algorithms. Learning algorithm

Network structure

Prediction accuracy (%) CO

SCG SCG SCG SCG SCG SCG SCG SCG SCG SCG SCG LM LM LM LM LM LM LM LM LM LM LM BFGS BFGS BFGS BFGS BFGS BFGS BFGS BFGS BFGS BFGS BFGS RP RP RP RP RP RP RP RP RP RP RP

4-5-1 4-6-1 4-7-1 4-8-1 4-9-1 4-10-1 4-11-1 4-12-1 4-13-1 4-14-1 4-15-1 4-5-1 4-6-1 4-7-1 4-8-1 4-9-1 4-10-1 4-11-1 4-12-1 4-13-1 4-14-1 4-15-1 4-5-1 4-6-1 4-7-1 4-8-1 4-9-1 4-10-1 4-11-1 4-12-1 4-13-1 4-14-1 4-15-1 4-5-1 4-6-1 4-7-1 4-8-1 4-9-1 4-10-1 4-11-1 4-12-1 4-13-1 4-14-1 4-15-1

HC

BSFC

AFR

Training set

Testing set

Training set

Testing set

Training set

Testing set

Training set

Testing set

90.89 93.09 91.97 93.44 92.52 96.06 95.59 94.51 97.24 95.69 96.96 93.86 92.76 95.08 96.33 97.02 98.98 97.56 98.54 98.80 98.59 98.69 92.13 93.24 93.05 93.62 97.13 97.71 95.39 97.05 98.13 98.51 98.69 90.78 91.76 90.11 91.86 91.25 92.05 93.08 93.04 93.01 94.00 92.89

84.50 84.37 89.37 86.13 87.88 85.59 90.06 83.32 87.13 84.93 86.56 87.97 84.26 91.44 85.98 83.96 82.57 85.02 83.10 81.60 84.38 82.85 88.93 88.45 90.66 88.17 86.92 86.18 84.56 77.84 76.58 74.92 75.07 87.59 90.47 89.67 87.37 88.05 89.00 89.07 85.46 87.71 84.82 86.28

94.63 96.16 97.23 96.37 97.82 97.71 97.85 97.57 98.90 99.20 98.90 95.69 97.73 98.07 98.41 98.74 99.37 99.38 99.45 99.27 99.37 99.34 96.29 96.12 97.52 97.22 97.87 97.89 98.29 98.88 99.32 99.20 99.30 95.50 95.29 96.11 96.91 96.44 97.13 96.99 97.02 96.72 98.02 97.41

93.20 94.53 93.66 94.89 93.48 94.43 96.26 91.59 93.37 92.16 91.87 94.15 95.05 95.14 92.11 90.33 89.47 87.07 86.62 89.82 83.99 88.24 91.97 92.00 92.78 94.52 91.89 92.38 92.73 91.66 91.43 88.80 90.26 94.33 94.45 93.76 93.08 95.13 94.46 94.08 95.85 94.99 96.48 93.90

97.34 98.06 98.59 98.48 98.63 99.03 98.68 98.48 99.01 99.02 98.57 98.05 98.49 98.57 98.74 98.77 98.78 98.95 98.97 99.08 99.12 98.91 95.04 98.02 97.84 98.69 98.56 98.76 98.83 98.95 99.01 98.99 98.83 96.14 96.90 98.02 98.18 97.74 97.80 97.99 98.26 98.29 98.67 98.05

97.03 96.91 97.40 96.08 94.75 96.38 96.14 95.41 95.60 94.21 94.03 96.05 96.79 96.11 95.37 94.71 94.56 95.88 95.35 95.58 93.62 94.83 95.12 96.56 96.45 94.58 96.13 96.72 96.87 95.22 94.44 95.63 95.63 94.02 95.74 95.57 95.71 95.57 96.51 96.66 96.68 96.27 95.54 95.21

97.22 97.29 98.42 98.59 98.40 98.35 98.43 98.67 99.11 98.90 99.19 98.37 98.49 99.22 99.09 98.81 99.24 99.40 99.22 99.29 99.25 99.26 98.17 98.09 98.45 98.71 98.77 99.17 98.93 99.21 99.31 99.31 99.35 96.88 96.84 97.71 97.61 98.18 98.27 98.27 98.17 98.49 98.19 98.30

93.12 94.00 95.95 95.25 95.08 94.97 96.31 93.43 94.68 94.53 94.06 95.03 96.00 94.77 95.51 93.46 94.01 94.81 93.48 94.30 93.77 93.97 94.62 94.93 95.44 94.58 94.56 94.35 96.72 95.28 93.29 93.59 92.87 94.73 94.56 94.76 94.59 95.87 94.80 94.11 95.09 95.08 94.97 92.52

monoxide, unburned hydrocarbon, break specific fuel consumption and airefuel ratio were normalised dividing by 5, 5000, 35, 26, 8, 1500, 4600 and 20, respectively. The digits for the fuel type to be entered into the artificial neural networks were determined

as methanol ¼ 1 and gasoline ¼ 2, because they do not have numerical values. Exhaust emissions and engine performance values predicted after ANN training were compared with the values obtained from the experimental study. The RMSE, MEP (mean error percentage) and R2 values were used for comparison. RMSE, MEP and R2 are represented in Eqs. (4)e(6).

0 11=2  X  2 1   @ tj  oj A RMSE ¼ p j   P    j tj  oj tj  100 MEP % ¼ p

R2 ¼ 1  Fig. 3. ANN architecture with a single hidden layer for carbon monoxide.

2 ! P j tj  oj P  2 j oj

(4)

(5)

(6)

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3. Results and discussion 3.1. Experimental evaluation of CO, HC, BSFC and AFR As can be seen in Fig. 4, performance values obtained when methanol was used as fuel were lower than gasoline. According to the engine performance curves, brake specific fuel consumption is more than the values where rotation values are low and high, whereas it is less in medium rotations such as 3000 rpm. This case shows the same feature for both gasoline and methanol fuels. When the engine is operated with gasoline, brake specific fuel consumption is minimum at 1800 and 3000 rpm interval, which are approximately 900 g/kWh. On the other hand, when it is operated with methanol, specific consumption is minimum at 1800e 3000 rpm intervals, which are 1800 g/kWh. As it can be seen from these values, motor performance values obtained when methanol was used as fuel became lower than gasoline for the same situation. This case is caused by the fact that evaporation heat of methanol is 3.28 times higher than the evaporation heat of gasoline, and that its

lower heating value is 2.24 times less than the lower heating value of gasoline. Therefore, when the engine is operated on methanol, its brake specific fuel consumption will be approximately two times higher than gasoline. As no modification was applied to the engine, engine performance was negatively affected as higher octane value of methanol was not benefited. Moreover, in case of insufficient intake manifold heating the fuel enters the cylinders as liquid. The negative aspects appearing can easily be seen when looking at the performance characteristic curves. Because the lower heating value of methanol is lower than the lower heating value of gasoline, if the same power and rotation value are aimed, the flow rate of the methanol sent to cylinders has to be increased. This case serves as one of the basic reasons of excessive increase in brake specific fuel consumption. With it is a high evaporation temperature, methanol is heated with the preheat process applied on suction manifold and an attempt is made to partially meet the energy needed. Although the manifold was heated from outside, it can be clearly seen from engine performance that this temperature was less than sufficient. Therefore, the

Fig. 4. Comparison of CO, HC, BSFC, and AFR values for gasoline and methanol engine.

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Table 3 Statistical data for CO, HC, BSFC and AFR using four different algorithms. Goal

Sequence

Learning algorithm

Network structure

Training set

Testing set

RMSE

R2

MEP

RMSE

R2

MEP

CO 1 2 3 4

LM BFGS RP SCG

4-7-1 4-7-1 4-6-1 4-11-1

0.020667 0.027081 0.035288 0.014658

0.996902 0.994665 0.990877 0.998443

4.922218 6.950387 8.239697 4.414095

0.049213 0.050708 0.046600 0.051715

0.977654 0.976326 0.979911 0.977706

8.564867 9.343023 9.531393 9.944888

1 2 3 4

RP SCG LM BFGS

4-14-1 4-11-1 4-7-1 4-8-1

0.012874 0.010802 0.011896 0.015316

0.999332 0.999529 0.999430 0.999054

1.975396 2.146407 1.927521 2.775601

0.018734 0.021846 0.027593 0.037676

0.998382 0.997842 0.996507 0.993543

3.515423 3.735359 4.855570 5.481618

1 2 3 4

SCG BFGS LM RP

4-7-1 4-11-1 4-6-1 4-12-1

0.007156 0.004075 0.006477 0.007890

0.999634 0.999881 0.999700 0.999555

1.413558 1.170858 1.505776 1.744768

0.010976 0.012582 0.013977 0.010183

0.998621 0.998207 0.997738 0.998841

2.595133 3.126220 3.210128 3.321998

1 2 3 4

BFGS SCG RP LM

4-11-1 4-11-1 4-9-1 4-8-1

0.006147 0.011275 0.012429 0.005067

0.999872 0.999569 0.999477 0.999913

1.073933 1.568444 1.819480 0.911500

0.032479 0.037634 0.028903 0.038115

0.996075 0.994873 0.997047 0.995011

3.277073 3.694142 4.130722 4.486403

HC

BSFC

AFR

fact that methanol which enters cylinders in liquid form cannot be burned completely and acts as the second reason for the decrease in motor performance. This case does not only lower performance, it also deteriorates the exhaust emissions. In Fig. 4, the change in exhaust CO emission values with engine rotation values can be seen. If the engine is operated with gasoline, CO emission varies between 2 and 5%, whereas when methanol is used as fuel, this rate varies between 1 and 2%. When CO emissions compared to gasoline and methanol fuels, there is a decrease by half. The BSFC and the CO values for both gasoline and methanol increase with increasing engine speed. Because of the lack of

combustion time, in these conditions, airefuel mixture combustion instabilities are the main reasons. The most prominent example of the fact that methanol enters cylinders mostly in liquid phase can be seen from HC emissions. In the case that engine runs with methanol, a significant increase is observed in HC emissions, which is one of the results of this situation. When gasoline is used as fuel, HC emissions occur at higher values such as 1300 ppm at lower speed; as the engine rotation and load increases, it reduces to the level of 700 ppm. When methanol is used as fuel, HC emissions vary between 800 ppm and 400 ppm. If sufficient pre-heating was conducted, the fuel would enter

Fig. 5. Matching of the experimental and ANN values for testing sets of CO, HC, BSFC and AFR.

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cylinders in vapour form, and HC emission values would be lower (Fig. 4). In Fig. 4, the change in AFR values with engine rotation values can be seen. If the engine is operated with gasoline, AFR varies between 13 and 15, whereas when methanol is used as fuel, this rate varies between 6 and 15. AFR compared to gasoline and methanol fuels, AFR values of gasoline are higher than methanol fuel values. Both gasoline and methanol fuels values are first decreasing but then they are nearly constant with engine speed. A stoichiometric mixture is the working point that modern engine management systems employing fuel injection attempt to achieve in light load cruise situations. For gasoline fuel, the stoichiometric airefuel mixture is approximately 14.7. The approximate mass of air is 14.7 mass of fuel. Any mixture less than 14.7 to 1 is considered to be a rich mixture; any more than 14.7 to 1 is a lean mixture. Whereas the methanol fuel stoichiometric air-to-fuel ratio is 6:1. 3.2. Prediction of engine performance and exhaust emissions using artificial neural network Artificial neural networks are an information-processing system which was inspired by biological neural networks and

CO ¼

HC ¼

In addition to the information given in Table 3, the R2 values were close to 1 for both the training and testing set. Similarly, RMSE and MEP were fairly low. In the training period, for BSFC, CO, HC and AFR, the mean relative error was found to be less than 5%. Other MEP results for the training and testing data were within acceptable error limits (5%), whereas MEP results for the carbon monoxide was about 8% for the testing data. Comparisons of the ANN predictions and experimental results for testing sets of output parameters are demonstrated in Fig. 5. It is seen that the test patterns consist of the results of 10 tests. The most striking point here is that the prediction values are very close to the experimental values. As shown in Fig. 5, the predictive ability of the network for BSFC, CO, HC and AFR was found to be satisfactory. This means that the selection of four input parameters as influencing factors for predictions of engine performance and exhaust emissions provides satisfactory results. The equations of the break specific fuel consumption, carbon monoxide, unburned hydrocarbon and airefuel ratio are given in Eqs. (7)e(10). Also, BSFC, CO, HC and AFR of the spark ignition engine which operates with methanol and gasoline fuels can be accurately calculated by these formulae.

1 1 þ eð0:47$F1þ26:5857$F2þ0:6243$F30:3029$F40:1169$F530:1875$F6þ2:5677$F7þ1:3785Þ

(7)

1 1 þ eð1:6669$F1þ0:7857$F2þ2:3483$F3þ2:3565$F4þ1:8671$F50:9765$F6þ3:7981$F71:2198$F80:7510$F91:9372$F101:2220$F112:5052$F12þ1:9013$F13þ1:8961$F143:6045Þ (8)

BSFC ¼

AFR ¼

1 1 þ eð1:3444$F1þ14:2160$F21:051$F33:7197$F4þ12:3523$F5þ2:5808$F6þ10:3370$F71:3444Þ

(9)

1 1 þ eð5:5227$F16:3870$F211:4906$F3þ3:1129$F4þ8:8843$F50:8038$F6þ1:7763$F73:9820$F84:6401$F9þ14:6868$F10þ9:6594$F110:4704Þ (10)

which includes some performance features comparable to biological neural networks. The use of an ANN model is considered as a practical and reliable approach for non-linear problems is to test the prediction ability of specific fuel consumption, air fuel ratio and exhaust emissions for a gasoline/methanol engine. The input parameters of the network are fuel type, engine speed, torque and fuel flow and its output parameters are also carbon monoxide, unburned hydrocarbon, break specific fuel consumption and aire fuel ratio. In this study, a computer program has been developed in MATLAB platform to predict CO, HC, BSFC and AFR of the engine. The optimum network structures and statistical parameters of ANN models for four learning algorithms were given in Table 3. It is apparent from Table 3, the prediction performances for both training and testing sets of HC, BSFC and AFR showed that all the approaches provide a quite satisfactory accuracy. Their R2 values are more than 0.99. R2 value of testing set of CO is only about 0.97 while R2 value of training set of CO is only more than 0.99. The best prediction results were obtained by LM, RP, SCG and BFGS learning algorithms for CO, HC, BSFC and AFR respectively. But, in general, SCG learning algorithm gave optimal or near optimal results for all engine values. The LM learning algorithm had the highest speed compared with the other learning algorithms and it reached to optimal solutions with small number of neurons in hidden layer.

where Fi (i ¼ 1, 2, 3, ..., 6 or 7) can be calculated according to Eq. (11).

Fi ¼

1 1 þ eEi

(11)

where Ei is the weighted sum of the inputs, and is calculated via the equations in Tables 4e7 respectively. The data flow was completed with the weights between the layers. The weight values of the input and hidden layers are given in the Tables 4e7. Here, the effect of the parameters that are at the input layer (fuel type, engine speed, torque, and fuel flow) on the BSFC, CO, HC and AFR can be observed. Table 4 Weights between input layer and hidden layer for carbon monoxide. Ei ¼ w1 x Ft þ w2 x N þ w3 x T þ w4 x m þ qi i

w1

w2

w3

w4

qi

1 2 3 4 5 6 7

20.5567 3.2179 8.8508 7.4488 2.3375 2.3561 3.0032

18.8115 2.6905 1.6973 5.2651 16.5006 2.5278 4.2582

43.5460 0.6381 1.6597 0.1136 34.2741 0.6399 0.9201

137.1448 2.1837 9.6879 10.3693 0.3408 2.0305 0.4256

64.8508 2.3224 1.5509 1.0095 16.1222 1.9868 0.9356

Y. Çay et al. / Energy 50 (2013) 177e186 Table 5 Weights between input layer and hidden layer for unburned hydrocarbon. Ei ¼ w1 x Ft þ w2 x N þ w3 x T þ w4 x m þ qi i

w1

w2

w3

w4

qi

1 2 3 4 5 6 7 8 9 10 11 12 13 14

2.3233 20.7017 37.6477 5.4834 20.8279 0.6687 3.1090 5.7232 2.1559 15.8216 9.5964 2.0175 5.1409 2.1905

14.8439 6.4541 1.1580 9.3051 3.4757 0.7165 0.1522 4.0079 50.9460 0.2831 1.2197 7.6085 11.3877 6.5494

8.8019 37.3731 0.5470 4.8038 12.8066 3.3239 8.3508 16.6259 1.9872 12.6757 3.8456 9.4049 0.1544 12.0538

2.0713 13.3634 10.9087 3.8739 53.1614 27.3673 2.5015 2.5595 39.0036 7.5211 5.6979 1.0777 1.7356 2.4864

7.9305 13.1148 28.9961 1.2853 0.3983 3.3342 4.3130 5.8816 12.8126 9.0443 1.9857 2.8794 9.4755 4.1366

Table 6 Weights between input layer and hidden layer for break specific fuel consumption. Ei ¼ w1 x Ft þ w2 x N þ w3 x T þ w4 x m þ qi i

w1

w2

w3

w4

qi

1 2 3 4 5 6 7

6.1367 0.9215 6.2304 8.2761 13.3334 2.0337 10.8711

8.6065 6.1443 7.5374 4.2407 9.6042 0.4886 0.8904

2.7542 9.7505 5.2516 2.2491 21.8834 0.1355 0.2756

1.1512 3.2887 2.1166 0.5642 11.6602 8.3803 2.6254

11.4027 4.3706 0.8362 1.4162 5.6802 0.3306 1.6316

185

is a good alternative to other conventional modelling techniques in the prediction of the break specific fuel consumption and exhaust emissions. According to the findings of this study, engine performance values when operated with methanol are lower than those obtained when operated with gasoline. As the higher octane values of methanol were not taken into consideration and engine’s compression rate was not modified as per this value, it affected the engine performance negatively. Nevertheless, as the heating of suction manifold is less than sufficient, methanol enters cylinders mostly in liquid phase. When the engine was operated with methanol, significant decrease was observed in the emissions compared to the gasoline case. It has been found out that the more than expected values of emission was caused by the fact that methanol entered cylinders in liquid phase due to its high evaporation heat. During experiments, methanol suction manifold was treated with pre-heat but it became obvious that this was not sufficient for the examination of performance and emission curves. That the evaporation temperature of methanol is high requires heating of methanol at suction manifold. The insufficient heating at suction manifold causes vibrated operation of the engine especially at higher rotations. When methanol is used as fuel in the spark ignition engine, sufficient heating of suction manifold is necessary in terms of motor performance and exhaust emission. In particular, during intercity travels, during when motor vehicles reach higher power rates, heating of suction manifold is even more necessary.

References

Table 7 Weights between input layer and hidden layer for air-fuel ratio. Ei ¼ w1 x Ft þ w2 x N þ w3 x T þ w4 x m þ qi i

w1

w2

w3

w4

qi

1 2 3 4 5 6 7 8 9 10 11

3.4724 0.6950 13.9449 7.6819 5.2721 0.6250 1.6734 16.8867 7.3617 0.4572 6.7352

6.0658 9.5892 8.1972 24.1519 12.4115 5.7735 21.6636 22.9654 5.8099 8.6139 4.9454

5.4282 7.2658 1.6756 0.0617 1.1646 16.3744 16.0527 1.7804 1.5742 0.7837 3.2717

4.9926 9.2055 9.9349 7.9679 7.2202 18.6978 18.8932 11.1511 6.1791 2.9229 3.8012

4.3480 24.3873 1.5099 5.4471 8.8788 16.5786 19.4814 14.2951 2.0851 4.8989 0.7176

4. Conclusions In this study, an ANN modelling was used for the prediction of the BSFC, CO, HC and AFR of a spark ignition engine which operates with methanol and gasoline fuels. For training period, different training algorithms such as BFGS, LM, RP and SCG back propagation were used. The best results in the prediction of BSFC, CO, HC and AFR were obtained by network architecture of 4-7-1, 4-7-1, 4-14-1 and 4-11-1 and the SCG, LM, RP and BFGS learning algorithms, respectively. In the ANN model, the coefficients of determination of the BSFC, CO, HC and AFR for both the training and the testing set were notably close to 1. It was determined that the ANN results obtained for the BSFC, CO, HC and AFR were within acceptable error limits (5%). These results show that the learning capacity of the ANN is quite powerful in the prediction of the BSFC, CO, HC and AFR. Therefore, the use of ANN is highly recommended for the prediction of break specific fuel consumption and exhaust emissions without conducting complicated, expensive, and timeconsuming experimental studies. This study shows that an ANN

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