Modeling of thermal conductivity and density of alumina/silica in water hybrid nanocolloid by the application of Artificial Neural Networks

Sathishkumar Kannaiyan; Chitra Boobalan; Fedal Castro Nagarajan; Srinivas Sivaraman

doi:10.1016/j.cjche.2018.07.018

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Modeling of thermal conductivity and density of alumina/silica in water hybrid nanocolloid by the application of Artificial Neural Networks

Materials and Product Engineering | Updated：2026-01-06

- Modeling of thermal conductivity and density of alumina/silica in water hybrid nanocolloid by the application of Artificial Neural Networks
- Chinese Journal of Chemical Engineering Vol. 27, Issue 3, Pages: 726-736(2019)
- Affiliations：
  
  2 Department of Mechanical Engineering, Aarupadai Veedu Institute of Technology, Paiyanoor,India
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2018.07.018
  CLC：
- Published：2019
- Accepted：
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Sathishkumar Kannaiyan, Chitra Boobalan, Fedal Castro Nagarajan, Srinivas Sivaraman. Modeling of thermal conductivity and density of alumina/silica in water hybrid nanocolloid by the application of Artificial Neural Networks[J]. Chinese Journal of Chemical Engineering, 2019, 27(3): 726-736.
DOI：

Sathishkumar Kannaiyan, Chitra Boobalan, Fedal Castro Nagarajan, Srinivas Sivaraman. Modeling of thermal conductivity and density of alumina/silica in water hybrid nanocolloid by the application of Artificial Neural Networks[J]. Chinese Journal of Chemical Engineering, 2019, 27(3): 726-736. DOI： 10.1016/j.cjche.2018.07.018.

摘要

Abstract

In this research work

the thermal conductivity and density of alumina/silica (Al

/SiO

) in water hybrid nanofluids at different temperatures and volume concentrations have been modeled using the artificial neural networks (ANN). The nanocolloid involved in the study was synthesized by the two-step method and characterized by XRD

TEM

SEM-EDX and zeta potential analysis. The properties of the synthesized nanofluid were measured at various volume concentrations (0.05%

0.1% and 0.2%) and temperatures (20 to 60℃). Established on the observational data and ANN

the optimum neural structure was suggested for predicting the thermal conductivity and density of the hybrid nanofluid as a function of temperature and solid volume concentrations. The results indicate that a neural network with 2 hidden layers and 10 neurons have the lowest error and a highest fitting coefficient of thermal conductivity

whereas in the case of density

the structure with 1 hidden layer consisting of 4 neurons proved to be the optimal structure.

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