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A Novel Approach for the Estimation of the Efficiency of Demulsification of Water-In-Crude Oil Emulsions.

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Title: A Novel Approach for the Estimation of the Efficiency of Demulsification of Water-In-Crude Oil Emulsions.
Authors: Nešić, Slavko1 (AUTHOR), Govedarica, Olga1 (AUTHOR) ogovedarica@uns.ac.rs, Jovičić, Mirjana1 (AUTHOR), Žeravica, Julijana1 (AUTHOR), Stojanov, Sonja1 (AUTHOR), Antić, Cvijan1 (AUTHOR), Govedarica, Dragan1 (AUTHOR)
Source: Polymers (20734360). Nov2025, Vol. 17 Issue 21, p2957. 22p.
Subjects: Demulsification, Artificial neural networks, Asphaltene, Petroleum industry, Emulsions, Mechanical efficiency, Response surfaces (Statistics)
Abstract: Undesirable water-in-crude oil emulsions in the oil and gas industry can lead to several issues, including equipment corrosion, high-pressure drops in pipelines, high pumping costs, and increased total production costs. These emulsions are commonly treated with surface-active chemicals called demulsifiers, which can break an oil–water interface and enhance phase separation. This study introduces a novel approach based on neural networks to estimate demulsification efficiency and to aid in the selection of demulsifiers under field conditions. The influence of various types of demulsifiers, demulsifier concentration, time required for demulsification, temperature and asphaltene content on the demulsification efficiency is analyzed. To improve model accuracy, a modified full-scale factorial design of experiments and the comparison of response surface method with multilayer perception neural networks were conducted. The results demonstrated the advantages of using neural networks over the response surface methodology such as a reduced settling time in separators, an improved crude oil dehydration and processing capacity, and a lower consumption of energy and utilities. The findings may enhance processing conditions and identify regions of higher demulsification efficiency. The neural network approach provided a more accurate prediction of maximum of demulsification efficiency compared to the response surface methodology. The automated multilayer perceptron neural network, with an architecture consisting of 3 input layers, 14 hidden layers, and 1 output layer, demonstrated the highest validation performance R2 of 0.991932 by utilizing a logistic output activation function and a hyperbolic tangent activation function for the hidden layers. The identification of shifted optimal values of time required from demulsification, demulsifier concentration, and asphaltene content along with sensitivity analysis confirmed advantages of automated neural networks over conventional methods. [ABSTRACT FROM AUTHOR]
Copyright of Polymers (20734360) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A Novel Approach for the Estimation of the Efficiency of Demulsification of Water-In-Crude Oil Emulsions.
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  Data: <searchLink fieldCode="JN" term="%22Polymers+%2820734360%29%22">Polymers (20734360)</searchLink>. Nov2025, Vol. 17 Issue 21, p2957. 22p.
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  Data: <searchLink fieldCode="DE" term="%22Demulsification%22">Demulsification</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Asphaltene%22">Asphaltene</searchLink><br /><searchLink fieldCode="DE" term="%22Petroleum+industry%22">Petroleum industry</searchLink><br /><searchLink fieldCode="DE" term="%22Emulsions%22">Emulsions</searchLink><br /><searchLink fieldCode="DE" term="%22Mechanical+efficiency%22">Mechanical efficiency</searchLink><br /><searchLink fieldCode="DE" term="%22Response+surfaces+%28Statistics%29%22">Response surfaces (Statistics)</searchLink>
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  Data: Undesirable water-in-crude oil emulsions in the oil and gas industry can lead to several issues, including equipment corrosion, high-pressure drops in pipelines, high pumping costs, and increased total production costs. These emulsions are commonly treated with surface-active chemicals called demulsifiers, which can break an oil–water interface and enhance phase separation. This study introduces a novel approach based on neural networks to estimate demulsification efficiency and to aid in the selection of demulsifiers under field conditions. The influence of various types of demulsifiers, demulsifier concentration, time required for demulsification, temperature and asphaltene content on the demulsification efficiency is analyzed. To improve model accuracy, a modified full-scale factorial design of experiments and the comparison of response surface method with multilayer perception neural networks were conducted. The results demonstrated the advantages of using neural networks over the response surface methodology such as a reduced settling time in separators, an improved crude oil dehydration and processing capacity, and a lower consumption of energy and utilities. The findings may enhance processing conditions and identify regions of higher demulsification efficiency. The neural network approach provided a more accurate prediction of maximum of demulsification efficiency compared to the response surface methodology. The automated multilayer perceptron neural network, with an architecture consisting of 3 input layers, 14 hidden layers, and 1 output layer, demonstrated the highest validation performance R2 of 0.991932 by utilizing a logistic output activation function and a hyperbolic tangent activation function for the hidden layers. The identification of shifted optimal values of time required from demulsification, demulsifier concentration, and asphaltene content along with sensitivity analysis confirmed advantages of automated neural networks over conventional methods. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Polymers (20734360) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/polym17212957
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 22
        StartPage: 2957
    Subjects:
      – SubjectFull: Demulsification
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Asphaltene
        Type: general
      – SubjectFull: Petroleum industry
        Type: general
      – SubjectFull: Emulsions
        Type: general
      – SubjectFull: Mechanical efficiency
        Type: general
      – SubjectFull: Response surfaces (Statistics)
        Type: general
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      – TitleFull: A Novel Approach for the Estimation of the Efficiency of Demulsification of Water-In-Crude Oil Emulsions.
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            – D: 01
              M: 11
              Text: Nov2025
              Type: published
              Y: 2025
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