Combining the computational fluid dynamics (CFD) and the design of experiments (DOE) methods, as a mixed approach in modeling was proposed so that to simultaneously benefit from the advantages of both modeling methods. The presented method was validated using a coal hydraulic classifier in an industrial scale. Effects of operating parameters including feed flow rate, solid content and baffle length, were evaluated on classifier overflow velocity and cut-size as the process responses. The evaluation sequence was as follows: the variation levels of parameters was first evaluated using industrial measurement, and then a suitable experimental design was constructed and the DOE matrix was translated to CFD input. Afterwards, the overflow velocity values were predicted by CFD and cut-size values were determined using industrial and CFD results. Overflow velocity and cut-size were statistically analyzed to develop prediction models for DOE responses; and finally, the main and the interaction effects were interpreted with respect to DOE and CFD results. Statistical effect plots along with CFD fluid flow patterns showed the type and the magnitude of operating parameters effects on the classifier performance and visualized the mechanism by which those effects occurred. The suggested modeling method seems to be a useful approach for better understanding the real operational phenomena within the fluid-base separation devices. Furthermore, individual and interaction effects can also be identified and used for interpretation of nonlinear process responses.