Fluid Phase Equilibria最新文献

{"title":"Hydrophobic interactions described using hetero-segmented PC-SAFT: 2. Surfactants and their aqueous solutions","authors":"Marius Rother,&nbsp;Gabriele Sadowski","doi":"10.1016/j.fluid.2025.114342","DOIUrl":"10.1016/j.fluid.2025.114342","url":null,"abstract":"<div><div>Despite their importance for industry and pharmaceuticals applications, description of aqueous solutions that contain surfactants is still a challenging task in thermodynamic modeling. As a first step towards a holistic modeling approach, which is also applicable for concentrated surfactant solutions, this work aimed to model the intrinsic behavior of surfactant molecules. For this purpose, we applied hetero-segmented PC-SAFT as a group contribution method to build surfactant molecules from different groups, which separately characterize the hydrophobic tail and the hydrophilic head of the surfactant. While the hydrophobic tail is modeled by the parameterization developed in the first part of this paper series (M. Rother, G. Sadowski, Fluid Phase Equilibria 582 (2024)), this work focuses on extending the parameter matrix to model the hydrophilic head. We considered the surfactant classes C_iG₁, C_iE_j and MEGA-i. The parameters for the surfactant head groups were adjusted to sorption data of surfactant/alcohol systems and to partition coefficients of the surfactants in n-alkane/water systems and n-alcohol/water systems. As a benchmark of the new parameterization, we modeled the critical micelle concentration as a function of temperature for these three surfactant classes using a newly developed, explicit equation for calculating this quantity. The results are in even quantitative agreement with the experimental data.</div></div>","PeriodicalId":12170,"journal":{"name":"Fluid Phase Equilibria","volume":"593 ","pages":"Article 114342"},"PeriodicalIF":2.8,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study of surface tension of CO2+water and CO2+ethanol solutions from combined CPA and PC-SAFT EoSs with gradient theory and artificial neural network","authors":"Parisa Tabarzadi ,&nbsp;Mohammad Niksirat ,&nbsp;Fatemeh Aeenjan ,&nbsp;Ariel Hernandez ,&nbsp;Shahin Khosharay","doi":"10.1016/j.fluid.2025.114338","DOIUrl":"10.1016/j.fluid.2025.114338","url":null,"abstract":"<div><div>The gradient theory of the interface was combined with the cubic plus association and perturbed chain statistical association fluid theory equations of state to describe the surface tension of (CO₂+ethanol) and (CO₂+water) systems. Two methods of phase equilibrium and two forms of influence parameters were applied to these systems. A novel influence parameter was also suggested for the gradient theory. The results of this study showed that the new proposed influence parameter results in the accuracy of the surface tension model. The lowest %AADs of surface tension were 2.37 and 6.02, for (CO₂+ethanol) and (CO₂+water) systems, respectively. Therefore, the accurate results of the surface tension were obtained for both systems. Then an artificial neural network model was developed to model the surface tension of the applied mixtures. The best results were obtained with 5 layers and 4 layers and using “trainlm” and “tansig” functions.</div></div>","PeriodicalId":12170,"journal":{"name":"Fluid Phase Equilibria","volume":"593 ","pages":"Article 114338"},"PeriodicalIF":2.8,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermodynamic perturbation coefficients for confined alkanes via Monte Carlo simulations","authors":"Rodolfo José Amancio,&nbsp;Luís Fernando Mercier Franco","doi":"10.1016/j.fluid.2025.114333","DOIUrl":"10.1016/j.fluid.2025.114333","url":null,"abstract":"<div><div>Modeling adsorption has been a challenge for more than a century. Different approaches within different scales have been proposed: from empirical models to equations of state, from classical Density Functional Theory to molecular simulations. Particularly equations of state are of interest for industrial applications. They are usually based on the assumption that the confinement effect can be simply added as a Helmholtz free energy contribution to the fluid–fluid Helmholtz energy. To verify this hypothesis, we propose a new conceptual framework to model the solid–fluid adsorption process, in which the reference fluid is a confined hard-chain, and the perturbation system contains the dispersion interactions among the fluid segments. Two strategies are employed: Barker–Henderson and Weeks–Chandler–Andersen. The solid material is conceived as an implicit wall imposing an external potential, a 10-4-3 Steele potential, on the fluid within a slit pore. The fluid–fluid interactions are described by a Mie potential. Applying Configurational-Bias Monte Carlo (CBMC) simulations, we compute the first- and second-order perturbation coefficients. Our findings show minimal confinement influence on the first perturbation coefficient. The second perturbation coefficient exhibits more complex behaviors, with divergences for short chains at high densities and long chains at low densities. These differences are due to preferred orientations and density peaks near confinement walls.</div></div>","PeriodicalId":12170,"journal":{"name":"Fluid Phase Equilibria","volume":"593 ","pages":"Article 114333"},"PeriodicalIF":2.8,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acidic deep eutectic systems and their capacity to increase drug bioavailability","authors":"Inês J. Ferreira,&nbsp;Cláudio C. Fernandes,&nbsp;Ana Rita C. Duarte","doi":"10.1016/j.fluid.2025.114332","DOIUrl":"10.1016/j.fluid.2025.114332","url":null,"abstract":"<div><div>The pharmaceutical industry faces several challenges concerning the bioavailability of novel medications mainly because of their limited permeability and/or solubility. These are two crucial features that influence how well a medication is absorbed. The biopharmaceutics categorization system is a crucial instrument for the classification of active pharmaceutical ingredients (API) based on their permeability and solubility. In this work we explored the possibility of deep eutectic systems (DES) to be used as solubility and permeability enhancers of four different drugs supplied by Boeringher Ingelheim. In this investigation, the API's were dissolved in various DES and their solubility measured in PBS at 37 °C. Our findings suggest that CA: Gly: W (1:1:1) was able to increase the solubility of all four drugs in PBS, as well as their permeability. In summary, BI0001 and BI0002 following pre-solubilization in that system drugs shifted from class III to from class I included, whereas BI0005 still kept its class III classification although having higher solubility and permeability. The encouraging outcomes highlight DES's potential as a technique to boost drug's bioavailability.</div></div>","PeriodicalId":12170,"journal":{"name":"Fluid Phase Equilibria","volume":"593 ","pages":"Article 114332"},"PeriodicalIF":2.8,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preface to the proceedings of the 16th international conference on properties and phase equilibria for product and process design (PPEPPD-2023) special issue","authors":"Lourdes F. Vega ,&nbsp;Fèlix Llovell","doi":"10.1016/j.fluid.2025.114337","DOIUrl":"10.1016/j.fluid.2025.114337","url":null,"abstract":"","PeriodicalId":12170,"journal":{"name":"Fluid Phase Equilibria","volume":"591 ","pages":"Article 114337"},"PeriodicalIF":2.8,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of ionic liquids’ speed of sound and isothermal compressibility by chemical structure based machine learning model","authors":"Yun Zhang ,&nbsp;Gulou Shen ,&nbsp;Die Lyu ,&nbsp;Xiaohua Lu ,&nbsp;Xiaoyan Ji","doi":"10.1016/j.fluid.2025.114334","DOIUrl":"10.1016/j.fluid.2025.114334","url":null,"abstract":"<div><div>The speed of sound (<em>u</em>) and isothermal compressibility coefficient (<em>K_T</em>) are important thermodynamic parameters of ionic liquids (ILs), crucial in describing their behavior, deriving additional thermodynamic properties, and developing the advanced equations of state. In this work, we developed an artificial neural network (ANN) model, integrated with the group contribution method (GCM), to predict the <em>u</em> and <em>K_T</em> of pure ILs. The model leverages a newly comprehensive dataset. GCM was employed to divide molecules of ILs into constituent groups and use these groups as input features for the ANN algorithm. The model offers simple and reliable predictions of <em>u</em> and <em>K_T</em> of ILs without relying on other properties. To achieve higher model generalizability, cross-validation was performed and two distinct dataset division strategies were applied: IL-division and datapoint-division. The model demonstrates exceptional predictive accuracy across both strategies. For the <em>u</em>-test set, the IL-division and datapoint-division achieve an average absolute relative deviation (AARD) of 0.9083 % and 0.4134 %, respectively. Similarly, for <em>K_T</em>, the IL-division and datapoint-division methods for the test set obtain AARD of 4.2679 % and 1.1651 %, respectively. In the datapoint-division method, the same IL was perhaps included in both training, validation, and test sets, yielding better results. However, the IL-division approach allows prediction on completely new ILs with no available experimental data. Furthermore, correlation analysis was conducted to explore the influence of molecular group occurrences on the model's predictions, offering deeper insights into the structure-property relationships of ILs.</div></div>","PeriodicalId":12170,"journal":{"name":"Fluid Phase Equilibria","volume":"592 ","pages":"Article 114334"},"PeriodicalIF":2.8,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High purity carbon dioxide captured with guanidinium sulfate clathrate from carbon dioxide/hydrogen mixtures","authors":"Chongwei Wang ,&nbsp;Shuanshi Fan ,&nbsp;Yanhong Wang ,&nbsp;Xuemei Lang ,&nbsp;Gang Li","doi":"10.1016/j.fluid.2025.114336","DOIUrl":"10.1016/j.fluid.2025.114336","url":null,"abstract":"<div><div>CO₂ capture based on clathrate technology is an environmentally friendly separation approach, but low capture efficiency limits further commercial applications. Therefore, an innovative gas separation technology based on the efficient capture of CO₂ by guanidinium sulfate (Gua₂SO₄) clathrate was proposed. First, the phase equilibrium data of Gua₂SO₄ solution with CO₂, mixture (50.0 mol.% CO₂-50.0 mol.% H₂) were reported with temperature range from 294.6 to 304.6 K and pressure range from 0.25 to 0.92 MPa. They effectively reduced the formation pressure of CO₂ clathrate rather than H₂ clathrate, which revealed that it could selectively enter into clathrate cages. With the support of this theory, 72.0 wt.% (72.0 wt.%) Gua₂SO₄ solution was applied to obtain CO₂ concentration of 99.2 mol.% in the clathrate phase under the conditions of a gas-liquid ratio of 9.7, temperature of 277.0 K, and pressure of 1.0 MPa, realizing high-selective CO₂ capture of the gas mixture with 50 mol.% CO₂-50 mol.% H₂. With the decrease of driving force, the separation efficiency was increased. Raman analysis results further showed that H₂ did not enter the clathrate cages in the presence of Gua₂SO₄ during the separation of the CO₂-H₂ mixture, which was consistent with the experimental results. Furthermore, the minimum theoretical work of separation was calculated to be only 56.2 kJ/kg CO₂. This approach of high-selective CO₂ capture with Gua₂SO₄ provides new ideas and methods for the application of clathrate technology in the field of gas separation and carbon capture, which lays the foundation for commercial development.</div></div>","PeriodicalId":12170,"journal":{"name":"Fluid Phase Equilibria","volume":"592 ","pages":"Article 114336"},"PeriodicalIF":2.8,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-solvation energies: Extended open database and GNN-based prediction","authors":"Hugo Marques ,&nbsp;Simon Müller","doi":"10.1016/j.fluid.2025.114335","DOIUrl":"10.1016/j.fluid.2025.114335","url":null,"abstract":"<div><div>Solvation energies play a crucial role in various chemical processes, ranging from chemical synthesis to separation techniques. To optimize these processes, it is essential to accurately predict solvation energies across different temperatures and solvents. However, most existing studies primarily focus on the standard temperature of 298.15 K. In this work, we address this limitation by creating an extensive database, which combines the DIPPR and Yaws databases. Our comprehensive dataset includes 5420 pure compounds, resulting in 71,656 data points spanning a wide range of temperatures. Additionally, besides the development of this novel database, another key contribution of this work is the coupling of the well-known Graph Convolutional Neural Network Chemprop, with our database with the aim of predicting self-solvation energies across diverse temperatures for the first time. The results presented here demonstrate the overall effectiveness of the model, evidenced by a low Mean Absolute Error (MAE) of 0.09 kcal mol⁻¹ and a high Determination Coefficient (R²) of 0.992. Additionally, the Average Relative Deviation (ARD) of the data is 2.2 %, further confirming the accuracy of the model. In fact, the model demonstrates robust predictive performance across data of varying quality, including a significant fraction of pseudo-experimental values derived from predictive schemes. However, it is worth noting that some groups of compounds, such as small sized compounds and low-numbered ring structures, exhibited somewhat larger deviations than expected. This suggests areas for further refinement and indicates that while the model is robust, there is still room for improvement in specific cases. This approach represents an overall improvement over previous models and offers enhanced versatility for practical applications in chemical synthesis and separation processes.</div></div>","PeriodicalId":12170,"journal":{"name":"Fluid Phase Equilibria","volume":"592 ","pages":"Article 114335"},"PeriodicalIF":2.8,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ethane solubility in triethylene glycol from an experimental and modeling perspective","authors":"Ali Rasoolzadeh ,&nbsp;Alireza Shariati ,&nbsp;Cor J. Peters","doi":"10.1016/j.fluid.2025.114331","DOIUrl":"10.1016/j.fluid.2025.114331","url":null,"abstract":"<div><div>The downstream units of the gas refinery could be impacted by even a small amount of water. In the gas dehydration unit, one of the methods used to extract water from water-saturated gas is the absorption of water with the glycol solutions. The key selection criterion for choosing the best solvent in the gas dehydration unit is while absorbing maximum amounts of water, does not tend to absorb other natural gas components like light hydrocarbons. Triethylene glycol (TEG) is a widely used solvent in the gas dehydration process. TEG has the potential to co-absorb various gas components, such as CO₂, methane, ethane, propane, and others, in addition to water from the gas stream. As a result, the proportions of gas components absorbed in TEG are crucial for optimizing glycol units, creating ideal regeneration environments, recovering energy, and saving money. In this contribution, the solubility of ethane in TEG was experimentally measured using the Cailletet apparatus, which operates based on the synthetic method. The ethane mole fraction range, the pressure range, and the temperature range are (0.0364 to 0.1263), (2.20 to 12.84) MPa, and (343.15 to 458.37) K, respectively. Additionally, a number of thermodynamic packages were utilized to determine the solubility of ethane in TEG. The findings showed that the van der Waals (vdW) mixing rules with the temperature-dependent parameter and the Wong-Sandler (WS) mixing rules combined with the Peng-Robinson (PR) equation of state (EoS) gave more accurate results with the average absolute deviation (AAD) in calculated pressures of 0.17 MPa and 0.18 MPa, respectively.</div></div>","PeriodicalId":12170,"journal":{"name":"Fluid Phase Equilibria","volume":"592 ","pages":"Article 114331"},"PeriodicalIF":2.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A stable solution method for natural gas density across a wide temperature range using the GERG-2008 equation of state","authors":"Wenlong Jia,&nbsp;Xiujuan Wang,&nbsp;Xia Wu,&nbsp;Changjun Li,&nbsp;Fan Yang,&nbsp;Yupeng Liao","doi":"10.1016/j.fluid.2024.114328","DOIUrl":"10.1016/j.fluid.2024.114328","url":null,"abstract":"<div><div>The GERG-2008 Equation of State (EoS) is recommended by ISO 20765–2 for computing the physical properties of natural gas. Due to the non-monotonic relationship between pressure and molar density described by the GERG-2008 EoS under low temperature conditions, multiple density solutions may exist, making the precise determination of density challenging. This paper first investigates the variation of molar densities with pressure at different temperatures. When the temperature is above <span><math><mrow><msub><mi>T</mi><mi>r</mi></msub><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow><mspace></mspace></mrow></math></span>(The composition-dependent reducing functions of the mixture temperature), the pressure described by the GERG-2008 EoS increases monotonically with the molar density, and only one molar density solution that satisfies the equation exists. However, when the temperature is below <span><math><mrow><msub><mi>T</mi><mi>r</mi></msub><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow><mspace></mspace></mrow></math></span>, the pressure described by GERG-2008 EoS no longer changes monotonically with the increase in molar density, resulting in multiple molar density solutions that satisfy the equation. To address this problem, this paper proposes a novel solution method that employs a combination of one-dimensional search and the Newton-Raphson iteration to obtain the required molar density solutions. The true molar density solution is then determined based on the Gibbs free energy criterion, ensuring the correct molar density solution is obtained across a wide temperature range. A total of 903 sets of natural gas density data, covering pressures from 0 to 200 MPa and temperatures from 100 to 450 K, were used to validate this method. The computational results indicate that, when the temperature is above<span><math><mrow><msub><mi>T</mi><mi>r</mi></msub><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow><mspace></mspace></mrow></math></span>, the average relative deviation (ARD) between the calculated density values and the experimental values ranges from 0.1 % to 0.59 %. For temperatures below <span><math><mrow><msub><mi>T</mi><mi>r</mi></msub><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow><mspace></mspace></mrow></math></span>, the ARD values using this method range from 0.01 % to 0.39 %. The proposed solution method enhances the stability and accuracy of solving the GERG-2008 equation, particularly for natural gas at low temperatures.</div></div>","PeriodicalId":12170,"journal":{"name":"Fluid Phase Equilibria","volume":"593 ","pages":"Article 114328"},"PeriodicalIF":2.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}