RESEARCH ARTICLE

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

  • Arunachalam VELMURUGAN ,
  • Marimuthu LOGANATHAN ,
  • E. James GUNASEKARAN
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  • Department of Mechanical Engineering, Annamalai University, Annamalainagar 608002, India

Received date: 24 Apr 2015

Accepted date: 28 Aug 2015

Published date: 29 Feb 2016

Copyright

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg

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 (NOx), hydrocarbon (HC), maximum pressure (Pmax) 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 R2 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.

Cite this article

Arunachalam VELMURUGAN , Marimuthu LOGANATHAN , E. James GUNASEKARAN . Prediction of performance, combustion and emission characteristics of diesel-thermal cracked cashew nut shell liquid blends using artificial neural network[J]. Frontiers in Energy, 2016 , 10(1) : 114 -124 . DOI: 10.1007/s11708-016-0394-x

1
Vedharaj S, Vallinayagam R, Yang W M, 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

DOI

2
Vedharaj S, Vallinayagam R, Yang W M, 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

DOI

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

DOI

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): 2344–2354nbsp;

DOI

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: 177–186nbsp;

DOI

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

DOI

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

DOI

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 

DOI

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

DOI

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

DOI

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

DOI

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 

DOI

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): 824–828nbsp;

DOI

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

DOI

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

DOI

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

DOI

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 

DOI

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

DOI

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

DOI

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 ethanol-gasoline blends. Energy, 2010, 35(1): 65–69nbsp;

DOI

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

DOI

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

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 

DOI

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