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Performance and Exhaust Emissions of a SI Two-stroke Engine with Biolubricants Using Artificial Neural Network
Available online at www.sciencedirect.com
ScienceDirect Energy Procedia 75 (2015) 3 – 9
The 7th International Conference on Applied Energy – ICAE2015
Performance and Exhaust Emissions of a SI Two-stroke Engine with Biolubricants Using Artificial Neural Network Masoud Dehghani Soufia, Barat Ghobadiana*, Gholamhassan Najafia, Mohammadreza Sabzimalekia, Farzad Jaliliantabara a
Department of Mechanics of Biosystem Engineering, Tarbia tModares University, Tehran, Iran.
Abstract Biolubricants are a new generation of renewable and eco-friendly vegetable-based lubricants which have attracted a lot of attention in recent years. In this research study a back-propagation neural network model has been developed for predicting the effect of different types of biolubricants on the performance and exhaust emissions of a 200 cc two stroke engine. The inputs of the model are lubricant type, lambda and engine speed. Model outputs are: engine brake power, torque, BSFC (brake specific fuel consumption) and exhaust emissions which include CO, CO2, UHC, O2 and NOx emissions. Engine’s brake power, torque and brake specific fuel consumption, as well as exhaust emissions have been predicted with the Artificial Neural Network (ANN) model. The relationship between input parameters and engine performance and emissions are determined using the network. The application of ANNs are highly recommended to predict the IC engine parameters under investigation.
©©2015 Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license 2015The The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/or peer-review under responsibility of ICAE Peer-review under responsibility of Applied Energy Innovation Institute Keywords: Artificial neural network; Biolubricant; Two-stroke engine; Engine oil; Engine performance; emissions.
1. Introduction Two-stroke engines are an idea specific to simple light weight engines which have been mostly used in portable engines and motorcycles [1,2]. The mixing of fuels and lubricants is one of the main reasons for the increase of emissions of these engines [3]. The lubrication of two-stroke gasoline engines is always done with mixing the lubricant with gasoline in the proper ratio. Conventionally, the lubricant has been
1876-6102 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of Applied Energy Innovation Institute doi:10.1016/j.egypro.2015.07.127
4
Masoud Dehghani Soufi et al. / Energy Procedia 75 (2015) 3 – 9
made up of petroleum based oils, additives of performance enhancers, and one solvent to improve the mixing of gasoline and lubricant [4]. Two major short comings of vegetable oils to be used as biolubricants are the high pour point and low oxidation stability, which are revisable through chemical modification [5]. Natural fatty acid oils such as castor oil, palm oil, rapeseed oil, soybean oil, sunflower oil, and tallow oil have been used in lubricants for years[6-8]. Castor oil has previously been used as a lubricant and can be replaced by petroleum-based lubricants because of the high pour point and low oxidation stability [9,10]. Palm oil is another vegetable oil which is used widely as biolubricants feedstock [11]. Among different methods such as Transesterification, hydrogenation, epoxidation, etc., transesterification with branched polyols like Trimethylolpropane (TMP) is one of the most common methods of chemical modification [12]. Sivasankaran (1988), produced mixtures of two-stroke engine oils based on the vegetable oil jojoba and examined the chemical and physical characteristics, fatigue, wear, and sediments at the engine [13]. Zhou and Ye (1998) tested two types of new two-stroke engine oils on gasoline burning scooters where oxygen additives and catalysts had been used [14]. Singh (2011) produced a 2T engine oil from castor plant oil through epoxidation and found that this engine oil reduces more than 50 percent of smoke compared to petroleum-based two-stroke engine oils [15]. ANNs are good for tasks involving incomplete data sets [16, 17]. In several research papers, the researchers have used the ANN modeling technique on the internal combustion engines [18]. Traver et al. [19] investigate ANN ability to predicting emission parameters of an engine. Dehkiani et al. [20] studied ANN to predict a diesel engine brake power, output torque and exhaust emissions. Rahimi-Ajdadi and AbbaspourGilandeh[21] used ANN to estimating fuel consumption. Yuan wang et. al. [22] presented a neural network model that predicts the exhaust emissions from an engine using the total cetane number. In addition, some other researcher used ANN algorithm to evaluating the engine parameters [23-26]. In this study, ANN was proposed to determine the performance and emissions of single cylinder SI engine which lubricated by biolubricants. This will perform by using a group of characteristic engine operating parameters as the ANN inputs.
2. Materials and Methods 2.1 Experimental investigation In this research, the two-stroke 200 cc SI engine was used under examination (Table 1). To measure the performance parameters of the engine, an eddy current dynamometer with the power of 15kW was applied (Fig. 1a). Two-stroke engine oil produced solely by Pars Oil Company and industrial oil SAE10 (for industrial use) were the petroleum-based lubricants used in the test. Castor oil, waste cooking oil and
5
Masoud Dehghani Soufi et al. / Energy Procedia 75 (2015) 3 – 9
palm oil based biolubricants were produced by means of transesterification with polyol Trimethylolpropane (Fig. 1b). They were used as a lubricant mixed with gasoline in the engine test (Fig. 1c). The features of each of the lubricants used in the test are given in Table 2. Table 1.The features of two-stroke engines used in the test. Engine type
2T Vespa with three orifice
Number of cylinders
Single-Cylinder
Engine Size
200 cc
Cooling system
Air cooling
Lubrication System
Mixed with fuel
Ignition system
CDI
b
a
c Fig. 1. (a)Engine test stand and the applied dynamometer, (b) Biolubricant synthesis process, (c) Different kind of biolubricants, Table 2. Physical features of of the lubricants used in the test. Density Details
at 15° C (gr/cm³)
Test Standard (ASTM)
D1298
Viscosity Index (VI) D2270
Viscosity at 100°C (cSt) D445
Viscosity at 40°C (cSt) D445
Two-stroke engine oil
0.883
95
9
71.73
SAE 10 oil
0.846
250
18.16
79.21
Castor oil based Biolubricant
0.953
82
8.67
75.82
Palm oil based Biolubricant
0.9058
390
4.90
12.67
Waste cooking oil based biolubricant
0.8316
166
2.67
8.04
6
Masoud Dehghani Soufi et al. / Energy Procedia 75 (2015) 3 – 9
Flowtronic 205 fuel gauge system was utilized to measure brake specific fuel consumption. This equipment had been connected to an auxiliary fuel tank containing biolubricant and gasoline with the mixing ratio of 1:10 (10% biolubricant). A mixing ratio of 10% was selected in consultation with experts in the laboratory and from the standard range of gasoline and lubricant mixing ratio. Measuring the emissions from the engine exhaust was done by the exhaust emission analyser MGT5.
3. Results and discussions The aim of using the ANN model as a practical approach, is to test the ability to predict performance and exhaust emissions of the tested two-stroke SI engine using biolubricants. Statistical values obtained for the network is given in Table 3. it was shown that R2 are close to 1. Predictive ability of the network for different parameters was found to be satisfactory. This is same as result of previus research works [26]. Figures 2-5 show the regression analysis between the network response (outputs) and the corresponding targets. A comparison between the predicted and the measured values of measured parameter are depicted in these figures. There is a good agreement between the predicted values using the neural network model and the measured values obtained from experimental tests (figures 2-5). The results show that the ANN is an appropriate technique, which can accurately predict performance and emissions.
Table 3. Statistical data for the effective power for different parameters. Parameter
CO
CO2
HC
O2
NOx
Power
Torque
BSFC
R2 train
0.89
0.92
0.97
0.99
0.87
0.99
0.95
0.93
0.90
0.98
0.98
0.99
0.88
0.99
0.93
0.76
2
R test
Fig .2. Comparison of Experimental and ANN Outputs for CO2.
Masoud Dehghani Soufi et al. / Energy Procedia 75 (2015) 3 – 9
Fig.3. Comparison of Experimental and ANN Outputs for HC.
Fig. 4. Comparison of Experimental and ANN Outputs for Power.
Fig.5. Comparison of Experimental and ANN Outputs for Torque.
5. Conclusions A multilayer perceptron neural network model with a 3–25–8 (number of input layer–hidden layer– output layer nodes) configuration was developed for performance (Power, torque and BSFC) and
7
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Masoud Dehghani Soufi et al. / Energy Procedia 75 (2015) 3 – 9
emissions (CO, CO2, HC, O2 and NOx) in a Diesel engine. Using some of the experimental data for training, an ANN model based on standard back propagation algorithm for the engine was developed. Then, the performance of the ANN was measured by comparing it's results with the experimental results. The results may easily be considered to be within the acceptable limits. This study shows that, as an alternative to classical modeling techniques, the ANN approach can be used to accurately predict the problems of internal combustion engines. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26.
Heywood, J.B., Internal combustion engine fundamentals. Vol. 930. 1988: Mcgraw-hill New York. Heywood, J.B. and E. Sher, The two-stroke cycle engine. Warrendale, PA: Society of Automotive Engineers, 1999. 472, 1999. Smith, A., Assessment of particulate sampling for two-stroke vehicles. AEA Technology Report submitted to the World Bank, Washington, DC, 2000. stroke 2 Stroke Exhaust Pollution: How It Happens. 2010. Salimon, J., N. Salih, and E. Yousif, Biolubricants: Raw materials, chemical modifications and environmental benefits. European journal of lipid science and technology, 2010. 112(5): p. 519-530. Rousseau, D., Chemical and physical properties of canola and rapeseed oil. Rapeseed and Canola Oil: Production, Processing, Properties and Uses, 2004: p. 79. Rudnick, L.R., Synthetics, mineral oils, and bio-based lubricants: chemistry and technology. 2013: CRC press. Syahrullail, S., et al., Experimental evaluation of palm oil as lubricant in cold forward extrusion process. International journal of mechanical sciences, 2011. 53(7): p. 549-555. Kirk, R.E. and D.F. Othmer, Encyclopedia of Chemical Technology. vol. 14. 1955. Burhoe, B., Uses for Castor Oil as a Lubricant, 2014: http://www.ehow.com/list_7483979_uses-castor-oil-lubricant.html. Salimon, J. and N. Salih, Chemical modification of oleic acid oil for biolubricant industrial applications. AIBAS, 2010. 4(7): p. 1999-2003. Nie, J., Synthesis and evaluation of polyol based biolubricants from vegetable oils. 2012. Sivasankaran, G., et al., Jojoba-oil-based two-stroke gasoline engine lubricant. Tribology international, 1988. 21(6): p. 327-333. Zhou, W. and S.-h. Ye, Effects of two new lubricants on the mutagenicity of scooter exhaust particulate matter. Mutation Research/Genetic Toxicology and Environmental Mutagenesis, 1998. 414(1): p. 131-137. Singh, A., Castor oil-based lubricant reduces smoke emission in two-stroke engines. Industrial crops and products, 2011. 33(2): p. 287-295. Kalogirou, S.A., Artificial intelligence for the modeling and control of combustion processes: a review. Progress in Energy and Combustion Science, 2003. 29(6): p. 515-566. O÷uz, H., I. SarÕtas, and H.E. Baydan, Prediction of diesel engine performance using biofuels with artificial neural network. Expert Systems with Applications, 2010. 37(9): p. 6579-6586. Özener, O., L. Yüksek, and M. Özkan, Artificial neural network approach to predicting engine-out emissions and performance parameters of a turbo charged diesel engine. Thermal Science, 2012(00): p. 220-220. Traver, M.L., R.J. Atkinson, and C.M. Atkinson, Neural network-based diesel engine emissions prediction using incylinder combustion pressure. SAE transactions, 1999. 108(4): p. 1166-1180. Deh Kiani, M.K., et al., Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends. Energy, 2010. 35(1): p. 65-69. Rahimi-Ajdadi, F. and Y. Abbaspour-Gilandeh, Artificial Neural Network and stepwise multiple range regression methods for prediction of tractor fuel consumption. Measurement, 2011. 44(10): p. 2104-2111. Yuanwang, D., et al., An analysis for effect of cetane number on exhaust emissions from engine with the neural network. Fuel, 2002. 81(15): p. 1963-1970. Yusaf, T.F., et al., CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network. Applied Energy, 2010. 87(5): p. 1661-1669. Garg, A.B., P. Diwan, and M. Saxena, Artificial Neural Networks based Methodologies for Optimization of Engine Operations. International Journal of Scientific & Engineering Research, 2012. Ghobadian, B., et al., Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network. Renewable Energy, 2009. 34(4): p. 976-982. Rezaei J., Shahbakhti M., Bahri B., Abdul Aziz A. Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks, Applied Energy 2015. 138: p. 460–473
Masoud Dehghani Soufi et al. / Energy Procedia 75 (2015) 3 – 9
Biography Dr. Barat Ghobadian is associate professor in Mechanics of biosystem engineering department , Tarbiat Modares University, Tehran, Iran. He got his Ph.D. at 1994 from I.I.T Roorkee university, India. His Ph.D. thesis title was : A Parametric Study on Diesel Engine Noise. He is expert in the fields of internal combustion engines and renewable energies.
9
ScienceDirect Energy Procedia 75 (2015) 3 – 9
The 7th International Conference on Applied Energy – ICAE2015
Performance and Exhaust Emissions of a SI Two-stroke Engine with Biolubricants Using Artificial Neural Network Masoud Dehghani Soufia, Barat Ghobadiana*, Gholamhassan Najafia, Mohammadreza Sabzimalekia, Farzad Jaliliantabara a
Department of Mechanics of Biosystem Engineering, Tarbia tModares University, Tehran, Iran.
Abstract Biolubricants are a new generation of renewable and eco-friendly vegetable-based lubricants which have attracted a lot of attention in recent years. In this research study a back-propagation neural network model has been developed for predicting the effect of different types of biolubricants on the performance and exhaust emissions of a 200 cc two stroke engine. The inputs of the model are lubricant type, lambda and engine speed. Model outputs are: engine brake power, torque, BSFC (brake specific fuel consumption) and exhaust emissions which include CO, CO2, UHC, O2 and NOx emissions. Engine’s brake power, torque and brake specific fuel consumption, as well as exhaust emissions have been predicted with the Artificial Neural Network (ANN) model. The relationship between input parameters and engine performance and emissions are determined using the network. The application of ANNs are highly recommended to predict the IC engine parameters under investigation.
©©2015 Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license 2015The The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/or peer-review under responsibility of ICAE Peer-review under responsibility of Applied Energy Innovation Institute Keywords: Artificial neural network; Biolubricant; Two-stroke engine; Engine oil; Engine performance; emissions.
1. Introduction Two-stroke engines are an idea specific to simple light weight engines which have been mostly used in portable engines and motorcycles [1,2]. The mixing of fuels and lubricants is one of the main reasons for the increase of emissions of these engines [3]. The lubrication of two-stroke gasoline engines is always done with mixing the lubricant with gasoline in the proper ratio. Conventionally, the lubricant has been
1876-6102 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of Applied Energy Innovation Institute doi:10.1016/j.egypro.2015.07.127
4
Masoud Dehghani Soufi et al. / Energy Procedia 75 (2015) 3 – 9
made up of petroleum based oils, additives of performance enhancers, and one solvent to improve the mixing of gasoline and lubricant [4]. Two major short comings of vegetable oils to be used as biolubricants are the high pour point and low oxidation stability, which are revisable through chemical modification [5]. Natural fatty acid oils such as castor oil, palm oil, rapeseed oil, soybean oil, sunflower oil, and tallow oil have been used in lubricants for years[6-8]. Castor oil has previously been used as a lubricant and can be replaced by petroleum-based lubricants because of the high pour point and low oxidation stability [9,10]. Palm oil is another vegetable oil which is used widely as biolubricants feedstock [11]. Among different methods such as Transesterification, hydrogenation, epoxidation, etc., transesterification with branched polyols like Trimethylolpropane (TMP) is one of the most common methods of chemical modification [12]. Sivasankaran (1988), produced mixtures of two-stroke engine oils based on the vegetable oil jojoba and examined the chemical and physical characteristics, fatigue, wear, and sediments at the engine [13]. Zhou and Ye (1998) tested two types of new two-stroke engine oils on gasoline burning scooters where oxygen additives and catalysts had been used [14]. Singh (2011) produced a 2T engine oil from castor plant oil through epoxidation and found that this engine oil reduces more than 50 percent of smoke compared to petroleum-based two-stroke engine oils [15]. ANNs are good for tasks involving incomplete data sets [16, 17]. In several research papers, the researchers have used the ANN modeling technique on the internal combustion engines [18]. Traver et al. [19] investigate ANN ability to predicting emission parameters of an engine. Dehkiani et al. [20] studied ANN to predict a diesel engine brake power, output torque and exhaust emissions. Rahimi-Ajdadi and AbbaspourGilandeh[21] used ANN to estimating fuel consumption. Yuan wang et. al. [22] presented a neural network model that predicts the exhaust emissions from an engine using the total cetane number. In addition, some other researcher used ANN algorithm to evaluating the engine parameters [23-26]. In this study, ANN was proposed to determine the performance and emissions of single cylinder SI engine which lubricated by biolubricants. This will perform by using a group of characteristic engine operating parameters as the ANN inputs.
2. Materials and Methods 2.1 Experimental investigation In this research, the two-stroke 200 cc SI engine was used under examination (Table 1). To measure the performance parameters of the engine, an eddy current dynamometer with the power of 15kW was applied (Fig. 1a). Two-stroke engine oil produced solely by Pars Oil Company and industrial oil SAE10 (for industrial use) were the petroleum-based lubricants used in the test. Castor oil, waste cooking oil and
5
Masoud Dehghani Soufi et al. / Energy Procedia 75 (2015) 3 – 9
palm oil based biolubricants were produced by means of transesterification with polyol Trimethylolpropane (Fig. 1b). They were used as a lubricant mixed with gasoline in the engine test (Fig. 1c). The features of each of the lubricants used in the test are given in Table 2. Table 1.The features of two-stroke engines used in the test. Engine type
2T Vespa with three orifice
Number of cylinders
Single-Cylinder
Engine Size
200 cc
Cooling system
Air cooling
Lubrication System
Mixed with fuel
Ignition system
CDI
b
a
c Fig. 1. (a)Engine test stand and the applied dynamometer, (b) Biolubricant synthesis process, (c) Different kind of biolubricants, Table 2. Physical features of of the lubricants used in the test. Density Details
at 15° C (gr/cm³)
Test Standard (ASTM)
D1298
Viscosity Index (VI) D2270
Viscosity at 100°C (cSt) D445
Viscosity at 40°C (cSt) D445
Two-stroke engine oil
0.883
95
9
71.73
SAE 10 oil
0.846
250
18.16
79.21
Castor oil based Biolubricant
0.953
82
8.67
75.82
Palm oil based Biolubricant
0.9058
390
4.90
12.67
Waste cooking oil based biolubricant
0.8316
166
2.67
8.04
6
Masoud Dehghani Soufi et al. / Energy Procedia 75 (2015) 3 – 9
Flowtronic 205 fuel gauge system was utilized to measure brake specific fuel consumption. This equipment had been connected to an auxiliary fuel tank containing biolubricant and gasoline with the mixing ratio of 1:10 (10% biolubricant). A mixing ratio of 10% was selected in consultation with experts in the laboratory and from the standard range of gasoline and lubricant mixing ratio. Measuring the emissions from the engine exhaust was done by the exhaust emission analyser MGT5.
3. Results and discussions The aim of using the ANN model as a practical approach, is to test the ability to predict performance and exhaust emissions of the tested two-stroke SI engine using biolubricants. Statistical values obtained for the network is given in Table 3. it was shown that R2 are close to 1. Predictive ability of the network for different parameters was found to be satisfactory. This is same as result of previus research works [26]. Figures 2-5 show the regression analysis between the network response (outputs) and the corresponding targets. A comparison between the predicted and the measured values of measured parameter are depicted in these figures. There is a good agreement between the predicted values using the neural network model and the measured values obtained from experimental tests (figures 2-5). The results show that the ANN is an appropriate technique, which can accurately predict performance and emissions.
Table 3. Statistical data for the effective power for different parameters. Parameter
CO
CO2
HC
O2
NOx
Power
Torque
BSFC
R2 train
0.89
0.92
0.97
0.99
0.87
0.99
0.95
0.93
0.90
0.98
0.98
0.99
0.88
0.99
0.93
0.76
2
R test
Fig .2. Comparison of Experimental and ANN Outputs for CO2.
Masoud Dehghani Soufi et al. / Energy Procedia 75 (2015) 3 – 9
Fig.3. Comparison of Experimental and ANN Outputs for HC.
Fig. 4. Comparison of Experimental and ANN Outputs for Power.
Fig.5. Comparison of Experimental and ANN Outputs for Torque.
5. Conclusions A multilayer perceptron neural network model with a 3–25–8 (number of input layer–hidden layer– output layer nodes) configuration was developed for performance (Power, torque and BSFC) and
7
8
Masoud Dehghani Soufi et al. / Energy Procedia 75 (2015) 3 – 9
emissions (CO, CO2, HC, O2 and NOx) in a Diesel engine. Using some of the experimental data for training, an ANN model based on standard back propagation algorithm for the engine was developed. Then, the performance of the ANN was measured by comparing it's results with the experimental results. The results may easily be considered to be within the acceptable limits. This study shows that, as an alternative to classical modeling techniques, the ANN approach can be used to accurately predict the problems of internal combustion engines. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26.
Heywood, J.B., Internal combustion engine fundamentals. Vol. 930. 1988: Mcgraw-hill New York. Heywood, J.B. and E. Sher, The two-stroke cycle engine. Warrendale, PA: Society of Automotive Engineers, 1999. 472, 1999. Smith, A., Assessment of particulate sampling for two-stroke vehicles. AEA Technology Report submitted to the World Bank, Washington, DC, 2000. stroke 2 Stroke Exhaust Pollution: How It Happens. 2010. Salimon, J., N. Salih, and E. Yousif, Biolubricants: Raw materials, chemical modifications and environmental benefits. European journal of lipid science and technology, 2010. 112(5): p. 519-530. Rousseau, D., Chemical and physical properties of canola and rapeseed oil. Rapeseed and Canola Oil: Production, Processing, Properties and Uses, 2004: p. 79. Rudnick, L.R., Synthetics, mineral oils, and bio-based lubricants: chemistry and technology. 2013: CRC press. Syahrullail, S., et al., Experimental evaluation of palm oil as lubricant in cold forward extrusion process. International journal of mechanical sciences, 2011. 53(7): p. 549-555. Kirk, R.E. and D.F. Othmer, Encyclopedia of Chemical Technology. vol. 14. 1955. Burhoe, B., Uses for Castor Oil as a Lubricant, 2014: http://www.ehow.com/list_7483979_uses-castor-oil-lubricant.html. Salimon, J. and N. Salih, Chemical modification of oleic acid oil for biolubricant industrial applications. AIBAS, 2010. 4(7): p. 1999-2003. Nie, J., Synthesis and evaluation of polyol based biolubricants from vegetable oils. 2012. Sivasankaran, G., et al., Jojoba-oil-based two-stroke gasoline engine lubricant. Tribology international, 1988. 21(6): p. 327-333. Zhou, W. and S.-h. Ye, Effects of two new lubricants on the mutagenicity of scooter exhaust particulate matter. Mutation Research/Genetic Toxicology and Environmental Mutagenesis, 1998. 414(1): p. 131-137. Singh, A., Castor oil-based lubricant reduces smoke emission in two-stroke engines. Industrial crops and products, 2011. 33(2): p. 287-295. Kalogirou, S.A., Artificial intelligence for the modeling and control of combustion processes: a review. Progress in Energy and Combustion Science, 2003. 29(6): p. 515-566. O÷uz, H., I. SarÕtas, and H.E. Baydan, Prediction of diesel engine performance using biofuels with artificial neural network. Expert Systems with Applications, 2010. 37(9): p. 6579-6586. Özener, O., L. Yüksek, and M. Özkan, Artificial neural network approach to predicting engine-out emissions and performance parameters of a turbo charged diesel engine. Thermal Science, 2012(00): p. 220-220. Traver, M.L., R.J. Atkinson, and C.M. Atkinson, Neural network-based diesel engine emissions prediction using incylinder combustion pressure. SAE transactions, 1999. 108(4): p. 1166-1180. Deh Kiani, M.K., et al., Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends. Energy, 2010. 35(1): p. 65-69. Rahimi-Ajdadi, F. and Y. Abbaspour-Gilandeh, Artificial Neural Network and stepwise multiple range regression methods for prediction of tractor fuel consumption. Measurement, 2011. 44(10): p. 2104-2111. Yuanwang, D., et al., An analysis for effect of cetane number on exhaust emissions from engine with the neural network. Fuel, 2002. 81(15): p. 1963-1970. Yusaf, T.F., et al., CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network. Applied Energy, 2010. 87(5): p. 1661-1669. Garg, A.B., P. Diwan, and M. Saxena, Artificial Neural Networks based Methodologies for Optimization of Engine Operations. International Journal of Scientific & Engineering Research, 2012. Ghobadian, B., et al., Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network. Renewable Energy, 2009. 34(4): p. 976-982. Rezaei J., Shahbakhti M., Bahri B., Abdul Aziz A. Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks, Applied Energy 2015. 138: p. 460–473
Masoud Dehghani Soufi et al. / Energy Procedia 75 (2015) 3 – 9
Biography Dr. Barat Ghobadian is associate professor in Mechanics of biosystem engineering department , Tarbiat Modares University, Tehran, Iran. He got his Ph.D. at 1994 from I.I.T Roorkee university, India. His Ph.D. thesis title was : A Parametric Study on Diesel Engine Noise. He is expert in the fields of internal combustion engines and renewable energies.
9