In the present study, measurement and optimization of the thermal conductivity of a hybrid nanofluid are carried out. SiO2 nanoparticles with average diameter of 20–30 nm and multi-walled carbon nanotube (MWCNT), with internal and external diameter of 2–6 and 5–20 nm, respectively, were dispersed in ethylene glycol and made the hybrid SiO2–MWCNT (85:15)–ethylene glycol nanofluid. The thermal conductivity of nanofluids in volume fractions of 0.05–1.95 % at temperatures between 30 and 50 C is measured experimentally. The results indicated that thermal conductivity ratio (TCR) of hybrid nanofluids increases nonlinearly with increasing temperature and concentration. Thus, the greatest increase in TCR at a concentration of 1.94 % and a temperature of 50 C was 22.2 %. Studying the cost of production and the suspension of hybrid nanofluid and nanofluid containing SiO2 and MWCNT particles illustrated that using the hybrid nanofluid could be the most optimal one in terms of cost and percentage of TCR. In order to model the thermal conductivity of hybrid nanofluid, two design methods and feed-forward neural network were provided. R2 value of new methods and artificial neural network (ANN) was obtained 0.9864 and 0.9981, respectively. Comparing these two data estimation methods with experimental data showed that both methods are accurate for predicting data. But ANN has much less error than the correlation outputs.
