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Economic analysis and TOPSIS approach to optimize the CI engine characteristics using span 80 mixed carbon nanotubes emulsified Sapindus trifoliatus (soapnut) biodiesel by artificial neural network prediction model

Author

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  • Muninathan, K.
  • Venkata Ramanan, M.
  • Monish, N.
  • Baskar, G.

Abstract

The rise in fuel use within the vehicle sector leads to a corresponding escalation in energy demand. To achieve optimal operating conditions, it is necessary to reduce energy usage. For that, the primary objective of this study is to utilize an artificial neural network (ANN) and the optimization approach as a technique for order performance by similarity to the ideal solution (TOPSIS) to forecast the optimal features of a compression ignition (CI) engine. The experiment was performed with a four-stroke mono-cylinder compression ignition (CI) engine under different load situations. The study considers six operational parameters, including brake thermal efficiency (BTE), specific fuel consumption (SFC), as well as emission characteristics including carbon monoxide (CO), hydrocarbon (HC), Nitrogen oxides (NOx), and smoke. From the ANN results, the correlation coefficients for BTE, SFC NOx, smoke, HC, and CO were 0.9712, 0.9862, 0.964, 0.941, 0.998, and 0.978, respectively. The SNBD25 + 30 mg/L SP80 + 30 mg/L CNT blend exhibited the maximum closeness coefficient under various load conditions. The maximum closeness coefficient obtained from the TOPSIS optimisation approach under full load conditions is 0.982386. From the result of the ANN prediction technique, for TOPSIS optimised blend SNBD25 + 30 mg/L SP80 + 30 mg/L CNT a 13.84% increase in BTE, 11.21% decrease in SFC, 16.67% decrease in CO, 13.87% decrease in NOx, 19.23% decrease in HC, and 32.53% decrease in smoke. Based on the outcomes obtained from the ANN and TOPSIS methodologies, it can be concluded that the SNBD25 + 30 mg/L SP80 + 30 mg/L CNT blend exhibits superior performance in terms of reduced NOx emissions and enhanced efficiency, surpassing the other blends under consideration. The incorporation of carbon nanotubes (CNTs) into a system leads to an increase in the carbon-to‑oxygen ratio, resulting in a reduction in the generation of nitrogen oxides (NOx). The cost of a mixture consisting of SNBD25 + 30 mg/L SP80 + 30 mg/L blend is 20% lower than the cost of diesel on a per-litre basis.

Suggested Citation

  • Muninathan, K. & Venkata Ramanan, M. & Monish, N. & Baskar, G., 2024. "Economic analysis and TOPSIS approach to optimize the CI engine characteristics using span 80 mixed carbon nanotubes emulsified Sapindus trifoliatus (soapnut) biodiesel by artificial neural network pr," Applied Energy, Elsevier, vol. 355(C).
    • Handle: RePEc:eee:appene:v:355:y:2024:i:c:s0306261923016732
    DOI: 10.1016/j.apenergy.2023.122309
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    1. Najafi, G. & Ghobadian, B. & Tavakoli, T. & Buttsworth, D.R. & Yusaf, T.F. & Faizollahnejad, M., 2009. "Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network," Applied Energy, Elsevier, vol. 86(5), pages 630-639, May.
    2. Canakci, Mustafa & Erdil, Ahmet & Arcaklioglu, Erol, 2006. "Performance and exhaust emissions of a biodiesel engine," Applied Energy, Elsevier, vol. 83(6), pages 594-605, June.
    3. Deb, Madhujit & Debbarma, Bishop & Majumder, Arindam & Banerjee, Rahul, 2016. "Performance –emission optimization of a diesel-hydrogen dual fuel operation: A NSGA II coupled TOPSIS MADM approach," Energy, Elsevier, vol. 117(P1), pages 281-290.
    4. Ağbulut, Ümit & Elibol, Erdem & Demirci, Tuna & Sarıdemir, Suat & Gürel, Ali Etem & Rajak, Upendra & Afzal, Asif & Verma, Tikendra Nath, 2022. "Synthesis of graphene oxide nanoparticles and the influences of their usage as fuel additives on CI engine behaviors," Energy, Elsevier, vol. 244(PA).
    5. Yesilyurt, Murat Kadir & Cesur, Cüneyt & Aslan, Volkan & Yilbasi, Zeki, 2020. "The production of biodiesel from safflower (Carthamus tinctorius L.) oil as a potential feedstock and its usage in compression ignition engine: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    6. Fayad, M.A. & Tsolakis, A. & Fernández-Rodríguez, D. & Herreros, J.M. & Martos, F.J. & Lapuerta, M., 2017. "Manipulating modern diesel engine particulate emission characteristics through butanol fuel blending and fuel injection strategies for efficient diesel oxidation catalysts," Applied Energy, Elsevier, vol. 190(C), pages 490-500.
    7. Sharma, Abhishek & Ansari, Naushad Ahmad & Pal, Amit & Singh, Yashvir & Lalhriatpuia, S., 2019. "Effect of biogas on the performance and emissions of diesel engine fuelled with biodiesel-ethanol blends through response surface methodology approach," Renewable Energy, Elsevier, vol. 141(C), pages 657-668.
    8. Sivaraja, C.M. & Sakthivel, G., 2017. "Compression ignition engine performance modelling using hybrid MCDM techniques for the selection of optimum fish oil biodiesel blend at different injection timings," Energy, Elsevier, vol. 139(C), pages 118-141.
    9. Channapattana, S.V. & Pawar, Abhay A. & Kamble, Prashant G., 2017. "Optimisation of operating parameters of DI-CI engine fueled with second generation Bio-fuel and development of ANN based prediction model," Applied Energy, Elsevier, vol. 187(C), pages 84-95.
    10. Tzeng, Gwo-Hshiung & Lin, Cheng-Wei & Opricovic, Serafim, 2005. "Multi-criteria analysis of alternative-fuel buses for public transportation," Energy Policy, Elsevier, vol. 33(11), pages 1373-1383, July.
    11. Murugesan, A. & Umarani, C. & Subramanian, R. & Nedunchezhian, N., 2009. "Bio-diesel as an alternative fuel for diesel engines--A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(3), pages 653-662, April.
    12. Mehra, Roopesh Kumar & Duan, Hao & Luo, Sijie & Rao, Anas & Ma, Fanhua, 2018. "Experimental and artificial neural network (ANN) study of hydrogen enriched compressed natural gas (HCNG) engine under various ignition timings and excess air ratios," Applied Energy, Elsevier, vol. 228(C), pages 736-754.
    13. EL-Seesy, Ahmed I. & Hassan, Hamdy, 2019. "Investigation of the effect of adding graphene oxide, graphene nanoplatelet, and multiwalled carbon nanotube additives with n-butanol-Jatropha methyl ester on a diesel engine performance," Renewable Energy, Elsevier, vol. 132(C), pages 558-574.
    14. Yusaf, Talal F. & Buttsworth, D.R. & Saleh, Khalid H. & Yousif, B.F., 2010. "CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network," Applied Energy, Elsevier, vol. 87(5), pages 1661-1669, May.
    15. Patel, Rupesh L. & Sankhavara, C.D., 2017. "Biodiesel production from Karanja oil and its use in diesel engine: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 464-474.
    16. Bhuiya, M.M.K. & Rasul, M.G. & Khan, M.M.K. & Ashwath, N. & Azad, A.K., 2016. "Prospects of 2nd generation biodiesel as a sustainable fuel—Part: 1 selection of feedstocks, oil extraction techniques and conversion technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 1109-1128.
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