prediction of optimum process parameters for karanja biodiesel production using support vector machine, genetic algorithm and particle swarm optimization

The growing energy demand and depletion of conventional energy resources presented a need for an alternative reliable source of energy that can readily replace conventional fuels like diesel and petrol. in the current work, biodiesel is synthesized from karanja oil by using transesterification. the yield is obtained at varying koh concentrations (1 wt %, 1.5 wt %, 2 wt %), varying molar ratios of methanol: oil (3:1, 4.5:1, 6:1), and varying times (15 min, 30 min, 45 min, 60 min). the optimal conditions from the experiment are obtained as a temperature of 50° c, a reaction time of 45 minutes, a methanol-oil ratio of 4.5:1, and a catalyst concentration of 1.5 %. the viscosity of biodiesel is found to be between 0.036 - 0.038 stokes. the optimum conditions obtained were compared with the statistics available in the literature. the produced biodiesel from karanja oil conforms to the astm d6751 standards. the produced biodiesel is characterized using fourier transform infra red (ft-ir) analysis and gas chromatography-mass spectrometry (gc-ms). further artificial intelligence techniques namely support vector machine, genetic algorithm, and particle swarm optimization have been used for predicting the optimum conditions of biodiesel production. the predicted yield with the support vector machine is compared with the yield obtained from experiments. the svm accurately predicted the experimental results with r2 = 0.999.  pso and ga can effectively be used as a tool for predicting the optimum parameters for biodiesel production.