Fluid Phase Equilibria最新文献

{"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}

{"title":"Density gradients in aqueous salt solutions: A challenging calculation for electrolyte equations of state","authors":"Iván Cubilla ,&nbsp;Marcos Cáceres ,&nbsp;Christian E. Schaerer ,&nbsp;Marcelo Castier","doi":"10.1016/j.fluid.2024.114327","DOIUrl":"10.1016/j.fluid.2024.114327","url":null,"abstract":"<div><div>In clinical laboratories around the world, isopycnic separations are routinely used to separate biological materials based on density differences. One of the techniques to form a density gradient for such separations is to centrifuge an aqueous salt solution. The high angular speeds, salt concentration and pressure can reach high values, creating challenging conditions for the modeling of centrifugation equilibrium. This paper addresses this problem and presents a formulation and a solution procedure for determining the thermodynamic equilibrium of electrolyte solutions during centrifugation. This is accomplished by a nested-loop algorithm; the outer loop iterates on the liquid volume; the inner loop minimizes the Helmholtz function at the given temperature and component amounts, for the current volume value. The Helmholtz function is evaluated as the summation of an intrinsic contribution given by the eSAFT-VR Mie equation of state, an external contribution of the centrifugal field, and induced electrostatic contribution associated with the possible displacement of charged species in the system. In general, qualitative agreement between the experimental and calculated density profiles was observed in the three systems studied.</div></div>","PeriodicalId":12170,"journal":{"name":"Fluid Phase Equilibria","volume":"592 ","pages":"Article 114327"},"PeriodicalIF":2.8,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155495","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":"Bubble points and densities of H2 (up to ∼ 5%) in CO2-rich binary systems","authors":"Franklin Okoro,&nbsp;Friday Junior Owuna,&nbsp;Antonin Chapoy,&nbsp;Pezhman Ahmadi,&nbsp;Rod Burgass","doi":"10.1016/j.fluid.2024.114321","DOIUrl":"10.1016/j.fluid.2024.114321","url":null,"abstract":"<div><div>In this study, experimental measurements of the bubble points of binary mixtures containing varying concentrations of CO₂ (99.5 %, 99 %, 98.5 %,98 %, and ∼ 95 %) with hydrogen (H₂) were made. These measurements were carried out from low temperatures (240.20 K) up to 294.84 K (with uncertainties of 0.14 K) using the constant composition expansion method. The experimental data were used to validate two thermodynamic models - the Peng-Robinson and Multi-Fluid Helmholtz Energy Approximation Equation of state (PR-EoS and MFHEA-EoS). From the results, the presence of H₂ in CO₂ at concentrations between 0.5 and 5 % caused a significant (∼19–980 %) positive deviation from the phase behaviour of CO₂ stream compared to that of pure CO₂. This effect intensified with higher concentrations of H₂ and decreased with rising temperatures. Both models demonstrated good agreement with the experimental bubble point data, exhibiting &lt;4 % average deviation for the system. Notably, the PR-EoS outperformed the MFHEA-EoS, showing &lt;3 % average deviation. Densities of CO₂ (99.5 %) with H₂ were measured at 278.14, 298.34, 323.55, and 348.40 K and pressures up to 35 MPa. While the densities of CO₂ (94.99 %) with H₂ were measured at 278.06, 288.13, 298.26, and 323.53 K and pressures up to 35 MPa using a vibrating tube densimeter which was calibrated using water and hydrogen. For the 99.5 % CO₂ binary mixture, the average absolute relative deviations (AARD) of the model predictions were 0.09 % and 0.26 % against MFHEA and PR EoS respectively. The AARD of the model predictions for the 94.99 % CO₂ were 0.33 % and 1.49 % with MFHEA and PR EoS respectively. Furthermore, even at low concentrations (0.5 %), the presence of H₂ led to a substantial reduction (&gt;35 %) in the density of the mixture compared to that of pure CO₂ at lower pressure conditions with this effect becoming more pronounced at higher temperatures and concentrations of H₂. Both models predicted the densities of the system well (with &lt;2 % deviations from the experimental data), though MFHEA-Eos was more accurate with &lt;0.4 % maximum relative deviation (MaxRD) for all the data points.</div></div>","PeriodicalId":12170,"journal":{"name":"Fluid Phase Equilibria","volume":"592 ","pages":"Article 114321"},"PeriodicalIF":2.8,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155498","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":"Studying the influence of temperature on the thermodynamic, structural, and dynamic properties of 11 recently reparametrized rigid water models via molecular dynamics simulations","authors":"Adnan Jaradat,&nbsp;Khadeejeh Abudalbouh,&nbsp;Ali Al-Mahmoud,&nbsp;Rakan Alsalman,&nbsp;Abdalla Obeidat","doi":"10.1016/j.fluid.2024.114329","DOIUrl":"10.1016/j.fluid.2024.114329","url":null,"abstract":"<div><div>Molecular dynamics simulations were conducted to investigate 11 recently reparametrized 3-site, 4-site, and 5-site rigid water models, including TIP4P/ε, OPC, TIP3P-FB, TIP4P-FB, TIP4P-D, SPC/ε, OPC3, TIP5P-2018, TIP3P-ST, TIP4P-ST, and FBA/ε. The study focused on the effects of temperature on the thermodynamic, structural, and dynamic properties of water. A wide range of physical properties was examined using both slab and closed systems across temperatures ranging from 270 to 630 Kelvin. The results were compared with older rigid and non-rigid water models. Among the models tested, TIP4P-FB demonstrated the best overall performance, while OPC3 was identified as the most accurate among the 3-site models tested in this study.</div></div>","PeriodicalId":12170,"journal":{"name":"Fluid Phase Equilibria","volume":"592 ","pages":"Article 114329"},"PeriodicalIF":2.8,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155494","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":"Influence of water content on thermophysical properties of aqueous glyceline solutions predicted by molecular dynamics simulations","authors":"Marcelle B.M. Spera ,&nbsp;Samir Darouich ,&nbsp;Jürgen Pleiss ,&nbsp;Niels Hansen","doi":"10.1016/j.fluid.2024.114324","DOIUrl":"10.1016/j.fluid.2024.114324","url":null,"abstract":"<div><div>Molecular simulations allow the prediction of a large variety of thermophysical properties for complex mixtures based on one underlying model, i.e. the force field. In the present work static and dynamic properties of aqueous 1:2 choline chloride:glycerol mixtures are computed by molecular dynamics simulations with the aim to report robust simulation protocols that allow for a thorough evaluation of the molecular model with regard to experimental data. In particular for the shear viscosity a rather strong dependence of the results on the simulation method can be found. The simulations do not only provide quantitative data but also insight into the effect of water on the microscopic structure of the fluid. The isobaric thermal expansivity shows a transition from DES-like to water-like behavior beyond a water mole fraction of 0.75. Moreover, inconsistencies in experimental datasets are identified. Molecular dynamics simulations serve as a powerful tool to support the decision for one or the other data set in case of contradictory experimental data.</div></div>","PeriodicalId":12170,"journal":{"name":"Fluid Phase Equilibria","volume":"592 ","pages":"Article 114324"},"PeriodicalIF":2.8,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155496","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":"A machine learning approach to predict CO2 diffusivity in liquid H2O over a wide pressure and temperature range","authors":"Georgios Gravanis ,&nbsp;Simira Papadopoulou ,&nbsp;Spyros Voutetakis ,&nbsp;Konstantinos Diamantaras ,&nbsp;Ioannis N. Tsimpanogiannis","doi":"10.1016/j.fluid.2024.114325","DOIUrl":"10.1016/j.fluid.2024.114325","url":null,"abstract":"<div><div>This study presents a machine learning approach for predicting the diffusivity of CO₂ in liquid H₂O over a wide range of temperatures and pressures. A comprehensive experimental dataset is compiled, including over 300 data points from existing literature, as well as, 75 newly identified diffusivity measurements. These data span a broad spectrum of temperatures and pressures. Various machine learning models namely, Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (kNN), and Autoencoders, are trained on this enhanced dataset and evaluated for their accuracy in diffusivity prediction. Results show that the Autoencoder model achieves superior performance, accurately predicting CO₂ diffusivity even in regions where experimental data is sparse. The model’s ability to generalize across a wide range of temperatures and pressures, demonstrates its potential for use in real-world applications, enabling fast, reliable predictions with minimized computational cost.</div></div>","PeriodicalId":12170,"journal":{"name":"Fluid Phase Equilibria","volume":"592 ","pages":"Article 114325"},"PeriodicalIF":2.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154554","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":"Separation of azeotropic mixture using a novel hybrid entrainer based on deep eutectic solvents","authors":"Yong Peng ,&nbsp;Yanhao Shen ,&nbsp;Junfeng Niu ,&nbsp;Xiaoyu Han","doi":"10.1016/j.fluid.2024.114326","DOIUrl":"10.1016/j.fluid.2024.114326","url":null,"abstract":"<div><div>In recent years, deep eutectic solvents (DESs) have emerged as environmentally friendly distillation entrainers. However, most research has focused on pure DESs, and developing new DESs remains challenging. This process requires significant time to screen suitable hydrogen bond donors and acceptors. This study explores a hybrid approach to enhance the efficacy of single DESs. Using the widely studied ethanol/water azeotropic mixture as a model system, Choline chloride (ChCl):urea (1:2, mol/mol) was selected as the benchmark entrainer. Among the 13 inorganic salts tested, CaCl₂ was chosen as an additive to prepare a hybrid entrainer (5 wt% CaCl₂ + 95 wt% ChCl:urea). Increasing the hybrid entrainer content from 0 to 36.9 wt% resulted in a 270% increase in relative volatility, outperforming pure DESs. This hybrid approach demonstrates potential to reduce ChCl:urea usage by 50%. Vapor-liquid equilibria were determined, with a good fit between experimental and theoretical data using the NRTL model.</div></div>","PeriodicalId":12170,"journal":{"name":"Fluid Phase Equilibria","volume":"592 ","pages":"Article 114326"},"PeriodicalIF":2.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155499","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":"The impact of inaccurate property data in process modelling","authors":"José M.S. Fonseca ,&nbsp;María Francisco Casal","doi":"10.1016/j.fluid.2024.114322","DOIUrl":"10.1016/j.fluid.2024.114322","url":null,"abstract":"<div><div>It is widely accepted that high-quality thermophysical property data are essential for the accurate modelling of chemical processes, both in the conceptual design phase of process development and in the optimization of existing processes. Unfortunately, over the last decades, the chemical industry has experienced the closure of many physical property data laboratories. This trend is not limited to experimental facilities, also applied thermodynamic groups have been significantly downsized or sometimes extinguished. Process engineers and modelling experts often take over the tasks of evaluating and testing the thermodynamic property packages used in their models. From the simulation industry, we see that it is increasingly common that users request out-of-the-box property data that can be used directly in their simulations. Estimation methods are sometimes being used without a proper acknowledgment of their limitations and associated uncertainties. We believe it is, therefore, important to raise awareness of how large the impact of potential property data errors on process modelling can be, more specifically on the modelling of typical downstream unit operations. In this work, we provide practical insights on this issue, by revisiting textbook examples and by delving into real-life industrial cases we have encountered over the years. Critical considerations on the direct use of data from large databanks and estimation methods are also presented. The last is particularly relevant, with an increasing number of research groups working on the development of machine learning methods as means to generate massive amounts of property data.</div></div>","PeriodicalId":12170,"journal":{"name":"Fluid Phase Equilibria","volume":"592 ","pages":"Article 114322"},"PeriodicalIF":2.8,"publicationDate":"2024-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155493","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}