Research Article
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Comparative analysis of various modelling techniques for emission prediction of diesel engine fueled by diesel fuel with nanoparticle additives

Year 2017, Volume: 1 Issue: 1, 15 - 23, 20.03.2017
https://doi.org/10.26701/ems.320490

Abstract

In this study, emissions of compression ignition engine fueled by diesel fuel with nanoparticle additives  was  modeled  by  regression  analysis,  artificial  neural  network  (ANN)  and  adaptive neuro  fuzzy  inference  system  (ANFIS)  methods.  Cetane  number  (CN)  and  engine  speed (rpm) were selected as input parameters for estimation of carbon monoxide (CO), oxides of nitrogen (NOx), and carbon dioxide (CO2) emissions. The results of estimation techniques were compared with each other and they showed that regression analysis was not accurate enough for prediction. On the other hand, ANN and ANFIS modelling techniques gave more accurate results with respect to regression analysis; linear and non-linear. Especially ANFIS models can be suggested as estimation method with minimum error compared to experimental results. 

References

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  • Özkan, İ.A., Ciniviz, M., and Candan, F. (2015). Estimating Engine Performance and Emission Values Using ANFIS/ANFIS Kullanılarak Motor Performans ve Emisyon Değerleri Tahmini. International Journal of Automotive Engineering and Technologies, vol. 4, no. 1, pp. 63-67.
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  • Tabari, H., Kisi, O., Ezani, A., and Talaee, P.H. (2012). SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment. Journal of Hydrology, vol. 444, pp. 78-89. 10.1016/j.jhydrol.2012.04.007
  • Bilgili, M., Sahin, B., Yasar, A., and Simsek, E. (2012). Electric energy demands of Turkey in residential and industrial sectors. Renewable and Sustainable Energy Reviews, vol. 16, no. 1, pp. 404-414. 10.1016/j.rser.2011.08.005
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Year 2017, Volume: 1 Issue: 1, 15 - 23, 20.03.2017
https://doi.org/10.26701/ems.320490

Abstract

References

  • Tosun, E., Yilmaz, A.C., Ozcanli, M., and Aydin, K. (2014). Determination of effects of various alcohol additions into peanut methyl ester on performance and emission characteristics of a compression ignition engine. Fuel, vol. 126, pp. 38-43. 10.1016/j.fuel.2014.02.037
  • Hussain, J., Palaniradja, K., Alagumurthi, N., and Manimaran, R. (2012). Effect of exhaust gas recirculation (EGR) on performance and emission characteristics of a three cylinder direct injection compression ignition engine. Alexandria Engineering Journal, vol. 51, no. 4, pp. 241-247. 10.1016/j.aej.2012.09.004
  • Ismail, H.M., Ng, H.K., Queck, C.W., and Gan, S. (2012). Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends. Applied Energy, vol. 92, pp. 769-777. 10.1016/j.apenergy.2011.08.027
  • Jahirul, M., Saidur, R., Masjuki, H., Kalam, M., and Rashid, M. (2009). Application of artificial neural networks (ANN) for prediction the performance of a dual fuel internal combustion engine. HKIE Transactions, vol. 16, no. 1, pp. 14-20.
  • Yusaf, T., Yousif, B., and Elawad, M. (2011). Crude palm oil fuel for diesel-engines: experimental and ANN simulation approaches. Energy, vol. 36, no. 8, pp. 4871-4878. 10.1016/j.energy.2011.05.032
  • Shanmugam, P., Sivakumar, V., Murugesan, A., and Ilangkumaran, M. (2011). Performance and exhaust emissions of a diesel engine using hybrid fuel with an artificial neural network. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 33, no. 15, pp. 1440-1450.
  • Ghazikhani, M. and Mirzaii, I. (2011). Soot emission prediction of a waste-gated turbo-charged DI diesel engine using artificial neural network. Neural Computing and Applications, vol. 20, no. 2, pp. 303-308. 10.1007/s00521-010-0500-7
  • Hosoz, M., Ertunc, H.M., Karabektas, M., and Ergen, G. (2013). ANFIS modelling of the performance and emissions of a diesel engine using diesel fuel and biodiesel blends. Applied Thermal Engineering, vol. 60, no. 1, pp. 24-32. 10.1016/j.applthermaleng.2013.06.040
  • Isin, O. and Uzunsoy, E.U. (2013). Predicting the Exhaust Emissions of a Spark Ignition Engine Using Adaptive Neuro-Fuzzy Inference System. Arabian Journal for Science and Engineering, vol. 38, no. 12, pp. 3485-3493. 10.1007/s13369-013-0637-7
  • Özkan, İ.A., Ciniviz, M., and Candan, F. (2015). Estimating Engine Performance and Emission Values Using ANFIS/ANFIS Kullanılarak Motor Performans ve Emisyon Değerleri Tahmini. International Journal of Automotive Engineering and Technologies, vol. 4, no. 1, pp. 63-67.
  • Al-Hinti, I., Samhouri, K., Al-Ghandoor, A., and Sakhrieh, A. (2009). The effect of boost pressure on the performance characteristics of a diesel engine: A neuro-fuzzy approach. Applied Energy, vol. 86, no. 1, pp. 113-121. 10.1016/j.apenergy.2008.04.015
  • Özgür, T. (2011) Investigation of nanoparticle additives to the biodiesel and diesel fuels for improvement of the performance and exhaust emissions in a compression ignition engine, M.Sc. Thesis, Mechanical Engineering, Cukurova University.
  • Tabari, H., Kisi, O., Ezani, A., and Talaee, P.H. (2012). SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment. Journal of Hydrology, vol. 444, pp. 78-89. 10.1016/j.jhydrol.2012.04.007
  • Bilgili, M., Sahin, B., Yasar, A., and Simsek, E. (2012). Electric energy demands of Turkey in residential and industrial sectors. Renewable and Sustainable Energy Reviews, vol. 16, no. 1, pp. 404-414. 10.1016/j.rser.2011.08.005
  • Krauss, G., Kindangen, J.I., and Depecker, P. (1997). Using artificial neural networks to predict interior velocity coefficients. Building and Environment, vol. 32, no. 4, pp. 295-303. 10.1016/S0360-1323(96)00059-5
  • Haykin, S.S., (2001) Neural networks: a comprehensive foundation. Tsinghua University Press.
  • Karimi, S., Kisi, O., Shiri, J., and Makarynskyy, O. (2013). Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia. Computers & Geosciences, vol. 52, pp. 50-59. 10.1016/j.cageo.2012.09.015
  • Jang, J.S.R. (1993). Anfis - Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems Man and Cybernetics, vol. 23, no. 3, pp. 665-685. Doi 10.1109/21.256541
There are 18 citations in total.

Details

Primary Language English
Subjects Mechanical Engineering
Journal Section Research Article
Authors

Erdi Tosun

Tayfun Ozgur

Ceyla Ozgur This is me

Mustafa Ozcanli

Hasan Serin

Kadir Aydin

Publication Date March 20, 2017
Published in Issue Year 2017 Volume: 1 Issue: 1

Cite

APA Tosun, E., Ozgur, T., Ozgur, C., Ozcanli, M., et al. (2017). Comparative analysis of various modelling techniques for emission prediction of diesel engine fueled by diesel fuel with nanoparticle additives. European Mechanical Science, 1(1), 15-23. https://doi.org/10.26701/ems.320490

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