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

Summary 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