Skip to main content

Advertisement

Log in

Prediction of performance, combustion and emission characteristics of diesel-thermal cracked cashew nut shell liquid blends using artificial neural network

  • Research Article
  • Published:
Frontiers in Energy Aims and scope Submit manuscript

Abstract

This paper explores the use of artificial neural networks (ANN) to predict performance, combustion and emissions of a single cylinder, four stroke stationary, diesel engine operated by thermal cracked cashew nut shell liquid (TC-CNSL) as the biodiesel blended with diesel. The tests were performed at three different injection timings (21°, 23°, 25°CA bTDC) by changing the thickness of the advance shim. The ANN was used to predict eight different engine-output responses, namely brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), exhaust gas temperature (EGT), carbon monoxide (CO), oxide of nitrogen (NO x ), hydrocarbon (HC), maximum pressure (P max) and heat release rate (HRR). Four pertinent engine operating parameters, i.e., injection timing (IT), injection pressure (IP), blend percentage and pecentage load were used as the input parameters for this modeling work. The ANN results show that there is a good correlation between the ANN predicted values and the experimental values for various engine performances, combustion parameters and exhaust emission characteristics. The mean square error value (MSE) is 0.005621 and the regression value of R 2 is 0.99316 for training, 0.98812 for validation, 0.9841 for testing while the overall value is 0.99173. Thus the developed ANN model is fairly powerful for predicting the performance, combustion and exhaust emissions of internal combustion engines.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Vedharaj S, Vallinayagam R, Yang WM, Chou S K, Chua K J E, Lee P S. Performance emission and economic analysis of preheated CNSL biodiesel as an alternate fuel for a diesel engine. International Journal of Green Energy, 2015, 12(4): 359–367

    Article  Google Scholar 

  2. Vedharaj S, Vallinayagam R, YangWM, Chou S K, Chua K J E, Lee P S. Experimental and finite element analysis of a coated diesel engine fuelled by cashew nut shell liquid biodiesel. Experimental Thermal and Fluid Science, 2014, 53: 259–268

    Article  Google Scholar 

  3. Vallinayagam R, Vedharaj S, Yang W M, Saravanan C G, Lee P S, Chua K J E, Chou S K. Impact of ignition promoting additives on the characteristics of a diesel engine powered by pine oil-diesel blend. Fuel, 2014, 117: 278–285

    Article  Google Scholar 

  4. Shivakumar, Srinivasa Pai P, Shrinivasa Rao B R. Artificial neural network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings. Applied Energy, 2011, 88(7): 2344–2354

    Google Scholar 

  5. Çay Y, Korkmaz I, Çiçek A, Kara F. Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network. Energy, 2013, 50: 177–186

    Article  Google Scholar 

  6. Mohamed Ismail H, Ng H K, Queck C W, Gan S. Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends. Applied Energy, 2012, 92: 769–777

    Google Scholar 

  7. Betiku E, Omilakin O R, Ajala S O, Okeleye A A, Taiwo A E, Solomon B O. Mathematical modeling and process parameters optimization studies by artificial neural network and response surface methodology: a case of non-edible neem (Azadirachta indica) seed oil biodiesel synthesis. Energy, 2014, 72: 266–273

    Article  Google Scholar 

  8. Arumugam S, Sriram G, Shankara Subramanian P R. Application of artificial to predict the performance and exhaust emissions of diesel engine using rapeseed oil methyl ester. Procedia Engineering, 2012, 38: 853–860

    Article  Google Scholar 

  9. 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. Renewable Energy, 2009, 34(4): 976–982

    Article  Google Scholar 

  10. Moradi G R, Dehghani S, Khosravian F, Arjmandzadeh A. The optimized operational conditions for biodiesel production from soybean oiland application of artificial neural networks for estimation of the biodiesel yield. Renewable Energy, 2013, 50: 915–920

    Article  Google Scholar 

  11. Najafi G, Ghobadian B, Tavakoli T, Buttsworth D R, Yusaf T F, Faizollahnejad M. Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network. Applied Energy, 2009, 86(5): 630–639

    Article  Google Scholar 

  12. Çelikten I, Mutlu E, Solmaz H. Variation of performance and emission characteristics of a diesel engine fueled with diesel, rapeseed oil and hazelnut oil methyl ester blends. Renewable Energy, 2012, 48: 122–126

    Article  Google Scholar 

  13. Parlak A, Islamoglu Y, Yasar H, Egrisogut A. Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a diesel engine. Applied Thermal Engineering, 2006, 26(8–9): 824–828

    Article  Google Scholar 

  14. Sayin C, Ertunc H M, Hosoz M, Kilicaslan I, Canakci M. Performance and exhaust emissions of a gasoline engine using artificial neural network. Applied Thermal Engineering, 2007, 27 (1): 46–54

    Article  Google Scholar 

  15. Canakci M, Erdil A, Arcaklioglu E. Performance and exhaust emissions of a biodiesel engine. Applied Energy, 2006, 83(6): 594–605

    Article  Google Scholar 

  16. Çelik V, Arcaklioglu E. Performance maps of a diesel engine. Applied Energy, 2005, 81(3): 247–259

    Article  Google Scholar 

  17. Can O, Celikten I, Usta N. Effects of ethanol addition on performance and emissions of a turbocharged indirect injection diesel engine running at different injection pressures. Energy Conversion and Management, 2004, 45(15–16): 2429–2440

    Article  Google Scholar 

  18. 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. Applied Energy, 2010, 87(5): 1661–1669

    Article  Google Scholar 

  19. Oguz H, Saritas I, Baydan H E. Prediction of diesel engine performance using biofuels with artificial neural network. Expert Systems with Applications, 2010, 37(9): 6579–6586

    Article  Google Scholar 

  20. Kiani Deh Kiani M, 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 ethanolgasoline blends. Energy, 2010, 35(1): 65–69

    Article  Google Scholar 

  21. Sayin C, Ertunc H M, Hosoz M, Kilicaslan I, Canakci M. Performance and exhaust emissions from a gasoline engine using artificial neural network. Applied Thermal Engineering, 2007, 27 (1): 46–54

    Google Scholar 

  22. Canakci M, Ozsezan A N, Arcaklioglu E, Erdil A. Predication of performance and exhaust emissions of a diesel engine fuelled with biodiesel produced from waste frying palm oil. Expert Systems with Applications, 2009, 86: 630–639

    Google Scholar 

  23. Velmurugan A, Loganathan M, James Gunasekran E. Experimental investigations on combustion, performance and emission characteristics of thermal cracked cashew nut shell liquid (TC-CNSL)-diesel blends in a diesel engine. Fuel, 2014, 132: 236–245

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Arunachalam Velmurugan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Velmurugan, A., Loganathan, M. & Gunasekaran, E.J. Prediction of performance, combustion and emission characteristics of diesel-thermal cracked cashew nut shell liquid blends using artificial neural network. Front. Energy 10, 114–124 (2016). https://doi.org/10.1007/s11708-016-0394-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11708-016-0394-x

Keywords

Navigation