Transcript OLI’s Mixed Solvent Electrolyte with Aspen PLUS

OLI Systems, Inc.
OLI’s Mixed Solvent
Electrolyte with Aspen
PLUS
THINK SIMULATION!
Opening new doors with Chemistry
Agenda
THINK
SIMULATION
OLI’s basic history
OLI’s history with Aspen Technologies
Advantages/disadvantages of Aspen PLUS OLI
Architecture of the Aspen PLUS OLI interface
Introduction to MSE
Overview of Aspen PLUS OLI (with MSE)
Demonstration
2
OLI’s basic history
THINK
SIMULATION
 Company founded in 1971 by Marshall Rafal
 First electrolyte simulator (ECES) 1973
■ Developed for OLIN Chemical
 First commercial sale of ECES 1975
■ Dupont
 The Environmental Simulation Program developed in
1991
 Linkage to simulators in 1995
 Windows program (Analyzers) became commercial in
2000
 Mixed-Solvent Electrolytes commercially available 2005
 Windows based process simulator (OLI Pro) to be
available in 2007
3
OLI’s history with Aspen Technologies
THINK
SIMULATION
 That “Other” chemical company that has a “D” in its name.
■ 25 years of process simulation experience with electrolyte
■ 1995 switched to Aspen PLUS as their process simulator
■ Wanted OLI’s electrolytes in Aspen PLUS
 1996 first Aspen PLUS OLI interface created
■ No model manager, version 8.2
 1997 Aspen PLUS OLI linked to model manager
■ Version 9.0
 2006 Aspen PLUS OLI updated for Aspen ONE 2006
■ Included change in concentration basis
■ Included MSE
 2007 Aspen PLUS OLI updated for Aspen ONE 2006.5
■ General release of MSE for all Aspen PLUS OLI clients
4
Advantages of Aspen PLUS OLI







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SIMULATION
User Interface
Learn one flow sheeting system
Multiple Property Options in same flowsheet
Different Non-electrolyte capability
Sizing
Costing
Two Software Venders
5
Disadvantages of Aspen PLUS OLI
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SIMULATION
 No Corrosion
 No advanced OLI technology
■ No Ion-exchange
■ No Surface Complexation
■ No Bio-kinetics
 No Scaling Tendencies
 Two Software Venders
6
Architecture of the Aspen PLUS OLI interface
OLI Chemistry
Generator
OLI
Databases
.BKP
THINK
SIMULATION
A+
Model Manager
.ASP/.INP
.DBS
OLI/A+
XREF
OLI Numerical
Solver/Engine
A+
Simulation Engine
Electrolyte Flash or Property
7
Architecture of the Aspen PLUS OLI interface
THINK
SIMULATION
 Aspen Unit Operations available with the OLI Property Set
■
■
■
■
■
■
■
■
■
■
■
■
■
■
■
■
MIXERS
FSPLIT
SEP
SEP2
HEATER
FLASH2
FLASH3
HEATX
MHEATX
RADFRAC
RSTOIC
RYIELD
RCSTR
RPLUG
PUMP
COMPR
8
Architecture of the Aspen PLUS OLI interface
THINK
SIMULATION
 Thermodynamic Properties from OLI used by Aspen
PLUS (OLI propset)
PHIVMX
PHILMX
HVMX
HLMX
GVMX
GLMX
SVMX
SLMX
VVMX
VLMX
MUVMX
MULMX
KVMX
KLMX
DVMX
DLMX
SIGLMX
PHIV
PHIL
HV
HL
GV
GL
SV
SL
VV
VL
MUV
MUL
KV
KL
DV
DL
SIGL
HSMX
PHIL
VV
MUVMXL
MUVLP
KVMXLP
KVLP
DHV
DHL
DHLPC
DGV
DGL
PHILPC
DSV
KVPC
9
Architecture of the Aspen PLUS OLI interface
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SIMULATION
 The OLI-Aspen Plus Cross Reference File (partial listing)
■ Full listing is available on your computer:
◊ C:\Program Files\OLI Systems\Alliance Suites\Aspen OLI
2006\Databanks\OLIAspenPlusCompXRef.lis
ESP-NAME
DB
================ =
AR
P
ABIETICAC
P
ACENAPHTHN
P
ACENITRILE
P
ACET2
P
ACETACID
P
ACETAL
P
ACETALDEHD
P
ACETAMIDEPPT
P
ACETAMIDE
P
ACETANHYD
P
ACETANILID
P
ACETATEION
P
ACETBR
P
ACETCL
P
ACETONE
P
ACETPHENON
P
ACETYLENE
P
ACRIDINE
P
ACROLEIN2
P
ACRYLAMIDEPPT
P
ACRYLAMIDE
P
8-CHAR
======
AR
ABIETICA
ACENAPHT
ACENTL
ACET2
ACETACID
ACETAL
ACEALD
ACETAM-S
ACETAMD
ACETAHYD
ACEANILD
ACETACETBR
ACETCL
ACETONE
ACEPHEN
ACETYLN
ACRIDINE
ACROLIN2
ACRAMI-S
ACRYAMID
ASP-ALIAS
=========
AR
C20H30O2
C12H10-D0
C2H3N
ASP-NAME
=====================================
ARGON
ABIETIC-ACID
ACENAPHTHENE
ACETONITRILE
C2H4O2-1
C6H14O2-D1
C2H4O-1
ACETIC-ACID
ACETAL
ACETALDEHYDE
C2H5NO-D1
C4H6O3
C8H9NO
CH3COO-
ACETAMIDE
ACETIC-ANHYDRIDE
ACETANILIDE
CH3COO-
C2H3CLO
C3H6O-1
C8H8O
C2H2
ACETYL-CHLORIDE
ACETONE
METHYL-PHENYL-KETONE
ACETYLENE
C3H4O
ACROLEIN
C3H5NO-D1
ACRYLAMIDE
7440-37-1
514-10-3
83-32-9
75-05-8
...........
64-19-7
105-57-7
75-07-0
60-35-5
60-35-5
108-24-7
Ar
C20H30O2
C12H10
C2H3N
C4H8O4
C2H4O2
C6H14O2
C2H4O
C2H5NO
C2H5NO
C4H6O3
...........
506-96-7
75-36-5
67-64-1
98-86-2
74-86-2
260-94-6
107-02-8
79-06-1
79-06-1
C2H3O2-1
C2H3BrO
C2H3ClO
C3H6O
C8H8O
C2H2
C13H9N
C3H4O
C3H5NO
C3H5NO
10
Architecture of the Aspen PLUS OLI interface
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SIMULATION
OLI added user blocks to Aspen PLUS
■ EFRACH
■ EFLASH
Available during Aspen PLUS Installation
■ Must be enabled at run-time
11
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SIMULATION
Architecture of the Aspen PLUS OLI interface
EFLASH
Vapor
(1)
Aqueous
(2)
Organic
(3)
Solid
(4)
Feeds
EFLASH
(Four outlet material streams)
Heat
Heat
12
Architecture of the Aspen PLUS OLI interface
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SIMULATION
Vapor or Liquid
EFRACH
Heat
1
Heat
DECANTER
Feeds
2
Organic
Products
3
Heat
Heat
Heat
N
Heat
13
Bottoms
Introduction to MSE
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SIMULATION
 Why develop a new thermodynamic model?
■ The Bromley-Zemaitis model (a/k/a Aqueous Model-AE)
had limitations
◊ Water was required as a solvent
◊ Mole fraction of all solutes was limited to approximately 0.35
◊ Limited in temperature (Approximately 300 oC)
◊ LLE predictions exclude critical solution points (limited to
strongly dissimilar phases)
■ A Mixed Solvent Electrolyte model (MSE) has advantages
◊ Water is not required
◊ Mole fraction of solute can approach and be equal to 1.0
◊ Temperature can be up to 0.9 Tc of solution
◊ Full range of LLE calculations including electrolytes in both
phases
14
THINK
SIMULATION
Introduction to MSE
Advantages and disadvantages between AE and MSE
 AE Model
■ Advantages:
◊ Larger existing databank
◊ The only model available for
rates of corrosion
■ Disadvantages:
◊ Limitations with respect to
composition (30 m with
respect to electrolytes,
x=0.3 with respect to
nonelectrolytes
◊ LLE predictions exclude
critical solution points
(limited to strongly dissimilar
phases)
 MSE model
■ Model advantages:
◊ No composition limitations
◊ Reliable predictions for
multicomponent concentrated
solutions
◊ Full range of LLE calculations
including electrolytes in both
phases
■ Methodological advantages
◊ Multi-property regressions
◊ Consistent use of
thermochemical properties
(no shortcuts like KFITs)
◊ Rigorous quality assurance
■ Disadvantages:
◊ A smaller in-place databank
but it is continuously
extended
15
Introduction to MSE
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SIMULATION
 Overview of species coverage between AE and MSE models.
8000
6000
Components
Growing with each build
4000
AE
2000
Build 7.0.54
MSE
0
0.0
1.0
Solute Mole Fraction
16
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SIMULATION
Structure of the thermodynamic model
Definition of species that may exist in the liquid,
vapor, and solid phases
Excess Gibbs energy model for solution
nonideality
Calculation of standard-state properties
■ Helgeson-Kirkham-Flowers equation for ionic and
neutral aqueous species
■ Standard thermochemistry for solid and gas
species
Algorithm for solving phase and chemical
equilibria
17
Outline of the model:
Solution nonideality
Excess Gibbs energy
LR
LC
II
THINK
SIMULATION
ex
ex
GLC
G ex GLR
GIIex



RT RT RT RT
Debye-Hückel theory for long-range electrostatic
interactions
Local composition model (UNIQUAC) for neutral
molecule interactions
Ionic interaction term for specific ion-ion and ionmolecule interactions


G IIex

   ni  xi x j Bij I x 
RT
 i
 i j
THINK
SIMULATION
Outline of the model:
Chemical equilibrium calculations
For a chemical reaction:
aA  bB  cC  dD
Standard-state
chemical
potential of i
At equilibrium
d
 xCc  x D
 Cc   Dd

 ln a
 a
b
b
RT
x

x



 A
B
A
B
G 0

with

G 0   v i  i0
i
 Infinite-dilution properties
■ Thermochemical databases for aqueous systems
■ Helgeson-Kirkham-Flowers model for T and P dependence
19
Outline of the model:
Constraints
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SIMULATION
 Activity coefficients are converted to unsymmetrical normalization to
work with infinite-dilution properties
 Constraining the parameters of the GE model to reproduce the Gibbs
energy of transfer
 ix , H 2 O , R
 ix , H 2 O , S
Activity coefficient of ion i in solvents R and
S in unsymmetrical, mole-fraction based
convention
20
Mixed-solvent electrolyte model:
Applicability
THINK
SIMULATION
 Simultaneous representation of multiple properties
■
■
■
■
■
■
■
■
Vapor-liquid equilibria
Osmotic coefficient/water activity and activity coefficients
Solid-liquid equilibria
Properties of electrolytes at infinite dilution, such as acidbase dissociation and complexation constants
Properties that reflect ionic equilibria, e.g., solution pH and
species distribution
Enthalpy (Hdil or Hmix)
Heat capacity
Density
21
Validity range
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SIMULATION
Concentrations from infinite dilution to
saturation or fused salt or pure solute limit
Temperatures up to 0.9Tc of mixtures
■ This translates into 300 C for H2O – dominated
systems
■ For concentrated inorganic systems, substantially
higher temperatures can be reached
Solvents: water, various organics or solvent
mixtures
22
Representative applications of the MSE
thermodynamic model
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SIMULATION
Strong acid systems
■ Simultaneous representation of phase equilibria
and speciation
Salt systems
■ Prediction of properties of multicomponent
systems
Organic – salt – water systems
■ Salt effects on VLE, LLE and SLE
Acid-base equilibria
■ pH of mixed-solvent systems
23
THINK
SIMULATION
VLE for H2SO4 + SO3 + H2O
1.0E+03
500ºC
1.0E+02
400ºC
 Phase
equilibria are
100ºC
accurately
50ºC
reproduced
25ºC
0ºC
from 0 C to
500 C
300ºC
200ºC
1.0E+01
1.0E+00
P, atm
1.0E-01
1.0E-02
1.0E-03
1.0E-04
1.0E-05
1.0E-06
1.0E-07
1.0E-08
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
x SO3
24
THINK
SIMULATION
Speciation for H2SO4 + SO3 + H2O:
100
90
80
HSO4-
mole percent
70
SO30
60
1957HR
H2SO40
50
1959YMS
1994CRP&1995CB
40
SO4-2
 Predicted
speciation in
concentrated
solutions agrees
with
spectroscopic
data
2000WYCH
30
20
10
0
0.0
0.2
0.4
0.6
(x SO3)
1/2
0.8
1.0
x (SO3 )=0.5
25
Partial pressures in the H2SO4 + SO3 + H2O system
 Partial pressures
of H2SO4, SO3
and H2O are
also correctly
reproduced
1.E-07
25C
30C
35C
25C
30C
35C
Perry 30C
1.E-08
H2SO4, atm
1.E-09
1.E-10
THINK
SIMULATION
1.E-11
1.E-12
1.E-13
Partial pressures of H2SO4
1.E-14
1.E-15
50
55
60
65
70
75
80
H2SO4, Wt%
26
100
NaNO3 – H2O
90
80
NaNO3 , weight %
THINK
SIMULATION
70
60
50
40
NaNO3
30
H2O(s)
20
Cal, NaNO3
10
Cal, H2O(s)
0
-20
0
20
40 60
80 100 120 140 160 180 200 220 240 260 280 300 320
Temperature, C
100
Mg(NO3)2 – H2O
90
Mg(NO 3 )2 , weight %
80
 Step 1: Binary systems
– solubility of solids
 The model is valid for
systems ranging from
dilute solutions to the
fused salt limit
70
H2O(s)
Mg(NO3)2.9H2O
Mg(NO3)2.6H2O
Mg(NO3)2.2H2O
Mg(NO3)2
Cal, H2O(s)
Cal, Mg(NO3)2.9H2O
Cal, Mg(NO3)2.6H2O
Cal, Mg(NO3)2.2H2O
Cal, Mg(NO3)2
60
50
40
30
20
10
Salt systems:
Na – K – Mg – Ca – Cl – NO3
0
-40
-20
0
20
40
60
80
100
Temperature, C
120
140
160
180
200
27
THINK
SIMULATION
Modeling salt systems:
Na – K – Mg – Ca – Cl – NO3
 Step 1: Binary
systems – solubility
of solids
 Water activity
decreases with salt
concentration until
the solution becomes
saturated with a
solid phase
1
0.9
Water activity
0.8
NaCl
0.7
1 - NaCl
0.6
6 - LiCl
0.5
11 - CaCl2
0.4
3 - Mg(NO3)2
12 - Ca(NO3)2
0.3
Ca(NO3 )2
0.2
LiCl
CaCl2 .2H2 O
0.1
Mg(NO3 )2
0
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
Total apparent salt, mole fraction
28
THINK
SIMULATION
90
80
NaNO3(s)
NaNO3 , weight %
70
60
50
40
NaNO3.KNO3(s)
0C
20C
30C
50C
100C
150C
200C
10C
25C
40C
75C
125C
175C
Step 2: Ternary systems
30
20
10
KNO3(s)
0
0
5
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
KNO3 , weight %
 Solubility in the system
NaNO3 – KNO3 – H2O
at various
temperatures
0.75
0.7
Water Activity
0.65
 Activity of water over
saturated NaNO3 –
KNO3 solutions at 90 C:
Strong depression at
the eutectic point
KNO3
0.6
0.55
NaNO3
0.5
0.45
0.4
NaNO3 +KNO3
0.35
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
NaNO3 , mole fraction (water free)
0.8
0.9
1
29
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SIMULATION
Step 3: Verification of predictions for multicomponent systems
 Deliquescence data
simultaneously
reflect solid
solubilities and
water activities
1
0.9
10 - NaNO3+KNO3
0.8
4 - NaNO3+KNO3+Ca(NO3)2+Mg(NO3)2
Water activity
0.7
0.6
0.5
NaNO3
0.4
NaNO3
0.3
NaNO3 +NaNO3 .KNO3
0.2
NaNO3 +Ca(NO3 )2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Total apparent salt, mole fraction
Mixed nitrate systems at 140 C
30
Electrolyte + organic systems:
Examples
THINK
SIMULATION
 Effect of electrolytes on phase equilibria in nonelectrolyte – water
systems
■ Salting out(in) effects
 Liquid-liquid equilibria in aqueous systems containing watersoluble polymers and salts
■ Liquid immiscibility is induced by the presence of a salt
31
LLE results – salt effect
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SIMULATION
0.00045
Solubility of benzene (x)
in aqueous salt solutions
0.00040
Solubility of
benzene in
aqueous
(NH4)2SO4 and
NaCl solutions
at 25ºC
25 C
0.00035
0.00030
0.00025
0.00020
0.00015
(NH4)2SO4
0.00010
0.00005
NaCl
0.00000
0
100
200
300
400
g salt/kg H2O
32
THINK
SIMULATION
Simultaneous representation of
thermodynamic properties:
NaCl-methanol-water
110
7.0
P=1 bar
6.0
mol NaCl/kg solvent
100
t/C
90
80
70
---- Salt-free
—— Saturated NaCl
25°C
5.0
4.0
3.0
2.0
1.0
0.0
60
0.0
0.2
0.4
0.6
0.8
1.0
x, y (methanol)
VLE: salting-out effect
0.0
0.2
0.4
0.6
0.8
1.0
x-methanol
Solubility
33
0.09
LLE in aqueous polymer –
salt systems
0.08
X - NaH 2PO4
0.07
0.06
THINK
SIMULATION
0.05
0.04
0.03
0.02
0.01
0.00
0.000 0.005 0.010 0.015 0.020 0.025
 PEG (MW=1000) + NaH2PO4 + H2O at
25 C
x - PEG1000
0.030
x - (NH4)2SO4
0.025
0.020
 PEG (MW=4000) + (NH4)2SO4 + H2O
at 25 C
0.015
0.010
0.005
0.000
0.0000 0.0005 0.0010 0.0015 0.0020 0.0025
x - PEG 4000
34
Acid-base and phase equilibria:
Treatment of pH in mixed solvents
THINK
SIMULATION
 Classical treatment
■ pH scale can be defined separately for each, pure or mixed,
solvent
■ pH scales can be converted using the Gibbs energy of transfer
of the proton
G Ht ,w  A
pH A  pH w 
RT ln 10
■ Such a conversion is inconvenient (availability of Gibbs energy of
transfer, extrathermodynamic assumptions)
■ However, it opens the possibility of a uniform calculation of pH
using an activity coefficient model as long as the model
accurately reproduces activity coefficients of individual species
and the Gibbs energy of transfer
35
THINK
SIMULATION
Treatment of pH in mixed-solvents
 Uniform treatment of apparent pH
■ Starting point: Aqueous definition of pH
pH   log a H 
 mH   H 
  log
0
m





■ Conversion to mole fraction scale and solvated proton basis
pH   log x H O   log  H O 
3
3
 1000
 log
 MH O
2


  log x H O  log  H O
2
2


■ Activity coefficients are obtained directly from the model
■ Values can be compared with measurements using glass
electrode
■ Does not require the presence of water – equivalent
expressions can be obtained for other solvents
36
THINK
SIMULATION
Speciation Effects
Apparent (Mixed Solvent-Based) Ionization Constants
Acetic Acid in EtOH-H2O
Acetic Acid in MeOH-H2O
12
12
cal
cal
Sen et al.
10
10
exp
pKa
pKa
Woolley
8
6
8
6
4
4
0.0
0.2
0.4
0.6
0.8
x-Ethanol
1.0
0.0
0.2
0.4
0.6
0.8
1.0
x-Methanol
Equilibrium constant
obtained from aqueous
solutions
37
THINK
SIMULATION
Parameters in the MSE Databank (1)
 Binary and principal ternary systems composed of the following primary ions
and their hydrolyzed forms
■ Cations: Na+, K+, Mg2+, Ca2+, Al3+, NH4+
■ Anions: Cl-, F-, NO3-, CO32-, SO42-, PO43-, OH-
 Aqueous acids, associated acid oxides and acid-dominated mixtures
■
■
■
■
■
■
■
■
■
H2SO4 – SO3
HNO3 – N2O5
H3PO4 – H4P2O7 – H5P3O10 – P2O5
H3PO2
H3PO3
HF
HCl
HBr
HI
•H3BO3
•CH3SO3H
•NH2SO3H
•HFSO3 – HF – H2SO4
•HI – I2 – H2SO4
•HNO3 – H2SO4 – SO3
•H3PO4 with calcium phosphates
•H – Na – Cl – NO3
•H – Na – Cl – F
38
Parameters in the MSE Databank (2)
THINK
SIMULATION
 Inorganic gases in aqueous systems
■
■
■
■
■
CO2 + NH3
H2S + NH3
SO2 + H2SO4
N2
O2
■ H2
 Transition metal aqueous systems
■
■
■
■
■
Fe(III) – H – O – SO4, NO3
Fe(II) – H – O – SO4, Br
Sn(II, IV) – H – O – CH3SO3
Zn(II) – H – SO4, NO3, Cl
Zn(II) – Li - Cl
39
Parameters in the MSE Databank (3)
THINK
SIMULATION
 Transition metal aqueous systems - continued
■
■
■
■
Cu(II) – H – SO4, NO3
Ni(II) – H – SO4, NO3, Cl
Mo(VI, IV) – H – O – Cl, SO4, NO3
W(VI) – H - O - Na – Cl, NO3
 Most elements from the periodic table in their elemental form
 Base ions and hydrolyzed forms for the majority of elements from the
periodic table
 Hydrogen peroxide chemistry
■ H2O2 – H2O – H - Na – OH – SO4 – NO3
40
Parameters in the MSE Databank (4)
THINK
SIMULATION
 Miscellaneous inorganic systems in water
■
■
■
■
■
■
NH2OH
NH4HS + H2S + NH3
LiCl – KCl
LiCl – CaCl2
Na2S2O3
LiOH – H3BO3 – H2O
 Organic acids in water, methanol and ethanol and their Na salts
■
■
■
■
■
■
■
■
Formic
Acetic (also K salt)
Citric
Adipic
Nicotinic
Terephthalic
Isophthalic
Trimellitic
41
Parameters in the MSE Databank (5)
THINK
SIMULATION
 Organic components and their mixtures with water
■ Hydrocarbons
◊ Straight chain alkanes: C1 through C30
◊ Isomeric alkanes: isobutane, isopentane, neopentane
◊ Alkenes: ethene, propene, 1-butene, 2-butene, 2-methylpropene
◊ Aromatics: benzene, toluene, o-, m-, p-xylenes, ethylbenzene, cumene,
naphthalene, anthracene, phenantrene
■ Alcohols
◊ Methanol, ethanol, 1-propanol, 2-propanol, cyclohexanol
■ Glycols
◊ Mono, di- and triethylene glycols, propylene glycol, polyethylene glycols
■ Phenols
◊ Phenol, catechol
■ Ketones
◊ Acetone, methylisobutyl ketone
■ Aldehydes
◊ Butylaldehyde
42
Parameters in the MSE Databank (6)
THINK
SIMULATION
 Organic solvents and their mixtures with water
■ Carbonates
◊ Diethylcarbonate, propylene carbonate
■ Amines
◊ Tri-N-octylamine, triethylamine, methyldiethanolamine
■ Nitriles
◊ Acetonitrile
■ Amides
◊ Dimethylacetamide, dimethylformamide
■ Halogen derivatives
◊ Chloroform
■ Aminoacids
◊ Methionine
■ Heterocyclic components
◊ N-methylpyrrolidone, 2,6-dimethylmorpholine
43
Parameters in the MSE Databank (7)
THINK
SIMULATION
 Polyelectrolytes
■ Polyacrylic acid
◊ Complexes with Cu, Zn, Ca
 Mixed-solvent organic systems
■
■
■
■
■
HAc – tri-N-octylamine – toluene – H2O
HAc – tri-N-octylamine – methylisobutylketone – H2O
HAc – MeOH – EtOH – H2O
HAc – MeOH – CO2 – H2O
Dimethylformamide – HFo – H2O
44
Parameters in the MSE Databank (8)
THINK
SIMULATION
 Mixed-solvent inorganic/organic system
■ Hydrocarbon – water – salt (Na, K, Ca, Mg, NH4, H, Cl, SO4, NO3) systems
■ Mono, di- and triethylene glycols - H – Na – Ca – Cl – CO3 – HCO3 - CO2 –
H2S – H2O
■ Phenol - acetone - SO2 - HFo - HCl – H2O
■ Benzene – NaCl and (NH4)2SO4 - H2O
■ Cyclohexane – NaCl - H2O
■ n-Butylaldehyde – NaCl - H2O
■ LiPF6 – diethylcarbonate – propylene carbonate
■ Ethanol – LiCl - H2O
■ Methanol - H2O + NaCl, HCl
45
Predictive character of the model
THINK
SIMULATION
Levels of predictivity
■ Prediction of the properties of multicomponent
systems based on parameters determined from
simpler (especially binary) subsystems
◊Extensively validated for salts and organics
■ Prediction of certain properties based on
parameters determined from other properties
◊Extensively validated (e.g., speciation or caloric
property predictions)
46
40
MSE (no ternary fits)
KH2PO4, weight %
35
30
25
20
15
0C
5C
10C
15C
20C
25C
30C
35C
40C
45C
50C
55C
60C
65C
70C
75C
THINK
SIMULATION
What does it mean for the
model to be predictive?
10
5
0
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
NaH2PO4, weight %
45
Aqueous model
(no ternary fits)
KH2PO4, weight %
40
35
30
25
20
0C
5C
10C
15C
20C
25C
30C
35C
40C
50C
45C
55C
60C
65C
70C
75C
15
10
 Parameters were determined
using only binary salt + H2O
data
 SLE for the ternary system
was predicted without making
any ternary fits
 MSE is clearly superior even
in the applicability range of
the aqueous model
 This can work only when the
ternary system does not
introduce a chemistry change
(e.g., double salts)
5
0
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
NaH2PO4, weight %
47
Predictive character of the model
THINK
SIMULATION
 Levels of predictivity - continued
■ Prediction of properties without any knowledge of
properties of binary systems
◊ Standard-state properties: Correlations to predict the
parameters of the HKF equation
∆ Ensures predictivity for dilute solutions
◊ Properties of solids: Correlations based on family analysis
◊ Parameters for nonelectrolyte subsystems
∆ Group contributions: UNIFAC estimation
∆ Quantum chemistry + solvation: CosmoTherm
estimation
» Also has limited applicability to electrolytes as long
as dissociation/chemical equilibria can be
independently calculated
48
Transport properties in the OLI
software
THINK
SIMULATION
Available transport properties:
■ Diffusivity
■ Viscosity
■ Electrical conductivity
OLI was the first to develop transport property
models for concentrated, multicomponent
aqueous solutions
More recently, the models have been extended
to mixed-solvent systems
49
Modeling diffusivity in electrolyte systems
 Limiting diffusivity –
Di0
 Long-range electrostatic interactions –
Relaxation effect:

ki

1  k
i

 Short-range interactions –
Hard-sphere contribution:
 Combination of the two effects:
k i
0

Di  Di  1 
ki





DiHS
Di0
 DiHS


 D 0

i
THINK
SIMULATION
MSA theory:
Important in
relatively dilute
solutions
Enskog theory:
Significant for
concentrated
solutions




50
THINK
SIMULATION
Calculation of diffusivity in MSE solutions
2.5
2.5
x LiCl=0
salt-free
x NaCl=0.005
x LiCl=0.02
1.5
1.0
2.0
2
x LiCl=0.01
9
2.0
D methanol*10 , m /s
D methanol *109, m 2/s
x LiCl=0.005
1.5
0.5
0.0
0.0
0.2
0.4
0.6
0.8
1.0
x 'methanol
Dmethanol in methanol-water-LiCl
system at 25ºC at various LiCl
concentrations
x NaCl=0.02
1.0
0.5
0.0
x NaCl=0.01
0.0
0.2
0.4
0.6
0.8
1.0
x 'methanol
Dmethanol in methanol-water-NaCl system
at 25ºC at various NaCl concentrations
51
THINK
SIMULATION
Computation of diffusion coefficients:
Species in NiCl2 solutions
D 109 (m2 s-1 )
2.5
2.0
Ni species - Stokes et
al. (1979)
1.5
Ni species - Salmon et
al. (1987)
1.0
Cl species - Stokes et
al. (1979)
0.5
 For complexed species,
measured diffusion
coefficients are
weighted averages of
diffusion coefficients of
individual complexes:
0.0
0
1
2
3
m NiCl2
4
5
D X T   iDQi X i
i
cQi X i
c X T
52
Modeling electrical conductivity
THINK
SIMULATION
The model includes the computation of
1.
2.
Limiting conductivities of ions as a function of temperature and
solvent composition
Dependence of electrical conductivity on electrolyte concentration
(the mean spherical approximation theory)
Limiting
conductivity
el

v i
0
 i   i 1  0
vi

Dependence of i on
electrolyte concentration
 X 
1 


X 

electrophoretic
correction
relaxation
effect
53
THINK
SIMULATION
Electrical conductivity model:
H2O – H2SO4 – SO3
specific conductivity (S.cm-1 )
10
1
0.1
0.01
0.001
0.0001
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
x-SO3
54
THINK
SIMULATION
Electrolytes in mixed solvents:
MgCl2 + ethanol + water
1.0E+00
specific conductivity, S ·cm
-1
0% EtOH
1.0E-01
20%
40%
60%
80%
1.0E-02
1.0E-03
1.0E-04
100% EtOH (---□---)
1.0E-05
0.0001
0.001
0.01
0.1
1
10
mol MgCl2/kg solvent
55
THINK
SIMULATION
Viscosity model
 In MSE solutions, -0 is found to show regularities with respect to
both electrolyte concentrations and solvent composition, and is the
most convenient quantity to define the model
viscosity of the
MSE solution
0
 mix
long-range
electrostatic
contribution
individual ion
interactions
contributions
between species
 
   
viscosity of the
solvent mixture
LR
s
Dependence of 
on electrolyte
concentration
ss
56
THINK
SIMULATION
Viscosities of solvent mixtures, 0mix
0
Mixing rule mix
   YiY jij
Viscosity (cP)
2.0
i
20C
1.5
25C
1.0
 ij 
50C
0.5
0.2


Modified volume fractions
0.0
0.0

1
 i0   0j 1  k ij
2
j
0.4
0.6
x-acetone
acetone + water
0.8
1.0
x i v i*
Yi 
*
 xl v l
l
vi*  v i0   xl1 4v l0 gil
l i
57
THINK
SIMULATION
Effects of electrolyte concentration

LR – From the analytical
model by Onsager and Fuoss
(1932)

s – defined for multiple
solvents

s-s
– defined for solvent
combinations

LR
1 2 N
 i
 2I 
 a 
 T 


 i zi 
n


4
r
c
s
  0 
 n 
i
n
0



i 
calculated using  and i0 in
the mixed solvent
 s    x j 0j ci Bi , j
j = solvent; i = ion or neutral
i
Bi,jjevaluated
based on
viscosities of
electrolyte in
pure solvent
Dik,jl(I,T) adjusted based on
experimental data
 s  s      x 'j xl' 0jl f i f k Dik,jl I 2
j
l
i k
j,l = solvent; i,k = ion or neutral
58
THINK
SIMULATION
Viscosity of H2O – H2SO4 – SO3
1000
 , cP
100
10
1
0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
x-SO3
59
Viscosity of salt – organic - water systems:
LiNO3-ethanol-H2O
THINK
SIMULATION
16
30 w t%
14
EtOH=0 w t%
70 w t%
12
 , cP
10
Viscosities of the ternary solutions
of LiNO3-ethanol-H2O as a function
of the molarity of LiNO3 at 25ºC and
at various ethanol weight percent.
100 w t%
8
6
4
2
0
0
5
10
15
c-LiNO3, mol/L
60
Sublimation /
calculations
sublimation pressure
10
NH4Cl
1
THINK
SIMULATION
salt
point
0.1
0.01
Stull-1947
OLI
0.001
150
200
250
300
350
 Solid-gas equilibrium
computations for pure
NH4Cl and NH4HS
t/C
sublimation pressure, atm
10
NH4HS
1
0.1
0.01
Stull-1947
P, atm (cal)
0.001
-60
-30
0
30
t/C
60
90
61
HI - I2 - H2O
THINK
SIMULATION
 H2O/HI/I2 full range of concentration, T
 The heart of the IS Process
 Challenge: presence of more than one LLE
region together with regions of VLE and SLE
62
MSE Challenge
THINK
SIMULATION
Will MSE work out-of-the-box?
 For systems within the AQ model limits, yes
■ Binary systems give reasonable results
when water remains the dominant solvent
 When the 2nd solvent, or different solvent
predominates
■ All major components must be studied with
respect to all solvents
◊ e.g., for MEG systems, MEG – Ca and MEG – Na
must be regressed, along with
63
Overview of Aspen PLUS OLI
(with MSE)
THINK
SIMULATION
Using the Aspen PLUS OLI Chemistry Generator
64
Aspen OLI Chemistry Generator
THINK
SIMULATION
65
Aspen OLI Chemistry Generator
THINK
SIMULATION
66
Aspen OLI Chemistry Generator
THINK
SIMULATION
67
Aspen OLI Chemistry Generator
THINK
SIMULATION
68
Aspen OLI Chemistry Generator
THINK
SIMULATION
69
Aspen OLI Chemistry Generator
THINK
SIMULATION
70
Aspen OLI Chemistry Generator
THINK
SIMULATION
71
Aspen OLI Chemistry Generator
THINK
SIMULATION
72
Aspen OLI Chemistry Generator
THINK
SIMULATION
73
Aspen OLI Chemistry Generator
THINK
SIMULATION
74
THINK
SIMULATION
75
Aspen OLI Chemistry Generator
THINK
SIMULATION
76
Aspen OLI Chemistry Generator
THINK
SIMULATION
77
Aspen OLI Chemistry Generator
THINK
SIMULATION
78
Aspen OLI Chemistry Generator
THINK
SIMULATION
79
Aspen OLI Chemistry Generator
THINK
SIMULATION
80
Aspen OLI Chemistry Generator
THINK
SIMULATION
81
Aspen OLI Chemistry Generator
THINK
SIMULATION
82
Aspen OLI Chemistry Wizard
THINK
SIMULATION
83
Aspen OLI Chemistry Wizard
THINK
SIMULATION
84
Aspen OLI Chemistry Wizard
THINK
SIMULATION
85
Aspen OLI Chemistry Wizard
THINK
SIMULATION
86
Aspen OLI Chemistry Wizard
THINK
SIMULATION
87
Aspen OLI Chemistry Wizard
THINK
SIMULATION
88
Aspen OLI Chemistry Wizard
THINK
SIMULATION
89
Aspen OLI Chemistry Wizard
THINK
SIMULATION
90
Aspen OLI Chemistry Wizard
THINK
SIMULATION
91
Aspen OLI Chemistry Wizard
THINK
SIMULATION
92
Aspen OLI Chemistry Wizard
THINK
SIMULATION
93
Aspen OLI Chemistry Wizard
THINK
SIMULATION
94
Aspen OLI Chemistry Wizard
THINK
SIMULATION
95
Aspen OLI Chemistry Wizard
THINK
SIMULATION
96
Aspen OLI Chemistry Wizard
THINK
SIMULATION
97
Aspen Plus 2006
THINK
SIMULATION
98
Aspen Plus 2006
THINK
SIMULATION
99
Aspen Plus 2006
THINK
SIMULATION
100
Aspen Plus 2006
THINK
SIMULATION
101
Aspen Plus 2006
THINK
SIMULATION
102
Aspen Plus 2006
THINK
SIMULATION
103
Aspen Plus 2006
THINK
SIMULATION
104
Aspen Plus 2006
THINK
SIMULATION
105
Aspen Plus 2006
THINK
SIMULATION
106
Aspen Plus 2006
THINK
SIMULATION
107
Aspen Plus 2006
THINK
SIMULATION
108
Aspen Plus 2006
THINK
SIMULATION
109
Aspen Plus OLI with EFRACH
THINK
SIMULATION
110
Aspen Plus OLI with EFRACH
THINK
SIMULATION
111
Aspen Plus OLI with EFRACH
THINK
SIMULATION
112
Aspen Plus OLI with EFRACH
THINK
SIMULATION
113
Aspen Plus OLI with EFRACH
THINK
SIMULATION
114
Aspen Plus OLI with EFRACH
THINK
SIMULATION
115
Aspen Plus OLI with EFRACH
THINK
SIMULATION
116
Aspen Plus OLI with EFRACH
THINK
SIMULATION
117
Aspen Plus OLI with EFRACH
THINK
SIMULATION
118
Aspen Plus OLI with EFRACH
THINK
SIMULATION
119
Aspen Plus OLI with EFRACH
THINK
SIMULATION
120
Aspen OLI
THINK
SIMULATION
More examples?
Live cases?
Questions?
121
Conclusion
THINK
SIMULATION
Using MSE in Aspen Plus is very similar to using
any property set.
The OLI property sets can be used with
standard Aspen PLUS unit operations or with OLI
unit operations
122