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

doi:10.1016/j.cjche.2018.07.018

Abstract

Abstract: In this research work, the thermal conductivity and density of alumina/silica (Al₂O₃/SiO₂) 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.

Key words: Thermal conductivity, Modeling, hybrid nanocolloids, ANN, thermal energy

摘要： In this research work, the thermal conductivity and density of alumina/silica (Al₂O₃/SiO₂) 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.

关键词: Thermal conductivity, Modeling, hybrid nanocolloids, ANN, thermal energy

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.

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