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Thermophysical Properties and Experimental and Modeling Density of Alkanol + Alkane Mixtures Using Neural Networks Developed with Differential Evolution Algorithm

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Abstract

Densities of pure 1-heptanol, 1-decanol, n-heptane, n-octane, n-nonane, n-decane, and their binary liquid mixtures were measured over the entire range of composition at (288.15, 293.15, 298.15, 303.15, 308.15, 313.15) K and at atmospheric pressure (0.1 MPa). The experimental data were used to determine several thermophysical properties including, the excess molar volume (\( V_{m}^{E} \)) and coefficient of thermal expansion (\( \alpha \)). These excess properties were used to analyze the inter–intra molecular interactions in the liquid mixtures. In addition, the densities of the considered mixtures were modelled using a combination of differential evolution algorithm and artificial neural networks. The proposed methodology determined good models that were able to efficiently predict the density with an average absolute relative error lower than 0.2 %.

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Authors and Affiliations

  1. Chemical Engineering Department, Faculty of Engineering, Shomal University, PO Box 731, Amol, Mazandaran, Iran

    Mohsen Pirdashti

  2. Faculty of Chemical Engineering, Babol University of Technology, PO Box, 484, Babol, Iran

    Kamyar Movagharnejad & Pegah Akbarpour

  3. Faculty of Chemical Engineering and Environmental Protection “Cristofor Simionescu”, “Gh. Asachi” Technical University, Bld Mangeron 73, 700050, Iasi, Romania

    Elena Niculina Dragoi

  4. Department of Chemical & Environmental Engineering, University of Nottingham Malaysia Campus, Jalan Broga, 43500, Semenyih, Selangor Darul Ehsan, Malaysia

    Ianatul Khoiroh

Authors
  1. Mohsen Pirdashti
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  2. Kamyar Movagharnejad
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  3. Pegah Akbarpour
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  4. Elena Niculina Dragoi
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  5. Ianatul Khoiroh
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Correspondence to Mohsen Pirdashti.

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Pirdashti, M., Movagharnejad, K., Akbarpour, P. et al. Thermophysical Properties and Experimental and Modeling Density of Alkanol + Alkane Mixtures Using Neural Networks Developed with Differential Evolution Algorithm. Int J Thermophys 41, 35 (2020). https://doi.org/10.1007/s10765-020-2609-y

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  • DOI: https://doi.org/10.1007/s10765-020-2609-y

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