Prediction of microemulsion phase behavior from surfactant and co-solvent structures
A predictive model was developed that captures quantitative structure-property relationships between the molecular structures of surfactants and co-solvents and microemulsion phase behavior. Both the optimum salinity and the optimum solubilization ratio (and thus the interfacial tension) are modeled...
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Veröffentlicht in: | Fuel (Guildford) 2019-02, Vol.237, p.494-514 |
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description | A predictive model was developed that captures quantitative structure-property relationships between the molecular structures of surfactants and co-solvents and microemulsion phase behavior. Both the optimum salinity and the optimum solubilization ratio (and thus the interfacial tension) are modeled as a function of both monovalent and divalent cations in the brines. A dataset consisting of 685 microemulsion phase behavior experiments with 24 unique crude oils, 85 surfactants (internal olefin sulfonates, alkylbenzene sulfonates, alcohol alkoxy sulfates and alcohol alkoxy carboxylates) and 18 co-solvents (alcohols and alcohol alkoxylates) was used for the model development and validation. Variations in the type of hydrophobe (carbon number, degree of branching, polydispersity, and aromaticity), number of propylene oxide groups, number of ethylene oxide groups, and the type of head group (sulfonate, benzene sulfonate, sulfate, carboxylate, hydroxyl) were studied. The oils were characterized using their equivalent alkane carbon number. The model includes the effect of soaps generated from the neutralization of acidic crude oils. The interfacial concentration of co-solvent is calculated using the pseudophase model. Previous models for optimum salinity have not included the effects of divalent cations, soap, and co-solvents among other limitations. Most importantly, the new model can be used to predict interfacial tension as well as optimum salinity and provide a better understanding of the impact of molecular structures on microemulsion phase behavior. The model is sufficiently accurate to provide a useful guide to experimental testing programs for the development of chemical formulations for enhanced oil recovery and other similar applications requiring low interfacial tension. |
doi_str_mv | 10.1016/j.fuel.2018.09.151 |
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Both the optimum salinity and the optimum solubilization ratio (and thus the interfacial tension) are modeled as a function of both monovalent and divalent cations in the brines. A dataset consisting of 685 microemulsion phase behavior experiments with 24 unique crude oils, 85 surfactants (internal olefin sulfonates, alkylbenzene sulfonates, alcohol alkoxy sulfates and alcohol alkoxy carboxylates) and 18 co-solvents (alcohols and alcohol alkoxylates) was used for the model development and validation. Variations in the type of hydrophobe (carbon number, degree of branching, polydispersity, and aromaticity), number of propylene oxide groups, number of ethylene oxide groups, and the type of head group (sulfonate, benzene sulfonate, sulfate, carboxylate, hydroxyl) were studied. The oils were characterized using their equivalent alkane carbon number. The model includes the effect of soaps generated from the neutralization of acidic crude oils. The interfacial concentration of co-solvent is calculated using the pseudophase model. Previous models for optimum salinity have not included the effects of divalent cations, soap, and co-solvents among other limitations. Most importantly, the new model can be used to predict interfacial tension as well as optimum salinity and provide a better understanding of the impact of molecular structures on microemulsion phase behavior. The model is sufficiently accurate to provide a useful guide to experimental testing programs for the development of chemical formulations for enhanced oil recovery and other similar applications requiring low interfacial tension.</description><identifier>ISSN: 0016-2361</identifier><identifier>EISSN: 1873-7153</identifier><identifier>DOI: 10.1016/j.fuel.2018.09.151</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Alcohols ; Alkanes ; Alkylbenzene sulfonate ; Aromaticity ; Benzene ; Brines ; Carbon ; Carboxylates ; Cations ; Chloride ; Co-solvent ; Divalent cations ; Enhanced oil recovery ; Ethylene oxide ; Formulations ; Hydrochloric acid ; Mathematical analysis ; Mathematical models ; Microemulsion ; Microemulsions ; Molecular structure ; Neutralization ; Oil ; Oil recovery ; Optimum salinity ; Organic chemistry ; Phase behavior ; Pollutants ; Polydispersity ; Prediction models ; Propylene oxide ; Salinity ; Salinity effects ; Soaps ; Solubilization ; Solubilization ratio ; Solvents ; Structure ; Sulfates ; Sulfonates ; Surface tension ; Surfactant ; Surfactants ; Tension</subject><ispartof>Fuel (Guildford), 2019-02, Vol.237, p.494-514</ispartof><rights>2018 Elsevier Ltd</rights><rights>Copyright Elsevier BV Feb 1, 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c328t-9e61edd4e293f5908b08a89b0c7eee36a2ffdc4c0021994d9d3a1e22cf72989e3</citedby><cites>FETCH-LOGICAL-c328t-9e61edd4e293f5908b08a89b0c7eee36a2ffdc4c0021994d9d3a1e22cf72989e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0016236118316971$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Chang, Leonard</creatorcontrib><creatorcontrib>Pope, Gary A.</creatorcontrib><creatorcontrib>Jang, Sung Hyun</creatorcontrib><creatorcontrib>Tagavifar, Mohsen</creatorcontrib><title>Prediction of microemulsion phase behavior from surfactant and co-solvent structures</title><title>Fuel (Guildford)</title><description>A predictive model was developed that captures quantitative structure-property relationships between the molecular structures of surfactants and co-solvents and microemulsion phase behavior. Both the optimum salinity and the optimum solubilization ratio (and thus the interfacial tension) are modeled as a function of both monovalent and divalent cations in the brines. A dataset consisting of 685 microemulsion phase behavior experiments with 24 unique crude oils, 85 surfactants (internal olefin sulfonates, alkylbenzene sulfonates, alcohol alkoxy sulfates and alcohol alkoxy carboxylates) and 18 co-solvents (alcohols and alcohol alkoxylates) was used for the model development and validation. Variations in the type of hydrophobe (carbon number, degree of branching, polydispersity, and aromaticity), number of propylene oxide groups, number of ethylene oxide groups, and the type of head group (sulfonate, benzene sulfonate, sulfate, carboxylate, hydroxyl) were studied. The oils were characterized using their equivalent alkane carbon number. The model includes the effect of soaps generated from the neutralization of acidic crude oils. The interfacial concentration of co-solvent is calculated using the pseudophase model. Previous models for optimum salinity have not included the effects of divalent cations, soap, and co-solvents among other limitations. Most importantly, the new model can be used to predict interfacial tension as well as optimum salinity and provide a better understanding of the impact of molecular structures on microemulsion phase behavior. The model is sufficiently accurate to provide a useful guide to experimental testing programs for the development of chemical formulations for enhanced oil recovery and other similar applications requiring low interfacial tension.</description><subject>Alcohols</subject><subject>Alkanes</subject><subject>Alkylbenzene sulfonate</subject><subject>Aromaticity</subject><subject>Benzene</subject><subject>Brines</subject><subject>Carbon</subject><subject>Carboxylates</subject><subject>Cations</subject><subject>Chloride</subject><subject>Co-solvent</subject><subject>Divalent cations</subject><subject>Enhanced oil recovery</subject><subject>Ethylene oxide</subject><subject>Formulations</subject><subject>Hydrochloric acid</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Microemulsion</subject><subject>Microemulsions</subject><subject>Molecular structure</subject><subject>Neutralization</subject><subject>Oil</subject><subject>Oil recovery</subject><subject>Optimum salinity</subject><subject>Organic chemistry</subject><subject>Phase behavior</subject><subject>Pollutants</subject><subject>Polydispersity</subject><subject>Prediction models</subject><subject>Propylene oxide</subject><subject>Salinity</subject><subject>Salinity effects</subject><subject>Soaps</subject><subject>Solubilization</subject><subject>Solubilization ratio</subject><subject>Solvents</subject><subject>Structure</subject><subject>Sulfates</subject><subject>Sulfonates</subject><subject>Surface tension</subject><subject>Surfactant</subject><subject>Surfactants</subject><subject>Tension</subject><issn>0016-2361</issn><issn>1873-7153</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LxDAQhoMouK7-AU8Fz6356EcCXkT8ggU9rOeQTSZsStusSbrgvzdlPXsaZnjfmXcehG4Jrggm7X1f2RmGimLCKywq0pAztCK8Y2VHGnaOVjirSspacomuYuwxxh1v6hXafgYwTifnp8LbYnQ6eBjnIS6Dw15FKHawV0fnQ2GDH4s4B6t0UlMq1GQK7cvohyPkNqYw6zQHiNfowqohws1fXaOvl-ft01u5-Xh9f3rclJpRnkoBLQFjaqCC2UZgvsNccbHDugMA1ipqrdG1xpgSIWojDFMEKNW2o4ILYGt0d9p7CP57hphk7-cw5ZOSkqamlNSUZxU9qfJrMQaw8hDcqMKPJFgu9GQvF3pyoSexkJleNj2cTJDzHx0EGbWDSWdYAXSSxrv_7L-KhXop</recordid><startdate>20190201</startdate><enddate>20190201</enddate><creator>Chang, Leonard</creator><creator>Pope, Gary A.</creator><creator>Jang, Sung Hyun</creator><creator>Tagavifar, Mohsen</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7T7</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope></search><sort><creationdate>20190201</creationdate><title>Prediction of microemulsion phase behavior from surfactant and co-solvent structures</title><author>Chang, Leonard ; Pope, Gary A. ; Jang, Sung Hyun ; Tagavifar, Mohsen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c328t-9e61edd4e293f5908b08a89b0c7eee36a2ffdc4c0021994d9d3a1e22cf72989e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Alcohols</topic><topic>Alkanes</topic><topic>Alkylbenzene sulfonate</topic><topic>Aromaticity</topic><topic>Benzene</topic><topic>Brines</topic><topic>Carbon</topic><topic>Carboxylates</topic><topic>Cations</topic><topic>Chloride</topic><topic>Co-solvent</topic><topic>Divalent cations</topic><topic>Enhanced oil recovery</topic><topic>Ethylene oxide</topic><topic>Formulations</topic><topic>Hydrochloric acid</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Microemulsion</topic><topic>Microemulsions</topic><topic>Molecular structure</topic><topic>Neutralization</topic><topic>Oil</topic><topic>Oil recovery</topic><topic>Optimum salinity</topic><topic>Organic chemistry</topic><topic>Phase behavior</topic><topic>Pollutants</topic><topic>Polydispersity</topic><topic>Prediction models</topic><topic>Propylene oxide</topic><topic>Salinity</topic><topic>Salinity effects</topic><topic>Soaps</topic><topic>Solubilization</topic><topic>Solubilization ratio</topic><topic>Solvents</topic><topic>Structure</topic><topic>Sulfates</topic><topic>Sulfonates</topic><topic>Surface tension</topic><topic>Surfactant</topic><topic>Surfactants</topic><topic>Tension</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chang, Leonard</creatorcontrib><creatorcontrib>Pope, Gary A.</creatorcontrib><creatorcontrib>Jang, Sung Hyun</creatorcontrib><creatorcontrib>Tagavifar, Mohsen</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Fuel (Guildford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chang, Leonard</au><au>Pope, Gary A.</au><au>Jang, Sung Hyun</au><au>Tagavifar, Mohsen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of microemulsion phase behavior from surfactant and co-solvent structures</atitle><jtitle>Fuel (Guildford)</jtitle><date>2019-02-01</date><risdate>2019</risdate><volume>237</volume><spage>494</spage><epage>514</epage><pages>494-514</pages><issn>0016-2361</issn><eissn>1873-7153</eissn><abstract>A predictive model was developed that captures quantitative structure-property relationships between the molecular structures of surfactants and co-solvents and microemulsion phase behavior. Both the optimum salinity and the optimum solubilization ratio (and thus the interfacial tension) are modeled as a function of both monovalent and divalent cations in the brines. A dataset consisting of 685 microemulsion phase behavior experiments with 24 unique crude oils, 85 surfactants (internal olefin sulfonates, alkylbenzene sulfonates, alcohol alkoxy sulfates and alcohol alkoxy carboxylates) and 18 co-solvents (alcohols and alcohol alkoxylates) was used for the model development and validation. Variations in the type of hydrophobe (carbon number, degree of branching, polydispersity, and aromaticity), number of propylene oxide groups, number of ethylene oxide groups, and the type of head group (sulfonate, benzene sulfonate, sulfate, carboxylate, hydroxyl) were studied. The oils were characterized using their equivalent alkane carbon number. The model includes the effect of soaps generated from the neutralization of acidic crude oils. The interfacial concentration of co-solvent is calculated using the pseudophase model. Previous models for optimum salinity have not included the effects of divalent cations, soap, and co-solvents among other limitations. Most importantly, the new model can be used to predict interfacial tension as well as optimum salinity and provide a better understanding of the impact of molecular structures on microemulsion phase behavior. The model is sufficiently accurate to provide a useful guide to experimental testing programs for the development of chemical formulations for enhanced oil recovery and other similar applications requiring low interfacial tension.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.fuel.2018.09.151</doi><tpages>21</tpages></addata></record> |
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subjects | Alcohols Alkanes Alkylbenzene sulfonate Aromaticity Benzene Brines Carbon Carboxylates Cations Chloride Co-solvent Divalent cations Enhanced oil recovery Ethylene oxide Formulations Hydrochloric acid Mathematical analysis Mathematical models Microemulsion Microemulsions Molecular structure Neutralization Oil Oil recovery Optimum salinity Organic chemistry Phase behavior Pollutants Polydispersity Prediction models Propylene oxide Salinity Salinity effects Soaps Solubilization Solubilization ratio Solvents Structure Sulfates Sulfonates Surface tension Surfactant Surfactants Tension |
title | Prediction of microemulsion phase behavior from surfactant and co-solvent structures |
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