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Geometric neuro-fuzzy transfer learning for in-cylinder pressure modelling of a diesel engine fuelled with raw microalgae oil

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  • Li, Ji
  • Wu, Dawei
  • Mohammadsami Attar, Hassan
  • Xu, Hongming

Abstract

Bioenergy attracts more attention owing to the reduction of both air pollution and greenhouse gas emissions in a whole life cycle compared to fossil fuels. As a third-generation biofuel, Microalgae Oil (MAO) can utilise carbon dioxide and light energy at an increased photosynthetic efficiency compared to energy crops for biomass. Due to the wide variety of MAO and their blends with diesel in different ratios, characterization of these biofuels’ engine performance is difficult to be standardized, e.g., in-cylinder pressure. This paper proposes a novel approach of geometric neuro-fuzzy transfer learning (GNFTL) for in-cylinder pressure modelling of a diesel engine fuelled with MAO. Inspired by computational geometry, this approach only utilizes limited experimental data obtained by geometric screening to learn a high-precise transfer model of the in-cylinder pressure with different MAO blending ratios. Followed by the process of MAO extraction and test cell description, the proposed approach of GNFTL is presented which comprises geometric transfer domain segmentation and neuro-fuzzy transfer learning. By a comprehensive study, the results demonstrate that the proposed approach can achieve a competitive prediction accuracy whilst significantly reducing experimental efforts on used biofuel by 47.8% and operation time by 41.5%, compared to the conventional manual design of experiment.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:306:y:2022:i:pa:s0306261921013143
    DOI: 10.1016/j.apenergy.2021.118014
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    1. Brennan, Liam & Owende, Philip, 2010. "Biofuels from microalgae--A review of technologies for production, processing, and extractions of biofuels and co-products," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(2), pages 557-577, February.
    2. Fagundez, J.L.S. & Lanzanova, T.D.M. & Martins, M.E.S. & Salau, N.P.G., 2020. "Joint use of artificial neural networks and particle swarm optimization to determine optimal performance of an ethanol SI engine operating with negative valve overlap strategy," Energy, Elsevier, vol. 204(C).
    3. Ramachandra, T.V. & Durga Madhab, Mahapatra & Shilpi, Samantray & Joshi, N.V., 2013. "Algal biofuel from urban wastewater in India: Scope and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 767-777.
    4. Joshi, Girdhar & Pandey, Jitendra K. & Rana, Sravendra & Rawat, Devendra S., 2017. "Challenges and opportunities for the application of biofuel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 850-866.
    5. 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.
    6. Rakopoulos, Constantine D. & Rakopoulos, Dimitrios C. & Mavropoulos, George C. & Kosmadakis, George M., 2018. "Investigating the EGR rate and temperature impact on diesel engine combustion and emissions under various injection timings and loads by comprehensive two-zone modeling," Energy, Elsevier, vol. 157(C), pages 990-1014.
    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. Susan C. Anenberg & Joshua Miller & Ray Minjares & Li Du & Daven K. Henze & Forrest Lacey & Christopher S. Malley & Lisa Emberson & Vicente Franco & Zbigniew Klimont & Chris Heyes, 2017. "Impacts and mitigation of excess diesel-related NOx emissions in 11 major vehicle markets," Nature, Nature, vol. 545(7655), pages 467-471, May.
    9. 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.
    10. Cocco Mariani, Viviana & Hennings Och, Stephan & dos Santos Coelho, Leandro & Domingues, Eric, 2019. "Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models," Applied Energy, Elsevier, vol. 249(C), pages 204-221.
    11. Mahabadipour, Hamidreza & Srinivasan, Kalyan K. & Krishnan, Sundar R., 2019. "An exergy analysis methodology for internal combustion engines using a multi-zone simulation of dual fuel low temperature combustion," Applied Energy, Elsevier, vol. 256(C).
    12. Najafi, Bahman & Akbarian, Eivaz & Lashkarpour, S. Mehdi & Aghbashlo, Mortaza & Ghaziaskar, Hassan S. & Tabatabaei, Meisam, 2019. "Modeling of a dual fueled diesel engine operated by a novel fuel containing glycerol triacetate additive and biodiesel using artificial neural network tuned by genetic algorithm to reduce engine emiss," Energy, Elsevier, vol. 168(C), pages 1128-1137.
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