Journal
RENEWABLE ENERGY
Volume 184, Issue -, Pages 753-764Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2021.11.091
Keywords
Wet microalgae; Biodiesel; Chlorella pyrenoidosa; Direct transesterification; Response surface methodology; Artificial neural network
Funding
- National Natural Science Foundation of China [22078308]
- Innovation Leadership Program in Sciences and Technologies for Central Plains Talent Plan [214200510009]
- Innovation Leadership Program in Sciences and Technologies for Zhengzhou Talent Gathering Plan, and Outstanding Contribution Talent Project in Sciences and Technologies for Zhengzhou Talent Gathering Plan [20180400042]
- Program for Science & Technology Innovative Research Team in the University of Henan Province [22IRTSTHN007]
- Zhengzhou University Young Teachers 'Special Research
- Taif University Researchers Support Project [TURSP-2020/106]
- Presidential Fellowship of Zhengzhou University (Chinese University Scholarship) , Henan, China
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This study investigates the optimal conditions and modeling for biodiesel production from wet microalgae, and validates the high quality of the produced biodiesel through experimental analysis in comparison with European and US standards.
This study investigates modeling and optimal conditions for biodiesel production from exceedingly wet microalgae Chlorella pyrenoidosa using the catalyst, hydrochloric acid. Three levels of Box-Behnken design response surface methodology were used to optimize individual and interactive effects of parameter time (120-240 min), temperature (120-160 degrees C), solvent-to-wet biomass ratio (2.0-4.67), and hydrochloric acid concentration (2-4 M). Temperature was the most significant factor for direct transesterification of wet microalgae (low p-value (0.0001) and high F-value (53.89). The highest yield (19.90%) of fatty acid methyl ester was obtained on dry biomass weight basis under the optimum conditions of 240 min, 146 degrees C, 2.83 (vol/wt), and 3.86 M acid concentration. The artificial neural network and response surface methodology were trained with Box-Behnken design data to predict responses, and to develop and compare each model's predictive abilities. The accuracy of results indicates that both models predict the experimental data for fatty acid methyl ester yields with high correlation coefficients (R2) 0.94 and 0.92, respectively for artificial neural network and response surface methodology. The potential for producing biodiesel from C. pyrenoidosa is validated by the high yields of C18 fatty acid methyl esters. Experimental analysis demonstrated biodiesel quality in comparison with European and US standards. (c) 2021 Elsevier Ltd. All rights reserved.
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