Prediction of thermophysical properties of deep eutectic solvent-based organic nanofluids: A machine learning approach

•COSMO-SAC calculation for Eutectic Point prediction.•Deep Eutectic Solvent (DES) comprises Dibenzyl Ether and Diphenyl Ether.•Nanofluids comprising DES and h-BN nanoparticles.•Thermophysical properties of Nanofluids.•Machine Learning approach for prediction of thermophysical properties. This study explores the potential of hexagonal boron nitride (h-BN) nanoparticles suspended in Deep Eutectic Solvent (DES) as a thermal medium or coolant. The eutectic point of the DES, which comprises dibenzyl ether and diphenyl ether as HBA and HBD, respectively, is predicted using the COnductor-like Screening MOdel-Segment Activity Coefficient (COSMO-SAC) thermodynamic model. The Nuclear Magnetic Resonance (NMR) spectroscopy technique is used to evaluate the hydrogen interaction of the DES. The nanofluids are synthesized at five different concentrations of h-BN nanoparticles: 0.01–0.12 wt%. The uniform dispersion of the nanoparticles in the bulk media is investigated by zeta potential stability analysis. The basic physical–chemical properties, namely thermal stability and freezing point, of the DES are further measured. The experimental evaluation of nanofluids comprises studying the effective thermophysical characteristics. The results demonstrate that the average thermal conductivity increase for Nanoparticles-Enhanced DES (NEDES2) is 7.4%. The specific heat capacity (average) of NEDES5 nanofluid increased by 33%. For a small quantity of nanoparticle dispersion, the viscosity of nanofluids increases somewhat relative to the base fluid and is temperature-dependent. While the density decreases with the addition of nanoparticles. Precise predictions of thermophysical properties are essential for addressing and improving a wide range of heat and mass transfer problems, especially in the field of engineering and associated domains. This is especially important for complex fluids, including nanofluids, which have a crucial impact on advancing new technologies. The importance of thermal performance parameters in heat transfer research is apparent. However, there is a noticeable lack of dependable thermophysical properties for nanofluids that can be consistently used in various issues and sophisticated industrial systems. A machine learning technique is used to forecast the thermophysical characteristics of nanofluids. For several models, the deviation between nanofluid’s predicted and experimental thermophysical characteristics is less than 6%. Apart from thermal conductivit