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Forcasting of an ANN model for predicting behaviour of diesel engine energised by a combination of two low viscous biofuels

  • Krishnamoorthy Ramalingam
  • Annamalai Kandasamy
  • Dhinesh Balasubramanian
  • Moulik Palani
  • Thiyagarajan SubramanianEmail author
  • Edwin Geo Varuvel
  • Karthikeyan Viswanathan
Recent Advances and Novel Concepts in Environmental Technologies
  • 41 Downloads

Abstract

This study is focused on artificial neural network (ANN) modelling of non-modified diesel engine keyed up by the combination of two low viscous biofuels to forecast the parameters of emission and performance. The diesel engine is energised with five different test fuels of the combination of citronella and Cymbopogon flexuous biofuel (C50CF50) with diesel at precise blends of B20, B30, B40, B50 and B100 in which these numbers represent the contents of combination of biofuel and the investigation is carried out from zero to full load condition. The experimental result was found that the B20 blend had improved BTE at all load states compared with the remaining biofuel blends. At 100% load state, BTE (31.5%) and fuel consumption (13.01 g/kW-h) for the B20 blend was closer to diesel. However, the B50 blend had minimal HC (0.04 to 0.157 g/kW-h), CO (0.89 to 2.025 g/kW-h) and smoke (7.8 to 60.09%) emission than other test fuels at low and high load states. The CO2 emission was the penalty for complete combustion. The NOx emission was higher for all the biodiesel blends than diesel by 6.12%, 8%, 11.53%, 14.81% and 3.15% for B20, B30, B40, B50 and B100 respectively at 100% load condition. The reference parameters are identified as blend concentration percentage and brake power values. The trained ANN models exhibit a magnificent value of 97% coefficient of determination and the high R values ranging between 0.9076 and 0.9965 and the low MAPE values ranging between 0.98 and 4.26%. The analytical results also provide supportive evidence for the B20 blend which in turn concludes B20 as an effective alternative fuel for diesel.

Keywords

Citronella oil Artificial neural network Bio-fuel Diesel engine Emission Simulation 

Abbreviations

CI

compression ignition

CV

calorific value

CN

cetane number

CO

carbon monoxide

NOx

oxides of nitrogen

CO2

carbon dioxide

HC

hydrocarbon

BSEC

brake specific energy consumption

BTE

brake thermal efficiency

ASTM

American Society for Testing and Materials

BP

brake power

ANN

artificial neural network

HSU

Hartridge smoke units

LGO

lemongrass oil

WCO

waste cooking oil

GC-MS

gas chromatography-mass spectrometry

FT-IR

Fourier transform infrared spectroscopy

MSE

mean square error

MAPE

mean absolute percentage error

Notes

Acknowledgements

One of the authors, Mr. Krishnamoorthy, state his extended thanks to ACRF for granting fellowship, and also, the researchers are conveying their hearty thanks to the Department of Automobile, MIT, Anna University, Chromepet, Chennai 44.

Funding information

This study was financially supported by ACRF.

References

  1. Alagumalai A (2015) Combustion characteristics of lemongrass (Cymbopogon flexuosus) oil in a partial premixed charge compression ignition engine. Alex Eng J 54:405–413.  https://doi.org/10.1016/j.aej.2015.03.021 CrossRefGoogle Scholar
  2. Amine M, Hadrich B, Kriaa K, Kechaou N (2018) Chemical engineering & processing : process intensification lab-scale extraction of essential oils from Tunisian lemongrass ( Cymbopogon fl exuosus). Chem Eng Process Process Intensif 124:164–173.  https://doi.org/10.1016/j.cep.2017.12.012 CrossRefGoogle Scholar
  3. Anand BP, Saravanan CG, Srinivasan CA (2010) Performance and exhaust emission of turpentine oil powered direct injection diesel engine. Renew Energy 35:1179–1184.  https://doi.org/10.1016/j.renene.2009.09.010 CrossRefGoogle Scholar
  4. Anandavelu K, Alagumurthi N, Saravannan CG (2011) Experimental investigation of using Eucalyptus oil and diesel fuel blends in Kirloskar TV1 direct injection diesel engine. J Sustain Energy Environ 2:93–97Google Scholar
  5. Ashok B, Nanthagopal K, Chaturvedi B, Sharma S, Thundil Karuppa Raj R (2018a) A comparative assessment on common rail direct injection ( CRDI ) engine characteristics using low viscous biofuel blends. Appl Therm Eng 145:494–506.  https://doi.org/10.1016/j.applthermaleng.2018.09.069 CrossRefGoogle Scholar
  6. Ashok B, Nanthagopal K, Saravanan B, Somasundaram P, Jegadheesan C, Chaturvedi B, Sharma S, Patni G (2018b) A novel study on the effect lemon peel oil as a fuel in CRDI engine at various injection strategies. Energy Convers Manag 172:517–528.  https://doi.org/10.1016/j.enconman.2018.07.037 CrossRefGoogle Scholar
  7. Ashok B, Thundil Karuppa Raj R, Nanthagopal K, Krishnan R, Subbarao R (2017) Lemon peel oil – a novel renewable alternative energy source for diesel engine. Energy Convers Manag 139:110–121.  https://doi.org/10.1016/j.enconman.2017.02.049 CrossRefGoogle Scholar
  8. Canakci M, Necati A, Arcaklioglu E, Erdil A (2009) Expert Systems with Applications Prediction of performance and exhaust emissions of a diesel engine fueled with biodiesel produced from waste frying palm oil. Expert Syst Appl 36:9268–9280.  https://doi.org/10.1016/j.eswa.2008.12.005 CrossRefGoogle Scholar
  9. Çay Y, Korkmaz I, Çiçek A, Kara F (2013) Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network. Energy 50:177–186.  https://doi.org/10.1016/j.energy.2012.10.052 CrossRefGoogle Scholar
  10. Devan PK, Mahalakshmi NV (2009) A study of the performance , emission and combustion characteristics of a compression ignition engine using methyl ester of paradise oil – eucalyptus oil blends. Appl Energy 86:675–680.  https://doi.org/10.1016/j.apenergy.2008.07.008 CrossRefGoogle Scholar
  11. Dharma S, Haji M, Chyuan H, Hanra A (2017) Experimental study and prediction of the performance and exhaust emissions of mixed Jatropha curcas-Ceiba pentandra biodiesel blends in diesel engine using arti fi cial neural networks. J Clean Prod 164:618–633.  https://doi.org/10.1016/j.jclepro.2017.06.065 CrossRefGoogle Scholar
  12. Dhinesh B, Annamalai M, Joshuaramesh I, Annamalai K (2017) Studies on the influence of combustion bowl modification for the operation of Cymbopogon flexuosus biofuel based diesel blends in a DI diesel engine. Appl Therm Eng 112:627–637.  https://doi.org/10.1016/j.applthermaleng.2016.10.117 CrossRefGoogle Scholar
  13. Dhinesh B, Bharathi RN, Joshuaramesh JI et al (2016a) An experimental analysis on the in fl uence of fuel borne additives on the single cylinder diesel engine powered by Cymbopogon fl exuosus biofuel. J Energy Inst:1–12.  https://doi.org/10.1016/j.joei.2016.04.010
  14. Dhinesh B, Isaac Joshua Ramesh Lalvani J, Parthasarathy M, Annamalai K (2016b) An assessment on performance, emission and combustion characteristics of single cylinder diesel engine powered by Cymbopogon flexuosus biofuel. Energy Convers Manag 117:466–474.  https://doi.org/10.1016/j.enconman.2016.03.049 CrossRefGoogle Scholar
  15. Dhinesh B, Maria Ambrose Raj Y, Kalaiselvan C, KrishnaMoorthy R (2018) A numerical and experimental assessment of a coated diesel engine powered by high-performance nano biofuel. Energy Convers Manag 171:815–824.  https://doi.org/10.1016/j.enconman.2018.06.039 CrossRefGoogle Scholar
  16. Dubey P, Gupta R (2018) Influences of dual bio-fuel ( Jatropha biodiesel and turpentine oil ) on single cylinder variable compression ratio diesel engine. Renew Energy 115:1294–1302.  https://doi.org/10.1016/j.renene.2017.09.055 CrossRefGoogle Scholar
  17. Dubey P, Gupta R (2017) Effects of dual bio-fuel (Jatropha biodiesel and turpentine oil) on a single cylinder naturally aspirated diesel engine without EGR. Appl Therm Eng 115:1137–1147.  https://doi.org/10.1016/j.applthermaleng.2016.12.125 CrossRefGoogle Scholar
  18. 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.  https://doi.org/10.1016/j.renene.2008.08.008 CrossRefGoogle Scholar
  19. Guedes AR, Reder A, De Souza C et al (2018) The Journal of supercritical Fluids Extraction of citronella grass solutes with supercritical CO 2 , compressed propane and ethanol as cosolvent : kinetics modeling and total phenolic assessment. J Supercrit Fluids 137:16–22.  https://doi.org/10.1016/j.supflu.2018.03.003 CrossRefGoogle Scholar
  20. Huang H, Liu Q, Shi C, Wang Q, Zhou C (2016) Experimental study on spray , combustion and emission characteristics of pine oil / diesel blends in a multi-cylinder diesel engine. Fuel Process Technol 153:137–148.  https://doi.org/10.1016/j.fuproc.2016.07.016 CrossRefGoogle Scholar
  21. Joy Prabu H, Johnson I (2015) Plant-mediated biosynthesis and characterization of silver nanoparticles by leaf extracts of Tragia involucrata, Cymbopogon citronella, Solanum verbascifolium and Tylophora ovata. Karbala Int J Mod Sci 1:237–246.  https://doi.org/10.1016/j.kijoms.2015.12.003 CrossRefGoogle Scholar
  22. Karthikeyan R, Mahalakshmi NV (2007) Performance and emission characteristics of a turpentine – diesel dual fuel engine. Energy 32:1202–1209.  https://doi.org/10.1016/j.energy.2006.07.021 CrossRefGoogle Scholar
  23. Kasiraman G, Geo VE, Nagalingam B (2016) Assessment of cashew nut shell oil as an alternate fuel for CI ( Compression ignition ) engines. Energy 101:402–410.  https://doi.org/10.1016/j.energy.2016.01.086 CrossRefGoogle Scholar
  24. Kasiraman G, Nagalingam B, Balakrishnan M (2012) Performance , emission and combustion improvements in a direct injection diesel engine using cashew nut shell oil as fuel with camphor oil blending. Energy 47:116–124.  https://doi.org/10.1016/j.energy.2012.09.022 CrossRefGoogle Scholar
  25. Kshirsagar CM, Anand R (2017) Artificial neural network applied forecast on a parametric study of Calophyllum inophyllum methyl ester-diesel engine out responses. Appl Energy 189:555–567.  https://doi.org/10.1016/j.apenergy.2016.12.045 CrossRefGoogle Scholar
  26. Kumar AN, Raju KB, Kishore PS, Narayana K (2018) ScienceDirect Some experimental studies on effect of exhaust-gas recirculation on performance and emission characteristics of a compression- ignition engine fuelled with diesel and lemon-peel oil blends. Mater Today Proc 5:6138–6148.  https://doi.org/10.1016/j.matpr.2017.12.220 CrossRefGoogle Scholar
  27. Li L, Jianxin W, Zhi W, Jianhua X (2015) Combustion and emission characteristics of diesel engine fueled with diesel / biodiesel / pentanol fuel blends. Fuel 156:211–218.  https://doi.org/10.1016/j.fuel.2015.04.048 CrossRefGoogle Scholar
  28. Mahmudul HM, Hagos FY, Mamat R, Adam AA, Ishak WFW, Alenezi R (2017) Production, characterization and performance of biodiesel as an alternative fuel in diesel engines – a review. Renew Sust Energ Rev 72:497–509.  https://doi.org/10.1016/j.rser.2017.01.001 CrossRefGoogle Scholar
  29. Manigandan S, Gunasekar P, Devipriya J, Nithya S (2019a) Emission and injection characteristics of corn biodiesel blends in diesel engine. Fuel 235:723–735.  https://doi.org/10.1016/j.fuel.2018.08.071 CrossRefGoogle Scholar
  30. Manigandan S, Gunasekar P, Poorchilamban S et al (2019b) Experimental investigation of the e ff ect of orifices inclination angle in multihole diesel injector nozzles . Part 2 – Spray characteristics. Fuel:1.  https://doi.org/10.1080/15567036.2019.1587048
  31. Manigandan S, Gunasekar P, Poorchilamban S, Nithya S, Devipriya J, Vasanthkumar G (2019c) Effect of addition of hydrogen and TiO2 in gasoline engine in various exhaust gas recirculation ratio. Int J Hydrog Energy 23;44(21):11205–18.Google Scholar
  32. Mostafa S, Pierantozzi M, Moghadasi J (2019) Viscosities of some fatty acid esters and biodiesel fuels from a rough hard sphere-chain model and artificial neural network. Fuel 235:1083–1091.  https://doi.org/10.1016/j.fuel.2018.08.088 CrossRefGoogle Scholar
  33. Naser A, Haque A, Remadevi R, Naebe M (2018) Lemongrass ( Cymbopogon ) : a review on its structure, properties, applications and recent developments. Cellulose 25:5455–5477.  https://doi.org/10.1007/s10570-018-1965-2 CrossRefGoogle Scholar
  34. Noor CWM, Noor MM, Mamat R (2018) Biodiesel as alternative fuel for marine diesel engine applications : a review. Renew Sust Energ Rev 94:127–142.  https://doi.org/10.1016/j.rser.2018.05.031 CrossRefGoogle Scholar
  35. Parthasarathy M, Isaac Joshua Ramesh Lalvani J, Dhinesh B, Annamalai K (2016) Effect of hydrogen on ethanol–biodiesel blend on performance and emission characteristics of a direct injection diesel engine. Ecotoxicol Environ Saf 134:433–439.  https://doi.org/10.1016/j.ecoenv.2015.11.005 CrossRefGoogle Scholar
  36. Petranović Z, Bešenić T, Vujanović M, Duić N (2017) Modelling pollutant emissions in diesel engines, influence of biofuel on pollutant formation. J Environ Manag 203:1038–1046.  https://doi.org/10.1016/j.jenvman.2017.03.033 CrossRefGoogle Scholar
  37. Purushothaman K, Nagarajan G (2009a) Performance, emission and combustion characteristics of a compression ignition engine operating on neat orange oil. Renew Energy 34:242–245.  https://doi.org/10.1016/j.renene.2008.03.012 CrossRefGoogle Scholar
  38. Purushothaman K, Nagarajan G (2009b) Experimental investigation on a C. I. engine using orange oil and orange oil with DEE. Fuel 88:1732–1740.  https://doi.org/10.1016/j.fuel.2009.03.032 CrossRefGoogle Scholar
  39. Rahman SMA, Van TC, Hossain FM et al (2019) Fuel properties and emission characteristics of essential oil blends in a compression ignition engine. Fuel 238:440–453.  https://doi.org/10.1016/j.fuel.2018.10.136 CrossRefGoogle Scholar
  40. Ramalingam K, Kandasamy A, James P, et al (2019) Production of eco-friendly fuel with the help of steam distillation from new plant source and the investigation of its influence of fuel injection strategy in diesel engineGoogle Scholar
  41. Ramalingam KM, Kandasamy A, Subramani L et al (2018) An assessment of combustion, performance characteristics and emission control strategy by adding anti-oxidant additive in emulsified fuel. Atmos Pollut Res:1.  https://doi.org/10.1016/j.apr.2018.02.007
  42. Rao KP, Babu TV, Anuradha G et al (2017) IDI diesel engine performance and exhaust emission analysis using biodiesel with an artificial neural network ( ANN ). Egypt J Pet 26:593–600.  https://doi.org/10.1016/j.ejpe.2016.08.006 CrossRefGoogle Scholar
  43. Roy S, Banerjee R, Das AK, Bose PK (2014) Journal of Natural Gas Science and Engineering Development of an ANN based system identi fi cation tool to estimate the performance-emission characteristics of a CRDI assisted CNG dual fuel diesel engine. J Nat Gas Sci Eng 21:147–158.  https://doi.org/10.1016/j.jngse.2014.08.002 CrossRefGoogle Scholar
  44. Sakthivel R, Ramesh K, Purnachandran R, Shameer PM (2018) A review on the properties, performance and emission aspects of the third generation biodiesels. Renew Sust Energ Rev 82:2970–2992.  https://doi.org/10.1016/j.rser.2017.10.037 CrossRefGoogle Scholar
  45. Sarıtas I, Emre H, Og H (2010) Expert systems with applications prediction of diesel engine performance using biofuels with artificial neural network. Expert Systems with Applications 37:6579–6586.  https://doi.org/10.1016/j.eswa.2010.02.128 CrossRefGoogle Scholar
  46. Sathiyamoorthi R, Sankaranarayanan G (2016) Effect of antioxidant additives on the performance and emission characteristics of a DICI engine using neat lemongrass oil – diesel blend. FUEL 174:89–96.  https://doi.org/10.1016/j.fuel.2016.01.076 CrossRefGoogle Scholar
  47. Sathiyamoorthi R, Sankaranarayanan G (2017) The effects of using ethanol as additive on the combustion and emissions of a direct injection diesel engine fuelled with neat lemongrass oil-diesel fuel blend. Renew Energy 101:747–756.  https://doi.org/10.1016/j.renene.2016.09.044 CrossRefGoogle Scholar
  48. Selvan SS, Pandian PS, Saravanan ASS (2018a) Comparison of response surface methodology ( RSM ) and artificial neural network ( ANN ) in optimization of Aegle marmelos oil extraction for biodiesel production. Arab J Sci Eng 43:6119–6131.  https://doi.org/10.1007/s13369-018-3272-5 CrossRefGoogle Scholar
  49. Selvan SS, Pandian PS, Subathira A, Saravanan S (2018b) Saraca asoca seeds – a novel candidature for biodiesel production : studies on yield optimization using ANN coupled GA and properties of biodiesel blends. Int J Green Energy 00:1–12.  https://doi.org/10.1080/15435075.2018.1529586 Google Scholar
  50. Srithar K (2017) Experimental investigations on mixing of two biodiesels blended with diesel as alternative fuel for diesel engines. J King Saud Univ - Eng Sci 29:50–56.  https://doi.org/10.1016/j.jksues.2014.04.008 Google Scholar
  51. Subramani L, Parthasarathy M, Balasubramanian D (2018) Novel Garcinia gummi-gutta methyl ester ( GGME ) as a potential alternative feedstock for existing unmodified DI diesel engine. Renew Energy 125:568–577.  https://doi.org/10.1016/j.renene.2018.02.134 CrossRefGoogle Scholar
  52. Suresh M, Jawahar CP, Richard A (2018) A review on biodiesel production , combustion , performance , and emission characteristics of non-edible oils in variable compression ratio diesel engine using biodiesel and its blends. Renew Sust Energ Rev 92:38–49.  https://doi.org/10.1016/j.rser.2018.04.048 CrossRefGoogle Scholar
  53. Szabados G, Bereczky Á, Ajtai T, Bozóki Z (2018) Evaluation analysis of particulate relevant emission of a diesel engine running on fossil diesel and different biofuels. Energy 161:1139–53Google Scholar
  54. Tamilselvan P, Nallusamy N, Rajkumar S (2017) A comprehensive review on performance, combustion and emission characteristics of biodiesel fuelled diesel engines. Renew Sust Energ Rev 79:1134–1159.  https://doi.org/10.1016/j.rser.2017.05.176 CrossRefGoogle Scholar
  55. Tosun E, Aydin K, Bilgili M (2016) Comparison of linear regression and artificial neural network model of a diesel engine fueled with biodiesel-alcohol mixtures. Alex Eng J 55:3081–3089.  https://doi.org/10.1016/j.aej.2016.08.011 CrossRefGoogle Scholar
  56. Uyumaz A (2018) Combustion , performance and emission characteristics of a DI diesel engine fueled with mustard oil biodiesel fuel blends at different engine loads. Fuel 212:256–267.  https://doi.org/10.1016/j.fuel.2017.09.005 CrossRefGoogle Scholar
  57. Vallinayagam R, Vedharaj S, Yang WM, Roberts WL, Dibble RW (2015) Feasibility of using less viscous and lower cetane (LVLC) fuels in a diesel engine: a review. Renew Sust Energ Rev 51:1166–1190.  https://doi.org/10.1016/j.rser.2015.07.042 CrossRefGoogle Scholar
  58. Vallinayagam R, Vedharaj S, Yang WM, Saravanan CG, Lee PS, Chua KJE, Chou SK (2014a) Impact of pine oil biofuel fumigation on gaseous emissions from a diesel engine. Fuel Process Technol 124:44–53.  https://doi.org/10.1016/j.fuproc.2014.02.012 CrossRefGoogle Scholar
  59. Vallinayagam R, Vedharaj S, Yang WM, Saravanan CG, Lee PS, Chua KJE, Chou SK (2014b) Impact of ignition promoting additives on the characteristics of a diesel engine powered by pine oil – diesel blend. Fuel 117:278–285.  https://doi.org/10.1016/j.fuel.2013.09.076 CrossRefGoogle Scholar
  60. Vallinayagam R, Vedharaj S, Yang WM, Lee PS, Chua KJE, Chou SK (2013) Combustion performance and emission characteristics study of pine oil in a diesel engine. Energy 57:344–351.  https://doi.org/10.1016/j.energy.2013.05.061 CrossRefGoogle Scholar
  61. Vallinayagam R, Vedharaj S, Yang WM, Lee PS, Chua KJE, Chou SK (2014c) Pine oil – biodiesel blends : a double biofuel strategy to completely eliminate the use of diesel in a diesel engine. Appl Energy 130:466–473.  https://doi.org/10.1016/j.apenergy.2013.11.025 CrossRefGoogle Scholar
  62. Verma P, Sharma MP (2016) Review of process parameters for biodiesel production from different feedstocks. Renew Sust Energ Rev 62:1063–1071.  https://doi.org/10.1016/j.rser.2016.04.054 CrossRefGoogle Scholar
  63. Vigneswaran R, Annamalai K, Dhinesh B, Krishnamoorthy R (2018) Experimental investigation of unmodified diesel engine performance, combustion and emission with multipurpose additive along with water-in- diesel emulsion fuel. Energy Convers Manag 172:370–380.  https://doi.org/10.1016/j.enconman.2018.07.039 CrossRefGoogle Scholar
  64. Wang Y, Gao W (2018) Environmental Effects Prediction of the water content of biodiesel using ANN-MLP : an environmental application. Energy Sources Part A Recover Util Environ Eff 40:987–993.  https://doi.org/10.1080/15567036.2018.1468510 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Krishnamoorthy Ramalingam
    • 1
  • Annamalai Kandasamy
    • 2
  • Dhinesh Balasubramanian
    • 3
  • Moulik Palani
    • 4
  • Thiyagarajan Subramanian
    • 5
    Email author
  • Edwin Geo Varuvel
    • 5
  • Karthikeyan Viswanathan
    • 6
  1. 1.Department of Mechanical EngineeringCuddaloreIndia
  2. 2.Department of Automobile Engineering, Madras Institute of Technology (MIT) CampusAnna UniversityChennaiIndia
  3. 3.Department of Mechanical Engineering, Mepco Schlenk Engineering College, Mepco NagarMepco Engineering College PostVirudhunagarIndia
  4. 4.Department of Industrial EngineeringTexas A & M UniversityCollege StationUSA
  5. 5.Green Vehicle Technology Research Centre, Department of Automobile EngineeringSRM Institute of Science and TechnologyKattankulathurIndia
  6. 6.Department of Mechanical EngineeringSri Krishna College of Engineering and TechnologyCoimbatoreIndia

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