Due to the different parameters affecting critical heat flux (CHF), four models were proposed in this study to estimate this parameter. The presented models were validated for mass fluxes of 100 < G [kg/m(2) s] < 2500 and working pressures of 100, 400, and 800 kPa. The nanoparticles used in this study are Alumina, Diamond, ZnO, and Graphene-Oxide. Based on the correlation coefficients, the mass flux and length of the tube have the greatest effect on the increasing and decreasing of the CHF, respectively. Also, the volume fraction and the thermal conductivity of the nanoparticle have the lowest impact on the CHF. It is observed that the most M.A.E. of previous studies is to estimate the CHF in microchannels. For the convenience of using the correlations, there is no need to calculate the nanofluid properties and the critical heat flux can be predicted only based on nanoparticle properties and volume concentration of nanoparticles in the nanofluid. Furthermore, a model was also proposed, in which there is no need for nanoparticle properties and is tuned according to the type of the nanoparticles (innovative parameter: Nanoparticle Number NO. In this study, two types of general and binary models were presented. Based on all existing experimental data, the general model has a mean absolute error (M.A.E.) of about 13%, and the binary model has an M.A.E. of 9%. The M.A.E. of the best model was observed for CHF < 400 [kW/m(2)] (M.A.E = 11%), and the M.A.E. for CHF > 400 [kW/m(2)] was 4.5%. Using the unique correlation for microchannels instead of using the Webber number coefficient, the model accuracy significantly increased. (C) 2019 Elsevier Ltd. All rights reserved.