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

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 (R-2) 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).

Automated summary

This study investigated the effects of various parameters on the biomass production and carbohydrate accumulation of Chlorella vulgaris FSP-E, with a focus on light intensity, initial nitrogen concentration, and nitrogen starvation duration. The research utilized response surface methodology (RSM) and artificial neural networks (ANN) to model and optimize the variables, achieving high correlation coefficients and obtaining a carbohydrate-rich biomass for bioethanol fermentation.