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

Nur Allif Fathurrahman, Cahyo Setyo Wibowo, Mohammad Nasikin, Munawar Khalil

Research output: Contribution to journalArticlepeer-review

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 564 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.

Original languageEnglish
JournalJournal of Industrial and Engineering Chemistry
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • B30 biodiesel blend
  • Cold-flow improver
  • RSM
  • Sorbitan monooleate
  • γ-AlO

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