Computational evaluation of microalgae biomass conversion to biodiesel

Since there are large requirements for green energy sustainability, in this study, optimization of in situ transesterification of microalgae slurry conversion into biodiesel was performed. The main aim was to perform a selection procedure of the optimal predictors for fatty acid methyl ester yield and exergy efficiency in situ transesterification process. The adaptive neuro fuzzy inference system (ANFIS) as a soft computing approach was used for the optimization of the predictors for the fatty acid methyl ester yield and exergy efficiency. Based on the obtained results, the optimal combination for fatty acid methyl ester yield was ultrasonic power and reaction time while the optimal combination for the exergy efficiency was concentrations of methanol and chloroform in oil. These selected predictors could be used effectively in order to maximize the fatty acid methyl ester yield and exergy efficiency.

Automated summary

This study optimized the predictors for fatty acid methyl ester yield and exergy efficiency in situ transesterification process. The results showed that the optimal combination for fatty acid methyl ester yield was ultrasonic power and reaction time, while the optimal combination for exergy efficiency was concentrations of methanol and chloroform in oil. These optimized predictors effectively improved the fatty acid methyl ester yield and exergy efficiency.