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Estimating phase behavior of the asphaltene precipitation by GA-ANFIS approach

Chen, M ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1080/10916466.2018.1493503
  3. Publisher: Taylor and Francis Inc , 2018
  4. Abstract:
  5. This study implements an adaptive neuro-fuzzy inference system (ANFIS) approach to predict the precipitation amount of the asphaltene using temperature (T), dilution ratio (Rv), and molecular weight of different n-alkanes. Results are then evaluated using graphical and statistical error analysis methods, confirming the model’s great ability for appropriate prediction of the precipitation amount. Mean squared error and determination coefficient (R2) values of 0.036 and 0.995, respectively are obtained for the proposed ANFIS model. Results are then compared to those from previously reported correlations revealing the better performance of the proposed model. © 2018, © 2018 Taylor & Francis Group, LLC
  6. Keywords:
  7. ANFIS ; asphaltene ; Heavy n-alkane ; Asphaltenes ; Fuzzy neural networks ; Fuzzy systems ; Mean square error ; Paraffins ; Temperature ; Adaptive neuro fuzzy inference systems (ANFIS) ; Asphaltene precipitation ; Determination coefficients ; Dilution ratio ; Mean squared error ; n-Alkanes ; Statistical error analysis ; Fuzzy inference
  8. Source: Petroleum Science and Technology ; Volume 36, Issue 19 , 2018 , Pages 1582-1588 ; 10916466 (ISSN)
  9. URL: https://www.tandfonline.com/doi/abs/10.1080/10916466.2018.1493503?journalCode=lpet20