Optimizing density, dynamic viscosity, thermal conductivity and specific heat of a hybrid nanofluid obtained experimentally via ANFIS-based model and modern optimization

Abstract

In this study, rGO/Co3O4 nanocomposite was synthesized, characterized, and then the thermophysical properties were obtained experimentally, after which the experimental data at varying values of temperature and particle loadings was used for optimization purposes. The study was concerned with different values of the controlling parameters. The in-situ/chemical reduction technique was used to synthesize the rGO/Co3O4 nanocomposite and then characterized with x-ray diffraction, transmission electron microscope, and magnetometry. The system was studied at temperature values ranging at 20, 30, 40, 50, and 60 °C and with particle loadings of 0.05%, 0.1%, and 0.2% wt%. The authors in this article have introduced a novel population-based algorithm that is known as Marine Predators Algorithm to obtain the optimal values of the controlling parameters (i.e., temperature and nanofluid mixture percentage) that minimize two controlled variables (i.e., density and viscosity) as well as maximize the other two controlled variables (thermal conductivity and specific heat). The rGO/Co3O4 nanocomposite nanofluid thermal conductivity and viscosity were investigated experimentally, and a maximum increment of 19.14% and 70.83% with 0.2% particle loadings at 60 °C was obtained. At 0.05%, 0.1%, and 0.2% particle loading wt%, the density increased by 0.115%, 0.23%, and 0.451% at a temperature of 20 °C; simultaneously, density increased by 0.117%%, 0.235%, and 0.469% at 60 °C, respectively as compared to water. At 0.2 wt%, the maximum decreased specific heat was 0.192% and 0.194% at 20 °C and 60 °C. When compared with water, no effect was observed with an increase in temperature/: a similar trend as that of the water was followed. The optimal values were found to be at a temperature of 60 °C and for 0.05% particle loading of the prepared nanofluid. However, among the conducted experiments, the optimizer pointed out that the optimal experiment was the one conducted at a temperature of 60 °C and a nanofluid percentage at 0.05. In conclusion, the proposed methodology of modelling with an artificial intelligence tool such as an adaptive network-based fuzzy inference system technique and then determining the optimal parameters with the marine predators algorithm accomplished the goal of the study with major success.