Optimization and modeling of carbohydrate production in microalgae for use as feedstock in bioethanol fermentation

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Billriz E. Condor
  • Mark Daniel G. de Luna
  • Ralf Ruffel M. Abarca
  • Yu-Han Chang
  • Yoong Kit Leong
  • Chun-Yen Chen
  • Po-Ting Chen
  • Jo-Shu Chang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Number of pages13
Journal / PublicationInternational Journal of Energy Research
Online published9 Feb 2022
Publication statusOnline published - 9 Feb 2022

Abstract

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.

Research Area(s)

  • artificial neural network, bioethanol, carbohydrates, central composite design, microalgae, response surface methodology

Citation Format(s)

Optimization and modeling of carbohydrate production in microalgae for use as feedstock in bioethanol fermentation. / Condor, Billriz E.; de Luna, Mark Daniel G.; Abarca, Ralf Ruffel M.; Chang, Yu-Han; Leong, Yoong Kit; Chen, Chun-Yen; Chen, Po-Ting; Lee, Duu-Jong; Chang, Jo-Shu.

In: International Journal of Energy Research, 09.02.2022.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review