Biodiesel synthesis (transesterification of triglycerides of higher fatty acid to methyl and ethyl esters) from castor oil and linseed oil using methanol and ethanol was investigated at subcritical and supercritical conditions of methanol and ethanol from 200 to 350 °C at 200 bar. The effect of molar ratio of alcohol to oil, temperature, and time was investigated in supercritical methanol and ethanol. The kinetics of the reaction was first order, and the activation energies were determined from the temperature dependence of the rate coefficients. Biodiesel was also synthesized enzymatically with Novozym 435 in supercritical carbon dioxide (ScCO2). The effect of various parameters such as enzyme loading, alcohol to oil molar ratio, temperature, and time was investigated in these systems. A simplified model, based on the Ping Pong Bi Bi with competitive inhibition mechanism, was proposed to describe the enzymatic transesterification kinetics for castor oil with methanol and ethanol.

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The present study estimates the prediction capability of response surface methodology (RSM) and artificial neural network (ANN) models for biodiesel synthesis from sesame (Sesamum indicum L.) oil under ultrasonication (20 kHz and 1.2 kW) using barium hydroxide as a basic heterogeneous catalyst. RSM based on a five level, four factor central composite design, was employed to obtain the best possible combination of catalyst concentration, methanol to oil molar ratio, temperature and reaction time for maximum FAME content. Experimental data were evaluated by applying RSM integrating with desirability function approach. The importance of each independent variable on the response was investigated by using sensitivity analysis. The optimum conditions were found to be catalyst concentration (1.79 wt%), methanol to oil molar ratio (6.69:1), temperature (31.92°C), and reaction time (40.30 min). For these conditions, experimental FAME content of 98.6% was obtained, which was in reasonable agreement with predicted one. The sensitivity analysis confirmed that catalyst concentration was the main factors affecting the FAME content with the relative importance of 36.93%. The lower values of correlation coefficient (R(2)=0.781), root mean square error (RMSE=4.81), standard error of prediction (SEP=6.03) and relative percent deviation (RPD=4.92) for ANN compared to those R(2) (0.596), RMSE (6.79), SEP (8.54) and RPD (6.48) for RSM proved better prediction capability of ANN in predicting the FAME content.

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