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Accurate predicting the viscosity of biodiesels and blends using soft computing models

Author

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  • Aminian, Ali
  • ZareNezhad, Bahman

Abstract

While the viscosity is an important factor influencing the atomization and combustion behavior of biodiesels, the viscosity prediction of biodiesels, blend of biodiesels, and blends of biodiesel-diesel fuels can be utilized for the replacement of conventional diesel fuels by the biodiesels from environmental pollution and renewability stand points. Therefore, a Support Vector Machine (SVM), an Adaptive Neuro Fuzzy Inference System (ANFIS), and feedforward neural network model trained by Genetic Algorithm (GA), Simulated Annealing (SA), and Levenberg-Marquardt (LM) are proposed for accurate prediction of the viscosity of various biodiesels based on a high number of experimental viscosity data. The performances of the developed models are compared to choose the one with the highest accuracy, which in turn led to pick up ANFIS model. Also, the neural network model trained by the stochastic optimization algorithms is provided better performance compared to other soft computing models while took into account new data. Also, the comparisons between the proposed model and the most well-known biodiesel viscosity models proofing the superiority of the developed model for predicting the viscosity of eighteen types of biodiesels with the correlation of determination of 0 .9964 and ARD of 2.51%.

Suggested Citation

  • Aminian, Ali & ZareNezhad, Bahman, 2018. "Accurate predicting the viscosity of biodiesels and blends using soft computing models," Renewable Energy, Elsevier, vol. 120(C), pages 488-500.
    • Handle: RePEc:eee:renene:v:120:y:2018:i:c:p:488-500
    DOI: 10.1016/j.renene.2017.12.038
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    Citations

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    Cited by:

    1. Gülüm, Mert & Onay, Funda Kutlu & Bilgin, Atilla, 2018. "Comparison of viscosity prediction capabilities of regression models and artificial neural networks," Energy, Elsevier, vol. 161(C), pages 361-369.
    2. Chater, Hamza & Asbik, Mohamed, 2024. "Innovative mathematical approach for hydrothermal carbonization process using an inverse method: Experimental analysis, rheology behavior, and numerical comparative investigation," Energy, Elsevier, vol. 290(C).
    3. Mujtaba, M.A. & Kalam, M.A. & Masjuki, H.H. & Razzaq, Luqman & Khan, Haris Mehmood & Soudagar, Manzoore Elahi M. & Gul, M. & Ahmed, Waqar & Raju, V. Dhana & Kumar, Ravinder & Ong, Hwai Chyuan, 2021. "Development of empirical correlations for density and viscosity estimation of ternary biodiesel blends," Renewable Energy, Elsevier, vol. 179(C), pages 1447-1457.
    4. T. M. Yunus Khan, 2020. "A Review of Performance-Enhancing Innovative Modifications in Biodiesel Engines," Energies, MDPI, vol. 13(17), pages 1-22, August.
    5. Bukkarapu, Kiran Raj & Krishnasamy, Anand, 2022. "A critical review on available models to predict engine fuel properties of biodiesel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).

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