ECH4301-Wk5 Application of Thermodynamics in process engineering

8/7/2019 ECH4301-Wk5 Application of Thermodynamics in process engineering

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Slide 1 of 93HMY\ ECH4301\ Semester 2 2009/2010

Application of Thermodynamics

in Solving Process EngineeringProblem


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Sommerfeld on thermodynamics

Thermodynamics is a funny subject. The first

time you go through the subject, you don't

understand it at all. The second time you go

through it, you think you understand it, except for

one or two small points. The third time you go

through it, you know you don't understand it, but

by that time you are so used to the subject that itdoesnt bother you any more.

- Arnold Sommerfeld (1868-1951)


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THERMODYNAMIC PROPERTY

MODELING FOR CHEMICAL PROCESS

AND PRODUCT ENGINEERING : SOME

PERSPECTIVE

John P. OConnell; Rafiqul Gani; Paul M. Mathias; Gerd Maurer; James D. Olson;

Peter A. Crafts; Ind. Eng. Chem. Res. 2009, 48, 4619-4637.DOI: 10.1021/ie801535a

Copyright 2009 American Chemical Society


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Introduction - 1

Chemical tech has broadened & deepened-

uses of thermodynamic properties much more

sophisticated

Process designs developed via computation,

based on accurate data & complex model

To reveal the condition need for desired product

quality

To optimize efficiency for sustainability

To suggest alternative molecular structures for novel

application (health, comfort & defence)


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As March 2009

45 000 000 organic & inorganic substances

61 000 000 chemical sequences in the CAS registry

Principles model for pure component & mixture PYTxequation of state

Excess Gibbs free energy

Introduction - 2

Stateproperties

of formation

Reactionequilibria

Volume Enthalpies

Entropies

Bulkproperties

Component properties

Chemical potential

FugacityEquipment sizing

Energy analysis


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Property model and process model

Process model set of mass and energy

balance equations, as well as imposed physical,

chemical and economic constrains, of process

situation 2 kind of properties i)measurable, ii) conceptual

Quantitative equilibrium or dynamic behaviour

Property model requires spec of desired

behaviour (properties & molecular structure) and

then establish the process steps


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Process and property model

relationships.

Published in: John P. OConnell; Rafiqul Gani; Paul M. Mathias; Gerd Maurer; James D. Olson; Peter A. Crafts; Ind. Eng. Chem. Res. 2009, 48,

4619-4637.DOI: 10.1021/ie801535aCopyright 2009 American Chemical Society


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Roles of property models in Process

and Product Engineering1. Common service role

1. A specified set of property values is provided when

requested

2. Process simulation

2. Service plus advice role1. Models provide information about feasibility

2. Process and product design

3. Integration role

1. Contribute to the technique of problem solution2. Developing efficient and flexible integrated simulation

design strategies

*Forward design approach vs Reverse approach


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Singlephase, fixed

T & P

T and cchange?

Multiplephase ?

Q in/out?Any

reaction?

Model SelectionOnly V (density) needed V(T,c) J for equilibrium phase

Enthalpy H (T, P, c, T)

Simultaneous chemical &

physical equilibriumProperties of formation

* For more complicated situations, model selection may not be straightforward


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Model Information Sources

Molecular simulation


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Estimation Level of empiricism

Fundamental behavior - unknown

or excessively complex.

Continuous variables - by curve-

fitting sparse experimental data

using, for e.g. polynomials,

loglog plots, analysis of variable

statistical methods (ANOVA), and

time-series analysis.Restricted to data interpolation.

Resulting equations with fitted

parameters should not be

extrapolated outside the space of

measured variables.

Computational

chemistry -

calculations from

first principles

where it may be

claimed that no

data are needed.

Include quantummechanics and

applied statistical

mechanics.

Uses rigorous equations or

models from chemical theory.

Collections of quantities include

parameters adjusted to fit data.

E.g. ~ activity coefficients,

compressibility factors, residual

enthalpies, and entropies and

fugacity coefficients.Relations include equations-of-

state, group-contribution

methods, and corresponding-

states formulations


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Model Development

Essential element appropriate specification of the

problem type and expected outcome

E.g. ~ Develop a model for the estimation of the

average density of polymers for the pressures andtemperatures encountered in extruding the product

A clear delineation of model type, application range and

expected users.

Develop a model to predict the activity coefficients ofliquid solutions

Must put constrain or the model must treat all types of system

under all conditions


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Development strategy Iterative steps.


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Schematic phase equilibrium diagram of thebinary system formaldehyde + water. V =vapor; L = liquid; S = solid. (insert) Lower

temperatures where an azeotrope exists atdilute formaldehyde.

Example 1 VLE for Formaldehyde

with Water

The liquidvapor phase region is restricted

to low formaldehyde concentrations since

solids characterized as oligomers of

poly(oxymethylene) glycols, formed from

varying numbers of formaldehyde and watermolecules, precipitate at higher

concentrations.

However, even at the low formaldehyde

concentrations, complicated phase behavior

arises, as shown in the insert of Figure 4.

This is attributed to the formation ofoligomers that remain in solution,

preventing formaldehyde from volatilizing

as well as complexing to form methylene

gycol which can appear in the vapor phase.


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Thermodynamic properties are usually insufficient to

determine speciation, because there are too many

different options, with too many parameters, that may

correlate the data satisfactorily.

Many of the models based on property data would be

unreliable for extrapolation.

The model was established some years ago when

computational chemistry would not have been adequate

to explore the most stable species. However, even now,

it is essential to validate calculations with appropriate

measurements.


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Figure 5 shows a schematic of the vaporand liquid species equilibria with

formaldehyde (FA), water (W), methylene

glycol (MG = HO(CH2O)H), and its

oligomers (MGi= HO(CH2O)iH, i> 1)

The liquid phase, instead of

being a binary, contains at

least four species.

The vapor phase is

considered as a ternary

mixture of the volatile species

FA, W, and MG, since theoligomers should have vapor

pressures that are extremely

low.


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The thermodynamic problem is simultaneous vaporliquid

equilibrium for FA, W, and MG and chemical-reaction

equilibrium for the formation of MG in both phases and the

formation of MGi in the liquid phase.

For the expected low-pressure distillation, the phase

equilibrium relation chosen here assumes ideal gas vapor andnon-ideal liquid solution:

where pis

is the saturation pressure of the three volatilizingspecies I (i = FA, W, and MG), with xi and yi being the molefractions of volatilizing species i in the liquid phase and in thevapor phase, respectively, and i being the activity coefficient of

species iin the liquid phase. The total pressure is indicated by p.

s

i i i ip x pyK !


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The chemical reaction in the vapor phase is the formation of methylene

glycol from formaldehyde and water:

The species concentrations in the ideal vapor phase are related to the equilibrium

constant, a function only of temperature, through

where p(0) = 0.1 MPa is the standard state pressure.For reaction 1, the equilibrium constant in the liquid phase KI can be expressed with K1gas

and the saturation pressures of the pure components and is related to the liquid phasespecies activities:

CH2O + H2O HOCH2OH (1)

(0 )

1 ( ) (2)gas MG

FA W

y pK T

y y p!

1 1 (0)( ) ( ) = (3)

s sgas FA W MG MG

s

MG FA W FA W

p p xK T K T

p p x x

K

K K!


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The formation of poly(oxymethylene) glycols involves other equilibria:

The equilibrium constant for the oligomer of degree n is

The variation with Tofpis(T) was the Antoine form, while that of variousequilibrium constants was the usual parametrized form of

These were selected because the range ofTwas limited and no more

complicated relations, i.e., added parameters, could be justified. The form

for i(T,x)was chosen to be the UNIFAC group contribution approach

because it is predictive and the number of groups in this system is limited.


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The vapor pressures of formaldehyde and water are available in the

literature, but none are found for MG, as it does not exist as a pure

substance.

Thus, pMGs(T) for MG was estimated or treated as an adjustable parameter

when regressing VLE data.

The parameters for the vapor reaction equilibrium constant, K1gas(T), were

determined by the correlation of experimental gas phase density data.

The parameters for the otherKj(T) were found by fitting experimental NMRdata.Some of the UNIFAC parameters for the species were adjusted from

those in the literature by regression of new VLE data.

Model Calculation and Development

The calculations for the simultaneous phase and reaction equilibria were

straightforward, and no new models needed to be developed.

Information Sources


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Figure 6 Equilibrium concentrations of methylene glycol (MG) and poly(oxymethylene)

glycols MG2 and MG3 in aqueous solutions of formaldehyde at 338 and 368 K:

(experiment) Hahnenstein et al.,(41) Balashov et al.;(42) (calculated) Albert et al.(43)


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Figure 6 shows the MG species concentrations in

aqueous formaldehyde mixtures as calculated from the

model in comparison with the experimental NMR data

used to obtain the equilibrium constants.

Since in the liquid phase more than 99% of the

formaldehyde is converted to methylene glycol and

poly(oxymethylene) glycols under the conditions shown

in Figure 6, the mole fraction of (monomeric)formaldehyde is too small to show.

Results


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Figure 7 Vaporliquid partitioning of formaldehyde in the system formaldehydewater at 363 and 413 K:

(experiment) Credali et al.,(45) Kogan,(46) Maurer,(47) Albert et al.,(43) Albert et al.;(44)

(calculated) Albert et al.(44)

Figure 7 shows a typical

comparison betweenexperimental data and

correlation results for the

vaporliquid equilibrium of

the binary system(formaldehyde + water) at

363 and 413 K.


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Formaldehyde also reacts with alcohols. - forms hemiformal and

poly(oxymethylene) hemiformals with methanol.

The above model was extended in a straightforward manner to the binary

formaldehyde + methanol, the ternary formaldehyde + water + methanol, and

to multicomponent systems containing trioxane and some reaction side

products.

The model correctly predicts that at low temperatures the presence of

methanol results (at very low methanol concentrations) in a higher volatility of

formaldehyde, whereas at higher methanol concentrations the volatility of

formaldehyde is lowered.

The thermodynamic model was also extended to describe caloric properties

of these mixtures.

This whole framework has been successfully applied by many companies to

do basic engineering of processes involving aqueous solutions of

formaldehyde. The important feature is that the species are established by,

and their predicted amounts under some conditions are compared with,

molecular measurements such as NMR and/or UV-VIS spectroscopy.

Extensions


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Example 2 - VLE for CO2 and H2S in

Aqueous Amine Solutions over Wide

Ranges of ConditionsSour gases, e.g., CO2 and H2S, are commonly removed from natural or

synthesis gas by chemical absorption in aqueous solutions of amines

(such as, 2,2-methyliminodiethanol = N-methyldiethanolamine =

MDEA) or amine mixtures (e.g., MDEA + piperazine).

While the competitive chemical absorption of CO2 and H2S is kinetically

controlled, departures from equilibrium are the driving forces of such

processes.

The reliable design and optimization of the separation equipmentrequires knowing the chemical reaction thermodynamics and the

vaporliquid equilibria, along with information about the energy to

vaporize/condense the mixtures.


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Gas absorption plants - run at ambient T & P up to 4 Mpa,

whereas solvent regeneration is in a stripper (i.e., gas desorption) at

elevated temperatures (over 390 K) and low pressures.

Composition measurements show that the liquids leaving the

absorption tower contain nearly no neutral amine and very small

amounts of neutral sour gases, though there are large amounts of

electrolyte reaction products such as protonated amines, bicarbonate,

carbonate, and carbamate.

In contrast, the liquids leaving the regeneration unit contain nearly no

electrolytes, the sour gases have been stripped off, and the amines

are mostly neutral.

The latest references addressing this approach and earlier works are

by Maurer and co-workers. A recent similar analysis related to

postcombustion carbon dioxide capture in aqueous ammonia is given

by Mathias et al.


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Figure 8 shows a speciation

scheme for the solubility of

CO2 in aqueous solutions ofMDEA and piperazine (PIP).

The vapor phase - have only

CO2 (C) and water (W),

though solvent volatilizationmight need to be treated in

full process design.

Liquid phase - extremely

complicated (more than adozen species, neutral and

ionic).Figure 8 VLE and chemical reactions in theCO2/MDEA/piperazine/H2O system.


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Broad range of species and compositions requires a model that is able to

describe phase behavior over a very wide range of T and P & high loading of

amine and CO2.

The VLE relation includes vapor-phase non-ideality and the effect of pressure

on the liquid phase. For water, the phase equilibrium relation is

while the extended Henrys law standard state on the molality scale

is used for carbon dioxide because it is supercritical at most conditions

of interest here. Consistent treatment of pressure and non-ideality withEqn 6 must be implemented

Model Relations and Selection


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The fugacity coefficients of saturated water vapor, water in

the vapor mixture, and CO2 in the vapor are

and respectively.

Predicted by the 2nd virial equation of state (since P were

not extremely high and the coefficients are generally more

reliable for aqueous systems than cubic equations of state

based on corresponding states

The Poynting factors for liquid phase pressure effects use

the pure liquid molar volume for water, vW, and the partialmolar volume at infinite dilution for CO2 in water, vCW

,

since these are good estimates and the effects on them

of composition and pressure can be ignored.


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For liquid phase properties - Chemical reactions dominate (see Fig 8).

Chemical equilibrium for reaction Rk is expressed using activities:

where the activity of a solute species i(i.e., all species except water)is the product of its stoichiometric molality and its activity coefficient

appropriate for the Henrys Law standard state, denoted with *:

For water, the activity, aw, is calculated via integration of theGibbsDuhem equation using the activities of all the solutes.


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The temperature-dependence of the chemical

equilibrium constant


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The scheme shown in Figure 8 involves extensive information.Parameters for the equilibrium constants were provided from the literature

cited above.

The Henrys law constant is that for unreacted carbon dioxide in water, which

along with pWs, vW, and the second virial coefficients, which, along with the

estimation method forvCW were taken from in the literature.

The Pitzer model for Gibbs excess energy requires binary and ternary

parameters to describe the interactions between solute species from low gas

loadings (i.e., at low partial pressures of carbon dioxide) to high gas loadings

(i.e., at high partial pressures of carbon dioxide).

The only source for obtaining reliably the most important parameter values is

experimental data of the solubility of CO2 in aqueous solutions of MDEA and of

piperazine at low, as well as at high, CO2 partial pressures.

Such investigations were performed with two different types of experimental

equipment.(50, 51, 60)

Information Sources


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Figure 9 Partial pressure of carbon dioxide (left diagram) and total pressure (right diagram) above

liquid mixtures of (CO2 + MDEA + H2O), mMDEA 2 molkg1: (experiment)(60) 313, 353, 393 K; (experiment)(61) 313, 333, 373, 393, 413 K; (calculation) correlation from all

data, - - - from only high pressure data.(60)


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Figure 10 Carbon dioxide partial pressure (left) and total pressure (right) above liquid mixtures of

(CO2 + MDEA + H2O), mMDEA 8 molkg1: (experiment)(59) 313, 353, 393 K;(experiment)(60, 61) 313.7, 354.4, 395 K; (calculations) correlation from all data, - - - from

only high pressure data.(60)


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The solid lines in Figure 9 and 10 show the excellent comparisons

between experimental(60, 61) and model results(60) for CO2 solubility

in aqueous solutions at low (Figure 9) and high (Figure 10) MDEA

molalities.

In addition, the broken lines in both figures show predictions at lowpartial pressures of carbon dioxide when all interaction parameters

were estimated by using only high-pressure gas solubility data.

These predictions agree well with low pressure experimental data at

low and moderate amine concentrations (high C/MDEA values) but are

less accurate at higher amine concentrations where parameters

characterizing important interactions between molecular MDEA and

other solute species were not included, as they cannot be determined

from high pressure gas solubility data.

Results


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This example illustrates the very great range of data thatmust be assembled in order to reliably develop a full model

for a truly complex system.

Note that every aspect was compared with data and

sensitivity to parameters was tested.

Compared to the first case, fewer molecular

measurements were available, though the speciation was

generally known.

It is possible that computational chemistry methods could

apply here, but this work was done before they were ready

for implementation.


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Example 3 LLE of carboxylic Acid from

Aqueous Solution Many carboxylic acid products are produced by fermentation. Product

recovery from the dilute aqueous solutions is achieved by reaction with

a hydrophobic component, e.g. tri-n-octylamine (TnOA), with theresulting complexes extracted into an organic phase.

Subsequently, the carboxylic acid must be separated and the auxiliary

component regenerated. The design of such extraction and recovery processes requires a

thermodynamic model for liquidliquid equilibria that accounts for

electrolytes in both aqueous and organic liquid solutions.

Our example is for citric acid partitioning between water and different

organic solvents in the presence of TnOA, including the effect of saltson the TnOA partitioning for recovery and regeneration.

The latest references summarizing this approach are by Maurer and

co-workers.


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At low aqueous-phase molalities of citric acid and fixed TnOA - the ratio of

organic to aqueous phase concentrations of citric acid increases with

increasing acid concentration.

It then passes through a maximum and decreases at the highestconcentrations.

This behavior is attributed to two competing effects.

At low acid concentrations in the aqueous phase, the dissociation equilibrium

for citric acid is shifted to its ionic species.

As only neutral acid molecules can be extracted into the organic solvent, the

partition coefficient increases when the amount of dissolved neutral citric acidincreases.

The decrease of the partition coefficient observed at high acid concentrations

results from complete complexation of the TnOA, so additional citric acid

cannot be bound, and the acid remains in the aqueous phase.

In such processes the aqueous phase may also contain strong electrolytes.

While most strong electrolytes reduce the solubility of an organic compoundin an aqueous phase, i.e., salt-out the organic compound, the presence ofsalt actually reduces the partitioning of a carboxylic acid to the organic phase

when a complexing agent, such as TnOA, is present.


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Figure 11 Partition coefficient of citric acid (PCit(m) = mCit(org)/mCit(aq)) in theaqueous/organic two-phase system (citric acid + water + MIBK + TnOA + NaNO3) at

298.15 K for equal volumes of the aqueous and the organic feed solutions at constant

TnOA concentration (mTnOA(org),(0) = 1.24 molal) in the organic feed and several saltconcentrations in the aqueous feed solution:(63) (experiment) mNaNO3(aq),(0) = 0,= 0.01, = 0.05, = 0.1 molal; prediction.

Figure 11 shows

the influence of

NaNO3 on thepartition

coefficient of citric

acid in the organic

phase relative to

aqueous phase

for the system(citric acid + water

+ MIBK + TnOA +

NaNO3)


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For this complex system, the fugacities of the coexisting liquid phases are

treated with the aqueous phase as an electrolyte solution as in eqs 6 and 8,

while the organic phase has only neutral species as in eq 1.

Pitzers excess Gibbs energy model is used in both phases.

It has terms associated with electrostatics and ions in the aqueous phase,

while a power-law equation is used for the organic phase.The organic-phase complexes of citric acid and TnOA are in chemical

reaction equilibrium; most contain water, as verified by IR-spectroscopy.

The stoichiometry of the complexes depends on the organic solvent and can

be complicated.

For example, two complexes (citric acid:TnOA:water = 2:3:2 and 1:1:1,

respectively) were found for toluene, whereas four complexes (1:0:3, 1:2:3,1:1:3, and 2:1:6) were in methyl isobutyl ketone (MIBK).

The equilibrium constants for the various reactions were expressed as in

eqs 4, 5, 7, and 8. Figure 12 shows the resulting speciation for liquidliquid

equilibria with toluene.

Model Relations, Selection, and

Information Sources


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Figure 12. Speciation in liquidliquid

phase equilibria for the system (citric

acid + water + toluene + TnOA).

Figure 12 shows the resulting

speciation for liquidliquid

equilibria with toluene.

The equilibrium constants for

the various reactions were

expressed as in eqs 4, 5, 7,

and 8. Figure 12 shows the

resulting speciation for

liquidliquid equilibria withtoluene.


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Comparisons between predictions and experimental data

are also shown in Figure 11; the agreement is within

experimental uncertainty.

When NaNO3 is added to equal volumes of aqueous andorganic feed solutions (NaNO3(aq),(0) = 0.05 mol kg1;

TnOA(org),(0) = 1.24 mol kg1) giving Cit.tot(aq) = 0.02

mol kg1 in the equilibrated aqueous solution, the

partition coefficient of citric acid to the organic phase is

about 0.4 compared to about 40 in the salt-free system.

When NaCl is the salt under the same conditions, the

value is about 10.

Results


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A number of applications have been made by adding other reactive

components to the scheme of Figure 12.

The partitioning of inorganic acids (HCl, HNO3, and H2SO4) in TnOA-

containing two-phase systems with toluene and MIBK and chloride

partitioning in the system (citric acid + water + MIBK + TnOA + NaCl) have

been studied.The addition of the salt of the carboxylic acid was predicted not to affect

the partitioning of the acid, as there is no competition of different acids for

the amine; this is found to be true for monocarboxylic acids like acetic acid,

though not for acids with more than one carboxylic group.

The model predicts all of the above behavior quantitatively, verifying the

thermodynamic framework.

Again, comprehensive use of, and comparisons with, many different data,

along with careful speciation, allows quantitative description of many

variations of these complex solutions. The results can be used for testing,

for exploring options in process synthesis, and for process optimization.

Extensions


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Example 4 VLE for oleum

Oleum, also known as fuming sulfuric acid, consists of SO3 dissolved in

100% H2SO4.

Thus, for example, 20 mol % oleum consists of 20% SO3 and 80% H2SO4by moles.

The modeling objective is to provide VLE and heat of vaporization

information for the system components in a case where corrosion andtoxicity make extensive measurements extremely challenging.

There are known to be many different species complexes in the liquid

phase of oleum in addition to the H2SO4 and SO3 components.

However, the dominant complex is H2S2O7 which is formed from 1 mol each

of H2SO4 and SO3 by the complexation reaction proposed by Nilges and

Schrage(67) and by Mathias et al.:(68)

No ions are considered to exist in the solution.


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Information Source

The phase equilibrium is obtained with liquid fugacity expressions of the

form of eq 1 and vapor fugacity expressions of the form of eqs 6 and 7.

The activity coefficients were obtained from the NRTL excess Gibbs energy

model since the non-ideality is strong.

For this system where SO3 is the principal volatile component, the effect on

VLE of parameters for the H2SO4H2S2O7 pair is very small and is thereforeignored.

However, parameters for the SO3H2SO4 and SO3H2S2O7 pairs must be

valid over a range of temperature both for VLE and for calorimetric

properties.

Since the pressure is elevated, there is vapor-phase non-ideality that was

described by the RedlichKwong equation with standard parameters basedon critical properties.

This is not expected to be rigorous, but the conditions are such that the

limited composition dependence of the vapor non-ideality minimally affects

the predicted behavior.


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Figure 13 Comparisons of experimental data(71) and model calculations () for vapour

pressures of oleum mixtures.

Published in: John P. OConnell; Rafiqul Gani; Paul M. Mathias; Gerd Maurer; James D. Olson; Peter A. Crafts; Ind. Eng. Chem. Res. 2009, 48,4619-4637.

DOI: 10.1021/ie801535aCopyright 2009 American Chemical Society


47/92


reaction equilibria of the system

Defined by the equilibrium constant for reaction III plus the non-idealities of

the three species in their liquid mixture:

where the temperature dependence ofKoleum(T) is that of eq 5 plus polynomialsin T.

Miles et al.(70) used two methods to measure the enthalpy of vaporization: (1)

evaporation of SO3 from oleum under reduced pressure and (2) heat of solution

(three sets of data) of addition of SO3 to oleum.

The enthalpy of mixing used for matching the calorimetry data is obtained from

the temperature dependence ofKoleum(T) and ofGE via the GibbsHelmholtz

relation


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Results

Figure 14 shows experimental data(70) and model calculations for the

enthalpy of vaporization of SO3 from oleum mixtures of various

concentrations at 30 C.

The enthalpy of vaporization here is the negative of the enthalpy change

that occurs at 30 C when 1 kg of gaseous SO3 is dissolved in a largequantity of an oleum mixture of the given concentration.


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Comparison between experimental data(70) and model calculations for

enthalpy of vaporization of SO3 from oleum mixtures of various

concentrations at 30 C.


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The sigmoidal shape of the enthalpy of vaporization curve is a clear

fingerprint that strong chemical reactions are involved.

At low SO3 concentrations, essentially all the added SO3 combines with

H2SO4 to form H2S2O7.

Hence, the total enthalpy of vaporization is approximately equal to theenthalpy of vaporization of pure SO3 ( 540 kJ kg

1) plus the heat of reaction

( 210 kJ kg1).

At about 50 mol % SO3 (45 wt % SO3), the amount of free H2SO4 has

substantially decreased, so reaction does not occur and the enthalpy of

vaporization rapidly decreases toward the enthalpy of vaporization of pure

SO3.

This is another example where success is obtained with an appropriate

conceptual model and multiproperty fitting of quality data, though no

molecular measurements were involved.

In particular, calorimetric data proved quite valuable in validating a proposed

chemistry model, given by the signature in the enthalpy of vaporization vsconcentration curve.

This case shows how a speciation can be validated by careful use of both

phase equilibrium and calorimetric data, giving a better chance of finding the

most appropriate description.


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Solvent Selection for

Pharmaceutical Production

Introduction

Challenges to develop efficient environmentally friendly process for API

only 1 in 10 new drug candidates survives through clinical trials to enter themarket

not economical to collect large quantities of thermophysical data since thesystems frequently change

predictive thermodynamic models to reduce the experimental search space

Modern drugs are functionally complex(72) and often fall beyond thecapabilities of traditional predictive models like UNIFAC

The reliable prediction of crystal structure and solid-state properties iscomputationally demanding, and still years away from mainstream application

3 examples of service and advise role

Typically, the industry deals with complex chemistry,(74) phase equilibriainvolving organic salts, and aqueous electrolytes. These factors make it clearthat pharmaceutical systems challenge the capability of modern property tools


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Example 1 - Anisole Removal during

Washing and Drying Operations

In this problem, an environmentally friendly solvent is needed to wash and dry

a crystalline pharmaceutical intermediate.

The solvent must efficiently remove anisole residues from upstream

chlorination and coupling reactions, where the product is precipitated as an

organic HCl salt and separated by pressure filtration to yield a 40% w/wanisole wet cake.

Due to anisoles low volatility, removing the residual anisole via inert gas drying

is very slow and not commercially viable.

Washing first with a more volatile solvent can increase the drying rate, and

MTBE was used in early process development.

However, at production scale, MTBE would possibly have required specificVOC abatement equipment, so a search for a better wash solvent was

initiated.

The final wash solvent was to be environmentally preferred, fully miscible with

anisole, and promote vaporization of residual anisole.


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Information SourcesTo solve the above solvent selection problem, the solvent needs are

translated into target properties.

Table 1 lists an appropriate set of pure component and mixture property

values.

The boiling point and melting point indicate the liquid range.

The key property to characterize the ease of anisole removal is the partialpressure, yip. As computed via eq 1, this quantity is increased by selecting awash solvent that gives anisole activity coefficients as large as possible.

Since the anisole will be dilute after washing, the relevant activity coefficient

for screening is its limiting value at infinite dilution, A,S.

Thus, the solvent power, Sp, is used to rank prospective wash solvents for

anisole volatilization


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The miscibility criterion is explicit. In addition to Sp, the Hansen

solubility parameters, h and p,(75, 76) were used to reduce the

solvent search space since the feasible solvent candidates are to

have low affinity for ionic solutes; i.e., small terms for hydrogen

bonding and polarity.

Finally, the functional groups were limited to those with goodenvironmental profiles.

The ProCAMD solvent search software(77) was used to find

solvent candidates matching the target properties listed in Table 1.

The pure component target properties were first estimated from

generated molecular structural information via the

ConstantinouGani or MarerroGani(78) method.

Those structures (molecules) satisfying the target values were then

examined for Sp and miscibility with the UNIFAC-LLE model.


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Results

A set of solvent candidates was identified among the 26837 molecular

structures generated by excluding 13698 substances by the hparameter; 3533 by p; 2925 by Tmelt; 6484 by Tboil; 62 by Sp; and 7 by

miscibility. That left 47 acceptable candidates as possible solvents;

Table 2 shows representative properties of four candidates.


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Of these, heptane was selected as the wash solvent for its Sp value,commercial availability, volatility, and environmental profile (fugitive

releases to the atmosphere).

Figures 15 and 16 show activity coefficients for anisole with the

original MTBE wash solvent and the heptane replacement as

predicted by the UNIFAC method.(79)

The value of ln A,S in heptane is about a factor of 3 greater than that

for MTBE.

In pilot plant trials with heptane, the drying step was considered rapidat about 7 h, and residual anisole levels were significantly reduced.

Perhaps most importantly, the VOC emissions were reduced from

0.15 mol fraction for MTBE to 0.02 mol fraction for heptane.


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Binary solution activity coefficients for MTBE

and anisole predicted by UNIFAC.(79)

Binary solution activity coefficients for

heptane and anisole predicted by

UNIFAC.(79)

This example of solvent substitution demonstrates how group contribution

methods may be applied in reverse and thus narrow the size of a search

space and minimize time-consuming laboratory experiments.


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Example 2 - Solvent Selection for an

Enantiomeric PharmaceuticalThe synthesis of drugs often results in intermediates containing racemic

mixtures of left- and right-handed enantiomers of chiral molecules. For esters,

it is sometimes possible to use a catalytic lipase enzyme in an aqueous

alcohol mixture to selectively dissociate the undesirable enantiomer into its

acid via the reaction:

After dissociation, the chirally resolved ester is easily separated from the acid

and alcohol using pH-buffered liquidliquid extraction. A final crystallization

yields the desired product. Finding the optimal conditions for the dissociation

in a pH-buffered liquid extraction depends on estimating accurate values of

the acid and ester dissociation equilibrium constants or pKa, while selection ofoptimal solvent(s) is needed for both partitioning and for crystallization.


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Information SourcesSeveral properties must be estimated for the process steps: the pKa valuesfor the ester and acid as well as LLE for extraction and SLE for

crystallization of the product with the proposed solvents. A review of pKapredicting methods is presented in ref80. Note that there are two pKas forthe ester and three for the acid. Software from ACD Laboratories(81) was

used to predict the pKas for these organic molecules with the values givenin Table 3. The OH groups on the reaction product alcohol and on the t-butanol are relatively stable and do not need to be considered.


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Using straightforward calculations, the distributions of the species can be

found as functions of pH. These are shown for the ester and acid in Figures

17 and 18. Above pH 10, the ester is present in the uncharged state, while

the acid is fully deprotonated with a charge of negative one. Under theseconditions, the partitioning gives maximum separation efficiency, since

charged species prefer the aqueous phase, while the neutral ester prefers

the organic phase. The pH is adjusted by adding a bicarbonate salt, since

this acts as a sufficiently strong inorganic base for buffering during extraction

but is not strong enough to hydrolyze the ester.


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Species distributions for racemic ester

dissociation as functions of pH.

Species distributions for racemic acid formation

as functions of pH.

After a water wash to remove the bicarbonate and subsequent decanting, thepurified ester product is in a stream rich in t-butanol and saturated withapproximately 30% w/w water. However, direct crystallization of the product

by cooling this solution gave a poor yield; the product solubility in aqueous t-butanol was too high. Thus, a solvent was sought where the product could be

extracted for crystallization.


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Experimental screening of product solubility suggested toluene would be a

good crystallization solvent, but the yield from the actual process solution

turned out to be poor. The reason is clear from the ternary LLE diagram of

Figure 19, predicted with the original UNIFAC LLE parameters.(79) It

shows tie lines and a binodal curve with t-butanol favoring the toluene-richorganic phase rather than the aqueous phase. Alternatives to water, while

keeping toluene, were sought using the search criteria of Table 4, similar

to the process above. In this case, the normal melting point and boiling

point were estimated from group-contribution methods for pure

component properties; for the liquid density, the Rackett equation(22) was

used, while for the selectivity of the product for the organic phase and

miscibility calculations, the UNIFAC-LLE model(79) with the associatedparameters was used.

Result


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Liquid phase compositions for aqueous t-butanol with toluene at T= 25 C, 1 atm from UNIFAC-LLE.(79)




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The search led to three substances expected to be commercially available,

with the properties shown in Table 5. With the best solvent, 1,3-propylene

glycol, the LLE phase diagram appeared as in Figure 20. The tie linesbetween the toluene-rich and glycol-rich phases show the desired

selectivity fort-butanol, but the two-liquid region extended too little towardthe t-butanol apex. It was concluded that toluene would not provide acommercially viable batch extraction process. Further solubility screening

identified cyclohexane as a potential crystallization solvent, so it was

examined as an extraction solvent, by modifying the polar solvent search

criteria given in Table 4 to include selectivity with cyclohexane and an

updated density limit of 0.8 g/mL.


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Ternary LLE fort-butanol, 1,3-propylene glycol, and toluene at T= 25 C, 1 atm from UNIFAC-LLE.(79)


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Again, the propylene glycols were identified as the top-ranking candidates.

The phase diagram of Figure 21 for 1,3-propylene glycol with cyclohexane

and t-butanol shows fully desirable characteristics. The two-liquid regionextends to a 50:50 volume ratio of cyclohexane to t-butanol, and the t-

butanol partition coefficients are more appropriate for productivity. In fact, justtwo washes with propylene glycol reduces the t-butanol content to about 3%w/w with a residual level of propylene glycol in the organic phase of only

0.5% w/w. This scheme gives good productivity in generic batch

manufacturing equipment, which is common to the pharmaceuticals industry.


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Ternary LLE fort-butanol, 1,3-propylene glycol, and cyclohexane at T= 25 C, 1 atm from UNIFAC-LLE.(79)

Adequate success in this conceptual design project was obtained by flexibly

searching for alternative solvents using phase equilibrium representations of

multicomponent systems as computed from group contribution methods.

Identifying the desired partitioning in the ternary system was particularly crucial.


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Example 3 - Selection of Binary Solvent

Mixtures for a Crystallization Process

This example concerns a pharmaceutical intermediate produced by reaction

in tetrahydrofuran (THF) with the desired product obtained by crystallization.

The yield from THF alone was found to be poor, and water was introduced as

an antisolvent to increase the yield. Laboratory test results showed

inconsistencies, and it was suggested that the actual amount of water in the

crystallization solvent was varying because of carryover from an upstream

washing step. Predictions of the solubility in the aqueous THF solution were

made to determine how water content could affect the product solubility.

Details of this application can be found in ref82. This example highlights the

use of techniques other than group contribution (GC) when the necessary

parameters for a GC-based method are not available.


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Information SourcesThe structure of many heterocyclic pharmaceutical molecules cannot be

treated with GC-based models like UNIFAC, due to missing functional

groups or interaction parameters. The NRTL-SAC model(83) is an

alternative approach which uses characteristic surface segments to describe

intermolecular interactions from surface charge density. The NRTL binary

interaction parameters between the segments are fixed, and there are

adjustable characteristic segment values for each molecular species. The

NRTL-SAC database contains segment profiles for 130 solvents derived

from available literature VLE and LLE data. For a particular solvent, the 4

solute segment parameters are regressed from solubilities in at least 4, and

up to 10, pure solvents spanning the expected surface segment values. This

will allow prediction of solid solubility of a solute, in pure or binary solvents of

the components in the database, with sufficient accuracy for solvent-ranking

and trends in ternary systems. For the present system, an NRTL-SAC model

was established from existing solubility data in 25 pure solvents over the

temperature range of 1080 C.


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Results

The solubility of the pharmaceutical intermediate in mixtures of THF and

water with the regression and prediction results shown in Figure 22, along

with solute solubilities in the mixed THFwater solvent shown in detail inFigure 23. While the calculated values were sometimes far from

experiment, they were adequate for the purposes of the problem. Figure 23

shows that water was acting unexpectedly. At low concentrations, it is a

cosolvent, increasing the solubility. At higher amounts, the water depresses

solubility. The strong variation with the water fraction suggested why the

laboratory tests on the process stream were not consistent: fluctuatingcarryover combined with extreme sensitivity to composition.


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Regression results of NRTL-SAC

model(83) parameters for 25 puresolvents.

Experimental and predicted solubilities of product

in THFwater mixtures.

From these results, a different and more robust crystallization process was

developed. The key was the estimation of solubilities and careful scrutiny of the

sensitivity of properties to variations in conditions.


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5. Emerging Methods for Property

Estimation

The examples given above demonstrate current approaches and capabilities

for property modeling when data sources are available or the opportunity for

new measurements exists. We now give two examples using advanced

techniques to overcome the limitations of current approaches such as group-

contribution methods. They use contemporary computational techniques

either directly or indirectly to predict phase equilibria. The first describes how

unavailable group-contribution parameters can be obtained with a new

method based only on chemical structure, with application to VLE. The

concept is appealing, and it suggests an avenue for future developments. The

other is for a separations process and compares different modeling

techniques, including molecular methods. The results have implications forefforts to improve predictive modeling capabilities.


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Estimation of Group-Contribution

ParametersRecently, Gani et al.(84) have suggested how already availableexperimental data might be used to predict group-contribution model

parameters that are missing in a host tabulation, such as the

MarreroGani(78) group-contribution method for pure component

properties. The basis is an atom-connectivity index, developed under the

principle of additivity of contributions of different descriptors for a specific

property that gives contributions to molecular properties by atoms and theirconnectivities. With atoms, many fewer parameters are needed to

represent groups of atoms. Further, index parameter values for

connectivity indices can be found from the same available experimental

data as for regressing group-contribution parameters. Combining known

group contributions (GC) with estimated group contributions from atom-

connectivity indexes (CI) results in an approach called GCplus. The methodcan be applied to any host group contribution model. Extension to a wide

range of property models for pure component properties and to average

properties of polymer repeat units has been made


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Gonzalez et al.(86) have applied the GCplus approach to predict missing

group-interaction parameters when the host method is the UNIFAC model for

activity coefficients (GC). The available experimental data used for UNIFAC

group contributions were employed in regressing the interaction parametersfor the atom-connectivity indices (CI). Then, the CI values were used to

estimate missing group-interaction parameters.

Examples applying GCplus to pure component properties are given in refs

8486. Here, we illustrate GCplus for mixture properties. In each case, the

chemicals and the phases of interest are given along with the host model and

the group(s) with missing values. Then predicted UNIFAC group parametersare given, along with comparison of predicted and measured phase behavior

results.


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VLE for 1,2-DichloroethaneDMSO

If the original UNIFAC-VLE model(87) is the base method, the missing

group interaction parameters are for the pair CCl and DMSO. Using the CI

method, estimates of the missing group interaction parameters are listed

in Table 6. Figure 24 shows TxyVLE comparisons from using theseparameters along with the GC parameters in the UNIFAC table with

measured VLE.(88) These data were not used for the CI-model parameterestimation, but the agreement is excellent


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TxyVLE diagram at 0.953 bar for 1,2-dichloroethane with DMSO from the UNFAC-CI method with parameters not used for the

CI model regression compared to measured values: (experiment)(88).



SLE f A i h


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SLE for Acetaminophen

(Paracetamol) with 1-Butanol

If a later revision of the original UNIFAC-VLE model(86) is the host for this

system, only the ACNH2CH2CO interactions are missing. However, for

illustration, we also present results when all the group interactionparameters are obtained via CI. Table 7 lists GCplus group interaction

parameters for the system where those for ACNH2/CH2CO (in bold) are

estimated with CI. Table 8 lists the parameters when all are estimated from

CI. The values are different. Figure 25 shows the comparisons of the results

for both cases with data.(89) Over the limited range of paracetamol

compositions, both sets of parameters describe the data well. Thus, whileparameter values may differ, the data can be adequately predicted with both

methods.


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Figure 25. Solubility of paracetamol in 1-butanol as a function of

temperature estimated with CI-generated values: only for

ACNH2/CH2CO groups (original UNIFAC(87) parameters for other

groups), - - - all parameters estimated from CI; (experiment)(89


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VLE for Methylethylketonen-Heptane

This system involves only the CH2 and CH2CO groups, but there is a

significant temperature variation to be dealt with. The later UNIFAC model(90)

has parameter values, so a comparison can be made among data, GC

prediction, and CI prediction. The CI-computed parameter matrix with

temperature dependence is given in Table 9


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VLE for the methylethylketonen-heptane system at 318.15 K: calculated with UNIFAC(90) group-contribution parameters, - -

- calculated with CI-estimated group-contribution parameters; (experiment)(90) y, x.




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Figure 26 shows the calculated Pxydiagram with the UNIFAC Dortmund parameters and the CI-computed parameters, along with data from ref91. The agreement for the CI method is not as good

as with the GC method over the whole data range, but the pressure and composition of the azeotrope

are given reasonably accurately.

VLE for the methylethylketonen-heptane system at 318.15 K: calculated with UNIFAC(90) group-contribution parameters, - -

- calculated with CI-estimated group-contribution parameters; (experiment)(90) y, x.


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These examples illustrate the possibilities of using models based on limited

information, such as connectivity indices, as well as a level of compromiseencountered when they are used in place of more elaborate methods such

as UNIFAC. In general, CI may be a reliable expedient to determine

unavailable, and perhaps less sensitive, parameters for use with incomplete

group contribution methods. It is not proposed as a replacement for

experiments, but rather to focus on a few experiments through which the

extension can be verified.


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5.2 Molecular Calculations

While methods such as CI are easily used to obtain group-contribution

parameters, their accuracy and generality may be limited. An alternative

which does not, in principle, require data for model parameter

regression is quantum chemistry calculations for inter- and

intramolecular force fields followed by molecular simulation or statistical

thermodynamic methods to obtain properties

S l t f E t ti Di till ti f


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Solvents for Extractive Distillation of

1,3-Butadiene

Mathias et al.(92) describe an investigation using quantum mechanics and

molecular simulation to improve process simulation for the classical problem

of 1,3-butadiene recovery from steam cracker C4 hydrocarbons by

determining the relative effectiveness ofn,n-dimethylformamide (DMF) and

acetonitrile (ACN) as extractive-distillation solvents. The principal propertiesobtained were the activity coefficients of the hydrocarbon components in the

presence of the extractive solvent for use in eq 1. Comparisons were made

among a quantum mechanical and statistical mechanical method, COSMO-

RS,(-36, 37) a molecular dynamics simulation approach, SPEADMD,(93)

group contributions from UNIFAC,(94) and thermodynamic intuition. Mathias

et al.(92) describe the methods and results in some detail; only a briefsummary is given here to indicate the findings.


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The COSMO-RS method reliably predicted the trends of infinite-dilution

activity coefficients with accuracy comparable to UNIFAC, but only with

systematic empirical corrections. This limited the true predictive capability

of the method. The SPEADMD molecular simulation used a force field fromthe principle of transferability,(95, 96) which assumes that forces inferred

from experimental data for one set of mixtures can be applied to other

compounds and mixtures. The computed results provided unique qualitative

structural and orientational insights at the molecular scale about the

solvation interactions between the polar solvents and the olefinic moieties

in the hydrocarbon compounds. The differences in accessibility for DMF

and ACN and the sizes and shapes that affect intermolecular contacts were

reliably characterized. However, to achieve accuracy for activity

coefficients, the molecular simulations required refinement of the interaction

potentials by regression to data, similar to finding UNIFAC parameters.

E t i f M l l


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Extensions of Molecular

CalculationsThe experience of Mathias et al.(92) suggests some of the limitations

and future prospects of molecular simulation, as do the International

Fluid Property Simulation Challenges (IFPSC).(97) The present

important question about the potential for molecular simulation as a

routine tool to provide quantitative property data for process and

product design is, Are we there yet?. In our opinion, the answer is a

qualified no. While progress is being made, the results are often like

the butadiene example: good, perhaps adequate for the advice role

without high accuracy, but not sufficient for the service role.


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A common shortcoming of molecular simulation methods is the lack of easily

available and suitable force fields(98) to solve the wide variety of problems

under consideration.(99) In particular, what should be done when noexperimental data exist for empirically fitting a force field? As an example,

consider the molecule whose chemical formula is C10H19N and structure is

the following

There are no experimental data and not even a CAS number has been

assigned. If this molecule is of interest in a product design for a particular

application or is an impurity that must be effectively removed in a processdesign, molecular simulation could not be used for property estimation unless

ab inito quantum methods alone could produce a force field. Figure 27

suggests a strategy to obtain force field parameters of molecules of industrial

interest


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Schematic for developing molecular simulation force fields for cases with different amounts of available data.




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Other limitations in simulations occur at lower temperatures and for larger

molecules (high density) and for transport properties. For quantum

calculations, the system size may be limiting, the fundamental basis setsmight not be accurate enough, and the way to improve results may not be

clear. Also, while only a few molecular-simulation researchers and reviewers

now list computing machinery and computing resources as a major limitation

in the extension of MC and MD to new fluid property applications,

computational capabilities beyond those currently available are needed for

computational chemistry to directly treat many practical systems.Finally, a key issue for practical application molecular simulation is the lack of

availability of tools for nonexpert users. There is no standard toolbox for

molecular simulation as pointed out by Wei.(100, 101) Our experience is that

even experts can have problems, giving one pause about very widespread

application of computation. For example, in at least two entries during IFPSC

contests, expert researchers incorrectly transcribed molecular parametersthat then produced nonrepresentative and erroneous results. In such cases,

what would nonexperts find? We mention two efforts to produce standard

molecular simulation tools which are the TOWHEE project(102) and the

LAMMPS project;(103) others should appear in the future

Documents

ECH4301-Wk5 Application of Thermodynamics in process engineering