Fluid catalytic cracking is an important means to improve quality of heavy oil. Proper mathematical models are required to investigate influence of operating conditions and raw material properties on product distribution. Selection of suitable input variables largely affects model performances. The input variable selection is currently dependent on understanding mechanisms of FCC process. From the viewpoint of data-driven modeling

a method of selecting Filter-Wrapper characteristic subset was proposed by combination of Filter method using classical RReliefF algorithm and Wrapper method using GA-SVR algorithm. With no requirement of prior FCC knowledge

this method chose input variables by spontaneous data-driven selection of characteristic variables. A model with good prediction accuracy and proper number of input variables was established by taking advantage of a FCCU operating data and selecting input variables for prediction model of the unit dry gas and coke yield. Present work can not only provide a method to variable selection in FCCU modeling and process analysis

but also extend to other industrial process analysis.