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Computational modeling of biodiesel production using supercritical methanol

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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 R 2 (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.
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Keywords: biodiesel; genetic algorithm; least square support vector machine; methanol; statistical learning theory; supercritical fluid [PQ1]

Document Type: Research Article

Affiliations: Chemical Engineering Department, Amirkabir University of Technology, Mahshahr Campus, Mahshahr, Iran

Publication date: January 2, 2019

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