Activity Models

Wilson <: ActivityModel
Wilson(components;
puremodel = PR,
userlocations = String[],
pure_userlocations = String[],
verbose = false,
reference_state = nothing)

Input parameters

• Tc: Single Parameter (Float64) - Critical Temperature [K]
• Pc: Single Parameter (Float64) - Critical Pressure [Pa]
• acentricfactor: Single Parameter (Float64) - Acentric Factor
• Mw: Single Parameter (Float64) - Molecular Weight [g/mol]
• g: Pair Parameter (Float64, asymetrical, defaults to 0) - Interaction Parameter

model parameters

• Tc: Single Parameter (Float64) - Critical Temperature [K]
• Pc: Single Parameter (Float64) - Critical Pressure [Pa]
• ZRA: Single Parameter (Float64) - Rackett compresibility factor
• Mw: Single Parameter (Float64) - Molecular Weight [g/mol]
• g: Pair Parameter (Float64, asymetrical, defaults to 0) - Interaction Parameter

Input models

• puremodel: model to calculate pure pressure-dependent properties

Description

Wilson activity model, with Rackett correlation for liquid volume:

Gᴱ = nRT∑xᵢlog(∑xⱼjΛᵢⱼ)
Λᵢⱼ = exp(-gᵢⱼ/T)*Vⱼ/Vᵢ
ZRAᵢ = 0.29056 - 0.08775ωᵢ
Vᵢ = (RTcᵢ/Pcᵢ)ZRAᵢ^(1 + (1-T/Tcᵢ)^2/7)

Model Construction Examples

# Using the default database
model = Wilson(["water","ethanol"]) #Default pure model: PR
model = Wilson(["water","ethanol"],puremodel = BasicIdeal) #Using Ideal Gas for pure model properties
model = Wilson(["water","ethanol"],puremodel = PCSAFT) #Using Real Gas model for pure model properties

# Passing a prebuilt model

my_puremodel = AbbottVirial(["water","ethanol"]; userlocations = ["path/to/my/db","critical.csv"])
mixing = Wilson(["water","ethanol"],puremodel = my_puremodel)

# Using user-provided parameters

# Passing files or folders
model = Wilson(["water","ethanol"];userlocations = ["path/to/my/db","wilson.csv"])

# Passing parameters directly
model = Wilson(["water","ethanol"],
        userlocations = (g = [0.0 3988.52; 1360.117 0.0],
                        Tc = [647.13, 513.92],
                        Pc = [2.19e7, 6.12e6],
                        acentricfactor = [0.343, 0.643],
                        Mw = [18.015, 46.069])
                        )

References

1. Wilson, G. M. (1964). Vapor-liquid equilibrium. XI. A new expression for the excess free energy of mixing. Journal of the American Chemical Society, 86(2), 127–130. doi:10.1021/ja01056a002

NRTL <: ActivityModel

function NRTL(components;
puremodel=PR,
userlocations = String[],
pure_userlocations = String[],
verbose = false,
reference_state = nothing)

Input parameters

• a: Pair Parameter (Float64, asymetrical, defaults to 0) - Interaction Parameter
• b: Pair Parameter (Float64, asymetrical, defaults to 0) - Interaction Parameter
• c: Pair Parameter (Float64, asymetrical, defaults to 0) - Interaction Parameter
• Mw: Single Parameter (Float64) (Optional) - Molecular Weight [g/mol]

Input models

• puremodel: model to calculate pure pressure-dependent properties

Description

NRTL (Non Random Two Fluid) activity model:

Gᴱ = nRT∑[xᵢ(∑τⱼᵢGⱼᵢxⱼ)/(∑Gⱼᵢxⱼ)]
Gᵢⱼ exp(-cᵢⱼτᵢⱼ)
τᵢⱼ = aᵢⱼ + bᵢⱼ/T

Model Construction Examples

# Using the default database
model = NRTL(["water","ethanol"]) #Default pure model: PR
model = NRTL(["water","ethanol"],puremodel = BasicIdeal) #Using Ideal Gas for pure model properties
model = NRTL(["water","ethanol"],puremodel = PCSAFT) #Using Real Gas model for pure model properties

# Passing a prebuilt model

my_puremodel = AbbottVirial(["water","ethanol"]; userlocations = ["path/to/my/db","critical.csv"])
mixing = NRTL(["water","ethanol"],puremodel = my_puremodel)

# Using user-provided parameters

# Passing files or folders
model = NRTL(["water","ethanol"];userlocations = ["path/to/my/db","nrtl_ge.csv"])

# Passing parameters directly
model = NRTL(["water","ethanol"],
userlocations = (a = [0.0 3.458; -0.801 0.0],
                b = [0.0 -586.1; 246.2 0.0],
                c = [0.0 0.3; 0.3 0.0])
            )

References

1. Renon, H., & Prausnitz, J. M. (1968). Local compositions in thermodynamic excess functions for liquid mixtures. AIChE journal. American Institute of Chemical Engineers, 14(1), 135–144. doi:10.1002/aic.690140124

aspenNRTL <: ActivityModel

function aspenNRTL(components;
puremodel=PR,
userlocations = String[],
pure_userlocations = String[],
verbose = false,
reference_state = nothing)

Input parameters

• a0: Pair Parameter (Float64, asymetrical, defaults to 0) - Interaction Parameter
• a1: Pair Parameter (Float64, asymetrical, defaults to 0) - Interaction Parameter
• t0: Pair Parameter (Float64, asymetrical, defaults to 0) - Interaction Parameter
• t1: Pair Parameter (Float64, asymetrical, defaults to 0) - Interaction Parameter
• t2: Pair Parameter (Float64, asymetrical, defaults to 0) - Interaction Parameter
• t3: Pair Parameter (Float64, asymetrical, defaults to 0) - Interaction Parameter

Input models

• puremodel: model to calculate pure pressure-dependent properties

Description

NRTL (Non Random Two Fluid) activity model:

Gᴱ = nRT∑[xᵢ(∑τⱼᵢGⱼᵢxⱼ)/(∑Gⱼᵢxⱼ)]
Gᵢⱼ exp(-αᵢⱼτᵢⱼ)
αᵢⱼ = αᵢⱼ₀ + αᵢⱼ₁T
τᵢⱼ = tᵢⱼ₀ + tᵢⱼ₁/T + tᵢⱼ₂*ln(T) + tᵢⱼ₃*T

Model Construction Examples

# Using the default database
model = aspenNRTL(["water","ethanol"]) #Default pure model: PR
model = aspenNRTL(["water","ethanol"],puremodel = BasicIdeal) #Using Ideal Gas for pure model properties
model = aspenNRTL(["water","ethanol"],puremodel = PCSAFT) #Using Real Gas model for pure model properties
# Passing a prebuilt model

my_puremodel = AbbottVirial(["water","ethanol"]; userlocations = ["path/to/my/db","critical.csv"])
mixing = aspenNRTL(["water","ethanol"],puremodel = my_puremodel)

# Using user-provided parameters

# Passing files or folders
model = aspenNRTL(["water","ethanol"];userlocations = ["path/to/my/db","nrtl.csv"])

# Passing parameters directly
model = aspenNRTL(["water","acetone"],
userlocations = (a0 = [0.0 0.228632; 0.228632 0.0],
                a1 = [0.0 0.000136; 0.000136 0.0],
                t0 = [0.0 13.374756; 16.081848 0.0],
                t1 = [0.0 -415.527344; -88.8125 0.0],
                t2 = [0.0 -1.913689; -2.930901 0.0],
                t3 = [0.0 0.00153; 0.005305 0.0])
            )

References

1. Renon, H., & Prausnitz, J. M. (1968). Local compositions in thermodynamic excess functions for liquid mixtures. AIChE journal. American Institute of Chemical Engineers, 14(1), 135–144. doi:10.1002/aic.690140124

UNIQUACModel <: ActivityModel

UNIQUAC(components;
puremodel = PR,
userlocations = String[], 
pure_userlocations = String[],
verbose = false,
reference_state = nothing)

Input parameters

• a: Pair Parameter (Float64, asymetrical, defaults to 0) - Binary Interaction Energy Parameter
• r: Single Parameter (Float64) - Normalized Van der Vals volume
• q: Single Parameter (Float64) - Normalized Surface Area
• q_p: Single Parameter (Float64) - Modified Normalized Surface Area
• Mw: Single Parameter (Float64) - Molecular Weight [g/mol]

Input models

• puremodel: model to calculate pure pressure-dependent properties

UNIQUAC (Universal QuasiChemical Activity Coefficients) activity model:

Gᴱ = nRT(gᴱ(comb) + gᴱ(res))
gᴱ(comb) = ∑[xᵢlog(Φᵢ/xᵢ) + 5qᵢxᵢlog(θᵢ/Φᵢ)]
gᴱ(res) = -∑xᵢqᵖᵢlog(∑θᵖⱼτⱼᵢ)
θᵢ = qᵢxᵢ/∑qᵢxᵢ
θᵖ = qᵖᵢxᵢ/∑qᵖᵢxᵢ
Φᵢ = rᵢxᵢ/∑rᵢxᵢ
τᵢⱼ = exp(-aᵢⱼ/T)

Model Construction Examples

# Using the default database
model = UNIQUAC(["water","ethanol"]) #Default pure model: PR
model = UNIQUAC(["water","ethanol"],puremodel = BasicIdeal) #Using Ideal Gas for pure model properties
model = UNIQUAC(["water","ethanol"],puremodel = PCSAFT) #Using Real Gas model for pure model properties

# Passing a prebuilt model

my_puremodel = AbbottVirial(["water","ethanol"]; userlocations = ["path/to/my/db","critical.csv"])
mixing = UNIQUAC(["water","ethanol"],puremodel = my_puremodel)

# Using user-provided parameters

# Passing files or folders
model = UNIQUAC(["water","ethanol"];userlocations = ["path/to/my/db","uniquac_ge.csv"])

# Passing parameters directly
model = UNIQUAC(["water","ethanol"],
        userlocations = (a = [0.0 378.1; 258.4 0.0], 
                        r = [0.92, 2.11],
                        q = [1.4, 1.97],
                        q_p = [1.0, 0.92],
                        Mw = [18.015, 46.069])
                    )

References

1. Abrams, D. S., & Prausnitz, J. M. (1975). Statistical thermodynamics of liquid mixtures: A new expression for the excess Gibbs energy of partly or completely miscible systems. AIChE journal. American Institute of Chemical Engineers, 21(1), 116–128. doi:10.1002/aic.690210115

ogUNIFACModel <: UNIFACModel

ogUNIFAC(components;
puremodel = PR, 
userlocations = String[],
group_userlocations = String[],
pure_userlocations = String[],
verbose = false,
reference_state = nothing)

Input parameters

• R: Single Parameter (Float64) - Normalized group Van der Vals volume
• Q: Single Parameter (Float64) - Normalized group Surface Area
• A: Pair Parameter (Float64, asymetrical, defaults to 0) - Binary group Interaction Energy Parameter

Input models

• puremodel: model to calculate pure pressure-dependent properties

UNIFAC (UNIQUAC Functional-group Activity Coefficients) activity model.

Original formulation.

The Combinatorial part corresponds to an GC-averaged modified UNIQUAC model. The residual part iterates over groups instead of components.

Gᴱ = nRT(gᴱ(comb) + gᴱ(res))

Combinatorial part:

gᴱ(comb) = ∑[xᵢlog(Φᵢ/xᵢ) + 5qᵢxᵢlog(θᵢ/Φᵢ)]
θᵢ = qᵢxᵢ/∑qᵢxᵢ
Φᵢ = rᵢxᵢ/∑rᵢxᵢ
rᵢ = ∑Rₖνᵢₖ for k ∈ groups
qᵢ = ∑Qₖνᵢₖ for k ∈ groups

Residual Part:

gᴱ(residual) = -v̄∑XₖQₖlog(∑ΘₘΨₘₖ)
v̄ = ∑∑xᵢνᵢₖ for k ∈ groups,  for i ∈ components
Xₖ = (∑xᵢνᵢₖ)/v̄ for i ∈ components 
Θₖ = QₖXₖ/∑QₖXₖ
Ψₖₘ = exp(-(Aₖₘ/T)

References

1. Fredenslund, A., Gmehling, J., Michelsen, M. L., Rasmussen, P., & Prausnitz, J. M. (1977). Computerized design of multicomponent distillation columns using the UNIFAC group contribution method for calculation of activity coefficients. Industrial & Engineering Chemistry Process Design and Development, 16(4), 450–462. doi:10.1021/i260064a004

ogUNIFACModel <: UNIFACModel

ogUNIFAC2(components;
puremodel = PR, 
userlocations = String[],
group_userlocations = String[],
pure_userlocations = String[],
verbose = false,
reference_state = nothing)

Input parameters

• R: Single Parameter (Float64) - Normalized group Van der Vals volume
• Q: Single Parameter (Float64) - Normalized group Surface Area
• A: Pair Parameter (Float64, asymetrical, defaults to 0) - Binary group Interaction Energy Parameter

Input models

• puremodel: model to calculate pure pressure-dependent properties

UNIFAC 2.0 (UNIQUAC Functional-group Activity Coefficients) activity model. Original formulation. The method is identical to ogUNIFAC but with a new parameters fitted by matrix completion methods.

References

1. Hayer, N., Wendel, T., Mandt, S., Hasse, H., Jirasek, F., Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0, Chemical Engineering Journal 504 (2025) 158667. 10.1016/j.cej.2024.158667.

UNIFACModel <: ActivityModel

UNIFAC(components;
puremodel = PR,
userlocations = String[],
group_userlocations = String[],
pure_userlocations = String[],
verbose = false,
reference_state = nothing)

Input parameters

• R: Single Parameter (Float64) - Normalized group Van der Vals volume
• Q: Single Parameter (Float64) - Normalized group Surface Area
• A: Pair Parameter (Float64, asymetrical, defaults to 0) - Binary group Interaction Energy Parameter
• B: Pair Parameter (Float64, asymetrical, defaults to 0) - Binary group Interaction Energy Parameter
• C: Pair Parameter (Float64, asymetrical, defaults to 0) - Binary group Interaction Energy Parameter

Input models

• puremodel: model to calculate pure pressure-dependent properties

Description

UNIFAC (UNIQUAC Functional-group Activity Coefficients) activity model. Modified UNIFAC (Dortmund) implementation. The Combinatorial part corresponds to an GC-averaged modified UNIQUAC model. The residual part iterates over groups instead of components.

Gᴱ = nRT(gᴱ(comb) + gᴱ(res))

Combinatorial part:

gᴱ(comb) = ∑[xᵢlog(Φ'ᵢ) + 5qᵢxᵢlog(θᵢ/Φᵢ)]
θᵢ = qᵢxᵢ/∑qᵢxᵢ
Φᵢ = rᵢxᵢ/∑rᵢxᵢ
Φ'ᵢ = rᵢ^(0.75)/∑xᵢrᵢ^(0.75)
rᵢ = ∑Rₖνᵢₖ for k ∈ groups
qᵢ = ∑Qₖνᵢₖ for k ∈ groups

Residual Part:

gᴱ(residual) = -v̄∑XₖQₖlog(∑ΘₘΨₘₖ)
v̄ = ∑∑xᵢνᵢₖ for k ∈ groups,  for i ∈ components
Xₖ = (∑xᵢνᵢₖ)/v̄ for i ∈ components
Θₖ = QₖXₖ/∑QₖXₖ
Ψₖₘ = exp(-(Aₖₘ + BₖₘT + CₖₘT²)/T)

References

1. Fredenslund, A., Gmehling, J., Michelsen, M. L., Rasmussen, P., & Prausnitz, J. M. (1977). Computerized design of multicomponent distillation columns using the UNIFAC group contribution method for calculation of activity coefficients. Industrial & Engineering Chemistry Process Design and Development, 16(4), 450–462. doi:10.1021/i260064a004
2. Weidlich, U.; Gmehling, J. A modified UNIFAC model. 1. Prediction of VLE, hE, and.gamma..infin. Ind. Eng. Chem. Res. 1987, 26, 1372–1381.

List of groups available

UNIFACModel <: ActivityModel

UNIFAC2(components;
puremodel = PR,
userlocations = String[],
group_userlocations = String[],
pure_userlocations = String[],
verbose = false,
reference_state = nothing)

Input parameters

• R: Single Parameter (Float64) - Normalized group Van der Vals volume
• Q: Single Parameter (Float64) - Normalized group Surface Area
• A: Pair Parameter (Float64, asymetrical, defaults to 0) - Binary group Interaction Energy Parameter
• B: Pair Parameter (Float64, asymetrical, defaults to 0) - Binary group Interaction Energy Parameter
• C: Pair Parameter (Float64, asymetrical, defaults to 0) - Binary group Interaction Energy Parameter

Input models

• puremodel: model to calculate pure pressure-dependent properties

Description

UNIFAC 2.0 activity model. Modified UNIFAC 2.0 (Dortmund) implementation. The method is identical to UNIFAC but with a new parameters fitted by matrix completion methods.

References

1. Hayer, N., Hasse, H., Jirasek, F., Modified UNIFAC 2.0 - A Group-Contribution Method Completed with Machine Learning. Arxive preprint (2024). 10.48550/arXiv.2412.12962

).

PSRKUNIFAC(components;
puremodel = BasicIdeal,
userlocations = String[],
group_userlocations = String[],
pure_userlocations = String[],
verbose = false,
reference_state = nothing)

Input parameters

• R: Single Parameter (Float64) - Normalized group Van der Vals volume
• Q: Single Parameter (Float64) - Normalized group Surface Area
• A: Pair Parameter (Float64, asymetrical, defaults to 0) - Binary group Interaction Energy Parameter
• B: Pair Parameter (Float64, asymetrical, defaults to 0) - Binary group Interaction Energy Parameter
• C: Pair Parameter (Float64, asymetrical, defaults to 0) - Binary group Interaction Energy Parameter

Input models

• puremodel: model to calculate pure pressure-dependent properties

Description

UNIFAC (UNIQUAC Functional-group Activity Coefficients) activity model.

Modified UNIFAC (Dortmund) implementation, with parameters tuned to the Predictive Soave-Redlich-Kwong (PSRK) EoS.

References

1. Fredenslund, A., Gmehling, J., Michelsen, M. L., Rasmussen, P., & Prausnitz, J. M. (1977). Computerized design of multicomponent distillation columns using the UNIFAC group contribution method for calculation of activity coefficients. Industrial & Engineering Chemistry Process Design and Development, 16(4), 450–462. doi:10.1021/i260064a004
2. Weidlich, U.; Gmehling, J. A modified UNIFAC model. 1. Prediction of VLE, hE, and.gamma..infin. Ind. Eng. Chem. Res. 1987, 26, 1372–1381.
3. Horstmann, S., Jabłoniec, A., Krafczyk, J., Fischer, K., & Gmehling, J. (2005). PSRK group contribution equation of state: comprehensive revision and extension IV, including critical constants and α-function parameters for 1000 components. Fluid Phase Equilibria, 227(2), 157–164. doi:10.1016/j.fluid.2004.11.002"

VTPRUNIFACModel <: UNIFACModel

VTPRUNIFAC(components;
puremodel = BasicIdeal,
userlocations = String[],
pure_userlocations = String[],
verbose = false,
reference_state = nothing)

Input parameters

• Q: Single Parameter (Float64) - Normalized group Surface Area
• A: Pair Parameter (Float64, asymetrical, defaults to 0) - Binary group Interaction Energy Parameter
• B: Pair Parameter (Float64, asymetrical, defaults to 0) - Binary group Interaction Energy Parameter
• C: Pair Parameter (Float64, asymetrical, defaults to 0) - Binary group Interaction Energy Parameter

Input models

• puremodel: model to calculate pure pressure-dependent properties

Description

UNIFAC (UNIQUAC Functional-group Activity Coefficients) activity model.

Modified UNIFAC (Dortmund) implementation, only residual part, activity model used for the Volume-Translated Peng-Robinson (VTPR) EoS.

The residual part iterates over groups instead of components.

Gᴱ = nRT(gᴱ(res))
gᴱ(res) = -v̄∑XₖQₖlog(∑ΘₘΨₘₖ)
v̄ = ∑∑xᵢνᵢₖ for k ∈ groups,  for i ∈ components
Xₖ = (∑xᵢνᵢₖ)/v̄ for i ∈ components
Θₖ = QₖXₖ/∑QₖXₖ
Ψₖₘ = exp(-(Aₖₘ + BₖₘT + CₖₘT²)/T)

References

1. Fredenslund, A., Gmehling, J., Michelsen, M. L., Rasmussen, P., & Prausnitz, J. M. (1977). Computerized design of multicomponent distillation columns using the UNIFAC group contribution method for calculation of activity coefficients. Industrial & Engineering Chemistry Process Design and Development, 16(4), 450–462. "doi:10.1021/i260064a004"
2. Weidlich, U.; Gmehling, J. A modified UNIFAC model. 1. Prediction of VLE, hE, and.gamma..infin. Ind. Eng. Chem. Res. 1987, 26, 1372–1381.
3. Ahlers, J., & Gmehling, J. (2001). Development of an universal group contribution equation of state. Fluid Phase Equilibria, 191(1–2), 177–188. doi:10.1016/s0378-3812(01)00626-4

UNIFACFVModel <: ActivityModel

UNIFACFV(components;
puremodel = PR,
userlocations = String[],
group_userlocations = String[],
pure_userlocations = String[],
verbose = false,
reference_state = nothing)

Input parameters

• volume: Single Parameter (Float64) - specific volume of species [g/cm^3]
• R: Single Parameter (Float64) - Normalized group Van der Vals volume
• Q: Single Parameter (Float64) - Normalized group Surface Area
• A: Pair Parameter (Float64, asymetrical, defaults to 0) - Binary group Interaction Energy Parameter
• Mw: Single Parameter (Float64) - Molecular weight of groups

Input models

• puremodel: model to calculate pure pressure-dependent properties

Description

UNIFACFV (UNIFAC Free Volume) activity model. specialized for solvent-polymer mixtures

The Combinatorial part corresponds to an GC-averaged modified UNIQUAC model.

Gᴱ = nRT(gᴱ(comb) + gᴱ(res) + gᴱ(FV))

References

UNIFACFVPolyModel <: ActivityModel

UNIFACFVPoly(components;
puremodel = PR,
userlocations = String[],
pure_userlocations = String[],
verbose = false,
reference_state = nothing)

Input parameters

• volume: Single Parameter (Float64) - specific volume of species [g/cm^3]
• c Single Parameter (Float64) - number of external degrees of freedom per solvent molecule
• R: Single Parameter (Float64) - Normalized group Van der Vals volume
• Q: Single Parameter (Float64) - Normalized group Surface Area
• A: Pair Parameter (Float64, asymetrical, defaults to 0) - Binary group Interaction Energy Parameter
• Mw: Single Parameter (Float64) - Molecular weight of groups

Input models

• puremodel: model to calculate pure pressure-dependent properties

Description

UNIFAC-FV (polymer) (UNIFAC Free Volume) activity model. specialized for polymer blends

The Combinatorial part corresponds to an GC-averaged modified UNIQUAC model.

Gᴱ = nRT(gᴱ(comb) + gᴱ(res) + gᴱ(FV))

tcPRWilsonRes <: WilsonModel
tcPRWilsonRes(components;
puremodel = BasicIdeal,
userlocations = String[],
pure_userlocations = String[],
verbose = false,
reference_state = nothing)

Input parameters

• g: Pair Parameter (Float64, asymetrical, defaults to 0) - Interaction Parameter
• v: Single Parameter (optional) (Float64) - individual volumes.

Input models

• puremodel: model to calculate pure pressure-dependent properties

Description

tc-PR-Wilson residual activity model, meant to used in combination with the tc-PR cubic EoS:

Gᴱᵣ = nRT( ∑xᵢlog(∑xⱼjΛᵢⱼ) - ∑xᵢ*(log(vᵢ/v) )
Λᵢⱼ = exp(-gᵢⱼ/T)*Vⱼ/Vᵢ 

Model Construction Examples

# Using the default database
model = tcPRWilsonRes(["water","ethanol"]) #Default pure model: PR
model = tcPRWilsonRes(["water","ethanol"],puremodel = BasicIdeal) #Using Ideal Gas for pure model properties
model = tcPRWilsonRes(["water","ethanol"],puremodel = PCSAFT) #Using Real Gas model for pure model properties

# Passing a prebuilt model

my_puremodel = AbbottVirial(["water","ethanol"]; userlocations = ["path/to/my/db","critical.csv"])
mixing = tcPRWilsonRes(["water","ethanol"],puremodel = my_puremodel)

# Using user-provided parameters

# Passing files or folders
model = tcPRWilsonRes(["water","ethanol"];userlocations = ["path/to/my/db","tcpr_wilson.csv"])

# Passing parameters directly
model = tcPRWilsonRes(["water","ethanol"],userlocations = (;g = [0.0 3988.52; 1360.117 0.0]))

References

1. Wilson, G. M. (1964). Vapor-liquid equilibrium. XI. A new expression for the excess free energy of mixing. Journal of the American Chemical Society, 86(2), 127–130. doi:10.1021/ja01056a002
2. Piña-Martinez, A., Privat, R., Nikolaidis, I. K., Economou, I. G., & Jaubert, J.-N. (2021). What is the optimal activity coefficient model to be combined with the translated–consistent Peng–Robinson equation of state through advanced mixing rules? Cross-comparison and grading of the Wilson, UNIQUAC, and NRTL aE models against a benchmark database involving 200 binary systems. Industrial & Engineering Chemistry Research, 60(47), 17228–17247. doi:10.1021/acs.iecr.1c03003

COSMOSAC02(components;
puremodel = PR,
userlocations = String[],
pure_userlocations = String[],
verbose = false,
reference_state = nothing)

Input parameters:

• Pi :Single Parameter{String}
• V: Single Parameter{Float64}
• A: Single Parameter{Float64}

Description

An activity coefficient model using molecular solvation based on the COSMO-RS method.

References

1. Klamt, A. (1995). Conductor-like screening model for real solvents: A new approach to the quantitative calculation of solvation phenomena. Journal of Physical Chemistry, 99(7), 2224–2235. doi:10.1021/j100007a062
2. Lin, S-T. & Sandler, S.I. (2002). A priori phase equilibrium prediction from a segment contribution solvation model. Industrial & Engineering Chemistry Research, 41(5), 899–913. doi:10.1021/ie001047w

COSMOSAC10(components;
puremodel = PR,
userlocations = String[],
pure_userlocations = String[],
verbose = false,
reference_state = nothing)

Input parameters:

• Pnhb :Single Parameter{String}
• POH :Single Parameter{String}
• POT :Single Parameter{String}
• V: Single Parameter{Float64}
• A: Single Parameter{Float64}

Description

An activity coefficient model using molecular solvation based on the COSMO-RS method. Sigma profiles are now split by non-hydrogen bonding, hydrogen acceptor and hydrogen donor.

References

1. Klamt, A. (1995). Conductor-like screening model for real solvents: A new approach to the quantitative calculation of solvation phenomena. Journal of Physical Chemistry, 99(7), 2224–2235. doi:10.1021/j100007a062
2. Lin, S-T. & Sandler, S.I. (2002). A priori phase equilibrium prediction from a segment contribution solvation model. Industrial & Engineering Chemistry Research, 41(5), 899–913. doi:10.1021/ie001047w
3. Hsieh, C-H., Sandler, S.I., & Lin, S-T. (2010). Improvements of COSMO-SAC for vapor–liquid and liquid–liquid equilibrium predictions. Fluid Phase Equilibria, 297(1), 90-97. doi:10.1016/j.fluid.2010.06.011

COSMOSACdsp(components;
puremodel = PR,
userlocations = String[],
pure_userlocations = String[],
verbose = false,
reference_state = nothing)

Input parameters:

• Pnhb :Single Parameter{String}
• POH :Single Parameter{String}
• POT :Single Parameter{String}
• V: Single Parameter{Float64}
• A: Single Parameter{Float64}
• epsilon: Single Parameter{Float64}
• COOH: Single Parameter{Float64}
• water: Single Parameter{Float64}
• hb_acc: Single Parameter{Float64}
• hb_don: Single Parameter{Float64}

Description

An activity coefficient model using molecular solvation based on the COSMO-RS method. Sigma profiles are now split by non-hydrogen bonding, hydrogen acceptor and hydrogen donor. A dispersive correction is included.

References

1. Klamt, A. (1995). Conductor-like screening model for real solvents: A new approach to the quantitative calculation of solvation phenomena. Journal of Physical Chemistry, 99(7), 2224–2235. doi:10.1021/j100007a062
2. Lin, S-T. & Sandler, S.I. (2002). A priori phase equilibrium prediction from a segment contribution solvation model. Industrial & Engineering Chemistry Research, 41(5), 899–913. doi:10.1021/ie001047w
3. Hsieh, C-H., Sandler, S.I., & Lin, S-T. (2010). Improvements of COSMO-SAC for vapor–liquid and liquid–liquid equilibrium predictions. Fluid Phase Equilibria, 297(1), 90-97. doi:10.1016/j.fluid.2010.06.011
4. Hsieh, C-H., Lin, S-T. & Vrabec, J. (2014). Considering the dispersive interactions in the COSMO-SAC model for more accurate predictions of fluid phase behavior. Fluid Phase Equilibria, 367, 109-116. doi:10.1016/j.fluid.2014.01.032

HANNA <: ActivityModel
HANNA(components;
puremodel = nothing,
userlocations = String[],
pure_userlocations = String[],
verbose = false,
reference_state = nothing)

Input parameters

• canonicalsmiles: canonical SMILES (using RDKit) representation of the components
• Mw: Single Parameter (Float64) (Optional) - Molecular Weight [g/mol]

Input models

• puremodel: model to calculate pure pressure-dependent properties

Description

Hard-Constraint Neural Network for Consistent Activity Coefficient Prediction (HANNA v1.0.0). The implementation is based on this Github repository. HANNA was trained on all available binary VLE data (up to 10 bar) and limiting activity coefficients from the Dortmund Data Bank. HANNA was only tested for binary mixtures so far. The extension to multicomponent mixtures is experimental.

To use the model, the package ClapeyronHANNA must be installed and loaded (see example below).

Example

using Clapeyron, ClapeyronHANNA

components = ["water","isobutanol"]
Mw = [18.01528, 74.1216]
smiles = ["O", "CC(C)CO"]

model = HANNA(components,userlocations=(;Mw=Mw, canonicalsmiles=smiles))
# model = HANNA(components) # also works if components are in the database 

References

1. Specht, T., Nagda, M., Fellenz, S., Mandt, S., Hasse, H., Jirasek, F., HANNA: Hard-Constraint Neural Network for Consistent Activity Coefficient Prediction. Chemical Science 2024. 10.1039/D4SC05115G.