Skip to main content
padlock icon - secure page this page is secure

Computational modeling of biodiesel production using supercritical methanol

Buy Article:

$60.00 + tax (Refund Policy)

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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

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

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more