Journals Books 2687-5527
Latest Issue Archive Future Issues About Us
Conference Proceedings

SETSCI - Volume 3 (2018)
ISAS2018-Winter - 2nd International Symposium on Innovative Approaches in Scientific Studies, Samsun, Turkey, Nov 30, 2018

Engine performance and exhaust gas temperature modeling in a diesel engine with butanol-diesel fuel blends: Comparison of artificial neural networks and regression analysis
Samet Gürgen1*, İsmail Altın2
1Iskenderun Teknik University, Hatay, Turkey
2Karadeniz Teknik University, Trabzon, Turkey
* Corresponding author:
Published Date: 2019-01-14   |   Page (s): 163-166   |    293     12

ABSTRACT In this study, the effect of using a mixture of butanol and diesel fuel in a small diesel engine on engine performance
and exhaust emissions is modeled. A comparison has been made with artificial neural networks, which is an up-to-date method,
and multiple linear regression methods. Motor performance parameters, torque, effective power and brake specific fuel
consumption are used as dependent variables. In addition, the exhaust gas temperature, which is an important parameter in the
engines, is taken as another dependent variable. In this study, independent variables are selected as butanol-diesel fuel mixture
ratio and engine speed. In order to compare two different modeling techniques, the mean squared error and mean absolute percent
error are calculated. As a result of this study, artificial neural networks give better results than multiple linear regression
techniques for four different dependent variables.  
KEYWORDS Diesel engine, butanol, engine performance, exhaust temperature, artificial neural network, multiple linear regression
REFERENCES [1] Gurgen, S., B. Unver, and I. Altin, Experimental Investigation On Cyclic Variability, Engine Performance, And Exhaust Emissions In A Diesel Engine Using Alcohol-Diesel Fuel Blends. Thermal Science, 2017. 21(1B): p. 581-589.
[2] Şahin, Z. and O.N. Aksu, Experimental investigation of the effects of using low ratio n-butanol/diesel fuel blends on engine performance and exhaust emissions in a turbocharged DI diesel engine. Renewable Energy, 2015. 77: p. 279-290.
[3] Kumar, S., et al., Advances in diesel–alcohol blends and their effects on the performance and emissions of diesel engines. Renewable and Sustainable Energy Reviews, 2013. 22: p. 46-72.
[4] Şahin, Z., O. Durgun, and O.N. Aksu, Experimental investigation of nbutanol/diesel fuel blends and n-butanol fumigation–evaluation of engine performance, exhaust emissions, heat release and flammability analysis. Energy Conversion and Management, 2015. 103: p. 778-789.
[5] Abu-Qudais, M., O. Haddad, and M. Qudaisat, The effect of alcohol fumigation on diesel engine performance and emissions. Energy conversion and management, 2000. 41(4): p. 389-399.
[6] Liu, H., et al., Experimental and simulation investigation of the combustion characteristics and emissions using n-butanol/biodiesel dual-fuel injection on a diesel engine. Energy, 2014. 74: p. 741-752.
[7] Zhu, Y., Z. Chen, and J. Liu, Emission, efficiency, and influence in a diesel n-butanol dual-injection engine. Energy conversion and management, 2014. 87: p. 385-391.
[8] Doğan, O., The influence of n-butanol/diesel fuel blends utilization on a small diesel engine performance and emissions. Fuel, 2011. 90(7): p. 2467-2472.
[9] Rakopoulos, D., et al., Combustion heat release analysis of ethanol or n-butanol diesel fuel blends in heavy-duty DI diesel engine. Fuel, 2011. 90(5): p. 1855-1867.
[10] Roy, S., R. Banerjee, and P.K. Bose, Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network. Applied Energy, 2014. 119: p. 330-340.
[11] Çay, Y., et al., Prediction of engine performance for an alternative fuel using artificial neural network. Applied Thermal Engineering, 2012. 37: p. 217-225.
[12] Parlak, A., et al., Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a Diesel engine. Applied Thermal Engineering, 2006. 26(8-9): p. 824-828.
[13] Gürgen, S., B. Ünver, and İ. Altın, Prediction of cyclic variability in a diesel engine fueled with n-butanol and diesel fuel blends using artificial neural network. Renewable Energy, 2018. 117: p. 538-544.
[14] Gülüm, M., F.K. Onay, and A. Bilgin, Comparison of viscosity prediction capabilities of regression models and artificial neural networks. Energy, 2018. 161: p. 361-369.
[15] de Oliveira, F.M., et al., Predicting Cetane Index, Flash Point, and Content Sulfur of Diesel–Biodiesel Blend Using an Artificial Neural Network Model. Energy & Fuels, 2017. 31(4): p. 3913-3920.
[16] Piloto-Rodríguez, R., et al., Prediction of the cetane number of biodiesel using artificial neural networks and multiple linear regression. Energy Conversion and Management, 2013. 65: p. 255-261.
[17] Meng, X., M. Jia, and T. Wang, Neural network prediction of biodiesel kinematic viscosity at 313K. Fuel, 2014. 121: p. 133-140.
[18] Çelebi, K., et al., Experimental and artificial neural network approach of noise and vibration characteristic of an unmodified diesel engine fuelled with conventional diesel, and biodiesel blends with natural gas addition. Fuel, 2017. 197: p. 159-173.
[19] Yang, F., et al., Artificial neural network (ANN) based prediction and optimization of an organic Rankine cycle (ORC) for diesel engine waste heat recovery. Energy Conversion and Management, 2018. 164: p. 15-26.
[20] Kshirsagar, C.M. and R. Anand, Artificial neural network applied forecast on a parametric study of Calophyllum inophyllum methyl ester-diesel engine out responses. Applied energy, 2017. 189: p. 555- 567

SET Technology - Turkey

eISSN  : 2687-5527    

E-mail :
+90 533 2245325

Tokat Technology Development Zone Gaziosmanpaşa University Taşlıçiftlik Campus, 60240 TOKAT-TURKEY
©2018 SET Technology