Computational modeling of biodiesel production using supercritical methanol

ABSTRACT

Renewable fuels such as biodiesel are introduced as promising environmental friendly fuels and they can be applied as alternative fuels instead of fossil fuels. In the present study, a modeling study based on statistical learning theory was investigated by the least square support vector machine (LSSVM) approach for non-catalytic biodiesel production in supercritical methanol. This model can estimate the biodiesel yield as a function of temperature, pressure, reaction time, and Methanol/oil ratio. The results indicated that the suggested LSSVM model was a satisfactory model to predict biodiesel yield that was confirmed by a high value of R² (0.9961) and low value of absolute deviation (1.17%). In addition, our model has been compared with another previous Artificial neural network (ANN)-based model and great estimations of both models were proved.

Keywords:

• biodiesel
• genetic algorithm
• least square support vector machine
• methanol
• statistical learning theory
• supercritical fluid [PQ1]