Enhancement of Mustard Oil Bio-Diesel Yield Using Evolutionary Algorithms

The requirement of alternative fuel is in demand and research is being done for biodiesel used as a fuel in diesel engines because of the scarcity of commercial diesel. This study focuses on enhancing the production yield of mustard oil biodiesel. The input variables for the transesterification of mustard oil have been selected and optimized using an ANFIS integrated with genetic algorithm to obtain the optimize the yield of mustard biodiesel. The molar ratio of methanol to mustard oil, catalyst concentration, reaction temperature, and reaction time has been selected as the input variable factors. A central composite design based on response surface approach has been taken into consideration for the experimental design and it recommended 30 combinations of input parameters. ANFIS has been utilized for training between inputs and output and further included in the GA population to investigate the optimum yield of mustard oil bio-diesel. The optimum yield of mustard biodiesel (96.4528%) has been achieved at the molar ratio 18:1, concentration of catalyst 6% w/v, temperature 63 °C, and time 135 min which is 7.8% more than the RSM predicted yield.