Biodiesel yield optimisation from a third-generation feedstock (microalgae spirulina) using a hybrid statistical approach

Abstract

The current study aims to find the optimum input parameters for high-quality biodiesel synthesis utilising response surface methodology (RSM), a genetic algorithm (GA), and the cuckoo search algorithm (CSA) approach. The L30 pre-designed and executed experiments explored the significance of four process parameters: the methanol to oil molar ratio, catalyst concentration, reaction temperature, reaction time, and their combined effect on biodiesel production. The optimum conditions for biodiesel production were a molar ratio of 7.54:1 (methanol to oil), a KOH catalyst concentration of 0.5 wt. %, a reaction temperature of 65 °C for 102.57 min of reaction time, and a corresponding value of yield of 97.76%. With a correlation coefficient (R2) of 98.23 and a root-mean-squared error (RMSE) of 0.9995, it was seen that RSM gave a robust and consistent model. Microalgae methyl ester fuel characteristics were evaluated and compared to ASTM standards and found acceptable. Thus, the synthesis of high-quality, high-yield biodiesel from microalgae is a feasible alternative.