thermodynamic property modeling for chemical process and product engineering some perspectives

8/13/2019 Thermodynamic Property Modeling for Chemical Process and Product Engineering Some Perspectives

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Thermodynamic Property Modeling for Chemical Process and Product

Engineering: Some Perspectives

John P. OConnell,*, Rafiqul Gani, Paul M. Mathias, Gerd Maurer,| James D. Olson, andPeter A. Crafts#

Department of Chemical Engineering, UniVersity of Virginia, CharlottesVille, Virginia 22904-4741, CAPEC,Technical UniVersity of Denmark, DK-2800, Lyngby, Denmark, Fluor Corporation, 3 Polaris Way, Aliso Viejo,California 92698, Applied Thermodynamics, UniVersity of Kaiserslautern, D-67653, Kaiserslautern, Germany,Dow Chemical Company, South Charleston, West Virginia 25303, AstraZeneca Pharmaceutical Ltd.,

Macclesfield, Cheshire, SK10 2NA, U. K.

Thermodynamic properties have always played essential roles in the engineering of chemical products and inthe processes that manufacture them. Further, contemporary and future chemical technologies depend morethan ever on property model formulation and application. This work explores how properties are utilized inprocess and product engineering, including opportunities and constraints of current property models, the currentstatus of data availability and needs, and the interplay of data and models. Several case studies are given toillustrate underlying concepts, strategies for development, and methods of application to some industrial systems.

1. Introduction

Over the past century, properties have been important inindustrial and engineering chemistry. In 1909, the first issue oftheJournal of Industrial and Engineering Chemistry1 contained6 editorials, 16 articles and notes, several book reviews andnew books notices, trade and industrial notes, and officialregulations and rulings. About half of these pieces used the wordproperties to identify standards and characteristics of products,while several book titles contained the word properties. Other

articles, such as A New Bomb Calorimeter, by C. J. Emersonand Report of the Committee on the Analysis of PhosphateRock, were also oriented toward describing the attributes ofchemical substances in commercial and personal use at the time,ranging from dyes to castor oil to whiskey. As chemicaltechnology has broadened and deepened, the uses of propertieshave become much more sophisticated. Process designs are nowdeveloped via computation based on accurate data and complexmodels to reveal the conditions needed to attain desired productcontent and quality, to optimize efficiencies for sustainability,and to suggest alternative molecular structures for novelapplications in health, comfort, and defense.

This work describes a view, and gives examples, of contem-

porary and future applications for properties in chemical processand product engineering. We seek to give some perspectives toboth property model developers and users who might benefitfrom seeing a conceptual framework along with some specificcases, to enhance their efficiency in performing process andproduct simulation and simulation of complex chemical systems.

We start with the nature of process and product engineeringand of property models as well as their possible roles in processand product design. This is followed by a brief discussion ofinformation sources for property descriptions and predictions.We then give a set of cases to provide current context and asense of future developments.

Of necessity, important situations have had to be omitted;our focus in this paper is on chemical and pharmaceuticalsystems. As a result, areas such as advanced biotechnology,nanotechnology, interfaces, polymers, near-critical systems,

petroleum fractions, and ionic liquids have not been included.Most of these are of great practical importance and representmany challenges. However, the nature of modeling theirproperties is either an extension of techniques we give here orinvolve concepts and details that require more discussion thanwe can provide. In any case, we hope that the approachesdescribed here will stimulate workers in such areas to find valuein our suggested modeling framework and methodologies, suchas careful data collection for maximum information content,shrewd model development for efficient application, and ap-propriate computational schemes for obtaining optimal results.

2. Property and Process Modeling in Chemical

EngineeringThe principal goals of product and process engineering are

to bring to commercial reality both innovations and enhance-ments for the myriad of items, substances, and systems thatnature allows by manipulation at the molecular and macroscopiclevels. Contemporary and future approaches build upon tradi-tional analysis and design, while using greater understandingof natural laws and relationships plus advanced tools, especiallymodern instruments and computers.

Incredible advances have been made in chemical technologyduring the last century. A comparison of article titles in a currentIndustrial and Engineering Chemistry Researchissue with thoseof the early issues of the Journal of Industrial and Engineering

* To whom correspondence should be addressed. Tel.: 1 (434) 924-3428. Fax: 1 (434) 982-2658. E-mail: [email protected].

University of Virginia. Technical University of Denmark. Fluor Corporation.| University of Kaiserslautern. Dow Chemical Company.# AstraZeneca Pharmaceutical Ltd.

Ind. Eng. Chem. Res. 2009, 48,46194637 4619

10.1021/ie801535a CCC: $40.75 2009 American Chemical SocietyPublished on Web 04/16/2009


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Chemistry leads to an appreciation of how the issues andtechniques have advanced. Several valuable perspectives onapplied thermodynamics in process modeling are available.2-5

Another illustration of change is in the kinds of failures andsystems investigated by Zudkevitch in his 1980 paper onForensic Thermodynamics6 compared to those of today.Earlier errors about phase boundaries and incorrect heat effectsare much less frequent for well-defined systems, though theyare still found in many current and complex systems. Thesedevelopments have come about because there is much fullerrecognition about what product and process engineering,especially design, require in order to be accurate and reliable.

It is important to recognize the enormous number andmanifold varieties of systems and substances for which modelsof many different properties are now expected to be found. Asof March 2009, there were over 45 000 000 organic andinorganic substances and nearly 61 000 000 chemical sequencesin the CAS registry.7 Dealing with the infinity of mixtures thatcan be generated from these compounds is daunting, butclassification and careful strategies, combined with computa-tional power, have allowed significant progress in treating moreand more complex situations in recent years. Gani andOConnell8 provide a list of some of the common types ofchemical systems for which property models may be found inthe literature, in process simulators, and specialty software. Ingeneral, for pure component and mixture thermodynamic

properties, the principal model forms arePVTxequations of stateand models for excess Gibbs energy, though these are generallylimited to fluids. Additionally standard state properties offormation of substances are needed for reaction equilibria. Theylead to bulk properties such as volumes, enthalpies, and entropiesfor equipment sizing and energy analyses, as well as tocomponent properties, such as chemical potentials and fugacitiesfor phase and reaction equilibria.

In our work, we distinguish process models and propertymodels (see Figure 1). Process models are the sets of mass andenergy balance equations, as well as imposed physical, chemical,and economic constraints, of a process situation. These containtwo kinds of properties: measurables (y), such as temperature

(T), pressure (p), composition (x), and mass flows (m) of thestreams, as well as conceptuals (), such as enthalpy, entropy,fugacity or chemical potential, etc. To obtain quantitativeequilibrium (no time dependence) or dynamic behavior (tvaries),process model relations require values of both property types,either directly by empirical correlation of measured data, or bycalculations with property models. Product engineering firstrequires specification of the desired behavior, commonlyexpressed in both properties and microscopic structure, and thenestablishment of the process steps to make the desired product.

Evaluations of process behavior or product performance oftenuse conservation of mass and/or energy; in such cases, theproperty variables are internal variables and are part of the setof constitutive equations used to find the system variables. On

the other hand, when process states or product qualities needto be determined, balance relations are not usually involved, so

property variables become the unknowns and are found on astand-alone basis. This means that product design problems aredistinct from process design problems. In processing, thechemicals are known and the behavior is to be solved for. Withproducts, the desired properties are known, but the chemicalidentities (molecular or atomic structure) or their mixturecompositions are unknown. For products, property models aretypically used in an iterative mannersthe generate-and-testparadigm. That is, for a generated molecular structure or

mixture, the target properties are calculated or tested. If theyare not within the desired range, another alternative is generated,and the calculation cycle is repeated until a generated set ofstructures meets the specifications. Note that the property modelsof a product design problem also become the constitutiveequations of the process models for designing the process tomanufacture the product. While process relations can be viewedas always true for the system under consideration, propertyrelations are expected to have limited accuracy and reliability,thereby introducing uncertainties to the design.

2.1. Roles of Property Models in Process and Product

Engineering.Gani and OConnell8 and OConnell and Neurock9

have described the roles of property models in computer aided

process and product engineering. Property models play threedistinctive roles, particularly in process and product design.There is the common serVice role, where a specified set ofproperty values is provided when requested. Additionally, thereis a serVice plus adVicerole, where models provide informationabout feasibility, in addition to property values. Finally, thereis the integration role where property models further, anddirectly, contribute to the technique of problem solution.Typically, properties play theserVicerole in process simulation,theadVicerole in process and product design, and theintegrationrole in developing efficient and flexible integrated simulation-design strategies.

Though property models are generally recognized for the

traditional serV

ice role, the other roles are actually morepowerful. The adVice and integration roles often improvedesigns, widening or narrowing search spaces and increasingsolution method efficiency. Simulation-based forward designapproaches can become bogged down with complex models.In fact, in many design and/or simulation problems, the targetconditions (which are usually dependent on properties) areknown but are only used to verify/analyze such simulationresults and as a basis for initiating a simulation for an alternativedesign. The reverse approach of searching with propertymodels for the substances and conditions that take the knowninputs to the desired outputs can be much more efficient thanforward trial methods.

The roles of property models need to be considered fordetermining strategies for their selection, use, and ultimateproblem solution as described in the work of Kontogeorgis andGani3 and Gani and OConnell.8

2.2. Property Models: What and How? We complete ourintroduction by describing the nature of property model relations,suggest some strategies for model selection, list informationsources for quantitative use of models, mention calculationalaspects, and end with techniques for model development. Thisis followed by several examples to illustrate how these elementscan be put together in some contemporary, and complex,systems.

2.2.1. Model Relations.Property models inevitably involvea mathematical relation from which properties can be computed

when the system is fully defined. This also means a propertymodel is a general algebraic relation containing parameters

Figure 1. Process and property model relationships.

4620 Ind. Eng. Chem. Res., Vol. 48, No. 10, 2009


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whose values characterize the system conditions and compo-nents. Property relations may be linear or nonlinear, and theparameters may be constants or functions of variables such astemperature, pressure or density, and composition. As indicatedbelow, there can be several possible sources of parameter values.

The most common model formulation is a generalizedexpression, often in the form of corresponding states where thevariables are scaled by with pure component critical propertiesand combining rules are used for mixtures, as determined from

a few substances and mixtures, and then applied to othersystems. The issue is whether the corresponding states similari-ties among substances and mixtures will hold for predicting theproperty for new systems. Thus, the acentric factor formulationwas established for normal fluidssnonpolar and weakly polarsubstances whose deviations from noble gases are principallyfrom the intermolecular forces of globularity and nonsphericity.One should not expect to accurately describe systems with largedipoles or, especially with association and solvation interactionssuch as hydrogen bonding, with simple rules.

2.2.2. Model Selection. The process or product situationdetermines which property model descriptions are needed.Consider specification of the volume of a tank to hold a liquid

mixture. If the conditions will be for a single phase at a specifiedtemperature and pressure, only a single density would be needed.However, if the temperature and composition of the phasevaried, a model for calculating liquid density as a function ofthese variables is necessary. When multiple phases could occurin the system, models for fugacity or activity to obtain theequilibrium phase compositions are required to determine thenumber, relative amounts, and density of all possible phases ofthe mixed system. If heat is to be added or removed tomanipulate the number of phases, values for mixture enthalpiesare needed at different conditions of state (temperature, pressure,composition, and phase). Finally, if reactions occur in the tank,the calculations involve simultaneous chemical and physicalequilibrium and require properties of formation for the speciesof the system. This apparently simple case demonstrates thepossible range of demands for adequate descriptions of chemicalsystems. For the more complicated situations, model selectionmay not be straightforward.

In all property roles, important decisions must be made aboutmodel form and content to balance effort with accuracy,reliability, and ease of prediction. Dependability, accessibility,generality, and effort usually need to be weighed carefully. Evenwhen expectations for outcomes are clear, the optimal procedureis often not apparent. For example, if vapor pressure is needed,should data be taken and correlated, Antoine parameters fromthe literature be accepted, corresponding states estimation beused, group or atom contribution methods be implemented, ordescriptors from molecular calculations be tried? Is the accuracyadequate for the service role in detailed process design? Is thegenerality suitable for the advising role when creating viablealternatives? Can the information content be sufficient for theintegration role for the elements of a process simulation?Different answers to these questions lead to different paths aswell as variations in design efficiency besides different outcomes.

Often it seems that no new model is needed, and the onlydecision is to choose the best model from among those available.But we note that, even for experienced users, the multitude ofmodel possibilities, and their complexities to deal with thevariations of pure components and mixtures, can make modeland data selection quite difficult. That means a property-model

user with limited knowledge and experience faces real chal-lenges to find a model for maximum effectiveness rather than

going with familiarity, hearsay, or ease of accessibility. Ourwarning is that there can be unpleasant consequences of usingan inappropriate property description (e.g., in either the modelor the parameters) with wrong results. The design can havebottlenecking or be oversized, as well as give too simplistic oroverly complex, process configurations.10,11

2.2.3. Model Information Sources. Property models areintended to give quantitative descriptions of nature. As a result,the information used must be reliable and appropriately accurate.

Three principal sources for property or parameter values areused:

Retrieval from literature or computerized databases Estimation via correlation, prediction, or computation New experimental measurement

These sources are given in increasing order of time andresource requirements. Below are some details of their uses.

Literature and Databases. A part of the contemporaryrevolution and explosion of information availability is storageand computerized access to property data. Electronic databasesare preferred because, in addition to easy searching, they canbe more readily corrected and kept up to date. Currentlyavailable electronic property and phase equilibria databases

include: Thermodynamics Research Center (TRC) now atNIST,12 NIST WebBook,13 AICHE Design Institute for PhysicalProperties (DIPPR),14 Physical Properties Data Service (PP-DS),15 National Standard Reference Data Series (NSRDS),16

Dortmund Data Bank,17 DECHEMA Chemistry Data Series,18

and the Engineering Science Data Unit-Glasgow.19 In addition,there are many handbooks and other printed sources of data, asrecently reviewed by Harvey.20

A key issue in retrieved data is data-quality review andinformation management. This issue has been explored par-ticularly well by Frenkel et al. in their discussion of theTHERMO_XML initiative.21 A great variety of errors ofinconsistency, tabulation, and omission are known to occur, and

users should always verify the reliability of vital data bycomparisons among sources and by using fundamental variationswith state variables as given by thermodynamic equations andderivatives. For example, the expected signs of propertyderivatives can often be checked for consistency with respectto temperature, pressure, and composition. Some journalspublishing properties work are now beginning a cooperationwith the Thermodynamics Research Center12 to screen manu-script submissions with new data for internal consistency, aswell as agreement, duplication, and plagiarism related to theliterature.

Estimation.Engineers may identify the estimation source ofinformation as prediction, calculation, correlation, data-exten-

sion, or, perhaps somewhat misleadingly, modeling. Estima-tion of values is required because, particularly in process-designengineering, all the needed data at all the states, and theirvariations with conditions, cannot possibly be measured. Anextensive compilation and review of estimation methods is givenby Poling, Prausnitz, and OConnell.22

The how to of estimation is most obviously related to thelevel of empiricism found in the different approaches highlightedin Figure 2. At the empirical end of the scale, the fundamentalbehavior of the needed quantity is unknown or excessivelycomplex. Obtaining the required continuous variables is bycurve-fitting sparse experimental data using, for example,polynomials, log-log plots, analysis of variable statistical

methods (ANOVA), and time-series analysis. This techniqueshould be restricted to data interpolation, and the resulting

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equations with fitted parameters should not be extrapolated

outside the space of measured variables.At the theoretical end of the scale lie the methods of

computational chemistry which are calculations from firstprinciples where it may be claimed that no data are needed.These include quantum mechanics and statistical mechanics. Theapplied statistical mechanics technique of molecular simulation,and its application to process and product design, is discussedbelow. In between a full empiricism/and basic theory is theapproach of enlightened empiricism, which uses actual oradapted forms of rigorous equations or models from chemicaltheory. Here, carefully selected collections of quantities includeparameters adjusted to fit data. Examples include the following:activity coefficients, compressibility factors, residual enthalpies,

and entropies and fugacity coefficients. Relations includeequations-of-state, group-contribution methods, and correspond-ing-states formulations, which may be predictive, or at leastallow cautious extrapolations outside the range of the dataregressed.

Molecular simulation computes properties using statisticalmechanical relationships for properties from assumed inter- andintramolecular potential functions, or force fields. Computationalchemistry obtains chemical energies and reaction paths fromapproximate solutions to quantum relations such as the Schro-dinger equation. In addition to potentially costing less time andmoney than real experiments, data values can be obtainedfor states that would be difficult, dangerous, or impossible to

measure in the laboratory. For example, the solubility of oxygenin flammable solvents and the formation properties of highlytoxic substances can be estimated from molecular simulationand quantum calculation. Molecular simulation can also validatetheoretically based models for properties,23-27 provide detailedmolecular structures for connections to properties, and confirmor debunk molecular speculations associated with process orproduct design.28

Our view is that, over the last twenty-five years, molecularsimulation methods have mainly provided qualitative mole-cular insight and identify erroneous assumptions about molecularstructure and behavior with particular force field models, whilefewer have provided quantitative estimation of properties and phase

equilibria by comparisons with experimental data. In addition totypical petrochemicals,25 molecular simulation methods have beenapplied to polymers, ordered materials, electronic materials,29 andto nanostructures.30 The American Institute of Chemical Engineershas a forum dedicated to Computational Molecular Science andEngineering (CoMSEF).31

Experiment.Finally, laboratory measurement is the slowestand most expensive route for getting information about productand process design. Measurements should be reserved, butstrongly considered, when uncertain quantities sufficientlyinfluence process condition and configuration outcomes to justifythe time and expense of reliable determination of properties.Experiments can be used to answer immediate property or design

questions, to provide the raw material from which estimationmethods are developed, or to provide validation of theoretical

methods. The importance of, and trends in, experimentalmeasurement have recently been reviewed by Gupta andOlson.32,33

2.2.4. Model Calculation. While there are relatively fewcalculational issues for property models, they can be significant.Algorithm efficiency is important only for extensive processsimulations and optimizations. However, convergence can beproblematic if models do not give consistent results, such asproper phase or partitioning, at conditions encountered away

from final states. Additional convergence difficulties can occurif reliable derivatives are not obtained from a model. Regressionto reliable parameter values may not occur if calculated valuesare too sensitive, or not sensitive enough, to parameter variation,or if some of the parameters are correlated so that uniquenesscannot be established. Thus, care about computational aspectsalso needs to be exercised in model formulation. Gani et al.34

have proposed a systematic property model analysis to identifythe complete and consistent set of property model equations tobe solved and classifications of the variables to be specified(model parameters, chemical system characteristics, and problemspecifications) to solve for the unknown properties.

2.2.5. Model Development. When no, or only inadequate,models exist, the level of investment to establish a reliableproperties model, from basis to operating code, demands anefficient development procedure. An essential element is ap-propriate specification of the problem type and the expectedoutcome. If one formulates the type precisely such as, developa model for the estimation of the average density of polymersfor the pressures and temperatures encountered in extruding theproduct, there is a clear delineation of model type, applicationrange, and expected users. The task is fairly clear. On the otherhand, if the statement is, develop a model to predict the activitycoefficients of liquid solutions, either constraints need to bemade on the types of systems and conditions for which themodel will be applicable or the model must treat all types ofsystems under all conditions. The latter may be impossible to

achieve, even with current knowledge and techniques.One development strategy for property models involves the

two-part, iterative process shown in Figure 3. Problem speci-fication, involving important decisions, is first, where problemtype, system classification, information sources, and expertiseof intended users are chosen. These must include accuracyrequirements on the properties to be obtained and the amountof effort to be expended in application. Then, the modeldevelopment is done via construction, solution, and verificationfor internal consistency. Gani et al.34 describe structuring modelsfor rapid coding as well as for recognizing the many relation-

Figure 2. Iterative steps of property model development.

Figure 3. Iterative steps of property model development.



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ships, the independent and dependent variables, and the param-etrizations embedded in models for simple and complex systems.It has also become common to formulate models in terms ofcontributions from groups of atoms, such as UNIFAC,35 as wellas with dielectric continuum solvation models with chargescreening, such as COSMO-RS36,37 and COSMO-SAC.38

3. Contemporary Examples of Property Modeling in

Process and Product Design

The above introduction describes our perspectives on propertymodeling, for process and product engineering, especially fordesign. The following is a set of specific examples whichillustrate aspects of property model implementation so thatreaders can appreciate current approaches to a variety of systemsand complexities. The first four cases involve different chemicalprocesses with complex chemistry, or speciation. They requirecareful analysis, somewhat elaborate development, and extensivedata. These models are principally used in the service role fordesign. Their success comes from ultimately achieving a robustand reliable functional form for both the chemical speciationand physical effect parameters as found from an array of data.The focus of our discussion is the final model form and con-

tent; the process of model development usually took consider-able time and persistence. The next set of three cases, drawnfrom the pharmaceutical industry, gives insights about theservice and advice role of properties in solvent selection. Finally,two emerging methodologies, estimation of group parametersand molecular calculations, illustrate extensions of currentapproaches to predicting behavior in broader applications.

3.1. Models for Complex Solutions.The design of stagedseparation processes such as distillation, extraction or crystal-lization typically uses phase equilibrium as a basis for connect-ing to real systems. Thus, property models for componentproperties in vapors and liquids are heavily involved. Manyimportant commercial chemicals strongly interact or dissociate

to form detectable or hypothesized species, especially in aqueousand alcoholic solutions. Thus, even a system made from onlytwo components can become a complex, multispecies mixture.These effects dramatically influence phase behavior and com-ponent partitioning. Describing such systems generally uses thesame properties as for simpler solutions, but the treatment mustdeal with phase and reaction equilibria simultaneously.

3.1.1. Vapor-Liquid Equilibria for Formaldehyde with

Water.A well-studied, and commercially important, system isthe aqueous binary of formaldehyde plus water. Figure 4 showsa schematic isothermal vapor-liquid equilibria (VLE) diagrambased on data referenced by Kuhnert et al.39 The liquid-vaporphase region is restricted to low formaldehyde concentrationssince solids characterized as oligomers of poly(oxymethylene)glycols, formed from varying numbers of formaldehyde andwater molecules, precipitate at higher concentrations. However,even at the low formaldehyde concentrations, complicated phasebehavior arises, as shown in the insert of Figure 4. This isattributed to the formation of oligomers that remain in solution,preventing formaldehyde from volatilizing as well as complexingto form methylene gycol which can appear in the vapor phase.

The selection of species for this case was confirmed by NMRspectroscopic measurements. Thermodynamic properties areusually insufficient to determine speciation, because there aretoo many different options, with too many parameters, that maycorrelate the data satisfactorily. Many of the models based onproperty data would be unreliable for extrapolation. The model

was established some years ago when computational chemistrywould not have been adequate to explore the most stable species.

However, even now, it is essential to validate calculations withappropriate measurements.

Figure 5 shows a schematic of the vapor and liquid speciesequilibria 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 atleast four species. The vapor phase is considered as a ternarymixture of the volatile species FA, W, and MG, since theoligomers should have vapor pressures that are extremely low.

Model Relations and Selection.The thermodynamic problem

is simultaneous vapor-liquid equilibrium for FA, W, and MGand chemical-reaction equilibrium for the formation of MG inboth phases and the formation of MG iin the liquid phase. Forthe expected low-pressure distillation, the phase equilibriumrelation chosen here assumes ideal gas vapor and nonideal liquidsolution:

where pis is the saturation pressure of the three volatilizing

species i (i ) FA, W, and MG), with xiand y i being the molefractions of volatilizing species i in the liquid phase and in thevapor phase, respectively, and i being the activity coefficient

of species i in the liquid phase. The total pressure is indicatedby p.

Figure 4. Schematic phase equilibrium diagram of the binary systemformaldehyde + water. V ) vapor; L ) liquid; S ) solid. (insert) Lowertemperatures where an azeotrope exists at dilute formaldehyde.

Figure 5. Vapor and liquid species equilibrium model for the systemformaldehyde-water.

pisxii ) pyi (1)



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The chemical reaction in the vapor phase is the formation ofmethylene glycol from formaldehyde and water:

The species concentrations in the ideal vapor phase are relatedto the equilibrium constant, a function only of temperature,through

where p(0) ) 0.1 MPa is the standard state pressure.For reaction I, 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 phase species

activities:

The formation of poly(oxymethylene) glycols involves otherequilibria:

The equilibrium constant for the oligomer of degree n is

The variation with Tofpis(T) was the Antoine form, while that

of various equilibrium constants was the usual parametrizedform of

These were selected because the range ofTwas limited and nomore complicated relations, i.e., added parameters, could be

justified. The form for i(T,x)was chosen to be the UNIFACgroup contribution approach because it is predictive and thenumber of groups in this system is limited.

Information Sources. The vapor pressures of formaldehydeand water are available in the literature, but none are found for

methylene glycol, as it does not exist as a pure substance. Thus,pMG

s (T) for MG was estimated or treated as an adjustable parameterwhen regressing VLE data. The parameters for the vapor reactionequilibrium constant,K1

gas(T), were determined by the correlationof experimental gas phase density data.40 The parameters for theother Kj(T) were found by fitting experimental NMR data.

41,42

Some of the UNIFAC parameters for the species were adjustedfrom those in the literature by regression of new VLE data.

Model Calculation and Development.The calculations forthe simultaneous phase and reaction equilibria were straight-forward, and no new models needed to be developed.

Results. Figure 6 shows the MG species concentrations inaqueous formaldehyde mixtures as calculated from the model43

in comparison with the experimental NMR data used to obtainthe equilibrium constants.41,42 Since in the liquid phase morethan 99% of the formaldehyde is converted to methylene glycol

and poly(oxymethylene) glycols under the conditions shown inFigure 6, the mole fraction of (monomeric) formaldehyde istoo small to show. Figure 7 shows a typical comparison betweenexperimental data43-47 and correlation results44 for the vapor-

Figure 6. Equilibrium concentrations of methylene glycol (MG) and poly(oxymethylene) glycols MG2 and MG3 in aqueous solutions of formaldehyde at338 and 368 K: (experiment) 0Hahnenstein et al.,41 OBalashov et al.;42 (calculated) sAlbert et al.43

CH2O + H2O h HOCH2OH (I)

K1gas(T) )

yMG

yFAyW

p(0)

p (2)

K1(T) K1gas(T)

pFAs

pMGs

pWs

p(0)

)xMG

xFAxW

MG

FAW(3)

HO(CH2O)n-1H + HOCH2OH h HO(CH2O)nH +

H2O n > 1 (II)

Kn(T) )xMG

n

xW

xMGn-1xMG

MGn

W

MGn-1MG

(n ) 2, 3, 4, 5,...) (4)

lnKj ) aj - bj/T (5)

Figure 7. vapor-liquid partitioning of formaldehyde in the system

formaldehyde-

water at 363 and 413 K: (experiment)3

Credali et al.,

454

Kogan,46 ]Maurer,47 9Albert et al.,43 b Albert et al.;44 (calculated) sAlbert et al.44



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liquid equilibrium of the binary system (formaldehyde + water)at 363 and 413 K.

Extensions. Formaldehyde also reacts with alcohols. Forexample, it forms hemiformal and poly(oxymethylene) hemiformalswith methanol. The above model was extended in a straightforwardmanner to the binary formaldehyde + methanol, the ternaryformaldehyde + water + methanol, and to multicomponent systemscontaining trioxane and some reaction side products.39 The modelcorrectly predicts that at low temperatures the presence of methanol

results (at very low methanol concentrations) in a higher volatilityof formaldehyde, whereas at higher methanol concentrations thevolatility of formaldehyde is lowered.

The thermodynamic model was also extended to describecaloric properties of these mixtures.48,49 This whole frameworkhas been successfully applied by many companies to do basicengineering of processes involving aqueous solutions of form-aldehyde. The important feature is that the species are estab-lished by, and their predicted amounts under some conditionsare compared with, molecular measurements such as NMR and/or UV-VIS spectroscopy.

3.1.2. VLE for CO2 and H2S in Aqueous Amine Solutions

over Wide Ranges of Conditions. Sour gases, e.g., carbondioxide and hydrogen sulfide, are commonly removed fromnatural or synthesis gas by chemical absorption in aqueoussolutions of amines (such as, 2,2-methyliminodiethanol )N-methyldiethanolamine ) MDEA) or amine mixtures (e.g.,MDEA + piperazine). While the competitive chemical absorp-tion of CO2 and H2S is kinetically controlled, departures fromequilibrium are the driving forces of such processes. Hence,the reliable design and optimization of the separation equipmentrequires knowing the chemical reaction thermodynamics andthe vapor-liquid equilibria, along with information about theenergy to vaporize/condense the mixtures.

Gas absorption plants are run at ambient temperatures and arange of pressures as high as 4 MPa, whereas solvent regenerationis in a stripper (i.e., gas desorption) at elevated temperatures (over

390 K) and low pressures. Composition measurements show thatthe liquids leaving the absorption tower contain nearly no neutralamine and very small amounts of neutral sour gases, though thereare large amounts of electrolyte reaction products such as proto-nated amines, bicarbonate, carbonate, and carbamate. In contrast,the liquids leaving the regeneration unit contain nearly noelectrolytes, the sour gases have been stripped off, and the aminesare mostly neutral. The latest references addressing this approachand earlier works are by Maurer and co-workers.50,51 A recentsimilar analysis related to postcombustion carbon dioxide capturein aqueous ammonia is given by Mathias et al.52

As a typical example, Figure 8 shows a speciation scheme forthe solubility of CO2in aqueous solutions of MDEA and piperazine

(PIP). The vapor phase is considered to have only CO2 (C) andwater (W), though solvent volatilization might need to be treatedin full process design. As can be seen, the liquid phase is extremelycomplicated, containing more than a dozen species, neutral andionic.

Model Relations and Selection. This broad range of speciesand compositions requires a model that is able to describe phasebehavior over a very wide range of temperature and pressure, aswell as high loading of amine and CO2. The vapor-liquidequilibrium relation includes vapor-phase nonideality and the effectof pressure on the liquid phase. For water, the phase equilibriumrelation is

while the extended Henrys law standard state on the molality scale,HCW

(m)(T;pWs ) is used for carbon dioxide because it is supercritical

at most conditions of interest here. Consistent treatment of pressureand nonideality with eq 6 must be implemented.

Here the solute composition is given in molality (moles solute perkilogram of water), mC, and its activity coefficient (normalizedaccording to Henrys law on the molality scale) in the liquid phaseis C*.

The fugacity coefficients of saturated water vapor, water inthe vapor mixture, and CO2 in the vapor are H2O

s , H2O , andCO2 , respectively. These were predicted by the second virialequation of state since the pressures were not extremely highand the coefficients are generally more reliable for aqueoussystems than cubic equations of state based on correspondingstates.

The Poynting factors for liquid phase pressure effects usethe pure liquid molar volume for water, VW, and the partial molarvolume at infinite dilution for CO2 in water, VCW

, since theseare good estimates and the effects on them of composition andpressure can be ignored.

Chemical reactions dominate the liquid phase properties asshown in Figure 8. As in eqs 2-4, chemical equilibrium forreaction Rkis expressed using activities:

where the activity of a solute species i (i.e., all species exceptwater) is the product of its stoichiometric molality and its activitycoefficient appropriate for the Henrys Law standard state,denoted with *:

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

The temperature-dependence of the chemical equilibriumconstant for the autoprotolysis of water (KR1) was adopted fromthe work of Edwards et al.,53 whileKR2andKR3for the formation

and dissociation of bicarbonate were taken from the work ofPatterson et al.54,55 For the protonation of methyldiethanolamine,

pWs W

s exp(V

W(p -

pWs

)RT )aW ) yWpW (6)

Figure 8. VLE and chemical reactions in the CO2/MDEA/piperazine/H2Osystem.

HCW(m) (T,pW

s ) exp(VCW (p - pW

s )

RT )mCC* ) yCpC (7)

KRk

(T) ) i

aii,Rk (7a)

ai ) m ii* (8)



8/19

KR4was derived from electrochemical investigations by Perez-Salado Kamps and Maurer.56 The corresponding constants forthe protonation and diprotonation of piperazine (KR5 and KR6)were taken from the work of Hetzer et al.,57 while those forformation of piperazine carbamate, piperazine dicarbamate, andprotonated piperazine carbamate (KR7, KR8, and KR9) weredetermined from the results of NMR-spectroscopic investiga-tions by Ermatchkov et al.58 These parametrizations were chosenbecause the correlations were carefully done with precise datafrom spectroscopy and other methods, which were directlyrelated to the reactions and species, rather than buried incombined phase and reaction equilibria.

Activity coefficients of both molecular and ionic solutespecies were calculated from a modification of Pitzer s equationfor the excess Gibbs energy of aqueous electrolyte solutions.59

This model has been shown to accurately describe dilute andconcentrated aqueous electrolytes.

Information Sources. The scheme shown in Figure 8

involves extensive information. Parameters for the equilibriumconstants were provided from the literature cited above. TheHenrys law constant is that for unreacted carbon dioxide inwater, which along with pW

s , VW, and the second virial coef-ficients, which, along with the estimation method for VCW

weretaken from in the literature.

The Pitzer model for Gibbs excess energy requires binaryand ternary parameters to describe the interactions betweensolute species from low gas loadings (i.e., at low partialpressures of carbon dioxide) to high gas loadings (i.e., at highpartial pressures of carbon dioxide). The only source forobtaining reliably the most important parameter values isexperimental data of the solubility of CO2in aqueous solutions

of MDEA and of piperazine at low, as well as at high, CO2partial pressures. Such investigations were performed with twodifferent types of experimental equipment.50,51,60

Results.The solid lines in Figure 9 and 10 show the excellentcomparisons between experimental60,61 and model results60 forCO2solubility in aqueous solutions at low (Figure 9) and high(Figure 10) MDEA molalities. In addition, the broken lines inboth figures show predictions at low partial pressures of carbondioxide when all interaction parameters were estimated by usingonly high-pressure gas solubility data. These predictions agreewell with low pressure experimental data at low and moderateamine concentrations (high mj C/mj MDEA values) but are lessaccurate at higher amine concentrations where parameterscharacterizing important interactions between molecular MDEA

and other solute species were not included, as they cannot bedetermined from high pressure gas solubility data.

This example illustrates the very great range of data that mustbe assembled in order to reliably develop a full model for atruly complex system. Note that every aspect was comparedwith 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 thatcomputational chemistry methods could apply here, but thiswork was done before they were ready for implementation.

3.1.3. LLE Extraction of Carboxylic Acids from

Aqueous Solutions. Many carboxylic acid products are pro-duced by fermentation. Product recovery from the dilute aqueoussolutions is achieved by reaction with a hydrophobic component,e.g. tri-n-octylamine (TnOA), with the resulting complexesextracted into an organic phase. Subsequently, the carboxylicacid must be separated and the auxiliary component regenerated.The design of such extraction and recovery processes requiresa thermodynamic model for liquid-liquid equilibria that ac-

counts for electrolytes in both aqueous and organic liquidsolutions.

Our example is for citric acid partitioning between water anddifferent organic solvents in the presence of TnOA, includingthe effect of salts on the TnOA partitioning for recovery andregeneration. The latest references summarizing this approachare by Maurer and co-workers.62,63

At low aqueous-phase molalities of citric acid and fixedTnOA, the ratio of organic to aqueous phase concentrations ofcitric acid increases with increasing acid concentration. It thenpasses through a maximum and decreases at the highestconcentrations. This behavior is attributed to two competingeffects. At low acid concentrations in the aqueous phase, the

dissociation equilibrium for citric acid is shifted to its ionicspecies. As only neutral acid molecules can be extracted intothe organic solvent, the partition coefficient increases when theamount of dissolved neutral citric acid increases. The decreaseof the partition coefficient observed at high acid concentrationsresults from complete complexation of the TnOA, so additionalcitric acid cannot be bound, and the acid remains in the aqueousphase.

In such processes the aqueous phase may also contain strongelectrolytes. While most strong electrolytes reduce the solubilityof an organic compound in an aqueous phase, i .e., salt-outthe organic compound, the presence of salt actually reduces thepartitioning of a carboxylic acid to the organic phase when a

complexing agent, such as TnOA, is present. Thus, Figure 11shows the influence of NaNO3 on the partition coefficient of

Figure 9.Partial pressure of carbon dioxide (left diagram) and total pressure(right diagram) above liquid mixtures of (CO2 + MDEA + H2O), mj MDEA2 mol kg-1: (experiment)60 4313, O 353, 0 393 K; (experiment)61 2313, [ 333, * 373, 9 393, 413 K; (calculation) scorrelation from alldata, - - - from only high pressure data.60

Figure 10.Carbon dioxide partial pressure (left) and total pressure (right)above liquid mixtures of (CO2 + MDEA + H2O), mj MDEA 8 mol kg

-1:(experiment)59 4 313,O 353, 0 393 K; (experiment)60,61 2 313.7,b 354.4,9 395 K; (calculations) scorrelation from all data, - - - from only highpressure data.60



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citric acid in the organic phase relative to the aqueous phasefor the system (water + methyl isobutyl ketone (MIBK) +TnOA).64

Model Relations, Selection, and Information Sources.Forthis complex system, the fugacities of the coexisting liquidphases are treated with the aqueous phase as an electrolytesolution as in eqs 6 and 8, while the organic phase has onlyneutral species as in eq 1. Pitzers excess Gibbs energy model59

is used in both phases. It has terms associated with electrostatics

and ions in the aqueous phase, while a power-law equation isused for the organic phase.The organic-phase complexes of citric acid and TnOA are in

chemical reaction equilibrium; most contain water, as verifiedby IR-spectroscopy. The stoichiometry of the complexesdepends on the organic solvent and can be complicated. Forexample, two complexes (citric acid:TnOA:water )2:3:2 and1:1:1, respectively) were found for toluene, whereas fourcomplexes (1:0:3, 1:2:3, 1:1:3, and 2:1:6) were in methylisobutyl ketone (MIBK). The equilibrium constants for thevarious reactions were expressed as in eqs 4, 5, 7, and 8. Figure12 shows the resulting speciation for liquid-liquid equilibriawith toluene.

Results.Comparisons between predictions and experimental

data are also shown in Figure 11; the agreement is withinexperimental uncertainty. When NaNO3 is added to equal

volumes of aqueous and organic feed solutions (mNaNO3(aq),(0)

)0.05mol kg-1; mTnOA

(org),(0))1.24 mol kg-1) giving mCit.tot

(aq))0.02 mol

kg-1 in the equilibrated aqueous solution, the partition coefficientof citric acid to the organic phase is about 0.4 compared toabout 40 in the salt-free system. When NaCl is the salt underthe same conditions, the value is about 10.

Extensions. A number of applications have been made byadding 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 toluene65

andMIBK64 and chloride partitioning in the system (citric acid +water + MIBK + TnOA + NaCl) have been studied. Theaddition of the salt of the carboxylic acid was predicted not toaffect the partitioning of the acid, as there is no competition ofdifferent acids for the amine; this is found to be true formonocarboxylic acids like acetic acid, though not for acids withmore than one carboxylic group.

The model predicts all of the above behavior quantitatively,verifying the thermodynamic framework. Again, comprehensiveuse of, and comparisons with, many different data, along withcareful speciation, allows quantitative description of manyvariations of these complex solutions. The results can be usedfor testing, for exploring options in process synthesis, and forprocess optimization.

3.1.4. Vapor-Liquid Equilibria for Oleum. Oleum, alsoknown as fuming sulfuric acid, consists of SO3 dissolved in100% H2SO4. Thus, for example, 20 mol % oleum consists of20% SO3and 80% H2SO4by moles. The modeling objective isto provide VLE and heat of vaporization information for thesystem components in a case where corrosion and toxicity makeextensive measurements extremely challenging.

There are known to be many different species complexes inthe liquid phase of oleum in addition to the H 2SO4 and SO3components. However, the dominant complex is H2S2O7,

66

which is formed from 1 mol each of H2SO4 and SO3 by thecomplexation reaction proposed by Nilges and Schrage67 and

by Mathias et al.:68

No ions are considered to exist in the solution.Model Relations, Selection, and Information Source. The

phase equilibrium is obtained with liquid fugacity expressionsof the form of eq 1 and vapor fugacity expressions of the formof eqs 6 and 7. The activity coefficients were obtained fromthe NRTL excess Gibbs energy model69 since the nonidealityis strong. For this system where SO3 is the principal volatilecomponent, the effect on VLE of parameters for theH2SO4-H2S2O7 pair is very small and is therefore ignored.

However, parameters for the SO3-

H2SO4 and SO3-

H2S2O7pairs must be valid over a range of temperature both for VLEand for calorimetric properties. Since the pressure is elevated,there is vapor-phase nonideality that was described by theRedlich-Kwong equation69 with standard parameters based oncritical properties. This is not expected to be rigorous, but theconditions are such that the limited composition dependenceof the vapor nonideality minimally affects the predictedbehavior.

The reaction equilibria of the system were defined by theequilibrium constant for reaction III plus the nonidealities ofthe three species in their liquid mixture:

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 organicfeed solutions at constant TnOA concentration (mTnOA

(org),(0)) 1.24 molal) in

the organic feed and several salt concentrations in the aqueous feedsolution:63 (experiment) s mNaNO3

(aq),(0)) 0, ] ) 0.01, O ) 0.05, 0 ) 0.1

molal; - b -prediction.

Figure 12.Speciation in liquid-liquid phase equilibria for the system (citricacid + water + toluene +TnOA). H2SO4+SO3 h H2S2O7 (III)

Koleum(T) )

xH2S2O7H2S2O7

xH2SO4xSO3

H2SO4SO3(9)



10/19

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

Miles et al.70 used two methods to measure the enthalpy ofvaporization: (1) evaporation of SO3from oleum under reducedpressure and (2) heat of solution (three sets of data) of additionof SO3 to oleum. The enthalpy of mixing used for matching

the calorimetry data is obtained from the temperature depen-dence ofKoleum(T) and ofGE via the Gibbs-Helmholtz relation

Results. Comparisons between VLE data71 and the model,as shown in Figure 13, demonstrate that the model providesessentially quantitative agreement with all of these data. Figure

14 shows experimental data

70

and model calculations for theenthalpy of vaporization of SO3from oleum mixtures of variousconcentrations at 30C. The enthalpy of vaporization here isthe negative of the enthalpy change that occurs at 30 C when1 kg of gaseous SO3is dissolved in a large quantity of an oleummixture of the given concentration.

The sigmoidal shape of the enthalpy of vaporization curve isa clear fingerprint that strong chemical reactions are involved.At low SO3 concentrations, essentially all the added SO3combines with H2SO4to form H2S2O7. Hence, the total enthalpyof vaporization is approximately equal to the enthalpy ofvaporization of pure SO3 ( 540 kJ kg

-1) plus the heat ofreaction ( 210 kJ kg-1). At about 50 mol % SO3 (45 wt %SO3), the amount of free H2SO4has substantially decreased, so

reaction does not occur and the enthalpy of vaporization rapidlydecreases toward the enthalpy of vaporization of pure SO 3.

This is another example where success is obtained with anappropriate conceptual model and multiproperty fitting of qualitydata, though no molecular measurements were involved. Inparticular, calorimetric data proved quite valuable in validatinga proposed chemistry model, given by the signature in theenthalpy of vaporization vs concentration curve. This case showshow a speciation can be validated by careful use of both phaseequilibrium and calorimetric data, giving a better chance offinding the most appropriate description.

4. Solvent Selection for Pharmaceutical Production

We give here three examples of the service and advice role,where more effective solvents were selected for solution separationby using group contribution and other molecular thermodynamicmethods. The pharmaceutical industry is increasingly challengedto develop more efficient and environmentally friendly processesfor API manufacture, in a modern development environment thatis characterized by high attrition. On average, only 1 in 10 newdrug candidates survives through clinical trials to enter the market.In early process development, there are usually few physicalproperty data available. Further, it is not economical to collect largequantities of thermophysical data since the systems frequently

change as new chemistry is explored and separation processes areadapted. Thus, pharmaceutical process design needs predictivethermodynamic models to reduce the experimental search spaceand focus laboratory effort in the areas of greatest potential success.Modern drugs are functionally complex72 and often fall beyondthe capabilities of traditional predictive models like UNIFAC. Thereliable prediction of crystal structure and solid-state properties iscomputationally demanding, and still years away from mainstreamapplication.73 Typically, the industry deals with complex chemis-try,74 phase equilibria involving organic salts, and aqueouselectrolytes. These factors make it clear that pharmaceutical systemschallenge the capability of modern property tools. The followingexamples demonstrate how current methods can still usefully

applied, as long as the problems are broken down into tractablesub-parts and appropriate modeling tools are used at each step.

4.1. Anisole Removal during Washing and Drying

Operations. In this problem, an environmentally friendlysolvent is needed to wash and dry a crystalline pharmaceuticalintermediate. The solvent must efficiently remove anisoleresidues from upstream chlorination and coupling reactions,where the product is precipitated as an organic HCl salt andseparated by pressure filtration to yield a 40% w/w anisolewet cake. Due to anisoles low volatility, removing theresidual anisole via inert gas drying is very slow and notcommercially viable. Washing first with a more volatilesolvent can increase the drying rate, and MTBE was used inearly process development. However, at production scale,MTBE would possibly have required specific VOC abatementequipment, 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 ofresidual anisole.

Model Relations, Selection, and Information Sources. Tosolve the above solvent selection problem, the solvent needs aretranslated into target properties. Table 1 lists an appropriate set ofpure component and mixture property values. The boiling pointand melting point indicate the liquid range. The key property tocharacterize the ease of anisole removal is the partial pressure,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 relevantactivity coefficient for screening is its limiting value at infinite

Figure 13.Comparisons of experimental data71 and model calculations (s)for vapor pressures of oleum mixtures.

Figure 14.Comparison between experimental data70 and model calculationsfor enthalpy of vaporization of SO3 from oleum mixtures of variousconcentrations at 30 C.

(lnKoleumT )Px ) -H

0

RT2

(10)

[(GE/RT)

T ]Px ) - h

E

RT2

(11)



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dilution, A,S . Thus, the solvent power, Sp, is used to rank

prospective wash solvents for anisole volatilization

The miscibility criterion is explicit. In addition to Sp, theHansen solubility parameters,handp,

75,76 were used to reducethe solvent search space since the feasible solvent candidatesare to have low affinity for ionic solutes; i.e., small terms forhydrogen bonding and polarity. Finally, the functional groupswere limited to those with good environmental profiles.

The ProCAMD solvent search software77 was used to findsolvent candidates matching the target properties listed in Table

1. The pure component target properties were first estimatedfrom generated molecular structural information via theConstantinou-Gani or Marerro-Gani78 method. Those struc-tures (molecules) satisfying the target values were then examinedfor Sp and miscibility with the UNIFAC-LLE model.79

Results. A set of solvent candidates was identified amongthe 26 837 molecular structures generated by excluding 13 698substances by thehparameter; 3533 byp; 2925 byTmelt; 6484byTboil; 62 by Sp; and 7 by miscibility. That left 47 acceptablecandidates as possible solvents; Table 2 shows representativeproperties of four candidates.

Of these, heptane was selected as the wash solvent for its Spvalue, commercial availability, volatility, and environmental

profile (fugitive releases to the atmosphere). Figures 15 and 16show activity coefficients for anisole with the original MTBEwash solvent and the heptane replacement as predicted by theUNIFAC method.79 The value of ln A,S

in heptane is about afactor of 3 greater than that for MTBE. In pilot plant trials withheptane, the drying step was considered rapid at about 7 h, andresidual anisole levels were significantly reduced. Perhaps mostimportantly, the VOC emissions were reduced from 0.15 molfraction for MTBE to 0.02 mol fraction for heptane.

This example of solvent substitution demonstrates how groupcontribution methods may be applied in reverse and thus narrowthe size of a search space and minimize time-consuminglaboratory experiments.

4.2. Solvent Selection for an Enantiomeric Pharmaceuti-

cal. The synthesis of drugs often results in intermediatescontaining racemic mixtures of left- and right-handed enanti-

omers of chiral molecules. For esters, it is sometimes possibleto use a catalytic lipase enzyme in an aqueous alcohol mixtureto selectively dissociate the undesirable enantiomer into its acidvia the reaction:

After dissociation, the chirally resolved ester is easilyseparated from the acid and alcohol using pH-bufferedliquid-liquid extraction. A final crystallization yields the desiredproduct. Finding the optimal conditions for the dissociation ina pH-buffered liquid extraction depends on estimating accuratevalues of the acid and ester dissociation equilibrium constants

Table 1. Selection Criteria for Wash Solvent with Anisole

property minimum maximum comments

Tboil, C 50 100 reasonable volatility for drying and allows recoveryby simple condensation

Tmelt, C none 0.0 liquid at ambient temperaturesSp 0.0 0.3 see eq 10; lower values mean lower residual anisole

after dryingHansen p, MPa

1/2 0.0 8.0 should be less polar than IPA (p 8)Hansen h, MPa

1/2 0.0 7.0 should have lower hydrogen bonding potential thanethyl acetate (h 7)

miscibility completely miscible with anisolepermissible solvent types

and functional groupsalcohols, ketones, aldehydes, acids,

phenols, esters, ethers, Cl, and FCH3, CH2, CH, C, OH, CH3CO, CH2CO, CHO,

CH3COO, CH2COO, HCOO, CH3O, CH2O, CH-O,COOH, CH2Cl, CHCl, CCl, CH2Cl2, CHCl2, CCl2,CCl3, CF3, CF2, CF, COO, CCl2F, HCCl2F, HCClF,CClF2, HCClF2, CClF3, CCl2F2, F

Table 2. A Selection of Wash Solvent Alternatives

solvent h, MPa1/2 p, MPa

1/2 Tmelt,C Tboil,C Sp

2,2-dimethylpentane 0 0 -124 79 0.081heptane 0 0 -91 98 0.088MTBE 5 3.4 -109 55.2 7.3CH2(OC2H5)2(ethylal) 6.9 4.5 -66.5 88 13.8

Sp ) 1

,S

MwA

MwS(12)

Figure 15. Binary solution activity coefficients for MTBE and anisolepredicted by UNIFAC.79

Figure 16. Binary solution activity coefficients for heptane and anisolepredicted by UNIFAC.79



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or pKa, while selection of optimal solvent(s) is needed for bothpartitioning and for crystallization.

Model Relations, Selection, and Information Sources.

Several properties must be estimated for the process steps: thepKavalues for the ester and acid as well as LLE for extractionand SLE for crystallization of the product with the proposedsolvents. A review of pKa predicting methods is presented inref 80. Note that there are two pKas for the ester and three forthe acid. Software from ACD Laboratories81 was used to predictthe pKas for these organic molecules with the values given inTable 3. The OH groups on the reaction product alcohol andon the t-butanol are relatively stable and do not need to beconsidered.

Using straightforward calculations, the distributions of thespecies can be found as functions of pH. These are shown forthe ester and acid in Figures 17 and 18. Above pH 10, the esteris present in the uncharged state, while the acid is fullydeprotonated with a charge of negative one. Under theseconditions, the partitioning gives maximum separation ef-ficiency, since charged species prefer the aqueous phase, whilethe neutral ester prefers the organic phase. The pH is adjustedby adding a bicarbonate salt, since this acts as a sufficientlystrong inorganic base for buffering during extraction but is notstrong enough to hydrolyze the ester.

After a water wash to remove the bicarbonate and subsequentdecanting, the purified ester product is in a stream rich int-butanol and saturated with approximately 30% w/w water.However, direct crystallization of the product by cooling thissolution gave a poor yield; the product solubility in aqueoust-butanol was too high. Thus, a solvent was sought where the

product could be extracted for crystallization.Results. Experimental screening of product solubility sug-

gested toluene would be a good crystallization solvent, but theyield from the actual process solution turned out to be poor.The reason is clear from the ternary LLE diagram of Figure19, predicted with the original UNIFAC LLE parameters.79 It

shows tie lines and a binodal curve with t-butanol favoring thetoluene-rich organic phase rather than the aqueous phase.Alternatives to water, while keeping toluene, were sought usingthe search criteria of Table 4, similar to the process above. Inthis case, the normal melting point and boiling point wereestimated from group-contribution methods for pure componentproperties; for the liquid density, the Rackett equation22 wasused, while for the selectivity of the product for the organicphase and miscibility calculations, the UNIFAC-LLE model79

with the associated parameters was used.

The search led to three substances expected to be com-mercially available, with the properties shown in Table 5. Withthe best solvent, 1,3-propylene glycol, the LLE phase diagramappeared as in Figure 20. The tie lines between 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 notprovide a commercially viable batch extraction process. Furthersolubility screening identified cyclohexane as a potential crystal-lization solvent, so it was examined as an extraction solvent,by modifying the polar solvent search criteria given in Table 4to include selectivity with cyclohexane and an updated densitylimit of 0.8 g/mL.

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 region extends to a 50:50 volume ratio of cyclohexane tot-butanol, and the t-butanol

Table 3. Estimated pKa

functional group pKas for ester pKas for acid

pyrazine N1 7.8 7.8pyrazine N2 3.4 3.9carboxylic H 2.9

Figure 17.Species distributions for racemic ester dissociation as functionsof pH.

Figure 18.Species distributions for racemic acid formation as functions ofpH.

Figure 19. Liquid phase compositions for aqueous t-butanol with tolueneat T )25 C, 1 atm from UNIFAC-LLE.79



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partition coefficients are more appropriate for productivity. Infact, just two washes with propylene glycol reduces thet-butanolcontent to about 3% w/w with a residual level of propyleneglycol in the organic phase of only 0.5% w/w. This scheme

gives good productivity in generic batch manufacturing equip-ment, which is common to the pharmaceuticals industry.

Adequate success in this conceptual design project wasobtained by flexibly searching for alternative solvents usingphase equilibrium representations of multicomponent systemsas computed from group contribution methods. Identifying thedesired partitioning in the ternary system was particularlycrucial.

4.3. Selection of Binary Solvent Mixtures for a

Crystallization Process.This example concerns a pharmaceuticalintermediate produced by reaction in tetrahydrofuran (THF) withthe desired product obtained by crystallization. The yield from THFalone was found to be poor, and water was introduced as an

antisolvent to increase the yield. Laboratory test results showedinconsistencies, and it was suggested that the actual amount of waterin the crystallization solvent was varying because of carryover froman upstream washing step. Predictions of the solubility in the

aqueous THF solution were made to determine how water contentcould affect the product solubility. Details of this application canbe found in ref 82. This example highlights the use of techniquesother than group contribution (GC) when the necessary parametersfor a GC-based method are not available.

Model Relations, Selection, and Information Sources.Thestructure of many heterocyclic pharmaceutical molecules cannotbe treated with GC-based models like UNIFAC, due to missingfunctional groups or interaction parameters. The NRTL-SACmodel83 is an alternative approach which uses characteristicsurface segments to describe intermolecular interactions fromsurface charge density. The NRTL binary interaction parametersbetween the segments are fixed, and there are adjustable

characteristic segment values for each molecular species. TheNRTL-SAC database contains segment profiles for 130 solventsderived from available literature VLE and LLE data. For aparticular solvent, the 4 solute segment parameters are regressedfrom solubilities in at least 4, and up to 10, pure solventsspanning the expected surface segment values. This will allowprediction of solid solubility of a solute, in pure or binarysolvents of the components in the database, with sufficientaccuracy for solvent-ranking and trends in ternary systems. Forthe present system, an NRTL-SAC model was established fromexisting solubility data in 25 pure solvents over the temperaturerange of 10-80 C.

Results. The solubility of the pharmaceutical intermediate inmixtures of THF and water with the regression and prediction

results shown in Figure 22, along with solute solubilities in themixed THF-water solvent shown in detail in Figure 23. While

Table 4. Selection Criteria for Polar Solvent for Extraction of Pharmaceutical Ester

property min max comments

Tboil, C 60 relatively low volatility to minimize VOC emissionsTmelt, C 0.0 be liquid at ambient temperature for ease of handlingdensity, g cm-3 0.9 more dense than toluene (0.86 g cc-1) to separate by gravity at the base

of the reactor.selectivity at

feed composition5.0 lower values mean higher residual t-butanol in polar phase. The feed

mole fraction oft-butanol is 0.3.miscibility immiscible with feed over a significant composition rangepermissible solvent

types and functional groups

alcohols, ketones, aldehydes, esters, ethers CH3, CH2, CH, C, OH, CH3CO, CH2CO, CHO, CH3COO, CH2COO,

HCOO, CH3O, CH2O, CH-O, COO

Table 5. Polar Solvents for Pharmaceutical Ester Crystallizationwith Toluene

solventTboil,C

Tmelt,C

density,g cm-3 selectivity

1,3-propylene glycol 215 -27 1.05 12.41,2-propylene glycol 187 -60 1.03 12.31-hydroxyacetone (acetol) 146 -17 1.08 7.8

Figure 20. Ternary LLE for t-butanol, 1,3-propylene glycol, and toluene

at T ) 25 C, 1 atm from UNIFAC-LLE.79

Figure 21. Ternary LLE for t-butanol, 1,3-propylene glycol, and cyclo-hexane at T ) 25 C, 1 atm from UNIFAC-LLE.79

Figure 22. Regression results of NRTL-SAC model83 parameters for 25pure solvents.



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the calculated values were sometimes far from experiment, theywere adequate for the purposes of the problem. Figure 23 shows

that water was acting unexpectedly. At low concentrations, it is acosolvent, increasing the solubility. At higher amounts, the waterdepresses solubility. The strong variation with the water fractionsuggested why the laboratory tests on the process stream were notconsistent: fluctuating carryover combined with extreme sensitivityto composition.

From these results, a different and more robust crystallizationprocess was developed. The key was the estimation of solubili-ties and careful scrutiny of the sensitivity of properties tovariations in conditions.

5. Emerging Methods for Property Estimation

The examples given above demonstrate current approaches and

capabilities for property modeling when data sources are availableor the opportunity for new measurements exists. We now give twoexamples using advanced techniques to overcome the limitationsof current approaches such as group-contribution methods. Theyuse contemporary computational techniques either directly orindirectly to predict phase equilibria. The first describes howunavailable group-contribution parameters can be obtained with anew method based only on chemical structure, with application toVLE. The concept is appealing, and it suggests an avenue for futuredevelopments. The other is for a separations process and comparesdifferent modeling techniques, including molecular methods. Theresults have implications for efforts to improve predictive modelingcapabilities.

5.1. Estimation of Group-Contribution Parameters.Re-cently, Gani et al.84 have suggested how already availableexperimental data might be used to predict group-contributionmodel parameters that are missing in a host tabulation, such as theMarrero-Gani78 group-contribution method for pure componentproperties. The basis is an atom-connectivity index, developedunder the principle of additivity of contributions of differentdescriptors for a specific property that gives contributions tomolecular properties by atoms and their connectivities. With atoms,many fewer parameters are needed to represent groups of atoms.Further, index parameter values for connectivity indices can befound from the same available experimental data as for regressinggroup-contribution parameters. Combining known group contribu-tions (GC) with estimated group contributions from atom-con-

nectivity indexes (CI) results in an approach called GCplus. Themethod can be applied to any host group contribution model.

Extension to a wide range of property models for pure componentproperties and to average properties of polymer repeat units hasbeen made.85

Gonzalez et al.86

have applied the GCplus

approach to predictmissing group-interaction parameters when the host method isthe UNIFAC model for activity coefficients (GC). The availableexperimental data used for UNIFAC group contributions wereemployed in regressing the interaction parameters for the atom-connectivity indices (CI). Then, the CI values were used toestimate missing group-interaction parameters.

Examples applying GCplus to pure component properties aregiven in refs 84-86. Here, we illustrate GCplus for mixtureproperties. In each case, the chemicals and the phases of interestare given along with the host model and the group(s) with missingvalues. Then predicted UNIFAC group parameters are given, alongwith comparison of predicted and measured phase behavior results.

VLE for 1,2-Dichloroethane-DMSO.If the original UNI-FAC-VLE model87 is the base method, the missing group interac-tion parameters are for the pair CCl and DMSO. Using the CImethod, estimates of the missing group interaction parameters arelisted in Table 6. Figure 24 shows TxyVLE comparisons fromusing these parameters along with the GC parameters in theUNIFAC table with measured VLE.88 These data were not usedfor the CI-model parameter estimation, but the agreement isexcellent.

SLE for Acetaminophen (Paracetamol) with 1-Butanol.

If a later revision of the original UNIFAC-VLE model86 is thehost for this system, only the ACNH2-CH2CO interactions aremissing. However, for illustration, we also present results whenall the group interaction parameters are obtained via CI. Table

7 lists GCplus group interaction parameters for the system wherethose for ACNH2/CH2CO (in bold) are estimated with CI. Table8 lists the parameters when all are estimated from CI. The valuesare different. Figure 25 shows the comparisons of the resultsfor both cases with data.89 Over the limited range of paracetamolcompositions, both sets of parameters describe the data well.Thus, while parameter values may differ, the data can beadequately predicted with both methods.

VLE for Methylethylketone-n-Heptane. This system in-volves only the CH2and CH2CO groups, but there is a significant

Figure 23.Experimental and predicted solubilities of product in THF-watermixtures.

Table 6. Calculated Group Interaction Parameters through the CIMethod for the 1,2-Dichloroethane-DMSO System

CCl DMSO

CCl 0.0 -198.22DMSO 259.82 0.0

Figure 24. Txy VLE diagram at 0.953 bar for 1,2-dichloroethane withDMSO from the UNFAC-CI method with parameters not used for the CImodel regression compared to measured values: (experiment).88



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temperature variation to be dealt with. The later UNIFAC model90

has parameter values, so a comparison can be made among data,GC prediction, and CI prediction. The CI-computed parametermatrix with temperature dependence is given in Table 9.

Figure 26 shows the calculated Pxy diagram with theUNIFAC Dortmund parameters and the CI-computed param-eters, along with data from ref 91. The agreement for the CImethod is not as good as with the GC method over the wholedata range, but the pressure and composition of the azeotropeare given reasonably accurately.

These examples illustrate the possibilities of using models basedon limited information, such as connectivity indices, as well as alevel of compromise encountered when they are used in place ofmore elaborate methods such as UNIFAC. In general, CI may bea reliable expedient to determine unavailable, and perhaps lesssensitive, parameters for use with incomplete group contributionmethods. It is not proposed as a replacement for experiments, butrather to focus on a few experiments through which the extensioncan be verified.

5.2. Molecular Calculations.While methods such as CI areeasily used to obtain group-contribution parameters, their accuracy

and generality may be limited. An alternative which does not, inprinciple, require data for model parameter regression is quantumchemistry calculations for inter- and intramolecular force fieldsfollowed by molecular simulation or statistical thermodynamicmethods to obtain properties.

Solvents for Extractive Distillation of 1,3-Butadiene.

Mathias et al.92 describe an investigation using quantum mechanicsand molecular simulation to improve process simulation for theclassical 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 properties obtained were theactivity coefficients of the hydrocarbon components in the presenceof the extractive solvent for use in eq 1. Comparisons were madeamong a quantum mechanical and statistical mechanical method,COSMO-RS,36,37 a molecular dynamics simulation approach,SPEADMD,93 group contributions from UNIFAC,94 and ther-modynamic intuition. Mathias et al.92 describe the methods andresults in some detail; only a brief summary is given here to indicatethe findings.

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 thetrue predictive capability of the method. The SPEADMD molecularsimulation used a force field from the principle of transferability,95,96

which assumes that forces inferred from experimental data for oneset of mixtures can be applied to other compounds and mixtures.The computed results provided unique qualitative structural andorientational insights at the molecular scale about the solvationinteractions between the polar solvents and the olefinic moietiesin the hydrocarbon compounds. The differences in accessibilityfor DMF and ACN and the sizes and shapes that affect intermo-lecular contacts were reliably characterized. However, to achieveaccuracy for activity coefficients, the molecular simulations requiredrefinement of the interaction potentials by regression to data, similarto finding UNIFAC parameters.

Extensions of Molecular Calculations. The experience ofMathias et al.92 suggests some of the limitations and futureprospects of molecular simulation, as do the International FluidProperty Simulation Challenges (IFPSC).97 The present importantquestion about the potential for molecular simulation as a routinetool to provide quantitative property data for process and productdesign is, Are we there yet?. In our opinion, the answer is aqualified no. While progress is being made, the results are oftenlike the butadiene example: good, perhaps adequate for the advicerole without high accuracy, but not sufficient for the service role.

A common shortcoming of molecular simulation methods isthe lack of easily available and suitable force fields98 to solvethe wide variety of problems under consideration.99 In particular,

what should be done when no experimental data exist forempirically fitting a force field? As an example, consider themolecule whose chemical formula is C10H19N and structure isthe following:

There are no experimental data and not even a CAS numberhas been assigned. If this molecule is of interest in a productdesign for a particular application or is an impurity that mustbe effectively removed in a process design, molecular simulation

could not be used for property estimation unless ab initoquantum methods alone could produce a force field. Figure 27

Table 7. Original UNIFAC87 and CI-Generated ACNH2/CH2COUNIFAC Interaction Parameters for the System Acetaminophen(Paracetamol) with 1-Butanol

CH2 ACH OH ACOH CH2CO ACNH2

CH2 0 61.13 932.65 1333 476.39 920.7ACH -11.12 0 636.100 1329 25.77 648.2OH 156.04 89.6 0 -259.7 84 -52.39ACOH 275.8 25.34 -451.60 0 -356.1 119.9CH2CO 26.76 140.1 164.5 -133.1 0 -44.56ACNH2 1139 247.5 -17.4 -253.1 4648 0

Table 8. CI-Generated UNIFAC Interaction Parameters for theSystem Acetaminophen (Paracetamol) with 1-Butanol

CH2 ACH OH ACOH CH2CO ACNH2

CH2 0 112.34 932 327.64 531.84 1232.09ACH -46.12 0 571.97 -207.56 195.38 966.75OH 52.07 98.03 0 -379.27 -135.03 -731.19ACOH 29.3 777.92 135.08 0 567.88 -318.1CH2CO -24.83 -60.15 432.62 423.34 0 -44.56ACNH2 35.65 371.2 -101.82 -1193.02 4648.0 0

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

temperature estimated with CI-generated values:s

only for ACNH2/CH2COgroups (original UNIFAC87 parameters for other groups), - - - all parametersestimated from CI; (experiment)89 [.

Table 9. CI-Computed Parameter Matrix for Dortmund GroupContributions90

aij bij cij

CH2 CH2CO CH2 CH2CO CH2 CH2CO

CH2 0 7824.76 0 -35.25 0 0CH2CO -1067.87 0 3.82 0 0 0



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suggests a strategy to obtain force field parameters of moleculesof industrial interest.

Other limitations in simulations occur at lower temperatures andfor larger molecules (high density) and for transport properties.For quantum calculations, the system size may be limiting, the

fundamental basis sets might not be accurate enough, and the wayto improve results may not be clear. Also, while only a fewmolecular-simulation researchers and reviewers now list computingmachinery and computing resources as a major limitation in theextension of MC and MD to new fluid property applications,computational capabilities beyond those currently available areneeded for computational chemistry to directly treat many practicalsystems.

Finally, a key issue for practical application molecularsimulation is the lack of availability of tools for nonexpert users.There is no standard toolbox for molecular simulation as pointedout by Wei.100,101 Our experience is that even experts can haveproblems, giving one pause about very widespread applicationof computation. For example, in at least two entries during

IFPSC contests, expert researchers incorrectly transcribed mo-lecular parameters that then produced nonrepresentative and

erroneous results. In such cases, what would nonexperts find?We mention two efforts to produce standard molecular simula-tion tools which are the TOWHEE project102 and the LAMMPSproject;103 others should appear in the future.

5.3. Other Aspects of Emerging Methods. Recent yearshave shown two particularly important developments in proper-ties modeling that combine molecular theory and thermodynamicrelations. One is the SAFT equation of state104 whose originalformulation was computationally complicated and initially hadrestricted applications. SAFT and PC-SAFT105 are now appear-ing in many variations with improved efficiency and reliability.In particular, formulations for polymers,106 electrolytes,107 ionicliquids,108 and group contributions109 have appeared. Another

development of the significance is the continued extensions andrefinement of COSMO methodology to a variety of systems.110,111

As time goes along, we expect both approaches to providepractical results for challenging systems and be worthy ofconsideration by model developers and users into the future.

6. Conclusions

For 100 years, the Industrial and Engineering Chemistryjournals have published indispensable information about proper-ties: data, models, behaviors, designs, and analyses. We havepresented a contemporary perspective of the importance, state-of-the-art, and the potential for continued improvement in

accuracy, reliability, and efficiency for thermodynamic proper-ties in the design and optimization of processes and products.Emphasis has been given to skillful modeling strategies,intelligent resource utilization, and adequate validation viaexperiment and consistency checks. Several examples focusedon complexing solutions for process simulation, where the roleof property models is in service; model development for solventsubstitution, where the role is service plus advice; and emergingtechniques for prediction of properties from molecular structure.The ranges of these systems illustrate the kinds of systems thatcan now be directly addressed when appropriate tools, experi-mental information, and computational methods are applied. Asthe sophistication of chemical products and processes increases,along with greater societal demands for sustainability, health,

safety, and economy, the ability to suitably estimate propertyvalues, an

thermodynamic property modeling for chemical process and product engineering some perspectives

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