Notice: file_put_contents(): Write of 211588 bytes failed with errno=28 No space left on device in /opt/frankenphp/design.onmedianet.com/app/src/Arsae/CacheManager.php on line 36

Warning: http_response_code(): Cannot set response code - headers already sent (output started at /opt/frankenphp/design.onmedianet.com/app/src/Arsae/CacheManager.php:36) in /opt/frankenphp/design.onmedianet.com/app/src/Models/Response.php on line 17

Warning: Cannot modify header information - headers already sent by (output started at /opt/frankenphp/design.onmedianet.com/app/src/Arsae/CacheManager.php:36) in /opt/frankenphp/design.onmedianet.com/app/src/Models/Response.php on line 20
Dimensionless Equation of State to Predict Microemulsion Phase Behavior | Langmuir
ACS Publications. Most Trusted. Most Cited. Most Read
Dimensionless Equation of State to Predict Microemulsion Phase Behavior
Advanced Search
    Interface Components: Nanoparticles, Colloids, Emulsions, Surfactants, Proteins, Polymers

    Dimensionless Equation of State to Predict Microemulsion Phase Behavior
    Click to copy article linkArticle link copied!

    View Author Information
    Department of Energy and Mineral Engineering, College of Earth and Mineral Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
    Other Access Options

    Langmuir

    Cite this: Langmuir 2016, 32, 35, 8969–8979
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.langmuir.6b02666
    Published August 9, 2016
    Copyright © 2016 American Chemical Society
    Request reuse permissions

    Abstract

    Click to copy section linkSection link copied!
    Abstract Image

    Prediction of microemulsion phase behavior for changing state variables is critical to formulation design of surfactant–oil–brine (SOB) systems. SOB systems find applications in various chemical and petroleum processes, including enhanced oil recovery. A dimensional equation-of-state (EoS) was recently presented by Ghosh and Johns1 that relied on estimation of the surfactant tail length and surface area. We give an algorithm for flash calculations for estimation of three-phase Winsor regions that is more robust, simpler, and noniterative by making the equations dimensionless so that estimates of tail length and surface area are no longer needed. We predict phase behavior as a function temperature, pressure, volume, salinity, oil type, oil–water ratio, and surfactant/alcohol concentration. The dimensionless EoS is based on coupling the HLD-NAC (Hydrophilic Lipophilic Difference–Net Average Curvature) equations with new relationships between optimum salinity and solubility. An updated HLD expression that includes pressure is also used to complete the state description. A significant advantage of the dimensionless form of the EoS over the dimensional version is that salinity scans are tuned based only on one parameter, the interfacial volume ratio. Further, stability conditions are developed in a simplified way to predict whether an overall compositions lies within the single, two-, or three-phase regions. Important new microemulsion relationships are also found, the most important of which is that optimum solubilization ratio is equal to the harmonic mean of the oil and water solubilization ratios in the type III region. Thus, only one experimental measurement is needed in the three-phase zone to estimate the optimum solubilization ratio, a result which can aid experimental design and improve estimates of optimum from noisy data. Predictions with changing state variables are illustrated by comparison to experimental data using standard diagrams including a new type of dimensionless fish plot. The results show that the optimum solubilization ratio and salinity using the tuned dimensionless EoS are within average errors of 2.44% and 1.17% of experimental values for the fluids examined. We then use the dimensionless equations and thermodynamic first-principles to derive the constant in Huh’s equation for interfacial tension prediction.

    Copyright © 2016 American Chemical Society

    Subjects

    what are subjects

    Read this article

    To access this article, please review the available access options below.

    Get instant access

    Purchase Access

    Read this article for 48 hours. Check out below using your ACS ID or as a guest.

    Recommended

    Access through Your Institution

    You may have access to this article through your institution.

    Your institution does not have access to this content. Add or change your institution or let them know you’d like them to include access.

    Cited By

    Click to copy section linkSection link copied!

    This article is cited by 44 publications.

    1. Akash Talapatra, Bahareh Nojabaei. Prediction of Viscosity of the Oil–Surfactant–Brine Microemulsion Phase Using Molecular Dynamics Simulations. Energy & Fuels 2024, 38 (9) , 7746-7757. https://doi.org/10.1021/acs.energyfuels.3c04902
    2. Jean-Louis Salager, Ronald Marquez, Jose G. Delgado-Linares, Miguel Rondon, Ana Forgiarini. Fundamental Basis for Action of a Chemical Demulsifier Revisited after 30 Years: HLDN as the Primary Criterion for Water-in-Crude Oil Emulsion Breaking. Energy & Fuels 2022, 36 (2) , 711-730. https://doi.org/10.1021/acs.energyfuels.1c03349
    3. V. A. Torrealba and R. T. Johns . Coupled Interfacial Tension and Phase Behavior Model Based on Micellar Curvatures. Langmuir 2017, 33 (47) , 13604-13614. https://doi.org/10.1021/acs.langmuir.7b03372
    4. Guillaume Lemahieu, Jesús F. Ontiveros, Valérie Molinier, Jean‐Marie Aubry. Rheology as a tool for identifying and characterizing optimal microemulsions formulations. Journal of Surfactants and Detergents 2025, 28 (3) , 627-638. https://doi.org/10.1002/jsde.12827
    5. Mohammad Mirzadeh, Zahra Javadi, Fatemeh Eslami. Water-in-Oil demulsification with the HLD and HLD-NAC approach and salt effect. Fuel 2025, 385 , 134170. https://doi.org/10.1016/j.fuel.2024.134170
    6. Qinzhi Li, Bing Wei, Qihang Ye, Qinyu Xie, Yiwen Wang, Shuoshi Wang, Jun Lu. Rapid design and optimization of complex surfactant microemulsions for high-efficiency oil extraction processes integrating experimental and modeling approaches. Separation and Purification Technology 2025, 354 , 128930. https://doi.org/10.1016/j.seppur.2024.128930
    7. Russell T. Johns, Daulet Magzymov. Microemulsion flash calculations using an HLD-NAC-based equation of state. 2025, 391-410. https://doi.org/10.1016/B978-0-12-821481-7.00004-6
    8. Edgar Acosta, Jeffrey H. Harwell. Net-Average-Curvature (NAC) fundamentals. 2025, 259-325. https://doi.org/10.1016/B978-0-12-821481-7.00013-7
    9. Steven Abbott. A formulator's guide to HLD-NAC. 2025, 235-258. https://doi.org/10.1016/B978-0-12-821481-7.00015-0
    10. Hanif F. Yoga, Nijat R. Gasimli, Russell T. Johns. Reliable Equivalent Alkane Carbon Number Determination for Dead and Live Crudes in Microemulsion Systems. SPE Journal 2024, 29 (09) , 4935-4949. https://doi.org/10.2118/221470-PA
    11. Guillaume Lemahieu, Jesús F. Ontiveros, Valérie Molinier, Jean-Marie Aubry. Exploring the impact of surfactant types and formulation variables on the EACN of crude and model oils. Colloids and Surfaces A: Physicochemical and Engineering Aspects 2024, 694 , 134029. https://doi.org/10.1016/j.colsurfa.2024.134029
    12. H. F. Yoga, N. R. Gasimli, R. T. Johns. Reliable EACN Determination for Dead and Live Crude in Microemulsion Systems. 2024https://doi.org/10.2523/IPTC-23685-MS
    13. Ronald Marquez, Jesús F. Ontiveros, Nelson Barrios, Laura Tolosa, Gerardo Palazzo, Véronique Nardello‐Rataj, Jean Louis Salager. Advantages and limitations of different methods to determine the optimum formulation in surfactant–oil–water systems: A review. Journal of Surfactants and Detergents 2024, 27 (1) , 5-36. https://doi.org/10.1002/jsde.12703
    14. Hadi Saboorian-Jooybari, Zhangxin Chen. Critical review of microemulsion phase behavior models: benefits, research gaps, industrial challenges, and roadmap. Journal of Industrial and Engineering Chemistry 2024, 129 , 1-23. https://doi.org/10.1016/j.jiec.2023.07.051
    15. Sung Hyun Jang, Gary A. Pope. Microemulsion phase behavior of live crude oil and revisiting the EACN framework for crude oils. Colloids and Surfaces A: Physicochemical and Engineering Aspects 2023, 670 , 131565. https://doi.org/10.1016/j.colsurfa.2023.131565
    16. Sung Hyun Jang, Gary A. Pope. Microemulsion phase behavior of active live oil. Fuel 2023, 345 , 128255. https://doi.org/10.1016/j.fuel.2023.128255
    17. Hadi Saboorian-Jooybari, Zhangxin Chen. New charged aggregate mathematical models to predict the behavior, structural parameters, and geometrical features of microemulsions. Colloids and Surfaces A: Physicochemical and Engineering Aspects 2023, 669 , 131290. https://doi.org/10.1016/j.colsurfa.2023.131290
    18. Daulet Magzymov, Russell T. Johns, Hafsa Hashim, Birol Dindoruk. Modeling of High-Pressure and High-Temperature Microemulsion Experiments using HLD-NAC-Based Equation of State. SPE Journal 2023, 28 (03) , 1202-1215. https://doi.org/10.2118/209470-PA
    19. Bruno Ramon Batista Fernandes, Kamy Sepehrnoori, Mojdeh Delshad. Challenges in modeling microemulsion phase behavior. Journal of Surfactants and Detergents 2023, 26 (3) , 311-333. https://doi.org/10.1002/jsde.12647
    20. Sung Hyun Jang, Gary A. Pope. An exception to linearity in EACN framework: Twin-tail lipophiles and n-alkanes interactions. Colloids and Surfaces A: Physicochemical and Engineering Aspects 2023, 665 , 131194. https://doi.org/10.1016/j.colsurfa.2023.131194
    21. Dito Fauzi Winanda, Sanggono Adisasmito. Addition of surfactants to Low Salinity Waterflooding in microfluidics system to increase oil recovery. Petroleum Research 2022, 7 (4) , 486-494. https://doi.org/10.1016/j.ptlrs.2021.12.011
    22. George Herman, Nicole Lichterfeld-Weber, Christian Bittner. Unique Synergistic Surfactant Mixture to Match Ultralow Interfacial Tension with Solubility at High Temperature and High Salinity. 2022https://doi.org/10.2118/211435-MS
    23. Daulet Magzymov, Russell T. Johns. Inclusion of variable characteristic length in microemulsion flash calculations. Computational Geosciences 2022, 26 (4) , 995-1010. https://doi.org/10.1007/s10596-022-10158-2
    24. Dong-Qi Wang, Dai-Yin Yin, Jun-Da Wang, Ya-Zhou Zhou, Cheng-Li Zhang. Prediction and programming of microemulsion phase behavior simulation. Petroleum Science 2022, 19 (3) , 1401-1410. https://doi.org/10.1016/j.petsci.2021.10.024
    25. Jean-Louis Salager, Ronald Marquez, Johnny Bullon, Ana Forgiarini. Formulation in Surfactant Systems: From-Winsor-to-HLDN. Encyclopedia 2022, 2 (2) , 778-839. https://doi.org/10.3390/encyclopedia2020054
    26. Daulet Magzymov, Russell T. Johns, Hafsa Hashim, Birol Dindoruk. Modeling of High Pressure and Temperature Microemulsion Experiments using HLD-NAC Based Equation of State. 2022https://doi.org/10.2118/209470-MS
    27. Xingang Bu, Ming Han, Abdulkareem AlSofi, Alhasan Fuseni. An Improved HLD-NAC Model for Microemulsion Phase Behavior Study. 2022https://doi.org/10.2118/200122-MS
    28. Gregory P. Dado, Paul W. Knox, Rachel M. Lang, Mona M. Knock. Non‐linear changes in phase inversion temperature for oil and water emulsions of nonionic surfactant mixtures. Journal of Surfactants and Detergents 2022, 25 (1) , 63-78. https://doi.org/10.1002/jsde.12557
    29. Hassan Ghasemi, Fatemeh Eslami. Design of industrial wastewater demulsifier by HLD-NAC model. Scientific Reports 2021, 11 (1) https://doi.org/10.1038/s41598-021-95485-7
    30. Ana M. Forgiarini, Ronald Marquez, Jean-Louis Salager. Formulation Improvements in the Applications of Surfactant–Oil–Water Systems Using the HLDN Approach with Extended Surfactant Structure. Molecules 2021, 26 (12) , 3771. https://doi.org/10.3390/molecules26123771
    31. Guillaume Lemahieu, Jesús F. Ontiveros, Nathaniel Terra Telles Souza, Valérie Molinier, Jean-Marie Aubry. Fast and accurate selection of surfactants for enhanced oil recovery by dynamic Salinity-Phase-Inversion (SPI). Fuel 2021, 289 , 119928. https://doi.org/10.1016/j.fuel.2020.119928
    32. Guillaume Lemahieu, Jesus F. Ontiveros, Valérie Molinier, Jean-Marie Aubry. Using the dynamic Phase Inversion Temperature (PIT) as a fast and effective method to track optimum formulation for Enhanced Oil Recovery. Journal of Colloid and Interface Science 2019, 557 , 746-756. https://doi.org/10.1016/j.jcis.2019.09.050
    33. Jean‐Louis Salager, Ana Forgiarini, Ronald Marquez. Extended Surfactants Including an Alkoxylated Central Part Intermediate Producing a Gradual Polarity Transition—A Review of the Properties Used in Applications Such as Enhanced Oil Recovery and Polar Oil Solubilization in Microemulsions. Journal of Surfactants and Detergents 2019, 22 (5) , 935-972. https://doi.org/10.1002/jsde.12331
    34. Verena Leitenmüller, Bernhard J. Rupprecht. A multidisciplinary approach for chemical EOR screening: Understanding alkali-oil interaction by the use of petroleum geochemistry. Journal of Petroleum Science and Engineering 2019, 180 , 967-981. https://doi.org/10.1016/j.petrol.2019.06.018
    35. Lucas Mejia, Mohsen Tagavifar, Ke Xu, Miguel Mejia, Yujing Du, Matthew Balhoff. Surfactant flooding in oil-wet micromodels with high permeability fractures. Fuel 2019, 241 , 1117-1128. https://doi.org/10.1016/j.fuel.2018.12.076
    36. Leonard Chang, Gary A. Pope, Sung Hyun Jang, Mohsen Tagavifar. Prediction of microemulsion phase behavior from surfactant and co-solvent structures. Fuel 2019, 237 , 494-514. https://doi.org/10.1016/j.fuel.2018.09.151
    37. Soumyadeep Ghosh, Adwait Chawathe, Sophany Thach, Harold C. Linnemeyer, Emily B. Tao, Varadarajan Dwarakanath, Anil Ambastha, Gayani Pinnawala Arachchilage. An Equation of State to Model Microemulsion Phase Behavior in Presence of Co-Solvents Using Average Solubilization Theory. 2018https://doi.org/10.2118/191530-MS
    38. Leonard Chang, Sung Hyun Jang, Mohsen Tagavifar, Gary A. Pope. Structure-Property Model for Microemulsion Phase Behavior. 2018https://doi.org/10.2118/190153-MS
    39. M. Tagavifar, S.H. Jang, L. Chang, K. Mohanty, G. Pope. Controlling the composition, phase volume, and viscosity of microemulsions with cosolvent. Fuel 2018, 211 , 214-222. https://doi.org/10.1016/j.fuel.2017.09.056
    40. Jean‐Louis Salager, Raquel E. Antón, María A. Arandia, Ana M. Forgiarini. How to Attain Ultralow Interfacial Tension and Three‐Phase Behavior with Surfactant Formulation for Enhanced Oil Recovery: A Review. Part 4: Robustness of the Optimum Formulation Zone Through the Insensibility to Some Variables and the Occurrence of Complex Artifacts. Journal of Surfactants and Detergents 2017, 20 (5) , 987-1018. https://doi.org/10.1007/s11743-017-2000-6
    41. Mehdi Nouraei, Edgar J. Acosta. Predicting solubilisation features of ternary phase diagrams of fully dilutable lecithin linker microemulsions. Journal of Colloid and Interface Science 2017, 495 , 178-190. https://doi.org/10.1016/j.jcis.2017.01.114
    42. Saeid Khorsandi, Changhe Qiao, Russell T. Johns. Simulation of Surfactant/Polymer Floods With a Predictive and Robust Microemulsion Flash Calculation. SPE Journal 2017, 22 (02) , 470-479. https://doi.org/10.2118/179566-PA
    43. V. A. Torrealba, R. T. Johns. Microemulsion Phase Behavior Model Using Empirical Trends in Chemical Potentials. 2017https://doi.org/10.2118/184555-MS
    44. Saeid Khorsandi, Russell T. Johns. Robust Flash Calculation Algorithm for Microemulsion Phase Behavior. Journal of Surfactants and Detergents 2016, 19 (6) , 1273-1287. https://doi.org/10.1007/s11743-016-1877-9
    Go to
    Get e-Alerts
    Get e-Alerts

    Langmuir

    Cite this: Langmuir 2016, 32, 35, 8969–8979
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.langmuir.6b02666
    Published August 9, 2016
    Copyright © 2016 American Chemical Society
    Request reuse permissions

    Article Views

    935

    Altmetric

    -

    Citations

    44
    Learn about these metrics

    Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.

    Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

    The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.