Process Optimization of Lipid Extraction and Transesterification for Enhanced Microalgal Biodiesel as a Green Energy Source | ||||
| Journal of Soil Sciences and Agricultural Engineering | ||||
| Article 3, Volume 16, Issue 4, April 2025, Page 69-76 PDF (739.02 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/jssae.2025.366630.1273 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Ahmed Jado   1; Tatiana Morosuk 2; Jinming Pan 3
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| 1Mansoura University, Department of Agricultural Engineering, Mansoura 35516, Egypt | ||||
| 2Institute for Energy Engineering, Technical University of Berlin, Berlin 10587, Germany | ||||
| 3Zhejiang University, Department of Biosystems Engineering, Hangzhou 310058, China | ||||
| Abstract | ||||
| This study aims to optimize the production of renewable biodiesel from Chlorella minutissima by enhancing both the extraction of algal oil and its subsequent transesterification into biodiesel. A systematic evaluation of key process parameters was conducted using response surface methodology (RSM). The lipid extraction process was investigated by varying the algal biomass-to-solvent ratio (BS), algae particle diameter (APD), and extraction-contact time (ET), while the transesterification phase was optimized by analyzing the effects of the methanol-to-oil ratio (MOR), catalyst concentration, reaction temperature, and reaction time. Statistical analysis using ANOVA and regression techniques revealed that ET had the most significant effect on oil yield (R² = 0.445, p < 0.001), followed by APD (R² = 0.303, p < 0.001) and BS (R² = 0.214, p < 0.001). For biodiesel production, all four parameters influenced conversion efficiency, with reaction temperature exhibiting the strongest positive effect (R² ≈ 0.994, p < 0.001). The predictive models, developed using RSM, achieved coefficient of determination (R²) values of 0.993 and 0.994 for algal oil and biodiesel yields, respectively, with corresponding root mean square error (RMSE) values of 0.0397 and 1.194. These results demonstrate the models' high predictive accuracy and robustness under varying operational conditions. Overall, this work provides critical insights into process interactions and establishes a reliable optimization framework for sustainable biodiesel production from microalgal biomass. | ||||
| Keywords | ||||
| Algal oil yield; Biodiesel; Transesterification; Microalgae; Sustainable energy | ||||
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