IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v247y2022ics0360544222003760.html
   My bibliography  Save this article

Artificial neural networks modeling of combustion parameters for a diesel engine fueled with biodiesel fuel

Author

Listed:
  • Can, Özer
  • Baklacioglu, Tolga
  • Özturk, Erkan
  • Turan, Onder

Abstract

In the present study, numerous artificial neural networks were employed to predict the combustion characteristics of a four-stroke, single-cylinder, naturally aspirated diesel engine, including multilayer perceptron (MLP), adaptive neuro-fuzzy interference system (ANFIS) and radial basis function network (RBFN). The actual data derived from measurements and calculations were applied in model training, cross-validation, and testing. Biodiesel fuel ratio, engine load, air consumption, and fuel flow rate data were considered as model-input parameters, which are related to main engine operating variables and also affect the combustion characteristics. These kinds of model-input data were especially preferred due to being commonly direct measurable with the main engine sensors or found in engine maps/look-up tables managed by the electronic control unit (ECU). The main parameters obtained from the direct analysis of the measured in-cylinder pressure data and the heat release analysis results were also determined as model-output parameters. Equally, to ensure a more accurate, straightforward and practical approach to prediction, in every category of neural networks, three training algorithms were adopted, including Levenberg-Marquardt (LM), back propagation (BP) and conjugate gradient (CG). Moreover, a sensitivity analysis was accomplished by assessing the strengths of the links between input and output parameters for every topology. As such, the researcher revealed that all proposed neural networks could predict maximum heat release rate (HRRmax), ignition delay (ID), maximum cylinder pressure (Pmax), maximum cylinder pressure location (θPmax), maximum in-cylinder pressure rise rate (dPmax), indicated mean effective pressure (IMEP), crank angle of center heat release rate (CA50), and combustion duration (CD), with a high accuracy rate. Results of model-output parameters are also important parameters in the combustion diagnostics which are influential on engine thermal efficiency and pollutant formation process. In addition, it is necessary to note that MLP architectures that incorporated the LM algorithm presented superior results. With regards to the optimal ANN model, the linear coefficient values: 0.999848, 0.999847, 0.999955, 0.999780, 0.999378, 0.999929, 0.999766, and 0.999216, were found for ID, HRRmax, Pmax, θPmax, dPmax, IMEP, CA50 and CD, respectively.

Suggested Citation

  • Can, Özer & Baklacioglu, Tolga & Özturk, Erkan & Turan, Onder, 2022. "Artificial neural networks modeling of combustion parameters for a diesel engine fueled with biodiesel fuel," Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222003760
    DOI: 10.1016/j.energy.2022.123473
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222003760
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.123473?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. Sundus, F. & Fazal, M.A. & Masjuki, H.H., 2017. "Tribology with biodiesel: A study on enhancing biodiesel stability and its fuel properties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 399-412.
    2. Baklacioglu, Tolga & Aydin, Hakan & Turan, Onder, 2016. "Energetic and exergetic efficiency modeling of a cargo aircraft by a topology improving neuro-evolution algorithm," Energy, Elsevier, vol. 103(C), pages 630-645.
    3. Adewole, Bamiji Z. & Abidakun, Olatunde A. & Asere, Abraham A., 2013. "Artificial neural network prediction of exhaust emissions and flame temperature in LPG (liquefied petroleum gas) fueled low swirl burner," Energy, Elsevier, vol. 61(C), pages 606-611.
    4. 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.
    5. Palash, S.M. & Kalam, M.A. & Masjuki, H.H. & Masum, B.M. & Rizwanul Fattah, I.M. & Mofijur, M., 2013. "Impacts of biodiesel combustion on NOx emissions and their reduction approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 473-490.
    6. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    7. Sun, Wei & Xu, Yanfeng, 2016. "Financial security evaluation of the electric power industry in China based on a back propagation neural network optimized by genetic algorithm," Energy, Elsevier, vol. 101(C), pages 366-379.
    8. Can, Özer & Öztürk, Erkan & Yücesu, H. Serdar, 2017. "Combustion and exhaust emissions of canola biodiesel blends in a single cylinder DI diesel engine," Renewable Energy, Elsevier, vol. 109(C), pages 73-82.
    9. Dwivedi, Gaurav & Sharma, M.P., 2014. "Impact of cold flow properties of biodiesel on engine performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 650-656.
    10. Haseeb Yaqoob & Yew Heng Teoh & Farooq Sher & Muhammad Umer Farooq & Muhammad Ahmad Jamil & Zareena Kausar & Noor Us Sabah & Muhammad Faizan Shah & Hafiz Zia Ur Rehman & Atiq Ur Rehman, 2021. "Potential of Waste Cooking Oil Biodiesel as Renewable Fuel in Combustion Engines: A Review," Energies, MDPI, vol. 14(9), pages 1-20, April.
    11. Taghavifar, Hamid & Mardani, Aref, 2014. "A comparative trend in forecasting ability of artificial neural networks and regressive support vector machine methodologies for energy dissipation modeling of off-road vehicles," Energy, Elsevier, vol. 66(C), pages 569-576.
    12. Al-geelani, Nasir A. & M. Piah, M. Afendi & Bashir, Nouruddeen, 2015. "A review on hybrid wavelet regrouping particle swarm optimization neural networks for characterization of partial discharge acoustic signals," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 20-35.
    13. Voyant, Cyril & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure, 2013. "Hybrid methodology for hourly global radiation forecasting in Mediterranean area," Renewable Energy, Elsevier, vol. 53(C), pages 1-11.
    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. Uzlu, Ergun & Akpınar, Adem & Özturk, Hasan Tahsin & Nacar, Sinan & Kankal, Murat, 2014. "Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey," Energy, Elsevier, vol. 69(C), pages 638-647.
    16. Moradi, G.R. & Dehghani, S. & Khosravian, F. & Arjmandzadeh, A., 2013. "The optimized operational conditions for biodiesel production from soybean oil and application of artificial neural networks for estimation of the biodiesel yield," Renewable Energy, Elsevier, vol. 50(C), pages 915-920.
    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. Chen, Mingfei & Zhou, Kaile & Liu, Dong, 2024. "Machine learning based technique for outlier detection and result prediction in combustion diagnostics," Energy, Elsevier, vol. 290(C).
    2. Jisieike, Chiazor Faustina & Ishola, Niyi Babatunde & Latinwo, Lekan M. & Betiku, Eriola, 2023. "Crude rubber seed oil esterification using a solid catalyst: Optimization by hybrid adaptive neuro-fuzzy inference system and response surface methodology," Energy, Elsevier, vol. 263(PB).
    3. Hu, Deng & Wang, Hechun & Wang, Binbin & Shi, Mingwei & Duan, Baoyin & Wang, Yinyan & Yang, Chuanlei, 2022. "Calibration of 0-D combustion model applied to dual-fuel engine," Energy, Elsevier, vol. 261(PB).
    4. 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).
    5. Karatuğ, Çağlar & Tadros, Mina & Ventura, Manuel & Soares, C. Guedes, 2024. "Decision support system for ship energy efficiency management based on an optimization model," Energy, Elsevier, vol. 292(C).
    6. Cao, Jiale & Li, Tie & Huang, Shuai & Chen, Run & Li, Shiyan & Kuang, Min & Yang, Rundai & Huang, Yating, 2023. "Co-optimization of miller degree and geometric compression ratio of a large-bore natural gas generator engine with novel Knock models and machine learning," Applied Energy, Elsevier, vol. 352(C).
    7. Wang, Huaiyu & Ji, Changwei & Yang, Jinxin & Wang, Shuofeng & Ge, Yunshan, 2022. "Towards a comprehensive optimization of the intake characteristics for side ported Wankel rotary engines by coupling machine learning with genetic algorithm," Energy, Elsevier, vol. 261(PB).
    8. 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).
    9. Huang, Congzhi & Li, Zhuoyong, 2023. "Data-driven modeling of ultra-supercritical unit coordinated control system by improved transformer network," Energy, Elsevier, vol. 266(C).
    10. Aliakbari, Karim & Ebrahimi-Moghadam, Amir & Pahlavanzadeh, Mohammadsadegh & Moradi, Reza, 2023. "Performance characteristics and exhaust emissions of a single-cylinder diesel engine for different fuels: Experimental investigation and artificial intelligence network," Energy, Elsevier, vol. 284(C).
    11. Li, Ji & Zhou, Quan & He, Xu & Chen, Wan & Xu, Hongming, 2023. "Data-driven enabling technologies in soft sensors of modern internal combustion engines: Perspectives," Energy, Elsevier, vol. 272(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. Suiuay, Chokchai & Laloon, Kittipong & Katekaew, Somporn & Senawong, Kritsadang & Noisuwan, Phakamat & Sudajan, Somposh, 2020. "Effect of gasoline-like fuel obtained from hard-resin of Yang (Dipterocarpus alatus) on single cylinder gasoline engine performance and exhaust emissions," Renewable Energy, Elsevier, vol. 153(C), pages 634-645.
    2. 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.
    3. Đozić, Damir J. & Gvozdenac Urošević, Branka D., 2019. "Application of artificial neural networks for testing long-term energy policy targets," Energy, Elsevier, vol. 174(C), pages 488-496.
    4. Silitonga, A.S. & Shamsuddin, A.H. & Mahlia, T.M.I. & Milano, Jassinne & Kusumo, F. & Siswantoro, Joko & Dharma, S. & Sebayang, A.H. & Masjuki, H.H. & Ong, Hwai Chyuan, 2020. "Biodiesel synthesis from Ceiba pentandra oil by microwave irradiation-assisted transesterification: ELM modeling and optimization," Renewable Energy, Elsevier, vol. 146(C), pages 1278-1291.
    5. Ağbulut, Ümit & Yeşilyurt, Murat Kadir & Sarıdemir, Suat, 2021. "Wastes to energy: Improving the poor properties of waste tire pyrolysis oil with waste cooking oil methyl ester and waste fusel alcohol – A detailed assessment on the combustion, emission, and perform," Energy, Elsevier, vol. 222(C).
    6. Torres-Ramírez, M. & Elizondo, D. & García-Domingo, B. & Nofuentes, G. & Talavera, D.L., 2015. "Modelling the spectral irradiance distribution in sunny inland locations using an ANN-based methodology," Energy, Elsevier, vol. 86(C), pages 323-334.
    7. Chen, Yong & Lu, Zhiyuan & Liu, Heng & Wang, Hu & Zheng, Zunqing & Wang, Changhui & Sun, Xingyu & Xu, Linxun & Yao, Mingfa, 2024. "Machine learning-based design of target property-oriented fuels using explainable artificial intelligence," Energy, Elsevier, vol. 300(C).
    8. Mat Yasin, Mohd Hafizil & Mamat, Rizalman & Najafi, G. & Ali, Obed Majeed & Yusop, Ahmad Fitri & Ali, Mohd Hafiz, 2017. "Potentials of palm oil as new feedstock oil for a global alternative fuel: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1034-1049.
    9. Omar Aboelazayem & Mamdouh Gadalla & Basudeb Saha, 2022. "Comprehensive Optimisation of Biodiesel Production Conditions via Supercritical Methanolysis of Waste Cooking Oil," Energies, MDPI, vol. 15(10), pages 1-22, May.
    10. 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.
    11. Vardast, Neda & Haghighi, Mohammad & Dehghani, Sahar, 2019. "Sono-dispersion of calcium over Al-MCM-41used as a nanocatalyst for biodiesel production from sunflower oil: Influence of ultrasound irradiation and calcium content on catalytic properties and perform," Renewable Energy, Elsevier, vol. 132(C), pages 979-988.
    12. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
    13. Meng, Ming & Niu, Dongxiao, 2011. "Modeling CO2 emissions from fossil fuel combustion using the logistic equation," Energy, Elsevier, vol. 36(5), pages 3355-3359.
    14. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
    15. Perumal, Varatharaju & Ilangkumaran, M., 2018. "Water emulsified hybrid pongamia biodiesel as a modified fuel for the experimental analysis of performance, combustion and emission characteristics of a direct injection diesel engine," Renewable Energy, Elsevier, vol. 121(C), pages 623-631.
    16. Yesilyurt, Murat Kadir & Eryilmaz, Tanzer & Arslan, Mevlüt, 2018. "A comparative analysis of the engine performance, exhaust emissions and combustion behaviors of a compression ignition engine fuelled with biodiesel/diesel/1-butanol (C4 alcohol) and biodiesel/diesel/," Energy, Elsevier, vol. 165(PB), pages 1332-1351.
    17. Singh, Paramvir & Varun, & Chauhan, S.R., 2016. "Carbonyl and aromatic hydrocarbon emissions from diesel engine exhaust using different feedstock: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 269-291.
    18. Bergthorson, Jeffrey M. & Thomson, Murray J., 2015. "A review of the combustion and emissions properties of advanced transportation biofuels and their impact on existing and future engines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1393-1417.
    19. Wong, Ka In & Wong, Pak Kin & Cheung, Chun Shun & Vong, Chi Man, 2013. "Modeling and optimization of biodiesel engine performance using advanced machine learning methods," Energy, Elsevier, vol. 55(C), pages 519-528.
    20. Zonggui Yao & Chen Wang, 2018. "A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting," Sustainability, MDPI, vol. 10(5), pages 1-33, May.

    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:247:y:2022:i:c:s0360544222003760. 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.