IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v202y2020ics0360544220309208.html
   My bibliography  Save this article

A hybrid ANN-Fuzzy approach for optimization of engine operating parameters of a CI engine fueled with diesel-palm biodiesel-ethanol blend

Author

Listed:
  • Dey, Suman
  • Reang, Narath Moni
  • Majumder, Arindam
  • Deb, Madhujit
  • Das, Pankaj Kumar

Abstract

This paper investigates use of artificial neural network (ANN) model in prediction of brake specific energy consumption (BSEC), nitrogen oxides (NOx), unburnt hydrocarbon (UHC), and carbon dioxide (CO2) emissions of a single cylinder diesel engine operates with diesel-palm biodiesel-ethanol blends. The engine is run at different load form 20–100% and 1500 rpm constant speed. The fuel used in this present study are diesel and six different diesel-palm biodiesel-ethanol blends. The Levenberg-Marquardt back propagation training algorithm with logistic-sigmoid activation function results best prediction of performance and emission characteristics with accurate overall correlation coefficient (R) (0.99329–0.99875) and minimum mean square error (MSE) (0.000179082–0.000465809). The mean absolute percentage errors (MAPE) are observed to be in range of 2.32–4.54% with the acceptable margin of mean square relative error (MSRE). Furthermore, experimental and ANN predicted data are compared in fuzzy interface system (FIS) to find optimum engine operating parameters. Compared to other blends, at 20% load, D85BD10E5 blend exhibits the highest MPCI (multi performance characteristics index) values of 0.718 and 0.705 for experimental and ANN predicted data respectively. Robustness and reliability of the proposed techniques clearly explain the application of ANN and fuzzy logic system in the prediction and optimization of engine parameters.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:202:y:2020:i:c:s0360544220309208
    DOI: 10.1016/j.energy.2020.117813
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220309208
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.117813?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. Javed, Syed & Baig, Rahmath Ulla & Murthy, Y.V.V. Satyanarayana, 2018. "Study on noise in a hydrogen dual-fuelled zinc-oxide nanoparticle blended biodiesel engine and the development of an artificial neural network model," Energy, Elsevier, vol. 160(C), pages 774-782.
    2. 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.
    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. Ghobadian, B. & Rahimi, H. & Nikbakht, A.M. & Najafi, G. & Yusaf, T.F., 2009. "Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network," Renewable Energy, Elsevier, vol. 34(4), pages 976-982.
    5. Al-Hinti, I. & Samhouri, M. & Al-Ghandoor, A. & Sakhrieh, A., 2009. "The effect of boost pressure on the performance characteristics of a diesel engine: A neuro-fuzzy approach," Applied Energy, Elsevier, vol. 86(1), pages 113-121, January.
    6. Bose, Probir Kumar & Deb, Madhujit & Banerjee, Rahul & Majumder, Arindam, 2013. "Multi objective optimization of performance parameters of a single cylinder diesel engine running with hydrogen using a Taguchi-fuzzy based approach," Energy, Elsevier, vol. 63(C), pages 375-386.
    7. Yusaf, T.F. & Yousif, B.F. & Elawad, M.M., 2011. "Crude palm oil fuel for diesel-engines: Experimental and ANN simulation approaches," Energy, Elsevier, vol. 36(8), pages 4871-4878.
    8. 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.
    9. Cheikh, Kezrane & Sary, Awad & Khaled, Loubar & Abdelkrim, Liazid & Mohand, Tazerout, 2016. "Experimental assessment of performance and emissions maps for biodiesel fueled compression ignition engine," Applied Energy, Elsevier, vol. 161(C), pages 320-329.
    10. Shahir, S.A. & Masjuki, H.H. & Kalam, M.A. & Imran, A. & Fattah, I.M. Rizwanul & Sanjid, A., 2014. "Feasibility of diesel–biodiesel–ethanol/bioethanol blend as existing CI engine fuel: An assessment of properties, material compatibility, safety and combustion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 379-395.
    11. Ankita Singh & Saurav Datta & Siba Sankar Mahapatra, 2013. "Principal component analysis and fuzzy embedded Taguchi approach for multi-response optimisation in machining of GFRP polyester composites: a case study," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 14(2), pages 175-206.
    12. Guido, Chiara & Beatrice, Carlo & Napolitano, Pierpaolo, 2013. "Application of bioethanol/RME/diesel blend in a Euro5 automotive diesel engine: Potentiality of closed loop combustion control technology," Applied Energy, Elsevier, vol. 102(C), pages 13-23.
    13. 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.
    14. Silitonga, A.S. & Masjuki, H.H. & Ong, Hwai Chyuan & Sebayang, A.H. & Dharma, S. & Kusumo, F. & Siswantoro, J. & Milano, Jassinnee & Daud, Khairil & Mahlia, T.M.I. & Chen, Wei-Hsin & Sugiyanto, Bamban, 2018. "Evaluation of the engine performance and exhaust emissions of biodiesel-bioethanol-diesel blends using kernel-based extreme learning machine," Energy, Elsevier, vol. 159(C), pages 1075-1087.
    15. Ma, Yinjie & Huang, Ronghua & Fu, Jianqin & Huang, Sheng & Liu, Jingping, 2018. "Development of a diesel/biodiesel/alcohol (up to n-pentanol) combined mechanism based on reaction pathways analysis methodology," Applied Energy, Elsevier, vol. 225(C), pages 835-847.
    16. 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.
    17. Deh Kiani, M. Kiani & Ghobadian, B. & Tavakoli, T. & Nikbakht, A.M. & Najafi, G., 2010. "Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends," Energy, Elsevier, vol. 35(1), pages 65-69.
    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. Li, Ji & Wu, Dawei & Mohammadsami Attar, Hassan & Xu, Hongming, 2022. "Geometric neuro-fuzzy transfer learning for in-cylinder pressure modelling of a diesel engine fuelled with raw microalgae oil," Applied Energy, Elsevier, vol. 306(PA).
    2. 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).
    3. Mendiburu, Andrés Z. & Lauermann, Carlos H. & Hayashi, Thamy C. & Mariños, Diego J. & Rodrigues da Costa, Roberto Berlini & Coronado, Christian J.R. & Roberts, Justo J. & de Carvalho, João A., 2022. "Ethanol as a renewable biofuel: Combustion characteristics and application in engines," Energy, Elsevier, vol. 257(C).
    4. Prabhakar Sharma & Ajay Chhillar & Zafar Said & Saim Memon, 2021. "Exploring the Exhaust Emission and Efficiency of Algal Biodiesel Powered Compression Ignition Engine: Application of Box–Behnken and Desirability Based Multi-Objective Response Surface Methodology," Energies, MDPI, vol. 14(18), pages 1-22, September.
    5. Gharehghani, Ayat & Abbasi, Hamid Reza & Alizadeh, Pouria, 2021. "Application of machine learning tools for constrained multi-objective optimization of an HCCI engine," Energy, Elsevier, vol. 233(C).
    6. Zandie, Mohammad & Ng, Hoon Kiat & Gan, Suyin & Muhamad Said, Mohd Farid & Cheng, Xinwei, 2023. "Multi-input multi-output machine learning predictive model for engine performance and stability, emissions, combustion and ignition characteristics of diesel-biodiesel-gasoline blends," Energy, Elsevier, vol. 262(PA).
    7. Suman Dey & Akhilendra Pratap Singh & Sameer Sheshrao Gajghate & Sagnik Pal & Bidyut Baran Saha & Madhujit Deb & Pankaj Kumar Das, 2023. "Optimization of CI Engine Performance and Emissions Using Alcohol–Biodiesel Blends: A Regression Analysis Approach," Sustainability, MDPI, vol. 15(20), pages 1-14, October.
    8. 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).
    9. Rajkumar, Sundararajan & Das, Arnab & Thangaraja, Jeyaseelan, 2022. "Integration of artificial neural network, multi-objective genetic algorithm and phenomenological combustion modelling for effective operation of biodiesel blends in an automotive engine," Energy, Elsevier, vol. 239(PA).
    10. Adhirath Mandal & Haengmuk Cho & Bhupendra Singh Chauhan, 2021. "ANN Prediction of Performance and Emissions of CI Engine Using Biogas Flow Variation," Energies, MDPI, vol. 14(10), pages 1-18, May.
    11. Viswanathan, Vinoth Kannan & Kaladgi, Abdul Razak & Thomai, Pushparaj & Ağbulut, Ümit & Alwetaishi, Mamdooh & Said, Zafar & Shaik, Saboor & Afzal, Asif, 2022. "Hybrid optimization and modelling of CI engine performance and emission characteristics of novel hybrid biodiesel blends," Renewable Energy, Elsevier, vol. 198(C), pages 549-567.
    12. Mohammad Talaei & Majid Astaneh & Elmira Ghiasabadi Farahani & Farzin Golzar, 2023. "Application of Artificial Intelligence for Predicting CO 2 Emission Using Weighted Multi-Task Learning," Energies, MDPI, vol. 16(16), pages 1-18, August.

    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. Rajkumar, Sundararajan & Das, Arnab & Thangaraja, Jeyaseelan, 2022. "Integration of artificial neural network, multi-objective genetic algorithm and phenomenological combustion modelling for effective operation of biodiesel blends in an automotive engine," Energy, Elsevier, vol. 239(PA).
    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. 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.
    4. Iftikhar Ahmad & Adil Sana & Manabu Kano & Izzat Iqbal Cheema & Brenno C. Menezes & Junaid Shahzad & Zahid Ullah & Muzammil Khan & Asad Habib, 2021. "Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions," Energies, MDPI, vol. 14(16), pages 1-27, August.
    5. 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.
    6. 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.
    7. 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.
    8. Ganesan, P. & Rajakarunakaran, S. & Thirugnanasambandam, M. & Devaraj, D., 2015. "Artificial neural network model to predict the diesel electric generator performance and exhaust emissions," Energy, Elsevier, vol. 83(C), pages 115-124.
    9. Domínguez-Sáez, Aida & Rattá, Giuseppe A. & Barrios, Carmen C., 2018. "Prediction of exhaust emission in transient conditions of a diesel engine fueled with animal fat using Artificial Neural Network and Symbolic Regression," Energy, Elsevier, vol. 149(C), pages 675-683.
    10. 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.
    11. 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.
    12. 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).
    13. 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).
    14. 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.
    15. Manieniyan, V. & Vinodhini, G. & Senthilkumar, R. & Sivaprakasam, S., 2016. "Wear element analysis using neural networks of a DI diesel engine using biodiesel with exhaust gas recirculation," Energy, Elsevier, vol. 114(C), pages 603-612.
    16. Mohammed I. Jahirul & Richard J. Brown & Wijitha Senadeera & Ian M. O'Hara & Zoran D. Ristovski, 2013. "The Use of Artificial Neural Networks for Identifying Sustainable Biodiesel Feedstocks," Energies, MDPI, vol. 6(8), pages 1-43, July.
    17. Bahri, Bahram & Shahbakhti, Mahdi & Kannan, Kaushik & Aziz, Azhar Abdul, 2016. "Identification of ringing operation for low temperature combustion engines," Applied Energy, Elsevier, vol. 171(C), pages 142-152.
    18. Bendu, Harisankar & Deepak, B.B.V.L. & Murugan, S., 2017. "Multi-objective optimization of ethanol fuelled HCCI engine performance using hybrid GRNN–PSO," Applied Energy, Elsevier, vol. 187(C), pages 601-611.
    19. 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.
    20. Bhowmik, Mrinal & Muthukumar, P. & Anandalakshmi, R., 2019. "Experimental based multilayer perceptron approach for prediction of evacuated solar collector performance in humid subtropical regions," Renewable Energy, Elsevier, vol. 143(C), pages 1566-1580.

    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:energy:v:202:y:2020:i:c:s0360544220309208. 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.journals.elsevier.com/energy .

    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.