As one of the reliable statistical techniques of DoE, Response Surface Methodology (RSM) can predict the relationship between the response and the independent variables, and it Calhex 231 is capable of optimizing the response surface function and predicting the future response . In this study, Design Expert 220.127.116.11 was used to perform RSM. Several types of designs such as Box–Behnken, Central Composite, One Factor, Miscellaneous, Optimal, User-Defined and Historical-Data are provided. The number of design factors was the most important selection criteria. In this work, the historical data design of RSM was deployed, because there is no limitation on the number of design factors and flame cell can be used for importing data that already exists. Previously obtained experimental data can be imported and analyzed via the historical data design interface . The three variables chosen for the study were viscosity, fuel injection pressure, air-assisted pressure designated as A, B, C whereas the predicted response was SMD designated as Y. The independent variables are coded to the (−1, 1) interval, and the low and high levels are coded −1 and 1 respectively. Table 2 shows the variables of lower, middle, upper design points for RSM in coded and actual values. Linearized regression equation between the response and the variables was used in this work.