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UNIFAC

UNIFAC (UNIversal Functional-group Activity Coefficient) is a semi-empirical group-contribution method for predicting activity coefficients in nonelectrolyte liquid mixtures, enabling the estimation of vapor-liquid equilibria (VLE), liquid-liquid equilibria (LLE), and related thermodynamic properties based solely on molecular structure.^[1] Developed in 1975 by Aage Fredenslund, Russell L. Jones, and John M. Prausnitz at the University of California, Berkeley, the original UNIFAC model extends the UNIQUAC equation by incorporating functional group contributions to separate the activity coefficient into combinatorial (size and shape effects) and residual (energetic interactions) parts.^[1] It uses two adjustable interaction parameters per pair of functional groups, regressed from experimental VLE data, and applies to a wide range of mixtures including hydrocarbons, alcohols, and water at temperatures from 275 K to 400 K with average errors of about 8-10% in activity coefficients.^[1] The method's predictive power stems from its reliance on a limited set of universal group parameters, allowing extrapolation to unmeasured systems without component-specific fitting.^[2] Over time, UNIFAC has evolved through revisions and extensions, notably the 1977 book by Fredenslund, Gmehling, and Rasmussen, which formalized its application to VLE calculations and expanded the parameter table to over 40 groups.^[2] A key advancement is the modified UNIFAC (Dortmund) model, introduced in the 1990s by Jürgen Gmehling and colleagues at the University of Oldenburg, which improves temperature dependence by using concentration-independent group interaction parameters derived from a broader database of VLE, LLE, and excess enthalpy data.^[3] This variant addresses limitations of the original, such as poor performance in dilute regions and asymmetric systems, achieving superior accuracy (e.g., mean deviations under 5% for many binaries) and extending applicability to higher temperatures up to 413 K.^[3] As of 2025, the modified UNIFAC parameter matrix, continuously updated through the UNIFAC Consortium, includes over 100 main groups and thousands of interaction pairs, with extensions for more complex mixtures like those involving ionic liquids.^[4]^[5]^[6] In chemical engineering practice, UNIFAC and its derivatives are integral to process simulation software like Aspen Plus and PRO/II for designing distillation columns, extraction processes, and reactive separations, where experimental data is scarce or costly.^[2] The model's reliability for multicomponent systems—up to dozens of components—makes it invaluable for industries such as petrochemicals, pharmaceuticals, and biofuels, though it assumes low pressures and neglects electrolytes or strong associations without extensions.^[3] Recent hybrid approaches, like modified UNIFAC 2.0 (introduced in 2024), integrate machine learning to complete parameter matrices and fill gaps, further enhancing predictive capabilities for emerging chemicals.^[7]

Introduction

Purpose and Scope

UNIFAC, or the UNIversal Functional-group Activity Coefficient method, is a group-contribution technique designed to estimate activity coefficients in multicomponent liquid mixtures of nonelectrolyte compounds.^[8] It combines the solution-of-groups concept, which treats molecules as assemblies of functional groups, with elements of the UNIQUAC equation of state to predict non-ideal solution behavior without relying on molecule-specific experimental parameters.^[2] The primary purpose of UNIFAC is to enable the prediction of vapor-liquid equilibria (VLE) and liquid-liquid equilibria (LLE) in systems where direct experimental data are limited or absent, particularly for multicomponent mixtures involving structurally similar or diverse components.^[8] This predictive capability is especially useful for estimating phase behavior in binary and higher-order systems, allowing extrapolation from available binary data to complex mixtures.^[9] In scope, UNIFAC applies to a wide array of organic mixtures, including hydrocarbons, alcohols, ketones, and esters, as well as select inorganic systems like those containing water, across temperature ranges of approximately 275–400 K and at low to moderate pressures where deviations from ideality are significant.^[10] It plays a central role in chemical engineering for process simulation, optimization of separation processes such as distillation and extraction, and the design of industrial units handling non-ideal fluids.^[2] By decomposing molecular structures into recurring functional groups—such as -CH3, -OH, or -C=O—UNIFAC leverages a database of group interaction parameters to compute thermodynamic properties efficiently and scalably.^[8]

Historical Development

The UNIFAC model was developed in 1975 by Aage Fredenslund (on sabbatical from the Technical University of Denmark), Russell L. Jones, and John M. Prausnitz at the University of California, Berkeley. Subsequent expansions involved collaborations with Jürgen Gmehling at the University of Dortmund, Peter Rasmussen, and Michael L. Michelsen at the Technical University of Denmark in Lyngby. This development built directly on the UNIQUAC equation of state for activity coefficients, adapting its local composition framework into a group-contribution approach to enable predictions for multicomponent mixtures without requiring binary interaction parameters for every pair. Influenced by prior group-contribution methods such as ASOG, the work aimed to address limitations in estimating vapor-liquid equilibria for systems lacking experimental data. The model's foundational publication appeared in 1975, introducing the UNIFAC method with its distinctive separation into combinatorial and residual contributions to the activity coefficient, allowing for systematic estimation of phase behavior in nonideal mixtures.^[1] This seminal paper demonstrated the approach's utility for designing multicomponent distillation columns, marking a significant advancement in predictive thermodynamics. In 1977, a comprehensive monograph by Fredenslund, Gmehling, and Rasmussen expanded on the theory, parameter estimation, and initial applications, solidifying UNIFAC's theoretical basis.^[2] Throughout the 1980s, expansions focused on compiling extensive parameter tables for additional functional groups, such as alcohols, ketones, and aromatics, which broadened the model's coverage to over 50 main groups and improved accuracy for diverse organic systems. These revisions, often led by Gmehling and Rasmussen, incorporated experimental vapor-liquid equilibrium data to refine group parameters, enhancing predictive reliability for industrial separations. By the decade's end, UNIFAC had gained widespread adoption in chemical engineering as a benchmark tool for phase equilibrium calculations, with integration into early process simulation software like Aspen Plus facilitating its routine use in process design and optimization. In the 1990s, further refinements addressed the original model's limitations in temperature extrapolation by introducing temperature-dependent binary interaction parameters in modified variants, particularly through the Dortmund group's efforts, which employed forms like \psi_{mn} = \exp\left( -\frac{a_{mn} + b_{mn} T + c_{mn} T^2}{T} \right) to better capture enthalpic and entropic effects. These updates, building on 1987 modifications, expanded applicability to excess enthalpies and wider temperature ranges without altering the core combinatorial-residual structure. As of 2025, the UNIFAC Consortium—established in 1996 to coordinate parameter development amid declining public funding—continues to release periodic updates, adding interaction parameters for emerging chemicals like pharmaceuticals and biofuels, ensuring relevance in modern applications. In recent years, advancements include modified UNIFAC 2.0 (2024), which incorporates machine learning to enhance parameter estimation and predictive accuracy for new systems.^[7] The core original UNIFAC formulation, however, has remained unchanged since the early 2000s, with focus shifting to database maintenance and variant extensions.

Fundamental Concepts

Group Contribution Method

The group contribution method in UNIFAC represents molecules as assemblies of functional groups rather than treating them as indivisible units, enabling the prediction of thermodynamic properties like activity coefficients for mixtures containing compounds without prior experimental data. This approach, rooted in the solution-of-groups concept, decomposes complex organic molecules into a finite set of recurring structural subunits, such as -CH₃ (methyl), -CH₂- (methylene), and -OH (hydroxyl), whose interactions are characterized by a limited number of universal parameters. By focusing on these groups, UNIFAC facilitates the estimation of mixture behavior across diverse chemical families, including hydrocarbons, alcohols, and water-based systems, at temperatures typically ranging from 275 K to 400 K.^[1] A primary advantage of this method is its ability to reduce the dependency on molecule-specific experimental data, as interactions are defined only between pairs of functional groups rather than entire molecules, allowing extrapolation to unstudied compounds and mixtures with just two adjustable parameters per group pair. In contrast to whole-molecule models like the Non-Random Two-Liquid (NRTL) equation, which require binary interaction parameters fitted for each specific pair of components, UNIFAC's group-based framework supports broader predictive capabilities with a more compact parameter table, enhancing its utility for screening large chemical spaces in process design. This efficiency has made UNIFAC particularly valuable for vapor-liquid equilibrium predictions in industrial applications involving novel or complex formulations.^[1] The process begins with identifying and assigning the functional groups within each molecule, followed by quantifying their relative contributions using structural parameters that account for molecular size (van der Waals volume, denoted as r) and shape (surface area, denoted as q). These parameters, derived from group additivity rules, allow the calculation of group mole fractions within the mixture, which in turn inform the overall activity coefficient through weighted group interactions. For instance, ethanol (C₂H₅OH) is decomposed into one -CH₃ group, one -CH₂- group, and one -OH group, enabling its thermodynamic behavior to be predicted solely from pre-tabulated group data without needing ethanol-specific measurements. This systematic decomposition ensures consistency and scalability across homologous series and multicomponent systems.^[1]

Activity Coefficients in Mixtures

In thermodynamics, the activity coefficient, denoted as \gamma_i for component i, serves as a correction factor that accounts for deviations from ideal behavior in mixtures, particularly in the context of fugacity calculations under modified Raoult's law. For non-ideal liquid mixtures, the fugacity of component i in the liquid phase is expressed as f_i^L = x_i \gamma_i f_i^0, where x_i is the mole fraction and f_i^0 is the fugacity of pure i, allowing the partial pressure in the vapor phase to deviate from the ideal p_i = x_i p_i^\circ. This adjustment is crucial because real mixtures rarely follow Raoult's law exactly due to intermolecular forces that alter the effective concentration and volatility of components.^[11]^[12] Activity coefficients capture the effects of molecular interactions in mixtures, such as hydrogen bonding, which leads to stronger attractions and negative deviations from ideality, and dispersion forces, which contribute to positive deviations through weaker, non-specific attractions. These interactions disrupt the random mixing assumed in ideal solutions, influencing properties like vapor-liquid equilibria (VLE) where accurate \gamma_i values are essential for predicting phase behavior without relying solely on experimental data. In electrolyte or polar systems, additional effects like ion-dipole interactions further necessitate these corrections to model solubility and partitioning accurately.^[13]^[14] The natural logarithm of the activity coefficient, \ln \gamma_i, is thermodynamically related to the molar excess Gibbs energy G^E via the partial derivative \ln \gamma_i = \left( \frac{\partial (G^E / RT)}{\partial x_i} \right)_{T,P}, where R is the gas constant and T is temperature; overall, G^E / RT = \sum x_i \ln \gamma_i. This connection decomposes G^E into entropic (configurational) and enthalpic (energetic) contributions, providing a framework for models like UNIFAC that predict \gamma_i through group contributions without detailed molecular simulations. Such relations ensure consistency in thermodynamic calculations across phases.^[15] Activity coefficients are vital for engineering applications in phase equilibria, enabling predictions of bubble points, azeotropes—where mixtures boil unchanged in composition—and solubilities in complex systems like biofuels or pharmaceuticals. By incorporating \gamma_i into VLE models, such as \gamma_i x_i p_i^\circ = y_i P, designers can optimize distillation processes or assess mixture stability, reducing the need for extensive vapor-liquid equilibrium measurements. This predictive power is particularly valuable in industries handling diverse, non-ideal mixtures where experimental data is scarce or costly.^[16]

Mathematical Formulation

Overall Model Equation

The UNIFAC model provides a predictive framework for estimating activity coefficients in multicomponent liquid mixtures by decomposing molecules into functional groups and accounting for both entropic and enthalpic contributions to nonideality. The overall expression for the natural logarithm of the activity coefficient of component i, \ln \gamma_i, is given by the sum of a combinatorial term, \ln \gamma_i^C, which captures differences in molecular size and shape, and a residual term, \ln \gamma_i^R, which accounts for group-specific energetic interactions:

\ln \gamma_i = \ln \gamma_i^C + \ln \gamma_i^R

This formulation originates from an adaptation of the UNIQUAC equation, which employs the local composition concept to describe solution nonideality, extended here to a group-contribution basis where interactions are parameterized between functional groups rather than entire molecules.^[1] The model's connection to thermodynamics is established through the molar excess Gibbs energy of mixing, G^E, normalized by RT:

\frac{G^E}{RT} = \sum_i x_i \ln \gamma_i

where the summation is over all components in the mixture, x_i denotes the mole fraction of component i, R is the universal gas constant, and T is the absolute temperature. In the combinatorial and residual contributions (detailed in subsequent sections), key parameters include the segment (volume) fraction \phi_i = \frac{x_i r_i}{\sum_j x_j r_j} and the surface area fraction \theta_i = \frac{x_i q_i}{\sum_j x_j q_j}, with r_i and q_i representing the relative volumes and surface areas of component i, respectively.^[1]

Combinatorial Contribution

The combinatorial contribution in the UNIFAC model captures the entropic effects arising from differences in molecular size and shape within liquid mixtures, approximating the Flory-Huggins entropy of mixing for hard-sphere molecules. This term, derived from the UNIQUAC equation, accounts for the non-ideal arrangement of molecules due to their relative volumes and surface areas, without considering energetic interactions. It is particularly relevant in systems where size asymmetry leads to deviations from ideal mixing entropy, such as in polymer solutions or mixtures of small and large molecules.^[1] The mathematical expression for the combinatorial activity coefficient, \ln \gamma_i^C, for component i is given by:

\ln \gamma_i^C = \ln \left( \frac{\phi_i}{x_i} \right) + \frac{z}{2} q_i \ln \left( \frac{\theta_i}{\phi_i} \right) + l_i - \frac{\phi_i}{x_i} \sum_j x_j l_j

where x_i is the mole fraction of component i, \phi_i is the volume fraction, \theta_i is the surface area fraction, q_i is the relative molecular surface area, r_i is the relative molecular volume, l_i = \frac{z}{2} (r_i - q_i) - (r_i - 1) is a shape-dependent parameter, and z = 10 is the lattice coordination number. The volume fraction is calculated as \phi_i = \frac{x_i r_i}{\sum_j x_j r_j}, while the surface area fraction is \theta_i = \frac{x_i q_i}{\sum_j x_j q_j}. These parameters r_i and q_i are obtained by summing group contributions for the molecules involved.^[1] To compute the combinatorial contribution, one first determines r_i and q_i for each component using predefined group volume and area parameters, then calculates \phi_i and \theta_i based on the mixture composition. The term l_i incorporates both size and shape effects, with the coordination number z = 10 approximating a typical lattice structure in liquids. This formulation ensures that the combinatorial term approaches zero for ideal mixtures of similar-sized molecules but becomes significant in asymmetric systems, reflecting the reduced configurational entropy due to packing inefficiencies.^[1]

Residual Contribution

The residual contribution in the UNIFAC model captures the enthalpic effects arising from interactions between functional groups in a mixture, distinct from the entropic effects handled by the combinatorial part. It models the non-ideal energetic contributions to activity coefficients by considering pairwise group interactions, enabling predictions for systems where molecular geometry alone is insufficient. This term is derived from the UNIQUAC equation's residual component, adapted to a group-contribution framework using the solution-of-groups concept. The residual activity coefficient for component i, denoted \ln \gamma_i^R, is given by

\ln \gamma_i^R = \sum_k \nu_k^{(i)} \left[ \ln \Gamma_k - \ln \Gamma_k^{(i)} \right],

where \nu_k^{(i)} represents the frequency of group k in molecule i, \Gamma_k is the residual activity coefficient of group k in the mixture, and \Gamma_k^{(i)} is the corresponding value in a hypothetical pure liquid of component i. This formulation decomposes the molecular residual contribution into additive group contributions, assuming that the overall non-ideality results from differences in group environments between the mixture and pure states. The group residual activity \ln \Gamma_k is further expressed as

\ln \Gamma_k = Q_k \left[ 1 - \ln \left( \sum_m \theta_m \psi_{m k} \right) - \sum_m \frac{\theta_m \psi_{k m}}{\sum_n \theta_n \psi_{n m}} \right],

with Q_k as the group surface area parameter, \theta_m = X_m Q_m / \sum_n X_n Q_n as the surface area fraction of group m (where X_m is the mole fraction of group m), and \psi_{m k} = \exp(-a_{m k} / T) as the binary group interaction parameter, which depends on temperature T and the adjustable parameter a_{m k}. These interaction parameters are fitted from experimental phase equilibrium data and account for differences in group affinities.^[1] Physically, the residual contribution accounts for van der Waals forces, polar interactions, and hydrogen bonding energies between dissimilar groups, treating the liquid mixture as a lattice where energetic non-idealities arise from unlike group pairings. It is grounded in Guggenheim's quasi-chemical approximation, which posits that interactions occur primarily between nearest-neighbor groups rather than assuming random mixing. The local composition concept is central here: each group interacts preferentially with its surrounding local environment, influenced by the relative surface areas and interaction strengths, rather than the bulk mixture composition; this local bias enhances accuracy for associating or polar systems like alcohol-water mixtures.^[1]

Model Parameters

Structural Parameters

In the UNIFAC model, the structural parameters consist of the relative van der Waals volume r_k and the relative van der Waals surface area q_k assigned to each functional group k. These parameters capture the geometric contributions to the combinatorial term of the activity coefficient, reflecting the size and shape differences among groups in a molecule. The values are derived from Bondi's tabulated van der Waals volumes V_k and surface areas A_k using the scaling relations r_k = V_k / 15.17 and q_k = A_k / 2.5, where the constants ensure normalization relative to a standard methylene (-CH₂-) group. For a given molecule i, the overall structural parameters R_i and Q_i are calculated by summing the contributions from all constituent groups, weighted by their occurrence frequencies \nu_k^{(i)}:

R_i = \sum_k \nu_k^{(i)} r_k, \quad Q_i = \sum_k \nu_k^{(i)} q_k.

Additionally, the model employs a fixed coordination number z = 10 to approximate the lattice structure in the combinatorial formulation, independent of the specific groups or molecules. These parameters remain constant across applications and are not temperature-dependent. Representative values illustrate the assignment process. For the methyl group (-CH₃), r_k = 0.9011 and q_k = 0.848. For more complex molecules, groups are identified based on molecular structure; for example, acetone (CH₃COCH₃) decomposes into two -CH₃ groups and one carbonyl group (>C=O) from the ketones main group, with r_k = 1.100 and q_k = 0.860 for >C=O. Thus, acetone's parameters are R_i = 2 \times 0.9011 + 1.100 = 2.9022 and Q_i = 2 \times 0.848 + 0.860 = 2.556. These sums enable the model's combinatorial part to account for entropic effects due to molecular size disparities without needing molecule-specific fitting.^[17] The original set of r_k and q_k values was introduced in the foundational UNIFAC work, drawing directly from Bondi's data for common groups like alkanes, alcohols, and ketones. Subsequent extensions for new functional groups, such as certain aromatics or ethers, have involved minor refinements to ensure consistency with experimental phase equilibria, but the core values for established groups have remained largely unchanged.

Binary Interaction Parameters

Binary interaction parameters in the UNIFAC model form a matrix a_{mn} that quantifies the energetic interactions between pairs of main groups m and n. These parameters are incorporated into the residual contribution of the activity coefficient through the group interaction factor defined as

\psi_{mn} = \exp\left( -\frac{a_{mn}}{T} \right),

where T is the absolute temperature in Kelvin and a_{mn} has units of Kelvin. In the original UNIFAC formulation, the parameters are asymmetric, such that a_{mn} \neq a_{nm}, reflecting directional differences in group interactions. The binary interaction parameters are regressed from experimental vapor-liquid equilibrium (VLE) and liquid-liquid equilibrium (LLE) data for binary mixtures involving the specific group pairs. This fitting process minimizes deviations between predicted and measured phase equilibria, ensuring the parameters capture the thermodynamic behavior of group interactions effectively. In the original model, a_{mn} values are temperature-independent constants; however, modified UNIFAC variants introduce temperature-dependent forms, such as linear (a_{mn} + b_{mn} T + c_{mn} T^2) or quadratic dependencies, to extend applicability across broader temperature ranges. The original 1977 parameter set covered interactions among 6 main groups, including CH₂, OH, and H₂O, derived from limited binary VLE data. Subsequent expansions by the DECHEMA UNIFAC consortium have grown the database to over 100 main groups by 2025, with parameters compiled from thousands of experimental datasets and published in peer-reviewed sources. These compilations are maintained by DDBST and accessible through the UNIFAC Consortium for consistent use in predictions.^[17] A representative example is the interaction between the CH₂ (alkane) and OH (alcohol) groups, where a_{\text{CH}_2,\text{OH}} = 986.5 K and a_{\text{OH},\text{CH}_2} = 156.4 K, demonstrating the pronounced energetic mismatch due to polarity differences that lead to significant deviations from ideal mixing in alcohol-hydrocarbon systems.

Applications and Implementation

Phase Equilibrium Predictions

UNIFAC enables the prediction of phase equilibria in multicomponent mixtures by estimating activity coefficients that account for non-ideal behavior, serving as the foundation for vapor-liquid (VLE) and liquid-liquid (LLE) calculations in chemical engineering processes such as distillation and extraction.^[2] The model decomposes molecules into functional groups, computes group contributions to activity coefficients, and integrates these into thermodynamic equilibrium conditions, allowing predictions without extensive experimental data for new systems.^[18] For VLE predictions, UNIFAC activity coefficients \gamma_i are combined with pure-component vapor pressures P_i^{\sat}, typically obtained from the Antoine equation \log_{10} P_i^{\sat} = A_i - \frac{B_i}{T + C_i}, to apply the modified Raoult's law. The bubble point pressure at a given temperature and liquid composition \mathbf{x} is determined by solving

P = \sum_i x_i \gamma_i P_i^{\sat},

while the dew point pressure for a given vapor composition \mathbf{y} involves solving

P = \left( \sum_i \frac{y_i}{\gamma_i P_i^{\sat}} \right)^{-1}.

Bubble and dew point algorithms iteratively adjust pressure or temperature until the equality of fugacities is satisfied, enabling the computation of phase diagrams for binary and multicomponent systems. These methods are particularly useful for isobaric or isothermal flash calculations in process simulation.^[2]^[19] In LLE predictions, UNIFAC facilitates the identification of phase splits by minimizing the total Gibbs free energy of the system, expressed as

G = \sum_k \sum_i n_{i,k} \left( \mu_i^0 + RT \ln (x_{i,k} \gamma_{i,k}) \right),

where k denotes phases, n_{i,k} and x_{i,k} are the moles and mole fractions of component i in phase k, and equilibrium requires equal chemical potentials \mu_i across phases. This approach is applied to systems like polymer-solvent mixtures or liquid-liquid extraction, where immiscibility arises from differing group interactions, such as in aqueous-organic separations. Optimization techniques, including successive substitution or Newton-Raphson methods, solve for the phase compositions that achieve minimum G.^[20] The typical workflow for UNIFAC-based phase equilibrium predictions begins with inputting the molecular structures of components, followed by decomposition into UNIFAC functional groups (e.g., -CH3, -OH). Group-specific parameters, including van der Waals volumes, surface areas, and binary interaction coefficients, are then used to calculate \gamma_i via combinatorial and residual contributions. These activity coefficients are incorporated into equilibrium relations to compute phase compositions and conditions, often yielding equilibrium constants K_i = y_i / x_i = \gamma_i P_i^{\sat} / P for VLE. This group-contribution strategy allows extrapolation to unstudied mixtures, streamlining design for separation processes.^[2] A representative case study involves the ternary ethanol-water-benzene system, where UNIFAC predicts the heterogeneous azeotrope at approximately 64.5 wt% ethanol and 64.9°C under atmospheric pressure with high fidelity, capturing the vapor-liquid-liquid equilibrium critical for ethanol dehydration processes. For many organic mixtures, UNIFAC achieves prediction accuracy within 5-10% average relative deviation in total pressure or composition, demonstrating its reliability for non-polar to moderately polar systems.^[21]^[19]^[22]

Software and Databases

The UNIFAC model is integrated into several commercial process simulation software packages, enabling its use for predicting phase equilibria and activity coefficients in chemical engineering applications. Prominent examples include Aspen Plus, where UNIFAC and its variants support vapor-liquid equilibrium (VLE) calculations through built-in property methods and estimation tools for missing binary interaction parameters; PRO/II, which incorporates UNIFAC for thermodynamic modeling in steady-state simulations; and ChemCAD, offering customization files for original UNIFAC and modified UNIFAC (Dortmund) to handle interaction parameters in flowsheet design.^[23]^[24]^[25]^[26] Standalone tools from the Dortmund Data Bank Software & Technology GmbH (DDBST) provide specialized access to UNIFAC functionalities, including regression, visualization, and prediction modules for phase equilibrium data. These tools draw from the extensive Dortmund Data Bank (DDB), which compiles vapor-liquid equilibrium (VLE) and liquid-liquid equilibrium (LLE) datasets to fit and validate UNIFAC parameters.^[27]^[28] Key databases for UNIFAC parameters are maintained by DECHEMA through DDBST, encompassing comprehensive VLE and LLE collections with over 100,000 data points for binary and multicomponent systems, updated continuously to incorporate new experimental measurements as of 2025. Seminal group parameter tables originated from Magnussen et al. (1981) for LLE predictions, with subsequent revisions expanding to over 90 main groups and thousands of interactions via the UNIFAC Consortium.^[29]^[28]^[30] Free online resources via DDBST allow public access to published UNIFAC parameters, including interaction matrices and subgroup assignments, alongside web-based calculators for activity coefficient predictions without software installation. For custom implementations, open-source libraries facilitate integration: in Python, the thermo package provides UNIFAC classes for activity coefficient computations using DDBST-sourced parameters; in MATLAB, user-contributed functions and toolboxes enable group decomposition and equilibrium solving based on standard UNIFAC formulations.^[17]^[31]^[32]^[33]^[34] As of 2025, the UNIFAC Consortium has incorporated new group definitions for pharmaceuticals (e.g., amide and ether subgroups) and electrolytes (e.g., ion-specific interactions in modified variants), derived from community-submitted experimental data to enhance predictions for complex mixtures in bioprocessing and ionic systems.^[23]^[30]

Limitations and Extensions

Key Assumptions and Shortcomings

The original UNIFAC model relies on several foundational assumptions that simplify the description of molecular interactions in liquid mixtures. The combinatorial contribution, which accounts for the entropy of mixing due to differences in molecular size and shape, is treated as athermal, exhibiting no explicit temperature dependence and derived from a group-contribution adaptation of the Flory-Huggins lattice model. This assumption implies that size and shape effects are purely entropic and independent of energetic factors or thermal expansion. Additionally, the model posits a random distribution of functional groups throughout the mixture in the combinatorial term, while the residual contribution incorporates local composition effects to capture non-random energetic interactions between groups, following the quasi-chemical approximation from UNIQUAC. Further assumptions include a fixed coordination number z = 10, which represents the average number of nearest neighbors in the lattice and is carried over unchanged from the UNIQUAC framework, limiting flexibility in describing varying molecular environments. The model also neglects conformational effects, such as chain flexibility, rotational isomerism, or intramolecular interactions, by treating molecules as rigid assemblies of functional groups without accounting for dynamic structural variations.^[3] These simplifications enable broad predictive capability but constrain the model's fidelity to complex molecular behaviors. Despite its widespread adoption, the original UNIFAC model exhibits significant shortcomings in accuracy and applicability, particularly for challenging systems. It frequently overpredicts activity coefficients in highly polar or associating mixtures, such as water-alcohol systems, where hydrogen bonding leads to stronger interactions than the residual term can adequately capture, resulting in unreliable phase equilibrium predictions.^[35] Performance degrades notably at elevated temperatures above 400 K, as the group interaction parameters were parameterized primarily for moderate conditions (typically 275–425 K), and the model assumes ideal gas behavior for the vapor phase, rendering it unsuitable for high-pressure scenarios without supplementary equations of state.^[36] Moreover, UNIFAC is restricted to low-molecular-weight organic compounds, showing poor extrapolation to polymers, electrolytes, or large molecules exceeding about ten functional groups, where size effects and long-range interactions dominate.^[37] In terms of quantitative accuracy for vapor-liquid equilibrium (VLE), the model shows reliable performance for non-polar hydrocarbon systems where dispersive forces prevail but larger deviations for hydrogen-bonding systems, underscoring the limitations in handling self-association and cross-association effects. Compared to fitted activity coefficient models like NRTL, which leverage system-specific data for superior accuracy in well-studied cases, UNIFAC underperforms in data-rich scenarios but provides a distinct advantage in pure predictive applications for novel mixtures lacking experimental parameters.^[3]

Modified UNIFAC Variants

To address limitations in the original UNIFAC model's temperature extrapolation capabilities, temperature-dependent group interaction parameters were introduced in the late 1980s and refined in the 1990s. One early formulation proposed a linear temperature dependence for the binary interaction parameters, \psi_{mn}(T) = \exp\left[-\left(a_{mn} + b_{mn}/T\right)/T\right], improving correlations for vapor-liquid equilibria (VLE) and excess enthalpies across wider temperature ranges. Later developments in the Modified UNIFAC (Dortmund) variant adopted a more flexible form, a_{mn}(T) = a_{mn} + b_{mn} T + c_{mn} \ln T, enabling better representation of non-ideal behaviors in systems like alcohols and water mixtures by accounting for enthalpic and entropic contributions more accurately.^[38]^[3] The Modified UNIFAC (Lyngby) model, developed in 1987, enhanced the combinatorial contribution by incorporating a free-volume term inspired by Flory-Huggins theory, replacing the original Staverman-Guggenheim approximation to better handle size asymmetries in mixtures. This update, combined with refitted residual parameters, extended applicability to over 50 functional groups, yielding improved predictions for phase equilibria and heats of mixing in asymmetric systems such as polymer-solvent blends. The model maintained the group-contribution framework while reducing systematic errors in combinatorial entropy estimates.^[39] Specialized variants have further tailored UNIFAC for challenging systems. The COSMO-UNIFAC approach integrates quantum density functional theory (DFT) calculations from COSMO-RS to generate or refine group interaction parameters a priori, particularly useful for novel molecules lacking experimental data; this hybrid reduces reliance on empirical fitting and enhances predictive accuracy for environmentally relevant compounds like pesticides and pharmaceuticals. For ionic solutions, the Electrolyte UNIFAC model extends the framework by adding long-range electrostatic interactions via Debye-Hückel terms and ionic group definitions, enabling reliable VLE and solubility predictions in mixed-solvent electrolyte systems such as brines with organics.^[40]^[41] Another notable extension is the NIST-modified UNIFAC model, introduced in the 2010s, which refits parameters using critically evaluated experimental data from the NIST ThermoData Engine, covering VLE, LLE, and excess properties for over 80 groups. This variant improves overall predictive reliability, particularly for diverse organic mixtures, and is continuously updated as of 2025.^[42] Recent advances up to 2025 incorporate machine learning to address sparse parameter matrices in UNIFAC variants. The Modified UNIFAC 2.0 employs matrix completion algorithms to infer missing group interactions from over 500,000 experimental data points, achieving broader coverage for complex mixtures without extensive refitting.^[7] Hybrid models combining UNIFAC with perturbed-chain statistical associating fluid theory (PC-SAFT) have been developed for polymers, where UNIFAC handles local composition effects and PC-SAFT captures chain connectivity and association, improving phase behavior predictions in polymer blends and solutions. These modifications collectively enhance performance, providing improved accuracy in VLE predictions for associating systems like hydrogen-bonding mixtures compared to the original UNIFAC.

References

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[7]
[PDF] Non-Ideality Through Fugacity and Activity - University of Delaware
Equation 42 is sometimes referred to as a modified Raoult's Law. The non-ideality of the liquid phase is totally contained in the activity coefficient, while ...
[8]
Raoult Law - an overview | ScienceDirect Topics
For non-ideal solutions, the activity coefficient depends in general on the concentration: for dilute solutions it is equal to a constant (γ0 = const., and ...
[9]
Prediction of hydrogen-bonding interaction energies with new ...
Mar 15, 2025 · In Molecular Thermodynamics, various approaches are used to account for the typically significant contribution of HB interactions to mixture non ...
[10]
[PDF] Dispersion activity coefficient models. Part 1 - TUE Research portal
Dec 1, 2019 · Methanol interacts mainly by hydrogen bonds and only slightly by dispersion, while carbon disulfide interacts mainly via a strong permanent ...
[11]
Excess Gibbs Energy - an overview | ScienceDirect Topics
Excess Gibbs free energy and activity coefficients are linked. From ... Because ln γi is a partial property, the following relation may be written:.
[12]
Vapor-liquid (azeotropic systems) and liquid-liquid equilibrium ...
Aug 15, 2019 · Activity coefficient plays an important role especially for mixtures with azeotrope point making it crucial to accurately predict the activity ...
[13]
Published Parameters UNIFAC - DDBST GmbH
This page shows the published parameters for original UNIFAC. Many new, updated, and revised parameters can be obtained from UNIFAC Consortium.
[14]
A modified UNIFAC model. 1. Prediction of VLE, hE, and .gamma..infin.
... Prediction Accuracy of an Infinite Dilution Activity Coefficient. Industrial & Engineering Chemistry Research 2024, 63 (19) , 8741-8750. https://doi.org ...
[15]
Prediction of liquid-liquid equilibria with UNIFAC: a critical evaluation
Prediction of liquid-liquid equilibria with UNIFAC: a critical evaluation | Industrial & Engineering Chemistry Research.
[16]
UNIFAC parameter table for prediction of liquid-liquid equilibriums
Generalized Nonrandom Two-Liquid (NRTL) Interaction Model Parameters for Predicting Liquid–Liquid Equilibrium Behavior. Industrial & Engineering Chemistry ...Missing: CH2 | Show results with:CH2
[17]
Simulation of Heterogeneous Azeotropic Distillation
The predictions of ternary azeotrope compositions and temperature, for the ethanol-water-benzene and isopropanol-water-toluene systems, are very accurate.
[18]
[PDF] The UNIFAC Consortium - DDBST
The combination of an improved model and the creation of new interaction parameters plus an optimized group assignment scheme based on the current Dortmund Data ...
[19]
[PDF] Don't Gamble With Physical Properties For Simulations
Most of the latter use a UNIFAC-based activity coefficient model as the default, but you can use any activity coefficient.
[20]
[PDF] UNIFAC and Modified UNIFAC (Dortmund) - Uni Oldenburg
The primary intention developing the original. UNIFAC method was the reliable prediction of. VLE-data for distillation processes. Because of some weakness of ...
[21]
[PDF] Component Based Development of Computer-aided Tools for ...
In stage 2, ProCAFD is also integrated external simulation tools like Aspen, PROII, where the generated designs are automatically transferred, and a rigorous ...
[22]
Software Package - DDBST GmbH
A large number of software tools have been developed to search, retrieve, export, visualize and regress the data.
[23]
Dortmund Data Bank - DDBST GmbH
The Dortmund Data Bank (DDB) now contains nearly all worldwide available phase equilibrium data, excess properties, transport properties and pure component ...
[24]
LLE – Liquid-Liquid Equilibria - DDBST
The LLE data bank contains a large amount of liquid-liquid equilibrium data and liquid solubility data for binary and higher systems.
[25]
Parameters of the Modified UNIFAC (Dortmund) Model - DDBST
This page shows published parameters for the modified UNIFAC (Dortmund) model, including interaction parameters, sub-groups, and main groups.
[26]
Property Estimation - DDBST GmbH
DDBST offers three free online tools: activity coefficients prediction with UNIFAC, limiting activity coefficient prediction with MOSCED, and Joback prediction ...
[27]
UNIFAC Gibbs Excess Model (thermo.unifac)
Class for representing an a liquid with excess gibbs energy represented by the UNIFAC equation. This model is capable of representing VL and LL behavior.
[28]
UNIFAC group contribution method activity calculator function
Oct 30, 2017 · This code is calculating the UNIFAC group contribution method for mixtures. It can be used in estimation of activity coefficients of mixtures.
[29]
Implementation of UNIFAC group contribution method in MATLAB
GitHub - aakanksha-gubbala/unifac: Implementation of UNIFAC group contribution method in MATLAB.
[30]
[PDF] Water activity in polyol/water systems: new UNIFAC parameterization
Mar 14, 2005 · The UNIFAC parameterization of alcohol/water systems shows two main weak- nesses: it underestimates the water uptake of substances with two ...
[31]
[PDF] unifac.pdf - ethesis
The UNIFAC (UNIQUAC Functional Group Activity Coefficients) group-contribution method is a reliable and fast method for predicting liquid-phase activity ...
[32]
https://thermo.readthedocs.io/thermo.unifac.html
[33]
On the temperature dependence of the UNIQUAC/UNIFAC models
The introduction of temperature dependent interaction parameters leads to considerable improvements of the simultaneous correlation.
[34]
A modified UNIFAC group-contribution model for prediction of phase ...
A modified UNIFAC group-contribution model for prediction of phase equilibria and heats of mixing. Click to copy article link.Missing: exact | Show results with:exact
[35]
A United Chemical Thermodynamic Model: COSMO-UNIFAC
The united chemical thermodynamic model can provide a moderate quantitative prediction for the systems (especially containing the toxic and harmful compounds) ...
[36]
Towards the extensionof UNIFAC to mixtures with electrolytes
Fredenslund et al., 1977. Aa. Fredenslund, J. Gmehling, P. Rasmussen. Vapor-Liquid Equilibria using UNIFAC, Elsevier, Amsterdam (1977). 1977. Guggenheim, 1935.
[37]
Application of CP-PC-SAFT and PC-SAFT for Simultaneous ...
Jun 18, 2024 · This study examines the accuracy of the simplest polarity- and association-neglecting forms of the critical point-based revision of ...
[38]
New modified UNIFAC parameters using critically evaluated phase ...
Feb 25, 2015 · Implementation of this algorithmic framework can be used to assess phase equilibrium data and screen out possible erroneous data sets.