IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v90y2018icp665-686.html
   My bibliography  Save this article

A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel

Author

Listed:
  • Yusri, I.M.
  • Abdul Majeed, A.P.P.
  • Mamat, R.
  • Ghazali, M.F.
  • Awad, Omar I.
  • Azmi, W.H.

Abstract

Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:rensus:v:90:y:2018:i:c:p:665-686
    DOI: 10.1016/j.rser.2018.03.095
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032118301916
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2018.03.095?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. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.
    3. Poompipatpong, Chedthawut & Kengpol, Athakorn, 2015. "Design of a decision support methodology using response surface for torque comparison: An empirical study on an engine fueled with waste plastic pyrolysis oil," Energy, Elsevier, vol. 82(C), pages 850-856.
    4. 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.
    5. Nina Hall, 2017. "What is adaptation to climate change? Epistemic ambiguity in the climate finance system," International Environmental Agreements: Politics, Law and Economics, Springer, vol. 17(1), pages 37-53, February.
    6. Pandian, M. & Sivapirakasam, S.P. & Udayakumar, M., 2011. "Investigation on the effect of injection system parameters on performance and emission characteristics of a twin cylinder compression ignition direct injection engine fuelled with pongamia biodiesel-d," Applied Energy, Elsevier, vol. 88(8), pages 2663-2676, August.
    7. 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.
    8. Almonacid, Florencia & Fernandez, Eduardo F. & Mellit, Adel & Kalogirou, Soteris, 2017. "Review of techniques based on artificial neural networks for the electrical characterization of concentrator photovoltaic technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 938-953.
    9. Azad, A.K. & Rasul, M.G. & Khan, M.M.K. & Sharma, Subhash C. & Hazrat, M.A., 2015. "Prospect of biofuels as an alternative transport fuel in Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 331-351.
    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. Mahmudul, H.M. & Hagos, F.Y. & Mamat, R. & Adam, A. Abdul & Ishak, W.F.W. & Alenezi, R., 2017. "Production, characterization and performance of biodiesel as an alternative fuel in diesel engines – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 497-509.
    12. Božnar, Marija Zlata & Grašič, Boštjan & Oliveira, Amauri Pereira de & Soares, Jacyra & Mlakar, Primož, 2017. "Spatially transferable regional model for half-hourly values of diffuse solar radiation for general sky conditions based on perceptron artificial neural networks," Renewable Energy, Elsevier, vol. 103(C), pages 794-810.
    13. 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.
    14. Shahbaz, Muhammad & Yusup, Suzana & Inayat, Abrar & Patrick, David Onoja & Pratama, Angga, 2016. "Application of response surface methodology to investigate the effect of different variables on conversion of palm kernel shell in steam gasification using coal bottom ash," Applied Energy, Elsevier, vol. 184(C), pages 1306-1315.
    15. 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.
    16. Hasan, M.M. & Rahman, M.M., 2017. "Performance and emission characteristics of biodiesel–diesel blend and environmental and economic impacts of biodiesel production: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 938-948.
    17. 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.
    18. Hosmath, R.S. & Banapurmath, N.R. & Khandal, S.V. & Gaitonde, V.N. & Basavarajappa, Y.H. & Yaliwal, V.S., 2016. "Effect of compression ratio, CNG flow rate and injection timing on the performance of dual fuel engine operated on honge oil methyl ester (HOME) and compressed natural gas (CNG)," Renewable Energy, Elsevier, vol. 93(C), pages 579-590.
    19. Atmanli, Alpaslan & Ileri, Erol & Yilmaz, Nadir, 2016. "Optimization of diesel–butanol–vegetable oil blend ratios based on engine operating parameters," Energy, Elsevier, vol. 96(C), pages 569-580.
    20. Yusri, I.M. & Mamat, R. & Najafi, G. & Razman, A. & Awad, Omar I. & Azmi, W.H. & Ishak, W.F.W. & Shaiful, A.I.M., 2017. "Alcohol based automotive fuels from first four alcohol family in compression and spark ignition engine: A review on engine performance and exhaust emissions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 169-181.
    21. Hasan, M.M. & Rahman, M.M., 2016. "Homogeneous charge compression ignition combustion: Advantages over compression ignition combustion, challenges and solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 282-291.
    22. 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.
    23. Najafi, Gholamhassan & Ghobadian, Barat & Yusaf, Talal & Safieddin Ardebili, Seyed Mohammad & Mamat, Rizalman, 2015. "Optimization of performance and exhaust emission parameters of a SI (spark ignition) engine with gasoline–ethanol blended fuels using response surface methodology," Energy, Elsevier, vol. 90(P2), pages 1815-1829.
    24. Sehatpour, Mohammad-Hadi & Kazemi, Aliyeh & Sehatpour, Hesam-eddin, 2017. "Evaluation of alternative fuels for light-duty vehicles in Iran using a multi-criteria approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 295-310.
    25. 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.
    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. Kumar, Thanikasalam & Mohsin, Rahmat & Majid, Zulkifli Abd. & Ghafir, Mohammad Fahmi Abdul & Wash, Ananth Manickam, 2020. "Experimental study of the anti-knock efficiency of high-octane fuels in spark ignited aircraft engine using response surface methodology," Applied Energy, Elsevier, vol. 259(C).
    3. Singh, Yashvir & Sharma, Abhishek & Tiwari, Sumit & Singla, Amneesh, 2019. "Optimization of diesel engine performance and emission parameters employing cassia tora methyl esters-response surface methodology approach," Energy, Elsevier, vol. 168(C), pages 909-918.
    4. Dhande, D.Y. & Nighot, D.V. & Sinaga, Nazaruddin & Dahe, Kiran B., 2021. "Extraction of bioethanol from waste pomegranate fruits as a potential feedstock and its blending effects on a performance of a single cylinder SI engine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    5. 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.
    6. 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).
    7. Zhang, Rongda & Wei, Jing & Zhao, Xiaoli & Liu, Yang, 2022. "Economic and environmental benefits of the integration between carbon sequestration and underground gas storage," Energy, Elsevier, vol. 260(C).
    8. Meroni, Andrea & Robertson, Miles & Martinez-Botas, Ricardo & Haglind, Fredrik, 2018. "A methodology for the preliminary design and performance prediction of high-pressure ratio radial-inflow turbines," Energy, Elsevier, vol. 164(C), pages 1062-1078.
    9. Varuvel, Edwin Geo & Seetharaman, Sathyanarayanan & Joseph Shobana Bai, Femilda Josephin & Devarajan, Yuvarajan & Balasubramanian, Dhinesh, 2023. "Development of artificial neural network and response surface methodology model to optimize the engine parameters of rubber seed oil – Hydrogen on PCCI operation," Energy, Elsevier, vol. 283(C).
    10. 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).
    11. Marco Bietresato & Carlo Caligiuri & Anna Bolla & Massimiliano Renzi & Fabrizio Mazzetto, 2019. "Proposal of a Predictive Mixed Experimental- Numerical Approach for Assessing the Performance of Farm Tractor Engines Fuelled with Diesel- Biodiesel-Bioethanol Blends," Energies, MDPI, vol. 12(12), pages 1-45, June.
    12. How, H.G. & Teoh, Y.H. & Krishnan, B. Navaneetha & Le, T.D. & Nguyen, H.T. & Prabhu, C., 2021. "Prediction of optimum Palm Oil Methyl Ester fuel blend for compression ignition engine using Response Surface Methodology," Energy, Elsevier, vol. 234(C).
    13. Li, Ji & Zhou, Quan & Williams, Huw & Xu, Pu & Xu, Hongming & Lu, Guoxiang, 2022. "Fuzzy-tree-constructed data-efficient modelling methodology for volumetric efficiency of dedicated hybrid engines," Applied Energy, Elsevier, vol. 310(C).
    14. Kale, Aneesh Vijay & Krishnasamy, Anand, 2024. "Experimental study on combustion, performance, and emission characteristics of a homogeneous charge compression ignition engine fuelled with multiple biofuel-gasoline blends," Energy, Elsevier, vol. 288(C).
    15. 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).
    16. Şenol, Halil & Ali Dereli̇, Mehmet & Özbilgin, Ferdi, 2021. "Investigation of the distribution of bovine manure-based biomethane potential using an artificial neural network in Turkey to 2030," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    17. Kuo Jiang & Hong Zeng & Zefan Wu & Jianping Sun & Cai Chen & Bing Han, 2023. "Study on the Effect of Parameter Sensitivity on Engine Optimization Results," Energies, MDPI, vol. 16(23), pages 1-16, December.
    18. Deshmukh, Minal & Pathan, Aadil, 2024. "Bambusa tulda: A potential feedstock for bioethanol and its blending effects on the performance of spark ignition engine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).

    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. 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.
    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. 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. 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).
    5. 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.
    6. 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.
    7. 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).
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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).
    15. 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.
    16. Hosseini, Seyyed Hassan & Taghizadeh-Alisaraei, Ahmad & Ghobadian, Barat & Abbaszadeh-Mayvan, Ahmad, 2020. "Artificial neural network modeling of performance, emission, and vibration of a CI engine using alumina nano-catalyst added to diesel-biodiesel blends," Renewable Energy, Elsevier, vol. 149(C), pages 951-961.
    17. 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.
    18. Najafi, Gholamhassan & Ghobadian, Barat & Yusaf, Talal & Safieddin Ardebili, Seyed Mohammad & Mamat, Rizalman, 2015. "Optimization of performance and exhaust emission parameters of a SI (spark ignition) engine with gasoline–ethanol blended fuels using response surface methodology," Energy, Elsevier, vol. 90(P2), pages 1815-1829.
    19. Krishnamoorthi, M. & Malayalamurthi, R. & Sakthivel, R., 2019. "Optimization of compression ignition engine fueled with diesel - chaulmoogra oil - diethyl ether blend with engine parameters and exhaust gas recirculation," Renewable Energy, Elsevier, vol. 134(C), pages 579-602.
    20. 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.

    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:rensus:v:90:y:2018:i:c:p:665-686. 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/600126/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.