Biodiesel is gaining acceptance as an alternative fuel in a scenario where fossil fuel reserves are being depleted rapidly. Therefore, it is considered as the fuel of the future due to its sustainability, renewable nature and environment friendly attributes. The optimal yield of biodiesel from cheap feed stock oils is a challenge to add cost effectiveness without compromising the fuel quality. In the current experiment, waste chicken fat oil was taken as the feedstock oil to produce biodiesel through the chemical and enzymatic route of transesterification. The process of chemical transesterification was performed using KOH and sodium methoxide, while enzymatic transesterification was done by using free Aspergillus terreus lipase and Aspergillus terreus lipase immobilized on functionalized Fe3O4 nanoparticles (Fe3O4_PDA_Lipase) as biocatalysts. The physico-chemical properties of the understudy feedstock oil were analyzed to check the feasibility as a feedstock for the biodiesel synthesis. The feedstock oil was found suitable for biodiesel production based upon quality assessment. Optimization of various reaction parameters (the temperature and time of reaction, catalyst concentration and methanol-to-oil mole ratio) was performed based on the response surface methodology (RSM). The maximum yield of biodiesel (90.6%) was obtained from waste chicken fat oil by using Fe3O4_PDA_Lipase as an immobilized nano-biocatalyst. Moreover, the above said optimum yield was obtained when transesterification was done using 6% Fe3O4_PDA_Lipase with a methanol-to-oil ratio of 6:1 at 42 °C for 36 h. Biodiesel production was monitored by FTIR spectroscopic analysis, whereas compositional profiling was done by GC–MS. The measured fuel properties—cloud point, pour point, flash point, fire point and kinematic viscosity—met the biodiesel specifications by American Society for Testing and Materials (ASTM).
An ultrasound-assisted system for the optimization of biodiesel production from chicken fat oil using a genetic algorithm and response surface methodology