Study on thermophysical properties of alumina nanoparticles enhanced ionic liquids (NEILs): A modeling approach

Abstract

Abstract In the present work, artificial neural networks have been developed to predict the relationship and influence of shear rate/temperature and particle loading on the viscosity, density, thermal conductivity and isobaric specific heat capacity of Al2O3 nanoparticles dispersed in a binary mixture of water and ionic liquid ([C2mim][CH3SO3]/water). The properties of the alumina nanoparticles enhanced ionic liquids with respect to the base fluids have been modeled using feed-forward back-propagation (BP) ANNs. The study has disclosed that the developed models predict the thermophysical properties of NEILs with reasonable accuracy. The regression coefficient (R) of developed models is noted to be greater than 0.99. Moreover, the root mean square errors for the developed models were found to be in the range of 0.0007–0.081, revealing an excellent compliance between the experimental and calculated thermophysical properties of NEILs.