IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v263y2020ics0306261920301240.html
   My bibliography  Save this article

Artificial neural network approach on forecasting diesel engine characteristics fuelled with waste frying oil biodiesel

Author

Listed:
  • Babu, D.
  • Thangarasu, Vinoth
  • Ramanathan, Anand

Abstract

The present work investigates the influence of advanced injection strategy on a common rail direct injection assisted diesel engine characteristics fuelled with biodiesel and conventional diesel. Also, an artificial neural network is employed to forecast engine characteristics. Engine test is conducted under 100% load condition through an optimized nozzle opening pressure of 500 bar. Pre-injection timing is fixed permanently at 30 °CA bTDC, main injection timing varied from 15 °CA to 21 °CA bTDC and post-injection varied from 6 °CA bTDC to 6 °CA aTDC sequentially. However, the pre, main and post-injection quantities are changed respectively from 5% to 15%, 70% to 90%, and 5% to 15%. Minimum carbon monoxide, unburned hydrocarbon and smoke emission of 0.01% vol., 8 ppm and 1.59 FSN are achieved with pre-injection timing of 30 °CA, main injection timing of 21 °CA bTDC, and post injection timing of 6 °CA bTDC for B100-15%-70%-15%. Maximum brake thermal efficiency and nitric oxide emission of 34.3% and 1114 ppm are achieved in B100-5%-90%-5% at advanced injection timing and higher nozzle opening pressure. Artificial neural network models conform to experimental results having a lower root mean square error and correlation coefficient values in a range of 0.01 to 0.02 and 0.980 to 0.998 respectively. An artificial neural network is mostly preferred over other theoretical and empirical models to higher accuracy in predicting the output. Hence, multiple injection strategy fuelled biodiesel significantly decreased the emission and improved the performance compared to mechanical, single and split injection strategy.

Suggested Citation

  • Babu, D. & Thangarasu, Vinoth & Ramanathan, Anand, 2020. "Artificial neural network approach on forecasting diesel engine characteristics fuelled with waste frying oil biodiesel," Applied Energy, Elsevier, vol. 263(C).
    • Handle: RePEc:eee:appene:v:263:y:2020:i:c:s0306261920301240
    DOI: 10.1016/j.apenergy.2020.114612
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920301240
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.114612?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. 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.
    2. 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.
    3. 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.
    4. Yadav, Jaykumar & Ramesh, A., 2018. "Injection strategies for reducing smoke and improving the performance of a butanol-diesel common rail dual fuel engine," Applied Energy, Elsevier, vol. 212(C), pages 1-12.
    5. Yap, Wai Kean & Karri, Vishy, 2011. "ANN virtual sensors for emissions prediction and control," Applied Energy, Elsevier, vol. 88(12), pages 4505-4516.
    6. Bhowmik, Subrata & Paul, Abhishek & Panua, Rajsekhar & Ghosh, Subrata Kumar & Debroy, Durbadal, 2018. "Performance-exhaust emission prediction of diesosenol fueled diesel engine: An ANN coupled MORSM based optimization," Energy, Elsevier, vol. 153(C), pages 212-222.
    7. Agarwal, Avinash Kumar & Dhar, Atul & Gupta, Jai Gopal & Kim, Woong Il & Lee, Chang Sik & Park, Sungwook, 2014. "Effect of fuel injection pressure and injection timing on spray characteristics and particulate size–number distribution in a biodiesel fuelled common rail direct injection diesel engine," Applied Energy, Elsevier, vol. 130(C), pages 212-221.
    8. Mohan, Balaji & Yang, Wenming & Raman, Vallinayagam & Sivasankaralingam, Vedharaj & Chou, Siaw Kiang, 2014. "Optimization of biodiesel fueled engine to meet emission standards through varying nozzle opening pressure and static injection timing," Applied Energy, Elsevier, vol. 130(C), pages 450-457.
    9. Elsanusi, Osama Ahmed & Roy, Murari Mohon & Sidhu, Manpreet Singh, 2017. "Experimental Investigation on a Diesel Engine Fueled by Diesel-Biodiesel Blends and their Emulsions at Various Engine Operating Conditions," Applied Energy, Elsevier, vol. 203(C), pages 582-593.
    10. Mohamed Ismail, Harun & Ng, Hoon Kiat & Queck, Cheen Wei & Gan, Suyin, 2012. "Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends," Applied Energy, Elsevier, vol. 92(C), pages 769-777.
    11. Chattopadhyay, Soham & Sen, Ramkrishna, 2013. "Fuel properties, engine performance and environmental benefits of biodiesel produced by a green process," Applied Energy, Elsevier, vol. 105(C), pages 319-326.
    12. Babu, D. & Anand, R., 2017. "Effect of biodiesel-diesel-n-pentanol and biodiesel-diesel-n-hexanol blends on diesel engine emission and combustion characteristics," Energy, Elsevier, vol. 133(C), pages 761-776.
    13. Babu, D. & Karvembu, R. & Anand, R., 2018. "Impact of split injection strategy on combustion, performance and emissions characteristics of biodiesel fuelled common rail direct injection assisted diesel engine," Energy, Elsevier, vol. 165(PB), pages 577-592.
    14. Arcaklioglu, Erol & Çelikten, Ismet, 2005. "A diesel engine's performance and exhaust emissions," Applied Energy, Elsevier, vol. 80(1), pages 11-22, January.
    15. Çay, Yusuf & Korkmaz, Ibrahim & Çiçek, Adem & Kara, Fuat, 2013. "Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network," Energy, Elsevier, vol. 50(C), pages 177-186.
    16. Sözen, Adnan & Arcaklioglu, Erol & Özkaymak, Mehmet, 2005. "Turkey's net energy consumption," Applied Energy, Elsevier, vol. 81(2), pages 209-221, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Thangarasu, Vinoth & M, Angkayarkan Vinayakaselvi & Ramanathan, Anand, 2021. "Artificial neural network approach for parametric investigation of biodiesel synthesis using biocatalyst and engine characteristics of diesel engine fuelled with Aegle Marmelos Correa biodiesel," Energy, Elsevier, vol. 230(C).
    2. S. Charan Kumar & Amit Kumar Thakur & J. Ronald Aseer & Sendhil Kumar Natarajan & Rajesh Singh & Neeraj Priyadarshi & Bhekisipho Twala, 2022. "An Experimental Analysis and ANN Based Parameter Optimization of the Influence of Microalgae Spirulina Blends on CI Engine Attributes," Energies, MDPI, vol. 15(17), pages 1-19, August.
    3. Pirmoradi, Neda & Ghaneian, Mohammad Taghi & Ehrampoush, Mohammad Hassan & Salmani, Mohammad Hossein & Hatami, Behnam, 2021. "The conversion of poultry slaughterhouse wastewater sludge into biodiesel: Process modeling and optimization," Renewable Energy, Elsevier, vol. 178(C), pages 1236-1249.
    4. Jeyaseelan, Thangaraja & El Samad, Tala & Rajkumar, Sundararajan & Chatterjee, Abhay & Al-Zaili, Jafar, 2023. "A techno-economic assessment of waste oil biodiesel blends for automotive applications in urban areas: Case of India," Energy, Elsevier, vol. 271(C).
    5. Elahi, Ehsan & Zhang, Zhixin & Khalid, Zainab & Xu, Haiyun, 2022. "Application of an artificial neural network to optimise energy inputs: An energy- and cost-saving strategy for commercial poultry farms," Energy, Elsevier, vol. 244(PB).
    6. Hüseyin Çamur & Ahmed Muayad Rashid Al-Ani, 2022. "Prediction of Oxidation Stability of Biodiesel Derived from Waste and Refined Vegetable Oils by Statistical Approaches," Energies, MDPI, vol. 15(2), pages 1-26, January.
    7. Salins, Sampath Suranjan & Kota Reddy, S.V. & Shiva Kumar,, 2021. "Experimental Investigation and Neural network based parametric prediction in a multistage reciprocating humidifier," Applied Energy, Elsevier, vol. 293(C).
    8. Panchal, Balaji & Zhu, Zheng & Qin, Shenjun & Chang, Tao & Zhao, Qiaojing & Sun, Yuzhuang & Zhao, Cunliang & Wang, Jinxi & Bian, Kai & Rankhamb, Santosh, 2022. "The current state applications of ethyl carbonate with ionic liquid in sustainable biodiesel production: A review," Renewable Energy, Elsevier, vol. 181(C), pages 341-354.
    9. R, Gopi & Thangarasu, Vinoth & Vinayakaselvi M, Angkayarkan & Ramanathan, Anand, 2022. "A critical review of recent advancements in continuous flow reactors and prominent integrated microreactors for biodiesel production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    10. Gavaskar, T. & Ramanan M, Venkata & Arun, K. & Arivazhagan, S., 2023. "The combined effect of green synthesized nitrogen-doped carbon quantum dots blended jackfruit seed biodiesel and acetylene gas tested on the dual fuel engine," Energy, Elsevier, vol. 275(C).
    11. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.

    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. 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).
    2. Song Hu & Stefano d’Ambrosio & Roberto Finesso & Andrea Manelli & Mario Rocco Marzano & Antonio Mittica & Loris Ventura & Hechun Wang & Yinyan Wang, 2019. "Comparison of Physics-Based, Semi-Empirical and Neural Network-Based Models for Model-Based Combustion Control in a 3.0 L Diesel Engine," Energies, MDPI, vol. 12(18), pages 1-41, September.
    3. Simsek, Suleyman & Uslu, Samet & Simsek, Hatice, 2022. "Proportional impact prediction model of animal waste fat-derived biodiesel by ANN and RSM technique for diesel engine," Energy, Elsevier, vol. 239(PD).
    4. Babu, D. & Karvembu, R. & Anand, R., 2018. "Impact of split injection strategy on combustion, performance and emissions characteristics of biodiesel fuelled common rail direct injection assisted diesel engine," Energy, Elsevier, vol. 165(PB), pages 577-592.
    5. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.
    6. 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.
    7. Dey, Suman & Reang, Narath Moni & Majumder, Arindam & Deb, Madhujit & Das, Pankaj Kumar, 2020. "A hybrid ANN-Fuzzy approach for optimization of engine operating parameters of a CI engine fueled with diesel-palm biodiesel-ethanol blend," Energy, Elsevier, vol. 202(C).
    8. 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.
    9. Subrata Bhowmik & Rajsekhar Panua & Subrata K Ghosh & Abhishek Paul & Durbadal Debroy, 2018. "Prediction of performance and exhaust emissions of diesel engine fuelled with adulterated diesel: An artificial neural network assisted fuzzy-based topology optimization," Energy & Environment, , vol. 29(8), pages 1413-1437, December.
    10. Sasanka Katreddi & Sujan Kasani & Arvind Thiruvengadam, 2022. "A Review of Applications of Artificial Intelligence in Heavy Duty Trucks," Energies, MDPI, vol. 15(20), pages 1-20, October.
    11. Hazar, Hanbey & Tekdogan, Remziye & Sevinc, Huseyin, 2021. "Determination of the effects of oxygen-enriched air with the help of zeolites on the exhaust emission and performance of a diesel engine," Energy, Elsevier, vol. 236(C).
    12. Ghadikolaei, Meisam Ahmadi & Wong, Pak Kin & Cheung, Chun Shun & Ning, Zhi & Yung, Ka-Fu & Zhao, Jing & Gali, Nirmal Kumar & Berenjestanaki, Alireza Valipour, 2021. "Impact of lower and higher alcohols on the physicochemical properties of particulate matter from diesel engines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    13. Lotfan, S. & Ghiasi, R. Akbarpour & Fallah, M. & Sadeghi, M.H., 2016. "ANN-based modeling and reducing dual-fuel engine’s challenging emissions by multi-objective evolutionary algorithm NSGA-II," Applied Energy, Elsevier, vol. 175(C), pages 91-99.
    14. Shameer, P. Mohamed & Ramesh, K., 2018. "Assessment on the consequences of injection timing and injection pressure on combustion characteristics of sustainable biodiesel fuelled engine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 45-61.
    15. Shahir, V.K. & Jawahar, C.P. & Suresh, P.R., 2015. "Comparative study of diesel and biodiesel on CI engine with emphasis to emissions—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 686-697.
    16. Talebian-Kiakalaieh, Amin & Amin, Nor Aishah Saidina & Zarei, Alireza & Noshadi, Iman, 2013. "Transesterification of waste cooking oil by heteropoly acid (HPA) catalyst: Optimization and kinetic model," Applied Energy, Elsevier, vol. 102(C), pages 283-292.
    17. Taghavifar, Hadi & Khalilarya, Shahram & Jafarmadar, Samad, 2014. "Diesel engine spray characteristics prediction with hybridized artificial neural network optimized by genetic algorithm," Energy, Elsevier, vol. 71(C), pages 656-664.
    18. 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.
    19. 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.
    20. Bishop, Justin D.K. & Stettler, Marc E.J. & Molden, N. & Boies, Adam M., 2016. "Engine maps of fuel use and emissions from transient driving cycles," Applied Energy, Elsevier, vol. 183(C), pages 202-217.

    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:263:y:2020:i:c:s0306261920301240. 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.