Optimization of sorbitan monooleate and g-Al2O3 nanoparticles as cold-flow improver in B30 biodiesel blend using response surface methodology (RSM)

The synergy of sorbitan monooleate (SMO) and g-Al2O3 nanoparticles, which was prepared via ultrasonicsonochemistry, as cold-flow improvers (CFI) in B30 biodiesel blend is presented in this work. Responsesurface methodology (RSM) was employed to study the influence of both CFIs on biodiesel's cold-flowproperties, i.e., cloud point (CP), cold-filter plugging point (CFPP),filter blocking tendency (FBT), andprecipitate. Based on the result, the relationship between CFI and CP's concentration was best expressedwith a quadratic model. Meanwhile, two-factor interaction (2FI) models were more suitable for CFPP, FBT,and precipitate. Based on the result, the most optimum concentration of SMO and g-Al2O3 nanoparticleswere achieved at 0.1% w/v and 50 ppm, respectively. At this condition, the predicted values of CP, CFPP,FBT, and precipitate of the sample were 8.52 C, 6.056 C, 7.208, and 564 mg/L, respectively. It is believedthat SMO's surface-activity and the ability of g-Al2O3 nanoparticles to form Pickering emulsion wereresponsible for the inhibition of excessive crystallization of saturated FAME but also enhancing theircolloidal stability at low temperature. KCI Citation Count: 3