SCI和EI收录∣中国化工学会会刊

Chinese Journal of Chemical Engineering ›› 2019, Vol. 27 ›› Issue (3): 726-736.doi: 10.1016/j.cjche.2018.07.018

• Materials and Product Engineering • Previous Articles    

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

Sathishkumar Kannaiyan1, Chitra Boobalan1, Fedal Castro Nagarajan2, Srinivas Sivaraman1   

  1. 1 Department of Chemical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai 603 110, India;
    2 Department of Mechanical Engineering, Aarupadai Veedu Institute of Technology, Paiyanoor, India
  • Received:2018-03-30 Revised:2018-07-02 Online:2019-03-28 Published:2019-04-25
  • Contact: Chitra Boobalan,E-mail address:chitrab@ssn.edu.in E-mail:chitrab@ssn.edu.in

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