Fluid Phase Equilibria最新文献

Thermodynamic modeling of poorly specified mixtures using NMR fingerprinting and group-contribution equations of state 使用核磁共振指纹和群体贡献状态方程的不良指定混合物的热力学建模

IF 2.8 3区工程技术 Q3 CHEMISTRY, PHYSICAL

Fluid Phase Equilibria

Pub Date : 2025-04-24 DOI: 10.1016/j.fluid.2025.114446

Jens Wagner, Zeno Romero, Kerstin Münnemann, Thomas Specht, Fabian Jirasek, Hans Hasse

Mixtures of which the composition is only partially known are ubiquitous in chemical and biotechnological processes and pose a significant challenge for process design and optimization since classical thermodynamic models require complete speciation, which cannot be obtained with reasonable effort in many situations. In prior work, we have introduced a framework combining standard nuclear magnetic resonance (NMR) experiments and machine-learning (ML) algorithms for the automated elucidation of the group composition of unknown mixtures and the rational definition of pseudo-components and have applied the results together with group-contribution (GC) models of the Gibbs excess energy. In the present work, we extend this approach to the application of group-contribution equations of state (GC-EOS), enabling the predictive modeling of basically all thermodynamic properties of such mixtures. As an example, we discuss the application of the SAFT-

γ

Mie GC-EOS for predicting the CO

_{2}

solubility in several test mixtures of known composition. However, the information on their composition was not used in applying our method; it was only used to generate reference results with the SAFT-

γ

Mie EOS that were compared to the predictions from our method. In addition, the CO

_{2}

solubility in the test mixtures was also determined experimentally by NMR spectroscopy. The results demonstrate that the new approach for modeling poorly specified mixtures also works with GC-EOS, which further extends its applicability in process design and optimization.

在化学和生物技术过程中，成分仅部分已知的混合物无处不在，这对过程设计和优化构成了重大挑战，因为经典热力学模型需要完全的物种形成，而在许多情况下，这是不可能通过合理的努力获得的。在之前的工作中，我们引入了一个结合标准核磁共振（NMR）实验和机器学习（ML）算法的框架，用于自动阐明未知混合物的基团组成和伪组分的合理定义，并将结果与吉布斯多余能量的基团贡献（GC）模型一起应用。在目前的工作中，我们将这种方法扩展到状态群贡献方程（GC-EOS）的应用中，从而能够对这种混合物的基本所有热力学性质进行预测建模。作为一个例子，我们讨论了SAFT-γ Mie GC-EOS在几种已知成分的测试混合物中预测CO2溶解度的应用。然而，在应用我们的方法时没有使用它们的成分信息；它仅用于与SAFT-γ Mie EOS生成参考结果，并将其与我们方法的预测进行比较。此外，还利用核磁共振光谱法测定了CO2在试验混合物中的溶解度。研究结果表明，该方法可以应用于GC-EOS，进一步扩展了其在工艺设计和优化中的适用性。

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引用次数: 0

Thermodynamic properties of some aqueous ionic liquid solutions using ion-SAFT-VR EOS 离子液体水溶液热力学性质的离子- saft - vr EOS研究

IF 2.8 3区工程技术 Q3 CHEMISTRY, PHYSICAL

Fluid Phase Equilibria

Pub Date : 2025-04-18 DOI: 10.1016/j.fluid.2025.114447

Ayeh Emrani, Ali Maghari

The statistical association fluid theory (SAFT) approach for attractive potentials of variable range combined with interionic interactions, to describe some aqueous solutions of imidazolium ionic liquids (ILs) with different anion structures. The anions analyzed encompass [SCN]^-, [DCA]^–, [BF4]^-, [NTF₂]^- and [PF₆]^- which show the effect of systematically change of the substituents on the anion. The proposed equation of state is a combination of a square-well variable range and a mean spherical approximation (MSA) corresponding to the restrictive primitive model to describe the long-range interactions of ILs. Water is characterized as a bipolar associating liquid combining the four-site model and a square-well potential with a constant dielectric continuum. The accurate predictions of volumetric properties for pure ILs and

p ρ T

of water as well as the thermodynamic properties of binary aqueous IL solutions are significant evidence of the capability and excellent performance of the ion-SAFT-VR model. Parameters of the new version of the model have been reported and the model tested for an impressively large number of data sets. For binary mixtures of ILs and water, binary interaction coefficients were obtained by fitting experimental data at constant pressure and phase concentrations. The results show the satisfactory predictability of the ion-SAFT-VR EOS in modeling the thermodynamic properties of bulk systems, which indicates the reliability of the employed EOS. Due to the excellent results obtained for pure IL densities and the satisfactory results observed for aqueous solution of ILs, we can confidently conclude that the approach provides an overall commendable description of most IL systems.

结合离子间相互作用，用统计关联流体理论（SAFT）方法描述了具有不同阴离子结构的咪唑类离子液体（ILs）水溶液。所分析的阴离子包括[SCN]-、[DCA] -、[BF4]-、[NTF2]-和[PF6]-，表明取代基的系统变化对阴离子的影响。所提出的状态方程是一个相对应于约束原始模型的平方井变量范围和平均球面近似（MSA）的组合，用于描述离子间的远程相互作用。水的特征是一种双极性缔合液体，结合了四点模型和具有恒定介电连续体的方阱电位。离子- saft - vr模型准确预测了纯IL和水的p / t的体积性质以及二元IL水溶液的热力学性质，证明了离子- saft - vr模型的能力和优异的性能。报告了新版本模型的参数，并对模型进行了令人印象深刻的大量数据集测试。在恒压、恒相浓度条件下，通过拟合实验数据，得到了双相作用系数。结果表明，离子- saft - vr EOS在模拟体系统热力学性质方面具有令人满意的可预测性，表明所采用的EOS是可靠的。由于纯IL密度获得了极好的结果，并且在IL水溶液中观察到令人满意的结果，我们可以自信地得出结论，该方法提供了大多数IL系统的总体值得赞扬的描述。

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引用次数: 0

Solubility of water in bitumen 水在沥青中的溶解度

IF 2.8 3区工程技术 Q3 CHEMISTRY, PHYSICAL

Fluid Phase Equilibria

Pub Date : 2025-04-15 DOI: 10.1016/j.fluid.2025.114442

B. Zuluaga, F.F. Schoeggl, H.W. Yarranton

Liquid/liquid-vapor (L/LV) and aqueous-liquid/aqueous-liquid-vapor (AL/ALV) boundaries of pseudo-binary mixtures of bitumen and water were measured using the isothermal stepwise volume expansion method at conditions relevant to in situ heavy oil operations (temperatures from 180 to 280 °C and pressures from 1.5 to 5 MPa). The L/AL boundary was determined from the intersection of the L/LV and AL/ALV boundaries. An activity coefficient model of the pseudo-binary system was used to check the self-consistency of the L/LV measurements. The Advanced Peng Robinson equation of state, which has a distinct alpha function for water, was used to model the VLE and VLLE data. For this model, the oil was characterized into pseudo-components based on a SimDist assay and the specific gravity and asphaltene content of the oil. Temperature dependent binary interaction parameters between bitumen pseudo-components and water were tuned such that the model fit the measurements to within the experimental error. Isothermal pressure-composition phase diagrams were generated for the pseudo-binary mixtures at each temperature. Finally, a straightforward correlation for the solubility limit of water in bitumen as a function of temperature was developed using the data in this study and from the literature. The average deviation of the correlation was 0.5 wt % below 340 °C.

采用等温逐步体积膨胀法测量了沥青和水的假二元混合物的液/液-气（L/LV）和水-液/水-液-气（AL/ALV）边界，测量条件与原地重油作业相关（温度为 180 至 280 °C，压力为 1.5 至 5 兆帕）。L/AL 边界是根据 L/LV 和 AL/ALV 边界的交叉点确定的。使用伪二元系统的活度系数模型来检查 L/LV 测量的自洽性。高级彭-罗宾逊状态方程对水具有独特的阿尔法函数，该方程被用来建立 VLE 和 VLLE 数据模型。在该模型中，根据 SimDist 分析以及油的比重和沥青质含量，将油表征为伪组分。对沥青伪组分和水之间与温度相关的二元相互作用参数进行了调整，使模型与测量结果的吻合度在实验误差范围内。为伪二元混合物生成了各温度下的等温压力-成分相图。最后，利用本研究和文献中的数据，建立了水在沥青中的溶解度极限随温度变化的直接相关关系。相关性的平均偏差在 340 °C 以下为 0.5 wt %。

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引用次数: 0

Thermodiffusion of CO2 mixtures in the extended critical region CO2混合物在扩展临界区域的热扩散

IF 2.8 3区工程技术 Q3 CHEMISTRY, PHYSICAL

Fluid Phase Equilibria

Pub Date : 2025-04-14 DOI: 10.1016/j.fluid.2025.114438

Gabriela Guevara-Carrion, Denis Saric, Jadran Vrabec

Thermodiffusion, also known as Soret effect, stands for the mass flux driven by a temperature gradient, leading to partial component separation in fluid mixtures. While typically small in magnitude and often neglected, thermodiffusion can become significant under specific conditions, such as in the extended critical region. In this work, the thermodiffusion behavior of diluted supercritical mixtures of carbon dioxide with methane, ethane, or isobutane is investigated with molecular simulation techniques. Thermodiffusion is studied along the

9 MPa

isobar over a temperature range from

T = 290

to 345 K, where singular thermodynamic and transport properties are observed. Attention is given to the crossover region near the critical point, where asymptotic power laws no longer apply, but long-range fluctuations still influence fluid behavior. Within this region, characterized by the Widom line, extreme values for the thermodiffusion and Soret coefficients are predicted. The underlying mechanisms responsible for this behavior are explored, and it is shown to be primarily driven by the extrema of the partial molar enthalpy of the solvent in the extended critical region.

热扩散，也被称为索雷特效应，是指由温度梯度驱动的质量通量，导致流体混合物中部分成分分离。虽然通常很小，经常被忽视，但热扩散在特定条件下可能变得重要，例如在扩展的临界区域。在这项工作中，用分子模拟技术研究了稀释的超临界二氧化碳与甲烷、乙烷或异丁烷混合物的热扩散行为。在T=290至345 K的温度范围内，沿着9MPa等压线研究了热扩散，观察到奇异的热力学和输运性质。注意在临界点附近的交叉区域，在那里渐近幂律不再适用，但长期波动仍然影响流体行为。在这一区域内，以智慧线为特征，预测了热扩散系数和索雷特系数的极值。对这种行为的潜在机制进行了探索，并表明它主要是由溶剂在扩展临界区域的偏摩尔焓的极值驱动的。

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引用次数: 0

ANN-based estimation of pure-component parameters of PC-SAFT equation of state using quantum chemical data 基于神经网络的量子化学PC-SAFT状态方程纯组分参数估计

IF 2.8 3区工程技术 Q3 CHEMISTRY, PHYSICAL

Fluid Phase Equilibria

Pub Date : 2025-04-14 DOI: 10.1016/j.fluid.2025.114444

Hiroaki Matsukawa, Yusuke Miyagi, Katsuto Otake

The perturbed chain-statistical associating fluid theory equation of state (PC-SAFT EoS) is a physical property estimation tool that can be used to calculate a wide range of substance types, temperatures, and pressures. To perform calculations using the PC-SAFT EoS, substance-specific pure-component parameters are required, which are generally determined from liquid density and saturated vapor pressure. Few studies have reported on these parameters, and methods that can obtain pure-component parameters without relying on measured physical properties remain elusive. In this study, an artificial neural network (ANN) is introduced to predict the pure-component parameters of the PC-SAFT EoS. The molecular information estimated from a Gaussian software was used as the input. In addition, we optimized the structure of the ANN by varying the transfer function, number of neurons, and number of hidden layers. The optimized ANN comprises a hard sigmoid transfer function composed of two hidden layers, with 20 and 10 neurons in the first and second layer, respectively. This model can determine the pure-component parameters of the PC-SAFT EoS for a wide range of substance types. Furthermore, SHapley Additive exPlanations analysis on the optimized ANN demonstrates that the contributions of the polarizability and dipole moment are large. However, the feature values related to the shape of the substance are lacking. These results contribute to expanding the range of applications for property estimation using EoS.

扰动链统计关联流体理论状态方程（PC-SAFT EoS）是一种物理性质估算工具，可用于计算各种物质类型、温度和压力。要使用 PC-SAFT EoS 进行计算，需要特定物质的纯组分参数，这些参数通常根据液体密度和饱和蒸汽压确定。有关这些参数的研究报告寥寥无几，而无需依赖测量物理性质就能获得纯组分参数的方法也仍未出现。本研究引入了人工神经网络（ANN）来预测 PC-SAFT EoS 的纯组分参数。我们使用高斯软件估算的分子信息作为输入。此外，我们还通过改变传递函数、神经元数量和隐层数来优化 ANN 的结构。优化后的 ANN 包含一个由两个隐藏层组成的硬 sigmoid 传递函数，第一层和第二层分别有 20 个和 10 个神经元。该模型可以确定 PC-SAFT EoS 的纯组分参数，适用于多种物质类型。此外，对优化 ANN 进行的 SHapley Additive exPlanations 分析表明，极化率和偶极矩的贡献很大。但是，缺乏与物质形状相关的特征值。这些结果有助于扩大使用 EoS 进行属性估计的应用范围。

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引用次数: 0

Using Physical Property Models and Artificial Intelligence to Design Chemical Products 利用物理性质模型和人工智能设计化工产品

IF 2.8 3区工程技术 Q3 CHEMISTRY, PHYSICAL

Fluid Phase Equilibria

Pub Date : 2025-04-11 DOI: 10.1016/j.fluid.2025.114441

Kevin G. Joback

Designing chemical products is the process of combining pieces, e.g., molecular fragments or ingredients, into an assembly, e.g., chemical structures or mixture formulations, whose properties satisfy a set of design constraints. We explain how computational techniques, specifically artificial intelligence techniques, can greatly assist this design process. We demonstrate how proper representation of knowledge is essential for enabling the computer to manipulate substructures, how combinatorial algorithms are used to generate structures and enumerate isomers, how rule-based systems help select the estimation techniques needed to test each generated candidate chemical, and how machine learning can be used to improve the models used to find promising candidates.

化工产品设计是将分子片段或成分等部件组合成化学结构或混合物配方等组件的过程，这些组件的性质满足一系列设计约束。我们解释了计算技术，特别是人工智能技术如何极大地协助这一设计过程。我们展示了正确的知识表示对于使计算机能够操纵子结构是至关重要的，如何使用组合算法来生成结构和枚举异构体，如何基于规则的系统帮助选择测试每种生成的候选化学物质所需的估计技术，以及如何使用机器学习来改进用于寻找有希望的候选化学物质的模型。

引用次数: 0

Artificial neural network-based estimation of interaction parameters between carbon dioxide and organic solvents using the Peng–Robinson equation of state with the van der Waals one-fluid mixing rule and quantum chemical data 基于van der Waals单流体混合规则的Peng-Robinson状态方程和量子化学数据的二氧化碳与有机溶剂相互作用参数的人工神经网络估计

IF 2.8 3区工程技术 Q3 CHEMISTRY, PHYSICAL

Fluid Phase Equilibria

Pub Date : 2025-04-11 DOI: 10.1016/j.fluid.2025.114443

Hiroaki Matsukawa, Emiri Kobayashi, Katsuto Otake

The Peng–Robinson (PR)-van der Waals (vdW) model, which combines the PR equation of state with the vdW one-fluid mixing rule, is often used to estimate the physical properties of CO₂/organic solvent mixtures. Calculating these properties using the PR-vdW model requires interaction parameters k_ij; however, reports on these parameters are limited. This article introduces an artificial neural network (ANN) to predict k_ij between CO₂ and organic solvents, using pure-component parameters and molecular information as inputs. The molecular information is obtained through the general-purpose quantum chemical calculation software Gaussian. In addition, the ANN is optimized by varying the transfer function, number of neurons, and number of hidden layers. The optimized ANN employs a tanh function as the transfer function for the hidden layers, with two hidden layers containing 40 and 10 neurons. This model effectively predicts k_ij for a wide range of substances and temperature conditions. Furthermore, SHapley Additive exPlanations analysis of the optimized ANN reveals a significant contribution from the quadrupole moment, likely due to quadrupole interactions between CO₂ and the organic solvents. These results support the estimation of the physical properties of CO₂/organic solvent mixtures.

Peng-Robinson (PR)-van der Waals （vdW）模型是将PR状态方程与vdW单流体混合规律相结合的模型，常用于估算CO2/有机溶剂混合物的物理性质。使用PR-vdW模型计算这些属性需要交互参数kij；然而，关于这些参数的报告是有限的。本文介绍了一种以纯组分参数和分子信息为输入的人工神经网络（ANN）来预测CO2与有机溶剂之间的kij。分子信息通过通用量子化学计算软件Gaussian获得。此外，通过改变传递函数、神经元数量和隐藏层数量来优化人工神经网络。优化后的ANN采用tanh函数作为隐藏层的传递函数，隐藏层分别包含40个和10个神经元。该模型有效地预测了各种物质和温度条件下的kij。此外，优化后的人工神经网络的SHapley加性解释分析揭示了四极矩的显著贡献，可能是由于CO2和有机溶剂之间的四极相互作用。这些结果支持了对CO2/有机溶剂混合物物理性质的估计。

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引用次数: 0

Vapor–liquid interfacial properties of the system acetone + CO2: Experiments, molecular simulation, density gradient theory, and density functional theory 丙酮+ CO2体系的气液界面性质：实验、分子模拟、密度梯度理论和密度泛函理论

IF 2.8 3区工程技术 Q3 CHEMISTRY, PHYSICAL

Fluid Phase Equilibria

Pub Date : 2025-04-11 DOI: 10.1016/j.fluid.2025.114436

Florian Fleckenstein, Stefan Becker, Hans Hasse, Simon Stephan

Vapor–liquid interfacial properties of the system acetone + CO

_{2}

were studied using pendant drop experiments as well as multiple theoretical approaches, namely molecular dynamics (MD) simulations, density gradient theory (DGT), and density functional theory (DFT). The surface tension as well as relative adsorption of CO

_{2}

were obtained from the experiments for temperatures between 303.15 K and 373.15 K. The experimental results were compared to predictions from the three theoretical approaches, which also provide insights into the structure of the interface and data on the interfacial enrichment of CO

_{2}

and the interfacial thickness, which is not feasible by the experiments alone. The results from all three theoretical approaches are found to be in good mutual agreement as well as in agreement with the experimental results. Additionally, MD, DGT, and DFT were used to study the nanoscopic structure at the interface.

采用垂坠实验以及分子动力学（MD）模拟、密度梯度理论（DGT）和密度泛函理论（DFT）等多种理论方法研究了丙酮+ CO2体系的气液界面性质。在303.15 ~ 373.15 K的温度范围内，测定了CO2的相对吸附量和表面张力。实验结果与三种理论方法的预测结果进行了比较，这也提供了对界面结构的见解，以及对CO2界面富集和界面厚度的数据，这是单独实验无法实现的。三种理论方法的计算结果与实验结果基本一致。此外，利用MD、DGT和DFT研究了界面处的纳米结构。

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引用次数: 0

Modeling flash points of biofuels using neural networks 用神经网络模拟生物燃料的闪点

IF 2.8 3区工程技术 Q3 CHEMISTRY, PHYSICAL

Fluid Phase Equilibria

Pub Date : 2025-04-01 DOI: 10.1016/j.fluid.2025.114439

Maurício Prado de Omena Souza , Débora Costa do Nascimento , Diego Tavares Volpatto , Gustavo Gondran Ribeiro , Antonio Marinho Barbosa Neto , Mariana Conceição da Costa

The search for renewable energy resources is driven by environmental hazards caused by petroleum derivatives, price fluctuations, and the unsustainability of fossil fuels. In Brazil, biodiesel and bioethanol are established renewable fuels, while butanol shows promise as an alternative fuel, requiring research into their safety and efficiency. The Flash Point (FP) is crucial for flammability assessment and safety in combustion processes, but its experimental measurement is resource-intensive. This study evaluates the capability of artificial neural networks (ANNs) to predict FP for some biofuels and their blends, using a dataset of 490 points. Notably, 24 of these points were newly acquired, while the remaining 466 were sourced from literature. A robust ANN model was trained using a 5-fold cross-validation with an 80/20 data split, incorporating average molar mass, vapor pressure natural logarithmic, and experimental method as input features. The final model, featuring three hidden layers determined through a parametric analysis, achieved a Root Mean Square Error (RMSE) of 4.22 K and a Mean Absolute Error (MAE) of 3.09 K for 98 unknown points. The model achieved satisfactory accuracy, with MAE ranging from 1.51 K to 3.63 K, and performed comparably to traditional UNIFAC thermodynamic models. These results highlight the potential of ANNs for FP prediction across diverse datasets.

石油衍生品、价格波动和化石燃料的不可持续性造成的环境危害推动了对可再生能源的探索。在巴西，生物柴油和生物乙醇是公认的可再生燃料，而丁醇作为替代燃料也很有希望，但需要对其安全性和效率进行研究。闪点（FP）是燃烧过程中可燃性评价和安全性评价的关键，但其实验测量耗费大量资源。本研究使用490个点的数据集，评估了人工神经网络（ann）预测一些生物燃料及其混合物FP的能力。值得注意的是，其中24点是新获得的，其余466点来自文献。将平均摩尔质量、蒸汽压自然对数和实验方法作为输入特征，采用80/20数据分割的5倍交叉验证训练了一个鲁棒的ANN模型。最终模型通过参数分析确定了三个隐藏层，对于98个未知点，其均方根误差（RMSE）为4.22 K，平均绝对误差（MAE）为3.09 K。该模型获得了令人满意的精度，MAE在1.51 ~ 3.63 K之间，与传统的UNIFAC热力学模型相当。这些结果突出了人工神经网络在不同数据集上预测FP的潜力。

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引用次数: 0

Development of equation of state for a double square-well fluid 双方井流体状态方程的发展

IF 2.8 3区工程技术 Q3 CHEMISTRY, PHYSICAL

Fluid Phase Equilibria

Pub Date : 2025-03-31 DOI: 10.1016/j.fluid.2025.114437

Mohammad Hossein Hadipanah, Seyed Hossein Mazloumi

A double square-well potential function is proposed to describe the interaction between unbounded particles. This model is constructed based on the Lennard-Jones potential function and has three adjustable parameters. Based on the two-layers local composition model, a coordination number model for this double square-well fluid is developed and then by using the generalized van der Waals partition function a new expression for the attractive part of equation of state is derived. Two new equations of state are presented by sum of the attractive term and repulsive expressions of Carnahan-Starling and van der Waals. These models have three adjustable parameters, which are obtained by simultaneously fitting vapor pressures and liquid densities of pure substances. The capability of these two models in correlation of the vapour pressure and liquid density and in prediction of the vapour molar volume and heat of vaporization of pure compounds is investigated. Good results obtained especially with the new EOS in which Carnahan-Starling repulsive term has been used. The results of this EOS are excellent even for large molecules such as long- chain alkanes from C₁₀ to C₂₀.

提出了一个双平方阱势函数来描述无界粒子间的相互作用。该模型基于Lennard-Jones势函数构造，具有三个可调参数。在双层局部组成模型的基础上，建立了双方井流体的配位数模型，并利用广义范德华配分函数导出了状态方程中吸引部分的新表达式。将Carnahan-Starling和van der Waals的吸引项和排斥表达式相加，给出了两个新的状态方程。这些模型有三个可调参数，它们是通过同时拟合纯物质的蒸汽压和液体密度得到的。研究了这两种模型在计算蒸汽压和液体密度的相关性以及预测纯化合物的蒸汽摩尔体积和汽化热方面的能力。特别是使用了Carnahan-Starling斥力项的新EOS得到了很好的结果。即使对于大分子，如从C10到C20的长链烷烃，EOS的结果也很好。

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引用次数: 0