Mustafa Kemal BALKİ, Cenk SAYIN, Volkan ÇAVUŞ, İsmail Umut DURAN, Resul TUNA

Experimental Study and Prediction of Performance and Emission in an SI Engine Using Alternative Fuel with Artificial Neural Network

In this study, firstly, the effect of compression ratio (CR), air excess coefficient (AEC) and ignition timing (IGT) on the performance and exhaust emissions in a spark ignition (SI) engine fueled with pure ethanol was experimentally investigated. The engine tests were conducted for four varied CR, three types of AEC and three different IGT at 2400 rpm, and the performance and exhaust emission of the engine were recorded. The results obtained from the pure ethanol were compared with those of the gasoline. Secondly, the performance and exhaust emission of SI engine for the same test conditions were estimated with a backpropagation artificial neural network (ANN) model. The ANN model was created using the C# programming language. The ANN model was trained with the data obtained from the experimental study. The engine torque, brake specific fuel consumption (BSFC), hydrocarbon (HC) emission and carbon dioxide (CO2) emission were estimated by the backpropagation ANN model. When the experimental data were compared with the predicted values, it was seen that the error percentages of the difference were acceptable. When the results were generally evaluated, it was observed that was improved of the performance and exhaust emissions as using pure ethanol at lowering the IGT and increasing the CR. In addition, it is seen that the ANN model can be used to estimate the performance and emissions of an SI engine using alternative fuels.

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B. Dhinesh, R. Niruban Bharathi, J. Isaac JoshuaRamesh Lalvani, M. Parthasarathy, K. Annamalai. An experimental analysis on the influence of fuel borne additives on the single cylinder diesel engine powered by Cymbopogon flexuosus biofuel. Journal of the Energy Institute. 90 (2017) 634-45.
M.K. Balki, C. Sayin. The effect of compression ratio on the performance, emissions and combustion of an SI (spark ignition) engine fueled with pure ethanol, methanol and unleaded gasoline. Energy. 71 (2014) 194-201.
M.K. Balki, C. Sayin, M. Canakci. The effect of different alcohol fuels on the performance, emission and combustion characteristics of a gasoline engine. Fuel. 115 (2014) 901-6.
M.K. Balki, C. Sayin, M. Sarıkaya. Optimization of the operating parameters based on Taguchi method in an SI engine used pure gasoline, ethanol and methanol. Fuel. 180 (2016) 630-7.
C.A. Srinivasan, C. Saravanan. Study of combustion characteristics of an SI engine fuelled with ethanol and oxygenated fuel additives. Journal of Sustainable Energy & Environment. 1 (2010) 85-91.
S. Yousufuddin. Experimental study on combustion duration and performance characteristics of a hydrogen-ethanol dual fueled engine. International Journal of Automotive Engineering and Technologies. 5 (2016) 85-101.
M. Parthasarathy, J. Isaac JoshuaRamesh Lalvani, B. Dhinesh, K. Annamalai. Effect of hydrogen on ethanol–biodiesel blend on performance and emission characteristics of a direct injection diesel engine. Ecotoxicol Environ Saf. 134 (2016) 433-9.
S. Dharma, M.H. Hassan, H.C. Ong, A.H. Sebayang, A.S. Silitonga, F. Kusumo, et al. 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. Journal of Cleaner Production. 164 (2017) 618-33.
C. Sayin, H.M. Ertunc, M. Hosoz, I. Kilicaslan, M. Canakci. Performance and exhaust emissions of a gasoline engine using artificial neural network. Appl Therm Eng. 27 (2007) 46-54.
M.B. Celik. Experimental determination of suitable ethanol–gasoline blend rate at high International Journal of Automotive Engineering and Technologies, IJAET 7 (1) [2018] 58-64 64 compression ratio for gasoline engine. Appl Therm Eng. 28 (2008) 396-404.
M. Koç, Y. Sekmen, T. Topgül, H.S. Yücesu. The effects of ethanol–unleaded gasoline blends on engine performance and exhaust emissions in a spark-ignition engine. Renew Energ. 34 (2009) 2101-6.
R.C. Costa, J.R. Sodré. Compression ratio effects on an ethanol/gasoline fuelled engine performance. Appl Therm Eng. 31 (2011) 278-83.
H.S. Yücesu, A. Sozen, T. Topgül, E. Arcaklioğlu. Comparative study of mathematical and experimental analysis of spark ignition engine performance used ethanol–gasoline blend fuel. Appl Therm Eng. 27 (2007) 358-68.
H.S. Yücesu, T. Topgül, C. Çinar, M. Okur. Effect of ethanol–gasoline blends on engine performance and exhaust emissions in different compression ratios. Appl Therm Eng. 26 (2006) 2272-8.
R. Daniel, G. Tian, H. Xu, S. Shuai. Ignition timing sensitivities of oxygenated biofuels compared to gasoline in a directinjection SI engine. Fuel. 99 (2012) 72-82.
T. Topgül, H.S. Yücesu, C. Çinar, A. Koca. The effects of ethanol–unleaded gasoline blends and ignition timing on engine performance and exhaust emissions. Renew Energ. 31 (2006) 2534-42.
C.-W. Wu, R.-H. Chen, J.-Y. Pu, T.-H. Lin. The influence of air–fuel ratio on engine performance and pollutant emission of an SI engine using ethanol–gasoline-blended fuels. Atmos Environ. 38 (2004) 7093-100.
C. Park, Y. Choi, C. Kim, S. Oh, G. Lim, Y. Moriyoshi. Performance and exhaust emission characteristics of a spark ignition engine using ethanol and ethanol-reformed gas. Fuel. 89 (2010) 2118-25.
I. Schifter, L. Diaz, R. Rodriguez, J.P. Gómez, U. Gonzalez. Combustion and emissions behavior for ethanol–gasoline blends in a single cylinder engine. Fuel. 90 (2011) 3586-92.
K. Prasada Rao, T. Victor Babu, G. Anuradha, B.V. Appa Rao. IDI diesel engine performance and exhaust emission analysis using biodiesel with an artificial neural network (ANN). Egyptian Journal of Petroleum. 26 (2017) 593-600.
S. Gürgen, B. Ünver, İ. Altın. Prediction of cyclic variability in a diesel engine fueled with n-butanol and diesel fuel blends using artificial neural network. Renew Energ. 117 (2018) 538-44.
J. Rezaei, M. Shahbakhti, B. Bahri, A.A. Aziz. Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks. Appl Energ. 138 (2015) 460-73.
MERC. Product specification; Germany. 2017.
H. Soukht Saraee, H. Taghavifar, S. Jafarmadar. Experimental and numerical consideration of the effect of CeO2 nanoparticles on diesel engine performance and exhaust emission with the aid of artificial neural network. Appl Therm Eng. 113 (2017) 663-72.
V. Cavus, A. Tuncer. An Application Interface Design for Backpropagation Artificial Neural Networks. ICCDMME 2015 International Conference On Computer Sciance, Data Mining and Mechanical Engineering, Bangkok (Thailand), 2015. pp. 100-3.