Evaluation of Machine Learning Assisted Phase Behavior Modelling of Surfactant–Oil–Water Systems
This paper evaluates the ability of machine learning (ML) algorithms to capture and reproduce complex multiphase behavior in surfactant–oil–water systems. The main objective of the paper is to evaluate the ability of machine learning algorithms to capture complex phase behavior of a surfactant–oil–w...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/100 |
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| author | Daulet Magzymov Meruyert Makhatova Zhassulan Dairov Murat Syzdykov |
| author_facet | Daulet Magzymov Meruyert Makhatova Zhassulan Dairov Murat Syzdykov |
| author_sort | Daulet Magzymov |
| collection | DOAJ |
| description | This paper evaluates the ability of machine learning (ML) algorithms to capture and reproduce complex multiphase behavior in surfactant–oil–water systems. The main objective of the paper is to evaluate the ability of machine learning algorithms to capture complex phase behavior of a surfactant–oil–water system in a controlled environment of known data generated via physical models. We evaluated several machine learning algorithms including decision trees, support vector machines (SVMs), k-nearest neighbors, and boosted trees. Moreover, the study integrates a novel graphical equation-of-state model with ML-generated compositional spaces to test ML’s effectiveness in predicting phase transitions and compares its performance to experimental data and a validated physical model. Our results demonstrate that the cubic SVM has the highest accuracy in capturing key behaviors, such as the shrinking of two-phase regions as salinity deviates from optimal conditions, and performs well even in near-extrapolated scenarios. Additionally, the graphical equation-of-state model aligns closely with both experimental data and the physical model, providing a robust framework for analyzing multiphase behavior. We do not suggest that machine learning models should replace traditional physical models, but rather should complement physical models by extending predictive capabilities, especially when experimental data are limited. This hybrid approach offers a promising method for investigating complex multiphase phenomena in surfactant systems. |
| format | Article |
| id | doaj-art-512d312eda9c4a7ea50bb76e1fcfc007 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-512d312eda9c4a7ea50bb76e1fcfc0072025-01-10T13:14:26ZengMDPI AGApplied Sciences2076-34172024-12-0115110010.3390/app15010100Evaluation of Machine Learning Assisted Phase Behavior Modelling of Surfactant–Oil–Water SystemsDaulet Magzymov0Meruyert Makhatova1Zhassulan Dairov2Murat Syzdykov3Oil and Gas Department, Atyrau Oil and Gas University, Atyrau 060027, KazakhstanOil and Gas Department, Atyrau Oil and Gas University, Atyrau 060027, KazakhstanOil and Gas Department, Atyrau Oil and Gas University, Atyrau 060027, KazakhstanOil and Gas Department, Atyrau Oil and Gas University, Atyrau 060027, KazakhstanThis paper evaluates the ability of machine learning (ML) algorithms to capture and reproduce complex multiphase behavior in surfactant–oil–water systems. The main objective of the paper is to evaluate the ability of machine learning algorithms to capture complex phase behavior of a surfactant–oil–water system in a controlled environment of known data generated via physical models. We evaluated several machine learning algorithms including decision trees, support vector machines (SVMs), k-nearest neighbors, and boosted trees. Moreover, the study integrates a novel graphical equation-of-state model with ML-generated compositional spaces to test ML’s effectiveness in predicting phase transitions and compares its performance to experimental data and a validated physical model. Our results demonstrate that the cubic SVM has the highest accuracy in capturing key behaviors, such as the shrinking of two-phase regions as salinity deviates from optimal conditions, and performs well even in near-extrapolated scenarios. Additionally, the graphical equation-of-state model aligns closely with both experimental data and the physical model, providing a robust framework for analyzing multiphase behavior. We do not suggest that machine learning models should replace traditional physical models, but rather should complement physical models by extending predictive capabilities, especially when experimental data are limited. This hybrid approach offers a promising method for investigating complex multiphase phenomena in surfactant systems.https://www.mdpi.com/2076-3417/15/1/100surfactantphase behaviorhybrid modelmachine learninggraphical equation of state |
| spellingShingle | Daulet Magzymov Meruyert Makhatova Zhassulan Dairov Murat Syzdykov Evaluation of Machine Learning Assisted Phase Behavior Modelling of Surfactant–Oil–Water Systems Applied Sciences surfactant phase behavior hybrid model machine learning graphical equation of state |
| title | Evaluation of Machine Learning Assisted Phase Behavior Modelling of Surfactant–Oil–Water Systems |
| title_full | Evaluation of Machine Learning Assisted Phase Behavior Modelling of Surfactant–Oil–Water Systems |
| title_fullStr | Evaluation of Machine Learning Assisted Phase Behavior Modelling of Surfactant–Oil–Water Systems |
| title_full_unstemmed | Evaluation of Machine Learning Assisted Phase Behavior Modelling of Surfactant–Oil–Water Systems |
| title_short | Evaluation of Machine Learning Assisted Phase Behavior Modelling of Surfactant–Oil–Water Systems |
| title_sort | evaluation of machine learning assisted phase behavior modelling of surfactant oil water systems |
| topic | surfactant phase behavior hybrid model machine learning graphical equation of state |
| url | https://www.mdpi.com/2076-3417/15/1/100 |
| work_keys_str_mv | AT dauletmagzymov evaluationofmachinelearningassistedphasebehaviormodellingofsurfactantoilwatersystems AT meruyertmakhatova evaluationofmachinelearningassistedphasebehaviormodellingofsurfactantoilwatersystems AT zhassulandairov evaluationofmachinelearningassistedphasebehaviormodellingofsurfactantoilwatersystems AT muratsyzdykov evaluationofmachinelearningassistedphasebehaviormodellingofsurfactantoilwatersystems |