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Soot emission prediction of a waste-gated turbo-charged DI diesel engine using artificial neural network

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Abstract

This study is about soot emission prediction of a waste-gated turbo-charged DI diesel engine using artificial neural network (ANN). For training the ANN model, six ranges of experimental data in previous study were used, and one range of data was kept for testing the accuracy of ANN predictions. The input parameters for the ANN are inlet manifold pressure, inlet manifold temperature, inlet air mass flow rate, fuel consumption, engine torque, and speed. Output parameter is the density of soot in the exhaust. The results show the ANN approach can be used to accurately predict soot emission of a turbo-charged diesel engine in different opening ranges of waste-gate (ORWG). Root mean-squared error (RMSE), fraction of variance (R 2), and mean absolute percentage error (MAPE) for predictions were found to be 1.19 (mg/m3), 0.9998, and 6.4%, respectively.

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Abbreviations

ANN:

Artificial neural network

C f :

Power correction factor

Eq. ratio:

Equivalence ratio

LM:

Levenberg–Marquardt

MAPE:

Mean absolute percentage error

MLP:

Multilayer perceptron

\( \dot{m}_{\text{f}} \) :

Fuel mass flow rate

\( \dot{m}_{\text{soot}} \) :

Soot mass flow rate

N:

Engine speed

ORWG:

Opening range of waste gate

P b,s :

Engine brake power

P exhaust :

Exhaust pressure

P in :

Inlet manifold pressure

RMSE:

Root mean-squared error

R 2 :

Fraction of variance

SCG:

Scaled conjugate gradient

T:

Engine torque

T exhaust :

Exhaust temperature

T 0 :

Ambient temperature

ρsoot :

Exhaust soot density

References

  1. George S, Balla S, Gautam V, Gautam M (2007) Effect of diesel soot on lubricant oil viscosity. USA J Tribol Int 40:809–818

    Article  Google Scholar 

  2. Tree DR, Svensson KI (2007) Soot processes in compression ignition engines. Progr Energy Combust Sci 33:272–309

    Article  Google Scholar 

  3. Pickett LM, Siebers DL (2003) Soot in diesel fuel jets: effect of ambient temperature, ambient density, and injection pressure. Combust Flame 138:114–135

    Article  Google Scholar 

  4. Ghazikhani M, Davarpanah M, Moosavi Shayegh SA (2008) An experimental study on the effects of different opening ranges of waste-gate on the exhaust soot emission of a turbo-charged DI diesel engine. Energy Convers Manag 49:2563–2569

    Article  Google Scholar 

  5. Haykin S (1999) Neural networks a comprehensive foundation. Prentice Hall, Englewood Cliffs

    MATH  Google Scholar 

  6. Yuanwang D, Meilin Z, Dong X, Xiaobei C (2002) An analysis for effect of cetane number on exhaust emissions from engine with the neural network. Fuel 81:1963–1970

    Article  Google Scholar 

  7. Çelik V, Arcaklioglu E (2005) Performance maps of a diesel engine. Appl Energy 81:247–259

    Article  Google Scholar 

  8. Durán A, Lapuerta M, Rodríguez-Fernández J (2005) Neural networks estimation of diesel particulate matter composition from transesterified waste oils blends. Fuel 84:2080–2085

    Article  Google Scholar 

  9. Gölcü M, Sekmen Y, Erduranlı P, Sahir Salman M (2005) Artificial neural-network based modeling of variable valve-timing in a spark-ignition engine. Appl Energy 81:187–197

    Article  Google Scholar 

  10. Canakci M, Erdil A, Arcaklioğlu E (2006) Performance and exhaust emissions of a biodiesel engine. Appl Energy 83:594–605

    Article  Google Scholar 

  11. Sayin C, Metin Ertunc H, Hosoz M, Kilicaslan I, Canakci M (2007) Performance and exhaust emissions of a gasoline engine using artificial neural network. Appl Therm Eng 27:46–54

    Article  Google Scholar 

  12. Atashkari K, Nariman-Zadeh N, Gölcü M, Khalkhali A, Jamali A (2007) Modelling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms. Energy Conserv Manag 48:1029–1041

    Article  Google Scholar 

  13. Ashhab MSS (2008) Fuel economy and torque tracking in camless engines through optimization of neural networks. Energy Conserv Manag 49:365–372

    Article  Google Scholar 

  14. Hu YH, Hwang J-N (2001) Handbook of neural network signal processing. CRC Press, New York

    Google Scholar 

  15. Hagan MT, Demuth HB, Beale M (2002) Neural network design. China Machine Press, China

  16. Beale R, Jackson T (1998) Neural computing: an introduction. Institute of Physics Publishing, Bristol

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Authors and Affiliations

  1. Department of Mechanical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    M. Ghazikhani & I. Mirzaii

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  1. M. Ghazikhani
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  2. I. Mirzaii
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Correspondence to M. Ghazikhani.

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Ghazikhani, M., Mirzaii, I. Soot emission prediction of a waste-gated turbo-charged DI diesel engine using artificial neural network. Neural Comput & Applic 20, 303–308 (2011). https://doi.org/10.1007/s00521-010-0500-7

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  • DOI: https://doi.org/10.1007/s00521-010-0500-7

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