Accurate modeling of biodiesel production from castor oil using ANFIS |
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| Authors: | Xiaoyun Yue Guoyang Chang |
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| Affiliation: | 1. School of Economics and Management, Yanshan University, Qinhuangdao, China;2. School of Mathematics and Information Technology, Hebei Normal University of Science and Technology, Qinhuangdao, China;3. High School, Tianjin Yinghua international school, Tianjin City, China |
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| Abstract: | Research for finding alternative fuel sources has been concluded that the renewable fuels such as biodiesel can be used as an alternative to fossil fuels because of the energy security reasons and environmental benefits. In this contribution, transesterification of castor oil with methanol to form biodiesel has been modeled by using artificial neural network fuzzy interference system (ANFIS) approach. Methanol to oil molar ratio, catalyst amount (C), temperature (T), and time (S) were used as input parameters and fatty acid methyl ester yield was used as output parameter for modeling the efficiency of biodiesel production from castor oil. Obtaining low value of absolute deviation (2.2391), high value of R-squared (0.98704), and other modeling results proves that ANFIS modeling is an effective approach for biodiesel production from castor oil. In conclusion, comparison between our model and other previous predictive models reported in open literature indicates the priority of our model. |
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| Keywords: | ANFIS Biodiesel castor oil FAME predictive model |
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