Modeling and global optimization of biodiesel synthesis using hybrid response surface methodology-crow search algorithm: Case study of papaya seed waste oil utilization

Biodiesel is potential renewable and clean energy, which can be produced form wide range of waste materials. This study employs a hybrid response surface methodology (RSM) and crow search algorithm (CSA) as novel tool for global optimization of transesterification reaction parameters to maximize biodiesel synthesis from papaya seed-derived waste oil. Catalyst (NaOH) dose, methanol to oil molar ratio (M:O), and reaction time were considered independent factors, while biodiesel yield was taken as a dependent variable. The experimentally produced biodiesel was characterized by gas chromatography–mass spectrometry analysis. The experiments were developed based on RSM with Box–Behnken design matrix, which was subsequently used for modeling, optimization and model validation. Initially, a quadratic regression model was developed following RSM technique, correlating the transesterification reaction parameters and biodiesel yield. Afterward, the CSA coupled with RSM approach was employed to assess the global optimization. A highest biodiesel yield of 99.48% was attained with a catalyst (NaOH) dose of 0.5 wt%, M:O of 8.5:1 at a reaction time of 40 min. The results acquired by RSM-CSA were also compared with the results achieved by desirability function-based optimization technique. Further, the optimal set for maximizing biodiesel yield was validated experimentally with an error margin of 2.0%. These observations indicate that the hybrid RSM-CSA is an efficient and economic approach to optimize the process conditions for biodiesel production from alternative sources.