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Estimating biofuel density via a soft computing approach based on intermolecular interactions

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  • Nabipour, Narjes
  • Daneshfar, Reza
  • Rezvanjou, Omid
  • Mohammadi-Khanaposhtani, Mohammad
  • Baghban, Alireza
  • Xiong, Qingang
  • Li, Larry K.B.
  • Habibzadeh, Sajjad
  • Doranehgard, Mohammad Hossein

Abstract

In this work, the density of biofuel is estimated using four intelligent models: a Least Square Support Vector Machine (LSSVM), a Radial Basis Function Artificial Neural Network (RBF-ANN), a Multi-layer Perceptron Artificial Neural Network (MLP-ANN), and an Adaptive Network-based Fuzzy Inference System (ANFIS). These models are used to estimate the density of biofuel based on intermolecular interactions and the van der Waals radii of the atoms. Various statistical analyses are performed on the original (experimental) and estimated data. It is found that the LSSVM model can provide more accurate predictions than the other three models. The R-squared value (R2) and the mean absolute relative error (MARE) for the LSSVM, RBF-ANN, MLP-ANN and ANFIS models are 0.847 & 0.056, 52.067 & 0.379, 57.385 & 0.371 and 65.096 & 0.678, respectively. This study shows that the LSSVM model is a promising tool for estimating the density of biofuel, offering an alternative to classic thermodynamic models.

Suggested Citation

  • Nabipour, Narjes & Daneshfar, Reza & Rezvanjou, Omid & Mohammadi-Khanaposhtani, Mohammad & Baghban, Alireza & Xiong, Qingang & Li, Larry K.B. & Habibzadeh, Sajjad & Doranehgard, Mohammad Hossein, 2020. "Estimating biofuel density via a soft computing approach based on intermolecular interactions," Renewable Energy, Elsevier, vol. 152(C), pages 1086-1098.
    • Handle: RePEc:eee:renene:v:152:y:2020:i:c:p:1086-1098
    DOI: 10.1016/j.renene.2020.01.140
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    References listed on IDEAS

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    1. Fatih Demirbas, M., 2009. "Biorefineries for biofuel upgrading: A critical review," Applied Energy, Elsevier, vol. 86(Supplemen), pages 151-161, November.
    2. Sipöcz, Nikolett & Tobiesen, Finn Andrew & Assadi, Mohsen, 2011. "The use of Artificial Neural Network models for CO2 capture plants," Applied Energy, Elsevier, vol. 88(7), pages 2368-2376, July.
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    Cited by:

    1. Roy Setiawan & Reza Daneshfar & Omid Rezvanjou & Siavash Ashoori & Maryam Naseri, 2021. "Surface tension of binary mixtures containing environmentally friendly ionic liquids: Insights from artificial intelligence," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(12), pages 17606-17627, December.
    2. Farrell, C.C. & Osman, A.I. & Doherty, R. & Saad, M. & Zhang, X. & Murphy, A. & Harrison, J. & Vennard, A.S.M. & Kumaravel, V. & Al-Muhtaseb, A.H. & Rooney, D.W., 2020. "Technical challenges and opportunities in realising a circular economy for waste photovoltaic modules," Renewable and Sustainable Energy Reviews, Elsevier, vol. 128(C).
    3. Zulfiqar Ahmad & Hua Zhong & Amir Mosavi & Mehreen Sadiq & Hira Saleem & Azeem Khalid & Shahid Mahmood & Narjes Nabipour, 2020. "Machine Learning Modeling of Aerobic Biodegradation for Azo Dyes and Hexavalent Chromium," Mathematics, MDPI, vol. 8(6), pages 1-17, June.
    4. 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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