Applications of artificial neural networks in prediction of performance, emission and combustion characteristics of variable compression ratio engine fuelled with waste cooking oil biodiesel

Journal of the Brazilian Society of Mechanical Sciences and Engineering

Volumn 37, Issue 3, 2015, Pages 915-928

  Muralidharan, K ^a   Vasudevan, D ^a  

^a PSNA COLLEGE OF ENGINEERING AND TECHNOLOGY   (India)

Author keywords

Artificial neural networks; Biodiesel; Combustion; Emission; Performance; Variable compression ratio engine

Indexed keywords

BACKPROPAGATION; BACKPROPAGATION ALGORITHMS; BIODIESEL; BRAKES; CARBON; CARBON DIOXIDE; CARBON MONOXIDE; COMBUSTION; ENGINE CYLINDERS; ENGINES; FORECASTING; GAS EMISSIONS; IGNITION; NEURAL NETWORKS; NEUTRON EMISSION; OIL SHALE; OILS AND FATS;

BRAKE THERMAL EFFICIENCY; COMBUSTION CHARACTERISTICS; FEED-FORWARD BACK PROPAGATION; INDICATED MEAN EFFECTIVE PRESSURE; PERFORMANCE; PREDICTION OF PERFORMANCE; SPECIFIC FUEL CONSUMPTION; VARIABLE COMPRESSION RATIO;

WASTE INCINERATION;

EID: 84928254339     PISSN: 16785878     EISSN: 18063691     Source Type: Journal    
DOI: 10.1007/s40430-014-0213-4     Document Type: Article

References (44)

1. Pramanik K (2003) Properties and use of jatropha curcas oil and diesel fuel blends in compression ignition engine. Renew Energy 28:239–248
2. Karthikeyan R, Mahalakshmi NV (2005) Performance and emission characteristics of a turpentine—diesel duel fuel engine. Energy 32:1202–1209
3. Phan AN, Phan TM (2008) Biodiesel production from waste cooking oils. Fuel 87:3490–3496
4. Huzayyin AS, Bawady AH, Rady MA, Dawood A (2004) Experimental evaluation of diesel engine performance and emission using blends of jojoba oil and diesel fuel. Energy Convers Manag 45:2093–2112
5. Pugazhvadivu M, Jeyachandran K (2005) Investigations on the performance and exhaust emissions of a diesel engine using preheated waste frying oil as fuel. Renew Energy 30:2189–2202
6. Rakopoulos CD, Antonopoulos KA, Rakopoulos DC, Hountalas DT, Giakoumis EG (2006) Comparative performance and emissions study of a direct injection diesel engine using blends of diesel fuel with vegetable oils or bio-diesels of various origins. Energy Convers Manag 47:3272–3287
7. Hebbal OD, Vijayakumar Reddy K, Rajagopal K (2006) Performance characteristics of a diesel engine with Deccan hemp oil. Fuel 85:2187–2194
8. Sureshkumar K, Velraj R, Ganesan R (2008) Performance and exhaust emission characteristics of a CI engine fueled with Pongamia pinnata methyl ester (PPME) and its blends with diesel. Renew Energy 33:2294–2302
9. Devan PK, Mahalakshmi NV (2009) A study of performance, emission and combustion characteristics of a compression ignition engine using methyl ester of paradise oil—eucalyptus oil blends. Appl Energy 86:675–680
10. Singh Pranil J, Khurma Jagjit, Singh Anirudh (2010) Preparation, characterization, engine performance and emission characteristics of coconut oil based hybrid fuels. Renew Energy 35:2065–2070
11. Aydin H, Bayindir H (2010) Performance and emission analysis of cottonseed oil methyl ester in a diesel engine. Renew Energy 35:588–592
12. Shanmugam P, Sivakumar V, Murugesan A, Umarani C (2011) Experimental study on diesel engine using hybrid fuel blends. Int J Green Energy 8(6):655–668
13. Senthil Kumar M, Ramesh A, Nagalingam B (2003) An experimental comparison of methods to use methanol and Jatropha oil in compression ignition engine. Biomass Bioenergy 25:309–318
14. Forson FK, Oduro EK, Hammond-Donkoh E (2004) Performance of jatropha oil blends in a diesel engine. Renew Energy 29:1135–1145
15. Ramdhas AS, Jeyaraj S, Muraleedharan C (2004) Use of vegetable oils as IC engines fuels—a review. Renew Energy 29:727–742
16. Lakshmi Narayana Rao G, Durga Prasad B, Sampath S, Rajagopal K (2007) Combustion analysis of diesel engine fueled with jatropha oil methyl ester—diesel blends. Int J Green Energy 4(6):645–655
17. Murugesan A, Umarani C, Subramanian R, Nedunchezhian N (2009) Bio-diesel as an alternative fuel for diesel engines—a review. Renew Sustain Energy Rev 13:653–662
18. Enweremadu CC, Rutto HL (2010) Combustion, emission and engine performance characteristics of used cooking oil biodiesel—a review. Renew Sustain Energy Rev 14:2863–2873
19. Xue J, Grift TE, Hansen AC (2011) Effect of biodiesel on engine performances and emissions. Renew Sustain Energy Rev 15:1098–1116
20. Satyanarayana M, Muraleedharan C (2010) Methyl ester production from rubber seed oil using two-step pretreatment process. Int J Green Energy 7(1):84–90
21. Kalogirou Soteris A (2001) Artificial neural networks in renewable energy systems applications: a review. Renew Sustain Energy Rev 5:376–401
22. Kalogirou SA (2003) Artificial intelligence for the modeling and control of combustion processes: a review. Prog Energy Combust Sci 29:515–566
23. Canakci M, Erdil A, Arcaklioglu E (2006) Performance and exhaust emissions of a biodiesel engine. Appl Energy 83:594–605
24. Canakci M, Ozsezen AN, Arcaklioglu E, Erdil A (2009) Prediction of performance and exhaust emissions of a diesel engine fuelled with biodiesel produced from waste frying palm oil. Expert Syst Appl 36:9268–9280
25. Ramdhas AS, Jayaraj S, Muraleedharan C, Padmajumari K (2006) Artificial neural networks used for the prediction of the cetane number of biodiesel. Renew Energy 31:2524–2533
26. Ghobadian B, Rahimi H, Nikbakht AM, Najafi G, Yusaf TF (2009) Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network. Renew Energy 34:976–982
27. Najafi G, Ghobadian B, Tavakoli T, Buttsworth DR, Yusaf TF, Faizollahnejad M (2009) Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network. Appl Energy 86:630–639
28. Rajendra M, Jena PC, Raheman H (2009) Prediction of optimized pretreatment process parameters for biodiesel production using ANN and GA. Fuel 88:868–875
29. Yusaf TF, Buttsworth DR, Saleh KH, Yousif BF (2010) CNG-Diesel engine performance and exhaust emission analysis with the aid of artificial neural network. Appl Energy 87:1661–1669
30. Oguz H, Sarıtas I, Baydan HE (2010) Prediction of diesel engine performance using biofuels with artificial neural network. Expert Syst Appl 37:6579–6586
31. Kiani Deh Kiani M, Ghobadian B, Tavakoli T, Nikbakht AM, Najafi G (2010) Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol–gasoline blends. Energy 35:65–69
32. Shivakumar, Srinivasa Pai P, Shrinivasa Rao BR (2011) 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. Appl Energy 88:2344–2354
33. Raheman H, Ghadge SV (2008) Performance of diesel engine with biodiesel at varying compression ratio and ignition timings. Fuel 87:2659–2666
34. Muralidharan K, Vasudevan D (2011) Performance, emission and combustion characteristics of a variable compression ratio engine using methyl esters of waste cooking oil and diesel blends. Appl Energy 88:3959–3968
35. Muralidharan K, Vasudevan D, Sheeba KN (2011) Performance, emission and combustion characteristics of biodiesel fuelled variable compression ratio engine. Energy 36:5384–5392
36. Panwar NL, Shrirame Hemant Y, Rathore NS, Jindal Sudhakar, Kurchania AK (2010) Performance evaluation of a diesel engine fueled with methyl ester of castor seed oil. Appl Therm Eng 30:245–249
37. Arul Mozhi Selvan V, Anand RB, Udayakumar M (2009) Combustion characteristics of diesohol using Bio diesel as an additive in a direct injection ignition engine under various compression ratios. Energy Fuels 23:5413–5422
38. Cybenko G (1989) Approximations of superposition of sigmoidal function. Math Control Signal Syst 2:303–314
39. Hornik K, Stinchcombe M, White H (1989) Multilayer feed-forward networks are universal approximations. Neural Netw 5(5):359–366
40. Barron AR (1993) Universal approximations bound for super positions of sigmoidal functions. IEEE Trans Inf Theory 39:930–945
41. Stern HS (1996) Neural networks in applied statistics. Technometrics 38(3):205–220
42. Vasudevan D, Selladurai V, Nagaraj P (2004) Determination of closed form solution for acceptance sampling using ANN. Qual Assur 11:43–61
43. Kwok TY, Yeung DY (1997) Constructive algorithms for structure learning in feed-forward neural network for regression problems. IEEE Trans Neural Netw 8:654–662
44. Baum EB, Haussler D (1989) What size nets gives valid generalization? Neural Comput 1:151–160