TY - JOUR
T1 - Optimization and modeling of carbohydrate production in microalgae for use as feedstock in bioethanol fermentation
AU - Condor, Billriz E.
AU - de Luna, Mark Daniel G.
AU - Abarca, Ralf Ruffel M.
AU - Chang, Yu Han
AU - Leong, Yoong Kit
AU - Chen, Chun Yen
AU - Chen, Po Ting
AU - Lee, Duu Jong
AU - Chang, Jo Shu
N1 - Publisher Copyright:
© 2022 John Wiley & Sons Ltd.
PY - 2022/10/25
Y1 - 2022/10/25
N2 - Microalgal biofuels have been long considered as potentially clean and sustainable alternatives to conventional fossil fuels. In this study, several parameters, including light intensity, initial nitrogen concentration, and nitrogen starvation duration were investigated for their effects on biomass production and carbohydrate accumulation of Chlorella vulgaris FSP-E by using two well-established modeling and optimization methods, namely response surface methodology (RSM) and artificial neural networks (ANN). RSM with central composite design (CCD) revealed that all investigated parameters and their interactions were significant (P <.01) to microalgal carbohydrate accumulation. Both RSM and ANN showed excellent performance in predicting the carbohydrate content of microalgae biomass with a high correlation coefficient (R2) of 0.9873 and 0.9959, respectively. Microalgal biomass with 59.53% carbohydrate content was obtained under optimized conditions. The carbohydrate-rich biomass was further used as feedstock for bioethanol fermentation, achieving a maximum productivity of 7.44 g L−1 h−1.
AB - Microalgal biofuels have been long considered as potentially clean and sustainable alternatives to conventional fossil fuels. In this study, several parameters, including light intensity, initial nitrogen concentration, and nitrogen starvation duration were investigated for their effects on biomass production and carbohydrate accumulation of Chlorella vulgaris FSP-E by using two well-established modeling and optimization methods, namely response surface methodology (RSM) and artificial neural networks (ANN). RSM with central composite design (CCD) revealed that all investigated parameters and their interactions were significant (P <.01) to microalgal carbohydrate accumulation. Both RSM and ANN showed excellent performance in predicting the carbohydrate content of microalgae biomass with a high correlation coefficient (R2) of 0.9873 and 0.9959, respectively. Microalgal biomass with 59.53% carbohydrate content was obtained under optimized conditions. The carbohydrate-rich biomass was further used as feedstock for bioethanol fermentation, achieving a maximum productivity of 7.44 g L−1 h−1.
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U2 - 10.1002/er.7709
DO - 10.1002/er.7709
M3 - Article
AN - SCOPUS:85124765500
SN - 0363-907X
VL - 46
SP - 19300
EP - 19312
JO - International Journal of Energy Research
JF - International Journal of Energy Research
IS - 13
ER -