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

Abstract The synergy of sorbitan monooleate (SMO) and γ-Al2O3 nanoparticles, which was prepared via ultrasonic sonochemistry, as cold-flow improvers (CFI) in B30 biodiesel blend is presented in this work. Response surface methodology (RSM) was employed to study the influence of both CFIs on biodiesel's cold-flow properties, i.e., cloud point (CP), cold-filter plugging point (CFPP), filter blocking tendency (FBT), and precipitate. Based on the result, the relationship between CFI and CP's concentration was best expressed with 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 γ-Al2O3 nanoparticles were 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 5.640 mg/L, respectively. It is believed that SMO's surface-activity and the ability of γ-Al2O3 nanoparticles to form Pickering emulsion were responsible for the inhibition of excessive crystallization of saturated FAME but also enhancing their colloidal stability at low temperature.