A new model to predict the densities of nanofluids using statistical mechanics and artificial intelligent plus principal component analysis

F. Yousefi; Z. Amoozandeh

doi:10.1016/j.cjche.2016.10.016

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A new model to predict the densities of nanofluids using statistical mechanics and artificial intelligent plus principal component analysis

Chemical Engineering Thermodynamics | Updated：2026-01-06

- A new model to predict the densities of nanofluids using statistical mechanics and artificial intelligent plus principal component analysis
- A new model to predict the densities of nanofluids using statistical mechanics and artificial intelligent plus principal component analysis
- 中国化学工程学报（英文版） 2017年25卷第9期页码：1273-1281
- Affiliations：
  
  Department of Chemistry, Yasouj University, Yasouj,Iran,75914-353
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2016.10.016
  中图分类号：
- 纸质出版：2017
- Accepted：
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F. Yousefi, Z. Amoozandeh. A new model to predict the densities of nanofluids using statistical mechanics and artificial intelligent plus principal component analysis[J]. 中国化学工程学报（英文版）, 2017,25(9):1273-1281.

F. Yousefi, Z. Amoozandeh. A new model to predict the densities of nanofluids using statistical mechanics and artificial intelligent plus principal component analysis[J]. , 2017, 25(9): 1273-1281.
F. Yousefi, Z. Amoozandeh. A new model to predict the densities of nanofluids using statistical mechanics and artificial intelligent plus principal component analysis[J]. 中国化学工程学报（英文版）, 2017,25(9):1273-1281. DOI： 10.1016/j.cjche.2016.10.016.

F. Yousefi, Z. Amoozandeh. A new model to predict the densities of nanofluids using statistical mechanics and artificial intelligent plus principal component analysis[J]. , 2017, 25(9): 1273-1281. DOI： 10.1016/j.cjche.2016.10.016.

摘要

Abstract

In this work

some thermodynamic properties of nanofluids such as Sb

; SnO

/(EG + H

ZnO/(EG + H

/(EG + H

ZnO/(PEG + H

ZnO/PEG

and TiO

/EG were estimated from the extended Tao-Mason equation of state

together with the Pak and Cho equation in various temperature

pressure

and volume fractions. The equations of state using minimum input data and density at room temperature as scaling constants

were developed to estimated densities of the above mentioned nanofluids. Furthermore

the artificial neural network plus principal component analysis (PCA) technique (with 20 neuron in hidden layer) was performed over the whole range of available conditions. The AADs of the calculated molar densities of all nanofluids using the EOS and ANN at various temperatures and volume fractions are 1.11% and 0.48%

respectively. In addition

the heat capacity and isentropic compressibility of the above mentioned nanofluids are predicted using obtained densities of EOS with the Pak and Cho equation.

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