| Autor: |
Tamador Ali Mahmood, Adel Sharif Hamadi, Muna Kheder Jassim |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
2ND INTERNATIONAL CONFERENCE ON MATERIALS ENGINEERING & SCIENCE (IConMEAS 2019). |
| ISSN: |
0094-243X |
| DOI: |
10.1063/5.0000117 |
| Popis: |
An accurate prediction of the dehydration of a crude oil is necessary for design, selection and petroleum operations. The aim of this research was to predict the separation of water using the FFANF artificial neural network. Factors studied include the effect of mixing time, mixing speed, emulsification temperature and shearing rate. The models of water separation were developed using experimental laboratory data. The results showed that the expected relationships had a perfect correlation with the tests achieved at R = 0.99992 and the top validation performance at 143.3434 and RC = 0.98. The results also showed that the model obtained was efficient in comparison with the conventional ANN in predicting water separation with an overall improvement of 39. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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