Enhanced biodiesel production from wet microalgae biomass optimized via response surface methodology and artificial neural network

Gul Muhammad, Ange Douglas Potchamyou Ngatcha, Yongkun Lv, Wenlong Xiong, Yaser A. El-Badry, Eylem Asmatulu, Jingliang Xu and Md Asraful Alam

Renewable Energy, 2022, vol. 184, issue C, 753-764

Abstract: 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 °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 °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.

Keywords: Wet microalgae; Biodiesel; Chlorella pyrenoidosa; Direct transesterification; Response surface methodology; Artificial neural network (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:184:y:2022:i:c:p:753-764

DOI: 10.1016/j.renene.2021.11.091

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