Abstract:
Nanofluids as new composite fluids have found their place as one of the attractive research areas. In recent years,
research has increased on using nanofluids as alternative heat transfer fluids to improve the efficiency of thermal systems
without increasing their size. Therefore, the examination and approval of different novel modelling techniques on nanofluid
properties have made progress in this area. Stability of the nanofluids is still an important concern. Research studies on
nanofluids have indicated that electrical conductivity and pH are two important properties that have key roles in the stability
of the nanofluid. In the present work, three different sizes of magnesium oxide (MgO) nanoparticles of 20, 40 and 100 nm at
different volume fractions up to 3% of the base fluid of ethylene glycol (EG) were studied for pH and electrical conductivity
modelling. The temperature of the nanofluids was between 20 and 70◦C for modelling. A genetic algorithm polynomial
neural network hybrid system and an adaptive neuro-fuzzy inference system approach have been utilized to predict the pH
and the electrical conductivity of MgO–EG nanofluids based on an experimental data set.
