The stability and thermophysical properties of Al<inf>2</inf>O<inf>3</inf>-graphene oxide hybrid nanofluids for solar energy applications: Application of robust autoregressive modern machine learning technique

This paper investigates the dispersion stability and thermophysical characteristics of water-based alumina (Al2O3), graphene oxide (GO) and their hybrid nanofluids (HNF) at different mixing ratios. Initially, the sol-gel and Hummer's method was employed for the synthesis of Al2O3 and GO nanoparticles (NPs) and they were characterized with X-ray diffraction analysis (XRD), ultraviolet–visible spectroscopy (UV–visible) and field emission scanning electron microscopy (FESEM). The effect of three different surfactants was analyzed on the stability of nanofluids (NFs). The properties such as thermal conductivity (TC) and viscosity (VST) were measured at different volume concentrations and temperatures ranging from 0.1 to 1 vol% and 30–60 °C, respectively. The maximum TC enhancement of GO is 43.9% higher than Al2O3 NF at 1 vol% at a temperature of 60 °C. The addition of GO content increases the TC and VST of HNF. The regression equations were developed to forecast the VST and TC of HNFs. Finally, two modern novel machine learning approaches, a Bayesian optimized support vector machine and a wide neural network, were used to model-predict the thermophysical properties of HNFs with a robust prognostic efficiency of 97.15–99.91%.