Biogas engine performance estimation using ANN

Engineering Science and Technology, an International Journal

Volumn 20, Issue 6, 2017, Pages 1563-1570

  Kurtgoz, Yusuf ^a   Karagoz, Mustafa ^a   Deniz, Emrah ^a  

^a KARABUK UNIVERSITY   (Turkey)

Author keywords

ANN; Biogas; BSFC; Estimating engine performance

Indexed keywords

EID: 85044452795     PISSN: None     EISSN: 22150986     Source Type: Journal    
DOI: 10.1016/j.jestch.2017.12.010     Document Type: Article

References (34)

1. Fanchi, J.R., Energy: Technology and Directions for the Future. 2004, Elsevier Academic Press, London.
2. Boudghene Stambouli, A., Koinuma, H., A primary study on a long-term vision and strategy for the realisation and the development of the Sahara Solar Breeder project in Algeria. Renewable Sustainable Energy Rev., 2012, 591–598.
3. Yettou, F., Azoui, B., Malek, A., Gama, A., Panwar, N.L., Solar cooker realizations in actual use: an overview. Renewable Sustainable Energy Rev. 37 (2014), 288–306.
4. Gomez Montoya, J.P.G., Amell Arrieta, A.A., Apata Lopez, A.F., Spark ignition engine performance and emissions in a high compression engine using biogas and methane mixtures without knock occurrence. Therm. Sci. 19 (2015), 1919–1930.
5. Porpatham, E., Ramesh, A., Nagalingam, B., Investigation on the effect of concentration of methane in biogas when used as a fuel for a spark ignition engine. Fuel 87 (2008), 1651–1659.
6. Porpatham, E., Ramesh, A., Nagalingam, B., Effect of swirl on the performance and combustion of a biogas fuelled spark ignition engine. Energy Convers. Manage. 76 (2013), 463–471.
7. Eyidogan, M., Özsezen, A.N., Çanakci, M., Turkcan, A., Impact of alcohol-gasoline fuel blends on the performance and combustion characteristics of an SI engine. Fuel 89 (2010), 2713–2720.
8. Çelik, M.B., Experimental determination of suitable ethanol-gasoline blend rate at high compression ratio for gasoline engine. Appl. Therm. Eng. 28 (2008), 396–404.
9. Park, C., Choi, Y., Kim, C., Oh, S., Lim, G., Moriyoshi, Y., Performance and exhaust emission characteristics of a spark ignition engine using ethanol and ethanol reformed gas. Fuel 89 (2010), 2118–2125.
10. Li, J., Gong, C.M., Su, Y., Dou, H.L., Liu, X.J., Effect of injection and ignition timings on performance and emissions from a spark-ignition engine fueled with methanol. Fuel 89 (2010), 3919–3925.
11. Celikten, I., Koca, A., Arslan, M.A., Comparison of performance and emissions of diesel fuel, rapeseed and soybean oil methyl esters injected at different pressures. Renewable Energy 35 (2010), 814–820.
12. Celikten, I., An experimental investigation of the effect of the injection pressure on engine performance and exhaust emission in indirect injection diesel engines. Appl. Therm. Eng. 23 (2003), 2051–2060.
13. Çelik, M.B., Özdalyan, B., Alkan, F., The use of pure methanol as fuel at high compression ratio in a single cylinder gasoline engine. Fuel 90 (2011), 1591–1598.
14. Çay, Y., Prediction of a gasoline engine performance with artificial neural network Yapay sinir ağı ile bir benzinli motorun performans tahmini. Fuel 111 (2013), 324–331.
15. Oğuz, H., Sarıtas, İ., Baydan, H.E., Prediction of diesel engine performance using biofuels with artificial neural network. Expert Syst. Appl. 37 (2010), 6579–6586.
16. Parlak, A., Islamoglu, Y., Yasar, H., Egrisogut, A., Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a diesel engine. Appl. Therm. Eng. 26 (2006), 824–828.
17. Kapusuz, M., Ozcan, H., Yamin, J.A., Research of performance on a spark ignition engine fueled by alcoholegasoline blends using artificial neural networks. Appl. Therm. Eng. 91 (2015), 525–534.
18. Soufi, M.D., Ghobadian, B., Najafi, G., Sabzimaleki, M., Jaliliantabar, F., Performance and exhaust emissions of a SI two-stroke engine with biolubricants using artificial neural network. Energy Proc. 75 (2015), 3–9.
19. Prasada Rao, K., Babu, T.V., Anuradha, G., Appa Rao, B.V., IDI diesel engine performance and exhaust emission analysis using biodiesel with an artificial neural network (ANN). Egypt. J. Petrol. 26 (2017), 593–600.
20. Syed, J., Baig, R.U., Algarni, S., Murthy, Y.V.V.S., Masood, M., Inamurrahman, M., Artificial neural network modeling of a hydrogen dual fueled diesel engine characteristics: an experiment approach. Int. J. Hydrogen Energy 42 (2017), 14750–14774.
21. Dharma, S., Hassan, M.H., Ong, H.C., Sebayang, A.H., Silitonga, A.S., Kusumo, F., Milano, J., Experimental study and prediction of the performance and exhaust emissions of mixed Jatropha curcas-Ceiba pentandra biodiesel blends in diesel engine using artificial neural networks. J. Clean. Prod. 164 (2017), 618–633.
22. Yusaf, T.F., Buttsworth, D.R., Saleh, K.H., Yousif, B.F., CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network. Appl. Energy 87 (2010), 1661–1669.
23. Roy, S., Banerjee, R., Das, A.K., Bose, P.K., Development of an ANN based system identification tool to estimate the performance-emission characteristics of a CRDI assisted CNG dual fuel diesel engine. J. Nat. Gas Sci. Eng. 21 (2014), 147–158.
24. Bietresato, M., Calcante, A., Mazzetto, F., A neural network approach for indirectly estimating farm tractors engine performances. Fuel 143 (2015), 144–154.
25. Togun, N.K., Baysec, S., Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks. Appl. Energy 87 (2010), 349–355.
26. Sayin, C., Ertunc, H.M., Hosoz, M., Kilicaslan, I., Canakci, M., Performance and exhaust emissions of a gasoline engine using artificial neural network. Appl. Therm. Eng. 27 (2007), 46–54.
27. Turkson, R.F., Yan, F., Ali, M.K.A., Hua, J., Artificial neural network applications in the calibration of spark-ignition engines: an overview. Eng. Sci. Technol. 19 (2016), 1346–1359.
28. Kathirvel, S., Layek, A., Muthuraman, S., Exploration of waste cooking oil methyl esters (WCOME) as fuel in compression ignition engines: a critical review. Eng. Sci. Technol. 19 (2016), 1018–1026.
29. Ozgoren, M., Bilgili, M., Sahin, B., Estimation of global solar radiation using ANN over Turkey. Expert Syst. Appl. 39 (2012), 5043–5051.
30. Rehman, S., Mohandes, M., Artificial neural network estimation of global solar radiation using air temperature and relative humidity. Energy Policy 36 (2008), 571–576.
31. Sozen, A., Arcaklioglu, E., Ozalp, M., Caglar, N., Forecasting based on neural network approach of solar potential in Turkey. Renewable Energy 30 (2005), 1075–1090.
32. Chow, S.K.H., Lee, E.W.M., Li, D.H.W., Short-term prediction of photovoltaic energy generation by intelligent approach. Energy Build. (Cool Roofs, Cool Pavements, Cool Cities, and Cool World) 55 (2012), 660–667.
33. Yadav, A.K., Chandel, S.S., Solar energy potential assessment of western Himalayan Indian state of Himachal Pradesh using J48 algorithm of WEKA in ANN based prediction model. Renewable Energy 75 (2015), 675–693.
34. Yadav, A.K., Malik, H., Chandel, S.S., Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models. Renewable Sustainable Energy Revi. 31 (2014), 509–519.