IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v355y2024ics0306261923016732.html
   My bibliography  Save this article

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

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923016732
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2023.122309?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Azad, A.K. & Rasul, M.G. & Khan, M.M.K. & Sharma, Subhash C. & Mofijur, M. & Bhuiya, M.M.K., 2016. "Prospects, feedstocks and challenges of biodiesel production from beauty leaf oil and castor oil: A nonedible oil sources in Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 302-318.
    2. Sun, Ping & Zhang, Jufang & Dong, Wei & Li, Decheng & Yu, Xiumin, 2023. "Prediction of oxygen-enriched combustion and emission performance on a spark ignition engine using artificial neural networks," Applied Energy, Elsevier, vol. 348(C).
    3. Kshirsagar, Charudatta M. & Anand, Ramanathan, 2017. "Artificial neural network applied forecast on a parametric study of Calophyllum inophyllum methyl ester-diesel engine out responses," Applied Energy, Elsevier, vol. 189(C), pages 555-567.
    4. Haruki Tajima & Takuya Tomidokoro & Takeshi Yokomori, 2022. "Deep Learning for Knock Occurrence Prediction in SI Engines," Energies, MDPI, vol. 15(24), pages 1-14, December.
    5. 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.
    6. 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.
    7. Yusri, I.M. & Abdul Majeed, A.P.P. & Mamat, R. & Ghazali, M.F. & Awad, Omar I. & Azmi, W.H., 2018. "A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 665-686.
    8. Roy, Sumit & Banerjee, Rahul & Bose, Probir Kumar, 2014. "Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network," Applied Energy, Elsevier, vol. 119(C), pages 330-340.
    9. Taghizadeh-Alisaraei, Ahmad & Motevali, Ali & Ghobadian, Barat, 2019. "Ethanol production from date wastes: Adapted technologies, challenges, and global potential," Renewable Energy, Elsevier, vol. 143(C), pages 1094-1110.
    10. Marietta Markiewicz & Łukasz Muślewski, 2019. "The Impact of Powering an Engine with Fuels from Renewable Energy Sources including its Software Modification on a Drive Unit Performance Parameters," Sustainability, MDPI, vol. 11(23), pages 1-16, November.
    11. Goel, Varun & Kumar, Naresh & Singh, Paramvir, 2018. "Impact of modified parameters on diesel engine characteristics using biodiesel: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2716-2729.
    12. Srinidhi, Campli & Madhusudhan, A. & Channapattana, S.V. & Gawali, S.V. & Aithal, Kiran, 2021. "RSM based parameter optimization of CI engine fuelled with nickel oxide dosed Azadirachta indica methyl ester," Energy, Elsevier, vol. 234(C).
    13. Rezaei, Javad & Shahbakhti, Mahdi & Bahri, Bahram & Aziz, Azhar Abdul, 2015. "Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks," Applied Energy, Elsevier, vol. 138(C), pages 460-473.
    14. Shivakumar & Srinivasa Pai, P. & Shrinivasa Rao, B.R., 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," Applied Energy, Elsevier, vol. 88(7), pages 2344-2354, July.
    15. Mao, Dongxu & Ghadikolaei, Meisam Ahmadi & Cheung, Chun Shun & Shen, Zhaojie & Cui, Wenzheng & Wong, Pak Kin, 2020. "Influence of alternative fuels on the particulate matter micro and nano-structures, volatility and oxidation reactivity in a compression ignition engine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    16. Arbab, M.I. & Masjuki, H.H. & Varman, M. & Kalam, M.A. & Imtenan, S. & Sajjad, H., 2013. "Fuel properties, engine performance and emission characteristic of common biodiesels as a renewable and sustainable source of fuel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 133-147.
    17. Roy, Sumit & Ghosh, Ashmita & Das, Ajoy Kumar & Banerjee, Rahul, 2015. "Development and validation of a GEP model to predict the performance and exhaust emission parameters of a CRDI assisted single cylinder diesel engine coupled with EGR," Applied Energy, Elsevier, vol. 140(C), pages 52-64.
    18. Isabella Yunfei Zeng & Shiqi Tan & Jianliang Xiong & Xuesong Ding & Yawen Li & Tian Wu, 2021. "Estimation of Real-World Fuel Consumption Rate of Light-Duty Vehicles Based on the Records Reported by Vehicle Owners," Energies, MDPI, vol. 14(23), pages 1-19, November.
    19. Wong, Pak Kin & Wong, Ka In & Vong, Chi Man & Cheung, Chun Shun, 2015. "Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search," Renewable Energy, Elsevier, vol. 74(C), pages 640-647.
    20. Thakkar, Kartikkumar & Kachhwaha, Surendra Singh & Kodgire, Pravin & Srinivasan, Seshasai, 2021. "Combustion investigation of ternary blend mixture of biodiesel/n-butanol/diesel: CI engine performance and emission control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:355:y:2024:i:c:s0306261923016732. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.