RSM optimization and yield prediction for biodiesel produced from alkali-catalytic transesterification of pawpaw seed extract: thermodynamics, kinetics, and Multiple Linear Regression analysis

Ngige, GA; Ovuoraye, PE; Igwegbe, CA; Fetahi, E; Okeke, JA; Yakubu, AD; Onyechi, PC

Abstract

Optimization of alkali transesterification of pawpaw seed extract to biodiesel using NaOH catalyst was carried out to analyze kinetics, thermodynamic parameters, and optimum conditions. Response Surface Methodology (RSM) and Multiple Linear Regression (MLR) algorithms were used to confirm the optimum yield results. GC chromatography and X-ray diffraction (XRD) were used to determine the fatty acid profile and characteristics of the pawpaw seed oil (PSO). The maximum biodiesel yield of 80% was obtained at optimum temperature, catalyst weight, and methanol to oil ratio of 60 °C, 1.0 wt%, and 3:1 via the RSM. Kinetics shows that the effect of NaOH on the overall reaction rate was feasible at 30 min while MLR predictions exercised outside the design matrix confirmed that increasing catalyst weights and temperature increases biodiesel yield within the optimum conditions. The finding obtained from the MLR was consistent with the experimentally determined percentage yield practicable based on the experimentally determined value conducted to verify the predicted output. The predicted output indicated a ± 0.025 standard deviation from the result practicable. Some key fuel properties derived from PSO satisfied ASTM (D6751) specifications and complied with EN141215 standards. The XRD patterns and GC/MC characterization confirm PSO is a good source for biodiesel production.

Citation

Ngige, G., Ovuoraye, P., Igwegbe, C., Fetahi, E., Okeke, J., Yakubu, A., & Onyechi, P. (2022). RSM optimization and yield prediction for biodiesel produced from alkali-catalytic transesterification of pawpaw seed extract: thermodynamics, kinetics, and Multiple Linear Regression analysis. Digital chemical engineering, 6, 100066. https://doi.org/10.1016/j.dche.2022.100066

• Journal Article Type: Article
• Acceptance Date: Nov 7, 2022
• Online Publication Date: Nov 18, 2022
• Publication Date: Nov 18, 2022
• Deposit Date: Dec 7, 2022
• Publicly Available Date: Dec 7, 2022
• Journal: Digital Chemical Engineering
• Publisher: Elsevier
• Volume: 6
• Pages: 100066
• DOI: https://doi.org/10.1016/j.dche.2022.100066
• Publisher URL: https://doi.org/10.1016/j.dche.2022.100066