Abstract
This paper presents a fuzzy logic-based prediction method to reveal the performance and emission characteristics of a single cylinder spark ignition (SI) engine, which uses different fuel mixtures (gasoline–water macro-emulsions, which contains isopropanol). Adaptive neuro-fuzzy inference system, ANFIS, was used to determine some characteristic parameters due to the combustion, such as exhaust emissions (CO, CO2, HCs). Experimental data such as engine power, torque, engine speed, brake mean effective pressure, brake specific fuel consumption were used as training and checking inputs for the ANFIS model to provide a predictive algorithm. The main purpose of this study is to provide a reliable model that can reveal different performance characteristics, which can be obtained from various gasoline–water macro-emulsions and doing this by the elimination of new experiments. The preliminary results show that an acceptable ANFIS model can also be used in experimental design procedures, by providing quick data handling and the results.
Similar content being viewed by others
References
Isin, O.: The experimental investigation of the gasoline–water emulsion fuels—their effect on spark ignition engine performance and exhaust emissions. Ph.D. Thesis, Dept. of Mech. Eng., Yildiz Technical University, Turkey (2000)
Isin, O.; Ergenc, A.T.; Ozkan, M.; Deniz, O.: The experimental investigation of isopropanol addition to the gasoline-water motor fuel blends on SI engine performance. 2nd Int. Conf. Appl. Therm. Proc. ATC’05 Istanbul, Turkey, pp. 425–429 (2005)
Peters, B.; Stebar, R.: Water–gasoline fuels—their effect on spark ignition engine emissions and performance. SAE Tech. Pap. 760547. doi:10.4271/760547 (1976)
Tsao, K.; Wang, C.; Miller, E.: Performance of gasoline-water fuel in a modified SI engine. SAE Tech. Pap. 841399, doi:10.4271/841399 (1984)
Togun, N.; Baysec, S.: Genetic programming approach to predict torque and brake specific fuel consumption of a gasoline engine. Appl. Energ. 87(11), 3401–3407 (2010)
Gabrys, B.; Howlett, R.J.; Jain, L.C.: Fuzzy and neuro-fuzzy techniques for modelling and control. Lect. Notes in Comp. Sci. 4251, 1206–1215. doi:10.1007/11892960_145 (2006)
Nguyen, H.H.; A Neural Fuzzy Approach to Modelling the Thermal Behavior of Power Transformers. MEng. Thesis, Sch. of Electrical Eng., Victoria University, Australia (2007)
Lee, S.H.; Howlett, R.J.; Walters, S.D.; Crua, C.: Fuzzy logic and neuro-fuzzy modelling of diesel spray penetration. Lect. Notes in Comp. Sci. 3682, 642–650. doi:10.1007/11552451_88 (2005)
Gopalakrishnan K.; Mudgal A.;Hallmark S.: Neuro-fuzzy approach to predictive modeling of emissions from biodiesel powered transit buses. Transport 26(4), 344–352 (2011)
Zhang, Q.; Tian, D.: Study of cws/diesel dual fuel engine emissions by means of rbf neural network. Chengdu, China. doi:10.1109/APPEEC.2010.5449248 (2010)
Obodeh, O.; Ajuva, C.I.:Evaluation of artificial neural network performance in predicting diesel engine NOx emissions. Eur. J. Sci. Res. 33(4), 642–653 (2009)
Karri, V.; Ho, T.N.: Predictive models for emission of hydrogen powered car using various artificial intelligent tools. Neural Comp. Appl. 18, 469–476. doi:10.1007/s00521-008-0218-y (2009)
Ghobadian, B.; Rahimi, H.; Nikbakht, A.M.; Najafi, G.; Yusaf, T.F.: Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network. Renew. Energ. 34(4), 976–982. doi:10.1016/j.renene.2008.08.008 (2009)
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. Energ. 87, 1661–1669. doi:10.1177/0957650911402546 (2010)
Kiani, M.K.D.; Ghobadian, B.; Tavakoli, T.; Nikbakht, A.M.; Najafi, G.: Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol–gasoline blends. Energ. 35, 65–69. doi:10.1016/j.energy.2009.08.034 (2010)
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, 46–54. doi:10.1016/j.applthermaleng.2006.05.016 (2007)
Yucesu, H.S.; Sozen, A.; Topgul, T.; Arcaklioglu, E.: Comparative study of mathematical and experimental analysis of spark ignition engine performance used ethanol–gasoline blend fuel. Appl. Therm. Eng. 27, 358–368 (2007)
Arcaklioglu, E.; Celikten, I.: A diesel engine’s performance and exhaust emissions. Appl. Energ. 80, 11–22. doi:10.1016/j.apenergy.2004.03.004 (2005)
Tutuncu, K.; Allahverdi, N.: Reverse modeling of a diesel engine performance by FCM and ANFIS. Int. Conf. Comp. Sys. Tech. CompSysTech’07. doi:10.1145/1330598.1330634 (2007)
Al-Hinti, I.; Samhouri, M.; Al-Ghandoor, A.; Sakhrieh, A.: The effect of boost pressure on the performance characteristics of a diesel engine: A neuro-fuzzy approach. Appl. Energ. 86(1), 113–121 (2009)
Li, H.; Chen, C.L.P.; Huang, H.P.: Fuzzy Neural Intelligent Systems: Mathematical Foundations and the Applications in Engineering. CRC Press, ISBN 0849323606 (2000)
Jang, J.S.R.; Sun, C.: Neuro-fuzzy modelling and control. Proc IEEE 83(3), 378–407 (1995)
Kalogirou, S.A.: Artificial intelligence for the modeling and control of combustion processes: a review. Prog. Energ. Combust. 29, 515–556 (2003)
Jang, J.S.R.: Input selection for ANFIS learning. Fuzz Syst. 2, 1493–1499 doi:10.1109/FUZZY.1996.552396 (1996)
Rezaeeian, A.; Koma, A.Y.;Shasti, B.; Doosthoseini, A.: ANFIS modeling and feedforward control of shape memory alloy actuators. Int. J. Math. Mod. Meth. Appl. Sci. 2(2), 228–235 (2008)
Sivara, O.; Brevern, P.: GUI based ANFIS modeling: backpropagation optimization method for CO2 laser machining. Int. J. Intell. Inf. Tech. Appl. 2(4), 191–198 (2009)
Matlab, Documentation files. http://www.mathworks.com/help/toolbox/nnet/. Accessed 20 November 2011 (2011)
Jain, S.; Khare, M.: Adaptive neuro-fuzzy modelling for prediction of ambient CO concentration at urban intersections and roadways. Air Qual Atmos. Health, 3, 203–212. doi:10.1007/s11869-010-0073-8 (2010)
Jassar, S.; Lian, Z.; Zaiyi, L.; Ng, K.L.R.: Parameter selection for training process of neuro-fuzzy systems for average air temperature estimation. Mechatr. Autom. ICMA 09, China. doi:10.1109/ICMA.2009.5246537 (2009)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Isin, O., Uzunsoy, E. Predicting the Exhaust Emissions of a Spark Ignition Engine Using Adaptive Neuro-Fuzzy Inference System. Arab J Sci Eng 38, 3485–3493 (2013). https://doi.org/10.1007/s13369-013-0637-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-013-0637-7