Industrial Needs in Physical Properties - Industrial & Engineering

Ind. Eng. Chem. Res. 2003, 42, 6359-6374

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Industrial Needs in Physical Properties Sumnesh Gupta* The Dow Chemical Company, 1400 Building, Midland, Michigan 48667

James D. Olson† The Dow Chemical Company, 740 Building, South Charleston, West Virginia 25303

Physical properties affect all aspects of the chemical industry. Authors share their experiences to describe the trends and needs regarding physical properties. They also provide suggestions regarding possible future approaches for improved measurements and models to meet these needs. Increased emphasis is being placed on accurate physical property data and models due to increasing capability and complexity of chemical process simulation software, trends in process simulation applications, and nontraditional applications of physical properties. Some of the nontraditional applications of properties include biotechnology, polymer solutions, reaction media and solvent selection, environmental applications, modeling of process safety scenarios, corrosion, and product design and formulation. The need for better models, for pure component as well as mixture properties, is described. Some of the current models and ongoing research are discussed including the application of molecular theory and simulation. In particular, it is important to properly account for the effect of underlying fundamental interactions at the molecular level on the macroscopic behavior of fluids. Here, molecular-based study of fluids can play an important role in meeting physical property-related needs of the chemical industry. Industrial projects, as well as the need to develop and validate better property models, are driving the need for increasingly complex and faster measurements with smaller samples under difficult conditions. These needs are reviewed along with some specific suggestions for future measurements. Introduction Physical properties have many applications in the chemical industry and, essentially, cover all aspects of chemical plant operation as well as product lifecycle. With the availability of high-speed personal computers and commercial simulation software, process simulation has become the main tool for the development, design, scale-up, and optimization of chemical processes. Physical properties (particularly phase equilibria) and kinetic data are the key inputs for the development of such process models. In addition to process simulation, physical properties are needed for regulatory compliance and product application literature. As industrial practitioners, we are often asked to provide information on the industrial need for physical properties research. The need for accurate physical property data1-3 and models4,5 is well-documented in the literature with many examples. Less understood are the areas of focus and the desired approaches. Hence, through this article, we attempt to move the discussion for industrial needs to the next level by pointing out specific areas and approaches where further research will benefit the chemical industry. This article focuses on the following: (a) a historical perspective of the use of physical properties data and models in the chemical industry, (b) trends in chemical process simulation and other important industrial applications of physical properties, (c) the need for better models for pure component and mixture properties, (d) the role of * To whom correspondence should be addressed. Tel: (989) 636-3446. Fax: (989) 638-6671. E-mail: [email protected]. † Tel: (304) 747-5789. Fax: (304) 747-3632.

molecular simulation and theory in physical properties research, and (e) the need for research in experimental measurements of physical properties. We also provide some final thoughts and suggestions. In writing this article, we have shown examples from published academic research rather than from our own in-house applications because of the following: (a) Academic research is usually published in refereed journals and is well-documented so that the assumptions and limitations are well-known. Further, these results can be easily replicated for verification or additional research. (b) A significant amount of academic research in the area of physical properties has a high degree of industrial relevance and we intend to provide positive reinforcement by showing these examples. Note that the discussion that follows in this paper is based upon the experience of the authors, which has been in the area of applying physical properties to chemical process development and design. If some physical property needs are not mentioned here, it is due to our unfamiliarity and not their unimportance. General Industrial Perspective and Historical Involvement Industrial use of physical properties has been driven by the actual or anticipated need. The main driving force has been the benefit provided by good physical properties in terms of reduced cost of manufacturing or reduced cycle time for the development of chemical processes. The specific needs may have changed with time, but these two issues remain. The latter issue is usually overlooked when discussing industrial needs but can often be critical to success. If improved properties

10.1021/ie030170v CCC: $25.00 © 2003 American Chemical Society Published on Web 06/27/2003

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can help avoid steps in the development of a new chemical process or product, they can have an enormous impact not only in terms of reduced development costs but also in terms of faster sales and higher profit margins. In the past, easier and faster availability of physical property data was an important issue for the industrial practitioner. A strong industrial interest in this issue often led to the development of physical property databases where the industry took the initiative and carried out significant in-house research, which was later published,6-9 or even carried out governmentsponsored research.10 While this continues in the form of significant industrial involvement in physical property databases, the role of industry has changed from that of a developer to (a) a sponsor of governmentindustry-academic consortia, for example, DIPPR database from the Design Institute of Physical Properties at the American Institute of Chemical Engineers and TRC/NIST SOURCE database from the National Institute of Standards and Technology of USA, or (b) as a client of the database publishers, for example, DETHERM database published by DECHEMA (the Society for Chemical Engineering and Biotechnology) and PPDS database of the National Engineering Laboratory of Scotland. However, industrial involvement in the development of physical property models in the past has been less. This is because the available physical property models, despite their deficiencies, were far ahead of what the industry members could effectively utilize in their inhouse process simulation software. This led to a situation where the chemical industry typically stayed away from providing any guidance or consortia support regarding industrial applications to developers of physical property models in academia or government institutions. However, needs as well as situations change. Changes in physical property needs are now driven mainly by trends in the chemical process simulation software, by how process simulations are applied in the chemical industry, and by nontraditional applications of physical properties within the chemical industry. These changes will be discussed next. Industrial Trends in Process Simulation Availability of inexpensive personal computers combined with the availability of user-friendly operating systems and other software has changed the way chemical industry develops and uses process simulation software. In the 1990s, the chemical industry moved away from in-house process simulation software to commercial software from vendors such as Aspen Technology, Inc. and Simulation Sciences, Inc. This was an important change. It has also led to lower costs due to elimination of the development and maintenance of inhouse software tools. In addition, it has also led to significantly increased productivity because the same tool or a set of linked tools provided by the vendor can be used for all the steps in the development and design of a chemical process. As recently as a decade ago, use of simulation software was a major bottleneck in the process design productivity. With this problem receding, the focus is changing to improve the capabilities of these tools, hence, more emphasis on more accurate and robust physical property, phase equilibria, and reaction kinetics models.

Figure 1. Links between property data sources, standard format databases, and user applications for the development of standards for property data exchange based upon the DIPPR PPDX project. This figure is from the presentation of Allan Fowler and Ashok Dewan at a DIPPR session at the 2000 AIChE National Meeting in Los Angeles. Figure courtesy of DIPPR.

Even so, some software- and database-related issues remain. An important need for the near future is the linkage between property databases and process simulation tools. Another need is the ability to transmit physical property and process simulation validationrelated data from the plant operation software and hardware to process simulation tools. These issues are being addressed through several consortia for the exchange of physical property data. Figure 1 shows these needs and the proposed general architecture from one such consortium (the DIPPR-PPDX project). Another important need is for easy analysis and modeling of physical property data. An important aspect of process simulation model development is the development of physical properties model package. This includes developing sets of pure component and mixture property interaction parameters. The actual work process may require importing and processing original experimental data for pure component and mixture physical properties (raw data) to obtain the property model parameters. Simulation software vendors are now beginning to focus their attention on improving their software for this work. However, raw data must be available in an easy-to-manipulate form and include meta-data such as uncertainties. Currently, the pure component physical property databases such as TRC/ NIST SOURCE do provide uncertainties for the pure component raw data. However, no such database is available which provides recommended experimental values of pure component properties for common compounds. For mixtures, the databases also need to include uncertainties for raw data as well as recommended experimental data for common mixtures. One data source that includes uncertainties is the IUPAC solubility data series. Recently, OLI Systems, Inc., started a consortium sponsored by the U.S. Government and the U.S. chemical industry to provide a database of recommended solubilities for aqueous solutions of electrolytes. Similar efforts are warranted for other types of mixtures and properties. It should be noted, however, that such compilations can be difficult to produce since many papers on experimental data do not provide sufficient direct information on uncertainties. Even with somewhat subjective uncertainties assigned to such data, these databases can reduce the effort needed for modeling of common mixtures.

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The most important need for physical properties concerns how process simulations are now being carried out. For process development and scale-up, the challenge is faster scale-up with larger scale factors. Taken together, this means simulating production scale plants even before building a pilot plant and reducing the amount of pilot plant work (or perhaps even bypassing the pilot plant testing altogether). While it may be difficult to avoid scale-up-related development steps for a reactor, good physical properties will reduce the development steps in the separations part of a chemical process and this, in general, is nearly half the manufacturing cost in a chemical process. For existing processes, the challenges are (a) simulation of a complete and integrated process as opposed to simulations of process sections or individual distillation columns in the past and (b) simulations for on-line or real time process optimization and control. Such simulations demand highly accurate property data and robust as well as accurate property models. These trends in process simulation applications require faster set-up of property models. Also, the simulation of a complete and integrated process should have the goal that preferably a single-property model be used for the entire chemical process or the main parts of it. Often environmental processes such as wastewater treatment or flue gas treatment need also to be integrated with the simulation of the main process. This creates additional difficulties since the environmental processes often involve phase equilibria of aqueous electrolyte solutions. In addition, because the chemical industry has become global, a single-property model package is preferred for all plants worldwide for a given process. Hence, the property data and models must be reliable enough to cover all the changes in plant design and operating conditions due to different site constraints. Nontraditional Applications of Physical Properties Due to their high impact, physical property applications in the chemical industry are wide ranging. In this section, some important nontraditional applications of physical properties are discussed. We first discuss these applications in terms of (a) different types of mixtures and then (b) in terms of new types of models and calculations. Chemical industry applications of physical properties go beyond the manufacture of typical small synthetic organic molecules. An important area for the future is biotechnology, particularly bioseparations. Use of biotechnology to produce small molecules such as antibiotics and amino acids creates challenges, in physical property modeling and prediction, which are familiar to users of physical properties of organic-aqueous mixtures. For the case of bioseparations involving small molecules, prediction and modeling of adsorption equilibrium for purification (particularly physical adsorption), ion exchange equilibria, and modeling of membrane separations will provide important challenges in the near future. Properties modeling and prediction is far more difficult for bioseparations involving proteins. Phase equilibria-related properties of protein solutions are difficult to predict or model because (a) proteins are electrolytic polymers and (b) protein folding can play an important role in how protein molecules interact with other

Figure 2. Solubility of Lysozyme in water for different temperatures, salts, salt concentrations, and pH values. B22 is the osmotic second virial coefficient, points are experimental data, and the line is prediction from a simple model which, at present, does not account for changes in the nature of the salt and the pH. This figure demonstrates the feasibility of using B22 for the modeling of protein precipitation. Figure from ref 11. Reproduced with permission from Biotechnol. Prog. 2001, 17, 182-187. Copyright 2001 American Chemical Society.

molecules, particularly other protein molecules. These problems become even more difficult due to the small amount of available laboratory sample or the sample shelf life, which may be too short for systematic studies. Of highest importance are crystallization, aqueous twophase systems and other similar liquid-liquid extractions, and membrane- and ultrafiltration-related properties of proteins and aqueous solutions. Despite significant difficulties involved, scientists are becoming increasingly successful in these areas also and Figure 2 provides an example for protein precipitation and crystallization. This figure from Rupert et al.11 shows a correlation between the osmotic second virial coefficient (B22) of Lysozyme and its solubility in aqueous solutions at different temperatures for various salts, salt concentrations, and pH values of the solution. This demonstrates the use of the osmotic second virial coefficient for modeling protein precipitation and crystallization. Recent work of Wilson and co-workers12 suggests a general range of B22 for protein molecules where they may easily precipitate as crystals from aqueous solutions. It may be difficult using the current scientific knowledge to calculate B22 in a process simulation as a function of some of these variables and particularly the pH of the solution. However, an urgent need here is to develop similar simple property prediction methods to guide and reduce the experimental work needed for the development of protein purification processes. Physical properties of polymer solutions is another area of increasing importance. Particularly important is the prediction and modeling of solid-liquid-liquid equilibria13 at high pressures encountered in polymer manufacturing processes. The traditional and popular model for the properties of polymer solutions is the Flory-Huggins theory, which is unable to predict the “lower critical solution temperature” (LCST) of polymer solutions without including an artificial temperature dependency in the χ interaction parameter.14 This model also does not easily account for the effect of supercritical components. Various models for predicting these phase equilibria are available in the literature and have been reviewed.14,15 Equation of state models that account for chain flexibility, for example, the SAFT series models,16-18 can easily model (with a minimum recourse to the use of binary interaction parameters) the lower


Figure 3. Prediction of liquid-liquid equilibrium in the lower critical solution temperature region for polyethylene in three different alkane solvents. Points are experimental data from deLoos et al.,19 and the lines are predictions using the SAFT model from Jog et al.18 This figure demonstrates the effects of proper accounting of chain term as well compressibility effects through the use of an equation of state model that incorporates the fundamental behavior of chain molecules. Figure from ref 18. Reproduced with permission from Ind. Eng. Chem. Res. 2002, 41, 887-891. Copyright 2002 American Chemical Society.

critical solution temperature as shown in Figure 3 for mixtures of polyethylene in alkanes. For the SAFT model calculations shown in this figure, a nominal value of -0.0035 was used for the binary interaction parameter (kij) based upon the modeling of hydrocarbon mixtures in the literature; this also demonstrates the predictive capability of these models. Models based on the SAFT framework also fulfill an important requirement in that they can also be applied to all types of mixtures (small molecules, large molecules, association, electrolytes, etc.) and recent reviews are available that describe the successes of the SAFT models.16,20 Shortcomings do remain in these models, particularly in the modeling of fluids of rigid nonspherical molecules such as styrene and the polymers of rigid nonspherical molecules such as polystyrene. The current equation of state models for polymer solutions also do not account for the effect of solvent on polymer folding. Recent theoretical improvements16 in the modeling of the reference term such as the use of dimers or n-mers also need to be put into practice. Also, parameters in these models have not yet been correlated to the extent that the parameters in the van der Waals-type equations of state have been correlated to pure component constants. Such parametrization will allow these models to be used in process simulation calculations without first developing pure component parameters through extensive fits of pure component data every time a new pure component is to be modeled. Developers of these models should also ensure that liquid heat capacity, heat of vaporization of volatile components, and to some extent, enthalpy effects of associating fluids are accurately correlated as these are also important physical properties for industrial use. In addition, need also exists for

viscosity and surface tension of polymer mixtures and these will be discussed later in detail. Properties of electrolyte mixtures are also important, particularly for environmental and regulatory-related applications. Treatment of wastewater and flue and vent gases often requires modeling of electrolyte solution properties. It is well-established that cations form hydrates with water and this behavior can be exploited to predict properties of electrolytes using theories such as the chemical hydration model of Robinson and Stokes.21 This hydration behavior is particularly important. It has a direct impact on the salting-in and salting-out phenomena as well as transport properties, density, and surface tension, which are important to the design of steam and air stripping equipment for the removal of volatile organic compounds from wastewater and other aqueous solutions. However, few practical activity coefficient models for electrolyte solutions incorporate this behavior, even though it has been shown to work well in the modeling of electrolyte solubility phenomena.14,21,22 Electrolyte models have been mainly limited to aqueous solutions with some extensions to nonaqueous electrolyte solutions.23,24 Such models should not be limited to predictions of vapor-liquid or liquidliquid equilibria alone. As shown in Figure 4, salts and organics in aqueous solutions work together to reduce the solubility of salts due to the presence of organics and to increase the relative volatility of organics due to the presence of salts. The interaction parameters developed in such models should simultaneously account for both types of behavior. Prediction of properties for the selection of reaction media and solvents25 is an important application of physical properties in chemical process development work. Other related needs have also been described.26 The traditional property estimation methods such as the UNIFAC method27 have worked well in the past. These methods are based upon the group contribution methodology for which the group parameters have been derived, mainly from mixtures of compounds containing only a few different chemical groups. These estimation methods work well for the classes of mixtures for which the parameters have been developed and are extremely popular in the chemical industry due to their simplicity and ease of use. However, to meet future needs, property prediction methods should be reliable in a “knowledge vacuum” and should not be limited to specific systems or conditions. Hence, these methods should be extended to multifunctional and multipolar molecules to increase the confidence in their usage. This is a general limitation of most group contribution methods and applies not only to activity coefficient predictions but other properties as well (with the exception of ideal gas properties). A key limitation in this regard is the lack of experimental data for model parameter development and testing. Mixture property predictions for phase equilibria can also help in reducing the number of phase equilibria measurements necessary for accurate process simulations. As a practical matter, design of chemical processes usually involves components that are considered “nonkey” in a separation, that is, such components are not important to the design of a specific section but may be important elsewhere in the process. In the case of distillation columns, for example, such components can be significantly heavier than the heavy key component or significantly lighter than the light key component. It may only be necessary to reasonably predict their


Figure 5. Headspace concentration of toluene (y-axis) in equilibrium with its aqueous solution at 25 °C (liquid-phase toluene concentration on x-axis). Solid line represents the approximate VLE calculations as described in the text, the two horizontal lines are the lower (small dashes) and the upper (dashes and dots) flammability limits for toluene in air, and the vertical dashed line is the limit of toluene solubility in water at 25 °C. This figure shows that the toluene concentration in water must stay below 160 ppm (wt) and well below its solubility limit at 25 °C to prevent the headspace from becoming flammable. This figure is an example of using physical property calculations for chemical process safety applications.

Figure 4. Part (a) shows a comparison of the model (lines) and experimental data (points) for the solubility of NaCl in methanolwater and ethanol-water mixtures at 25 °C. Part (b) shows the effect of NaCl on vapor-liquid of methanol-water mixtures at 760 mmHg total pressure (solid lines are for electrolyte solution and dashed lines are for the case of pure water). When the salt concentration is high, the relative volatility of the ethanol-water mixture changes significantly. When there is a large amount of ethanol present, however, the salt solubility goes down and the relative volatility of ethanol-water mixture remains essentially the same. This demonstrates the need to model together the salting effects on the relative volatility of the organic in water as well as the effect of organic solvents on the salt solubility. Figure 4a is reprinted from Wang, P.; Anderko, A.; Young, R. D. A speciationbased model for mixed-solvent electrolyte systems. Fluid Phase Equilib. 2002, 203, 141-176. Copyright 2002 with permission from Elsevier. Figure 4b courtesy of OLI Systems, Inc.

properties so that they go in the right direction in a distillation column and do not adversely affect the properties of other components in solution for that specific section of the chemical process. Noncondensable gases can also build up in a chemical process. Often,

design of equipment to remove such light components only requires a reasonable estimation of gas solubility. The group contribution approach taken by Catte et al.28 is very promising in this regard and needs to be extended to other solvents that were not considered by these authors. Another important application of physical properties is in the area of chemical process safety (for evaluation of hazards and determination of safe operating conditions for chemical processes and for the design of relief and other safety devices). Use of thermochemical methods for hazard evaluation and screening has been described by Stull29 and a recent description of predictions and measurements of heat effect in chemical processes is also available.30 Design calculations for relief devices may involve physical properties for the following situations: (a) modeling of vapor-liquidliquid equilibria, (b) mixtures involving products of chemical reactions and polymerization, (c) extrapolation of properties to high temperatures and pressures, and (d) fluid flow situations involving multiple phases. Programs for carrying out these calculations are often as complex as the chemical process simulation software. In addition, physical properties can also be useful in determining the safe operating conditions for chemical processes or chemical streams. Figure 5 shows such an application of phase equilibria. This figure shows the results of a simple “Gamma-Phi” model calculation14 for the vapor-phase concentration of toluene in equilibrium with its dilute aqueous solution at 25 °C and compares these results with the lower and upper flammability limits of toluene in air. The activity coefficients used in this calculation were simply assumed to be the inverse of the recommended values for the solubility of toluene in water at 25 °C.31 The upper and lower flammability limit values for toluene are also taken from the published literature.32 This figure shows that the concentration of toluene in the aqueous phase would have to be 160 ppm(wt) or less to keep the vapor-phase concentration of toluene below the flammability limit. This figure also shows that this concentration of toluene in the aqueous solution is much less than its solubility in


Figure 7. Comparison of flash point temperatures for ethanolwater mixtures from closed-cup flash point experiments (points) and prediction (line) as a function of water concentration in the liquid phase. This demonstrates the feasibility of using property prediction methods to develop solvent blends and other chemical products having desired flash point values to meet regulatory standards. Prediction of safety related properties can reduce the time and amount of work needed to develop new products.

Figure 6. E-pH diagram for chromium in water (a) and methanol (b) at 25 °C. This shows the impact of changing the solvent on the corrosion-related chemical equilibria which can be predicted from standard state thermodynamic properties. As solvent changes from water to methanol, the region of formation of chromium oxide has shrunk considerably, leading to possible loss of passive protection of steel by chromium oxide. Figures courtesy of OLI Systems, Inc.

water. As this example demonstrates, the ability to quickly and reliably predict liquid-phase activity coefficients and phase equilibria can be important for flammability and other process safety calculations. A recent application involves the use of mixture chemical thermodynamics and standard state properties in developing the Pourbaix diagrams33 (electrode potential vs pH diagrams) for aqueous and nonaqueous electrolyte solutions for the purpose of corrosion protection in the chemical industry. Figure 6 shows an example of such Pourbaix diagrams for stainless steel in water and methanol at 25 °C. This figure was developed using the calculational methodology described by Anderko et al.34 Formation of a microscopic and passive chromium oxide layer on the metal surface protects stainless steel from further corrosion in aqueous solutions. As shown in Figure 6a for the case of water as the solvent, the region of formation of this layer

covers the neutral pH ) 7 region. However, in Figure 6b for the case of methanol as the solvent, the region of formation of this chromium oxide layer is much smaller, leading to a much smaller zone for the safe operation of the equipment constructed with stainless steel in liquids containing methanol. Selection of proper material of construction is extremely important for the safe and long-term operation of chemical processes. Most of the fundamental knowledge in this area, however, has been limited to aqueous systems and, as this example shows, use of physical properties can easily extend this knowledge to nonaqueous solutions. These are far more prevalent in the chemical industry than aqueous solutions. Another important application of physical properties is the design of products by their properties. In particular, significant interest exists in developing products that meet the environmental and safety standards. As an example, we show in Figure 7 a prediction of flash point of ethanol-water mixtures in air. The calculations for these predictions involved the UNIFAC method27 for liquid-phase activity coefficients to predict the vaporphase concentration of ethanol and water in equilibrium with their liquid-phase concentration, which is shown on the x-axis of Figure 7. These calculations are based upon the idea of using the adiabatic flame temperature of a fuel/oxidant mixture to predict the lower flammability limit29,35 and the relationship between the lower flammability limit and the flash point.36 The calculated vapor-phase compositions were then subjected to adiabatic-constant volume flame temperature calculations to determine the flame temperatures. For any specified ethanol-water mixture concentration, the liquid-phase temperature was varied to adjust the vapor-phase compositions to obtain the limiting adiabatic flame temperatures of 1500 K. Adiabatic flame temperature of 1500 K is considered to be the minimum temperature at which flame propagation can occur 37 for the combustion of organic compounds in air and this can be considered to be the criteria for calculation of the lower flammability limit or the flash point. Results of these calculations are then compared in Figure 7 with


the closed-cup flash point data from the literature.32 Figure 7 shows an excellent comparison between the calculations and the experimental data, showing how physical properties can be successful in predicting product safety properties. Other similar applications include prediction of properties for solvent mixtures and additives for alternative cleaning agents and lowering of vapor pressure to meet environmental regulations.38 In general, the property-related issues for product safety are similar to reliable and quick prediction of properties for chemical process development. However, a new important area in this regard is the prediction of properties of mixtures containing interfaces and micelles. Significant advances have been made in this area, particularly, in predicting critical concentrations for aggregate formation.39 However, little work has been done in developing thermodynamic framework and prediction methods for solute distribution between the aggregate phase and bulk phases (vapor or liquid) and this is of significant practical importance. Need for Better ModelssPure Component and Mixture Properties Recently, as many as 15000 pure components were identified as those of interest to physical property databases.40 Wide-ranging experimental physical property data of good quality, however, are available only for a limited number of chemicals.41 Usually, these are chemicals of common commercial interest, for example, methanol, ethanol, and small hydrocarbons or those for which property data have been provided by the manufacturers or well-measured in academia. Hence, proprietary measurements must be carried out to meet the property needs of industrial projects. Physical properties are expensive and time-consuming to measure, especially at temperatures other than near room temperature. Obtaining samples of sufficient purity can also be difficult at times. Hence, cost of measurements as well as short lead times available for industrial projects leads to a need to minimize the physical property measurements and to increase the emphasis on reliable physical property extrapolation methods. For pure component properties, it is important that the correlations used to model physical properties provide reliable extrapolation to higher and lower temperatures with the measurement of as few data as possible. This means using correlations which provide reasonable extrapolation and by limiting the number of coefficients in such correlations to be fitted to match the amount of available data. An example is the Rackett-Campbell-Thodos equation42 for liquid density. Here, a few liquid density data points measured at low temperatures can be extrapolated to the critical point (estimated or measured) by the use of a single parameter. An additional parameter can then be introduced to better match the liquid density data, if available, at higher temperatures. Furthermore, higher confidence in the selected raw experimental data and the fitted model are more important than obtaining a “perfect” fit to data. A recent test43 of many pure component property correlations showed that most correlations fitted the best quality experimental data to the same level of low uncertainty. In most cases, the uncertainty in the fit is at a level that may be attributed to uncertainties in the experimental data. Hence, the quality of fit to experimental data is no longer the necessary criterion for selecting a

Figure 8. Correlation of parameters A (a) and B (b) in the liquid thermal conductivity correlation of Jamieson and Cartwright as a function of the number of carbon atoms in a given homologous series. The line for halogenated hydrocarbons has been removed from Figure 8a for the sake of clarity. Figures are from ref 44. Reproduced with permission from J. Chem. Eng. Data 1980, 25, 199-201. Copyright 1980 American Chemical Society.

pure component property model to meet the industrial need. What is needed are correlations whose parameters follow discernible patterns, for example, a monotonic relationship to the number of carbon atoms within a homologous series of organic compounds or an ability to utilize default values for some parameters when experimental data are limited. An excellent example that meets both of these criteria is the equation for liquid thermal conductivity developed by Jamieson and Cartwright.44,45 This equation models the liquid thermal conductivity, λ, as a function of temperature:

λ ) A(1 + Bτ1/3 + Cτ2/3 + Dτ)

(1)

where τ ) (1 - T/Tc) and Tc is the critical temperature. As shown in Figure 8 (a and b), the parameters A and B in the above equation are functions of carbon number for several homologous series. These functions are monotonic for compounds with more than five carbon atoms and this is the area of greatest need. Parameters C and D in this equation can be set to multiples of parameter B in this equation44,45 if sufficient data are not available at higher temperatures. Property models should incorporate fundamental theoretical behavior to provide confidence in extrapolation. Examples of this include behavior at limiting


temperatures (e.g., infinite viscosity at freezing point for liquids and low- and high-temperature limits for the ideal gas heat capacity) and limiting density (e.g., second virial region for equation of state limits). Models that directly incorporate the behavior derived from molecular theory (e.g., models for transport properties of gases based upon Chapman-Enskog theory for transport properties of gases45) are also useful. Another desired feature is the ability to incorporate mixing rules within the models for pure component properties. Further, such mixing rules should readily account for different standard states of components (e.g., supercritical components and solids) at temperatures and pressures of interest. Only a few models for pure component properties (e.g., Rackett equation for liquid density) provide these two features at the current level of scientific knowledge. For mixtures, phase equilibria modeling and prediction is most important. In selecting a physical property model for chemical process simulation, the goal is to use a single phase equilibria model for the entire integrated process simulation, hence, the need to utilize a single model for phase equilibria of mixtures containing electrolytes, associating or reacting mixtures, complex formation, small molecules as well as chain molecules, and components which are not liquid at temperatures of interest. Both the traditional standard approaches of modeling phase equilibria, the “Gamma-Phi” approach using liquid-phase activity coefficients and the “Phi-Phi” approach using fluid equations of state, have limitations in meeting this goal. However, both of these approaches provide some solution, at least in principle, to this problem. While the activity coefficient models are well-developed for mixtures of small molecules as well as electrolyte mixtures, they may not be suitable for practical modeling at conditions close to the mixture critical point for small molecules where the liquid phase may be highly compressible. The latter concern may also apply to the case of polymer solutions at high pressures where the LCST behavior may occur. On the other hand, equations of state in practical usage have not been welladapted to modeling electrolyte solutions, even though thermodynamic framework exists, and are only now beginning to account for self-association and complex formation. In the past, the available equations of state were limited to modeling regular solutions. However, excellent research in the past decade has produced methods of combining the activity coefficients modelbased mixing rules with the equations of state models46,47 that extend the range of application of these models. Another concern about equations of state is that most past research work has focused on extensions of the van der Waals-type cubic equation of state. This includes the above-mentioned combining of activity coefficient models with the equations of state models for prediction of phase equilibria for highly nonideal mixtures.46,47 This has occurred despite the now famous recommendation of Henderson.48 No matter how sophisticated a mixing rule, the use of van der Waals-type cubic equations of state force their inherent limitations on the users. These are the ability to reasonably predict only the vapor pressure of a select series of components and only an approximate modeling of the effect of liquid density and compressibility. van der Waals-type cubic equations are unable to accurately model other liquidphase properties, for example, enthalpy and heat capac-

Figure 9. Relative volatility of formic acid-water mixtures as a function of the liquid-phase mole fraction of water at two isobars (200 and 760 mmHg). Points are experimental data and the lines are predictions using the SAFT association model. The experimental data are from ref 52 for the isobaric data at 760 mmHg and ref 53 for the isobaric data at 200 mmHg. The SAFT calculations were carried out using a zero binary interaction parameter and include self-association of formic acid and water. This figure shows the importance of proper accounting of selfassociation in the modeling of the vapor-liquid equilibria of mixtures of otherwise very similar and close boiling pure components. (Unpublished calculations by A. Ghosh, Rice University, and P. K. Jog and S. Gupta, The Dow Chemical Company, 2002).

ity,45,49 and also phase equilibria at high pressures (particularly the mixture critical locus critical50). Despite their limitations, van der Waals-type cubic equations of state have met the phase-equilibria-related property needs of the chemical industry well for the past 50 years, particularly for hydrocarbon and other slightly nonideal mixtures. In addition, with newer mixing rules they have been applied to other types of mixtures despite several problems with these mixing rules.46,47 However, research in the past 2 decades has started to focus, and should continue to do so, on the use of more realistic reference fluid equations, for example, Carnahan-Starling equation for hard spheres.14,16,50 While the use of a more realistic term provides a good starting point in the equation of state development, significant further development work is needed to meet industrial needs and some of the research needs will be pointed out in the sections that follow. Development of the Statistical Association Fluid Theory of Wertheim51 by Gubbins and co-workers16,17 into an equation of state form suitable for process design calculations is particularly significant. This equation incorporates rigorously not only the behavior of chain molecules (as discussed in the previous section of this paper) but also the molecular self-association and complex formation into a single approach. Advantages include a reduced need for the development of pure component parameters and mixing rules. Figure 9 shows an example of the straightforward modeling of mixtures containing associating molecules for vaporliquid equilibria of formic acid-water mixtures. These components are close boiling and form a nearly ideal solution in the liquid phase. However, the relative volatilities obtained in this mixture are far from those for an ideal solution and this is due mainly to self-


association of formic acid and, to some extent, that of water. The SAFT model used here incorporates dimer formation for formic acid and dimer, trimer, and tetramer formation for water. The same pure component parameters were used for both the monomer and oligomer species, except for the chain length. The chain length for the oligomer was taken as the corresponding multiple of the chain length of the monomer. In addition, known equilibrium constants from the literature are used for the monomer-dimer chemical equilibria. The results from these calculations are compared with the experimental data of Conti et al.52 and Ito and Yoshida.53 In this figure, we compare the relative volatility, which is defined as

RV(1,2) ) (y1/x1)/(y2/x2)

(2)

for a binary mixture of components 1 and 2. As shown in Figure 9, the SAFT equation of state model predicts the relative volatility for this mixture without the use of any binary interaction parameters or any special treatment of the gas phase. Our SAFT model calculations also showed a higher fraction of dimerization (or oligomerization) for these two components in the liquid phase than in the gas phase and this is as expected. A recent review paper of Mueller and Gubbins16 provides details of the important recent developments in this area as well as desired features and shortcomings of SAFT models. An important goal for phase equilibria is the simultaneous modeling of vapor-liquid, liquid-liquid, infinite dilution activity coefficients, and heats of mixing data. For the case of liquid-phase activity coefficients, the local-composition models are most common in the current industrial practice. In general, these models cannot simultaneously model several different types of phase-equilibria-related data.16 This limitation can lead to fitting of mixture model parameters which are specific to the particular project needs and are, therefore, not general. This can create significant risk when using the same mixture model parameters for prediction of different excess properties and types of phase equilibria, for example, in predicting liquid-liquid equilibria from mixture model parameters derived from vapor-liquid equilibria data. Models based on rigorous molecular theory can work well for such cases and an example is shown in Figure 10 (a and b) for the vapor-liquid and liquid-liquid equilibria of methanol-hexane mixtures. A self-association version of the SAFT equation of state along with a small and temperature-independent value of the binary interaction parameter, kij, was used by Kahl and Enders54 for the model predictions. The SAFT predictions are shown to be in reasonable agreement both for the vapor-liquid equilibria of this mixture at low pressures and the liquid-liquid equilibria at high pressures. (Kahl and Enders54 also showed the results of calculations using the Peng-Robinson equation of state, which does not model these data as well when using a simple mixing rule.) Another important industrial need regarding mixture phase equilibria is a reliable extrapolation of measured data from lower temperatures and pressures. Utilization of a temperature-independent kij parameter by Kahl and Enders54 for an excellent prediction of phase equilibria, over a wide temperature range for this extremely nonideal mixture, shows that it is possible to develop mixture property models to meet such needs.

Figure 10. Comparison of the SAFT (solid lines) and PengRobinson (dotted lines) equations of state with the experimental data (points) for methanol-hexane mixtures for liquid-liquid equilibrium (a) and vapor-liquid equilibrium (b). An association term was used in the SAFT equation of state for methanol along with a constant nonzero binary interaction parameter. This figure demonstrates that it is possible to model multiple phase equilibria simultaneously and accurately, provided a model is available that takes into account the fundamental behavior at the molecular level. Figure provided by S. Enders in electronic form. Kahl, H.; Enders, S. Phys. Chem. Chem. Phys. 2002, 4, 931-936. Reproduced with permission of the PCCP owner societies.

Modeling of multicomponent mixtures raises issues similar to those encountered when attempting to simultaneously fit different phase equilibria properties. While the local-composition models for liquid activity coefficients usually provide reasonable predictions of multicomponent phase equilibria for mixtures of similar molecules, occasionally they provide a model that is inconsistent between binary and multicomponent mixtures. Examples include prediction of vapor-liquid and liquid-liquid phase equilibria for ternary mixtures, such as the water-alcohol-hydrocarbon mixtures, from parameters derived only from the binary mixture data. In such cases, when multicomponent data are used for model validation, parameters may need to be further adjusted from the values fit to the binary data. However, this can limit the range of such a model. Further, measuring phase equilibria data for multicomponent mixtures involves higher degrees of difficulties in experiments and higher uncertainties among individual variables. Together, these issues lead to a need for reexamination of the extrapolation of models developed


on the basis of binary data to predict properties for processes involving multicomponent mixtures. Mixture properties other than phase equilibria are also important and a serious need exists for research in these properties. In particular, models for surface tension and viscosity of liquid mixtures are of particular importance. Models are available for the case of mixtures of small molecules, for example, for surface tension by Suarez et al.55 However, models are lacking for more generalized applications, for example, polymer solutions, electrolytes, and components that may have a different standard state at the temperature of interest (supercritical components and solids). Particularly interesting would be the development of a single model to account for all of these types of mixtures. Molecular theory is well-developed for interfacial properties and, in principle, can account for these two issues.56 However, availability of practical and easy to use models remains an important challenge. A similar situation also exists for mixture models for liquid viscosity. Models are available for viscosity of mixtures of small molecules45 and also for electrolyte solutions.57 Even these models are based upon very simple mixing rules and do not account for molecular behavior such as self-association. An example is the viscosity of methanol-water mixtures which, at near room-temperature conditions, shows a maximum as a function of the mixture concentration. This type of behavior is extremely difficult to model with the use of current models if a single interaction parameter is utilized. Mixture models that are generally applicable (as described in the previous paragraph regarding the types of mixtures and standard state conditions) are needed for liquid viscosity also. Another important phase equilibria property is the prediction of Henry’s Law constants, particularly for environmental applications.58 Also needed are the generally applicable methods of predicting thermal conductivity, enthalpies, and densities of the wide variety of mixtures discussed above.

Figure 11. Relationship between physical property experiments, molecular theory, and molecular simulation. The items along the arrow describe the unknowns to be validated between any of the two methodologies. Molecular simulation can play the role of computer experiment in conjunction with molecular theory with the only unknown being the assumptions in molecular theory. In this way, molecular simulations can first be used in the development of molecular theory. Theory developed thus can then be further refined using experimental data for real systems. This avoids testing of theory directly against experimental data where there are two unknowns together, assumptions in theory and uncertainties in the intermolecular potential of the real systems, and it is possible that the errors in these two unknowns may cancel out. Molecular simulations can also be used in the role of computer models to predict real system properties when intermolecular forces are the unknown. Hence, the molecular study of fluids using molecular theory and simulation provides a means to separately study the effect of intermolecular forces and assumptions in molecular theory.

Role of Molecular Theory and Simulation As discussed above, the need for reliable models and mixing rules for physical properties is creating a situation where the successful models will be those that are based upon fundamental relationships between the underlying inter- and intramolecular forces and the resulting macroscopic behavior of fluids. Molecularbased study of fluids is well-suited to achieving this goal, as pointed out by Mansoori and Haile.59 Molecularbased study of fluids has two distinct aspects: (a) development of molecular theory, role of molecular simulation as computer experiments, and application of molecular theory to real systems; and (b) direct application of molecular simulations as computer models to model and predict physical properties of real systems. Both of these aspects are described in Figure 11 using a triangular diagram popularized by Gubbins60 and others. Physical property models developed from molecular theory of fluids can provide distinct advantage over other models. An example is the development of the polar SAFT model by Jog et al.60 based upon the u-expansion for the dipolar fluids.60 Figure 12 shows the relative volatility of acetone-n-hexane mixtures at 55 °C calculated using the polar SAFT model and compared with experimental TPxy data as well as the Redlich-

Figure 12. Comparison of relative volatility of acetone/n-hexane mixtures at 55 °C from experimental data (points), polar-SAFT equation of state model (long dashes) with kij ) 0, and RK-Soave model incorporating the simple van der Waals mixing rule (small dashes). The RK-Soave model utilized here requires the use of a binary interaction parameter (kij) which was initially set to zero (kij ) 0) and then fitted to experimental data (kij ) 0.108) for the correct modeling of the vapor-liquid equilibria. This figure demonstrates the advantage of using methods based upon molecular theory (polar SAFT) over the methods that do not properly account for the intermolecular forces between the molecules (RKSOAVE). Figure based upon calculations described in Jog et al.61 Experimental data are from Kudryavtseva and Susarev.62

Kwong-Soave (RK-Soave) equation of state using the simple quadratic mixing rule.61 Here, acetone was modeled as a dipolar fluid in the polar SAFT model and n-hexane was considered nonpolar. The results from this polar SAFT model compare well with the experimental data without the use of any adjustable mixture parameters. In comparison to the polar SAFT model with kij ) 0, a large value of kij ) 0.108 is needed with the RKSoave equation of state to obtain a good comparison with the experimental data. Figure 12 (along with Figure 10)


Figure 13. Comparison of excess Gibbs free energy for binary fluids of LJ spheres for a fixed size ratio of σAA/σBB ) 2 from thermodynamic perturbation theory (lines) and molecular simulations (points). The x-axis shows the liquid mole fraction of the smaller molecule. Comparisons are shown for three different temperatures and pressures This figure not only shows thermodynamic perturbation theory to work well for mixtures of spheres which differ only in the size but also points out the asymmetry in excess free energy caused by the difference in the size of the molecules. Figure redrawn in electronic form by K. P. Shukla and based upon calculations described in ref 64. Reproduced with permission from Shukla, K. P. ; Haile, J. M. Mol. Phys. 1987, 62, 617-636. Copyright 1987 Taylor and Francis.

demonstrates the utility of proper accounting of the fundamental behavior to accurately model mixture properties. Another successful example in this area is the development by Prof. Mansoori and co-workers63 of mixing rules based upon statistical mechanical equilibrium correlation functions. With just two adjustable mixture parameters, this mixing rule has been successfully applied to the prediction of properties of several highly nonideal mixtures and this includes the prediction of the liquid-liquid equilibria of mixtures for a reasonably wide temperature range. Application of molecular simulations as computer experiments for testing and development of molecular theories is extremely useful in this regard since important variables such as size and shape of molecules can be varied in a manner not possible in laboratory experiments. An example, shown in Figure 13, is the testing of a version64 of thermodynamic perturbation theory of Weeks, Chandler, and Andersen65 for mixtures of spherical molecules. Here, extensive molecular simulations were carried out for various sets of reduced temperatures and pressures (reduced using the units of the Lennard-Jones (12:6) potential model) for mixtures of two Lennard-Jones (12:6) fluids for which the ratio of diameters is 2. Such a unique manipulation of molecular sizes cannot be carried out in physical experiments where the ratio of energy parameters will also vary. The comparison in Figure 13 validates perturbation theory for these particular types of mixtures. In addition, this figure demonstrates that asymmetry of activity coefficients can exist simply due to differences in the size of molecules. As compared to direct testing of theories with experimental data shown in Figure 12, testing of molecular theory using molecular simulation as computer experiments eliminates the intermolecular potential as an unknown, leading to a straightforward evaluation of molecular theory. After testing, the model development can then incorporate some experimental data also to make the model more realistic. Such testing and devel-

opment of predictive methods has been useful in extending thermodynamic perturbation theories to pure fluids and mixtures consisting of spherical and nonspherical molecules, chain molecules, and polar fluids and associating fluids. Property models developed in this manner will have an enormous impact on accurate prediction and modeling of physical properties with a minimum recourse to experimental data. Such applications of molecular theory and simulation need not be limited to property model development. They can also be used to develop precise knowledge of the impact of changes in intra- and intermolecular forces on the macroscopic properties of fluids. A simple example in this regard would be to study how the locations of various important functional groups in a molecule affect its physical properties, for example, boiling point. Such knowledge can then be utilized in property estimation using molecular structure methods.45 Molecular simulations can also be used to directly predict physical properties of real systems. Here, two important questions arise: (a) Are the computational methods suitable for reliable computations of the desired physical properties? And, (b) are the intermolecular potentials sufficiently accurate to model the desired real systems? While significant advances have taken place regarding the first question, challenges still remain, for example, accurate computation of free energy, activity coefficients, and other phase-equilibriarelated thermodynamic quantities.66 The second question is also extremely important if not overriding. As an example, we show the results of modeling the density of liquid naphthalene67 using molecules of different shapes in Figure 14. Modeling of the density of liquid naphthalene is straightforward when using the macroscopic correlations available in the literature. However, use of molecular simulation methodology requires an intermolecular potential model with sufficient details to mimic the shape of the naphthalene molecule to reasonably predict the liquid density. This shows that a proper accounting of intermolecular forces is neces-


Figure 14. Comparison of molecular dynamics simulations for the density of saturated liquid naphthalene (solid line, molecular simulations using dumbbell of oblate ellipsoids; dashed line, molecular simulations using dumbbell of spheres) and experimental data (points). This shows the importance of proper modeling of intermolecular forces and the increased burden placed upon those who seek to carry out molecular modeling for property predictions. Reproduced with permission from J. Chem. Phys. 1989, 90, 1888-1900. Copyright 1989 American Institute of Physics.

Figure 15. Prediction of Henry’s constant for oxygen solubility in benzene from molecular dynamics simulation (diamonds) and RK-Soave equations of state (triangles) compared with the recommended values based on experimental data (circles) from ref 69. Reprinted from Murad, S; Gupta, S. Molecular dynamics simulation for Henry’s constant of oxygen in benzene. Fluid Phase Equilib. 187-188, 29-37. Copyright 2001 with permission from Elsevier.

sary, even for some simple properties of some simple nonpolar fluids, if accurate results are to be obtained from molecular simulations to predict physical properties of real fluids. However, results from molecular simulations can be extremely useful once such hurdles are overcome. Figure 15 shows a comparison of molecular simulation results68 for Henry’s constant of oxygen in benzene with the experimental data69 and predictions from the Mathias version70 of the RK-Soave equation of state (with kij ) 0). These simulations were carried out using the intermolecular potential models for oxygen and benzene from the literature and without any special tuning to

Figure 16. Physical properties for which molecular simulations could be used as a prediction tool. The x-axis ranks properties according to the relevance of the physical properties as compared to degree of measurement difficulty whereas the y-axis ranks properties according to their importance in chemical process design and development.

gas solubility data. With use of only a value of zero for the interaction parameter in the mixing rules for the cross interactions, the molecular simulation results agree well with the experimental data whereas a nonzero binary interaction parameter (kij ) 0.234) was needed in the Redlich-Kwong-Soave equation of state predictions to achieve the same level of comparison. Flammability hazards due to the presence of oxygen make such measurements extremely difficult and molecular simulations provide a good way to predict the oxygen solubility where moderate accuracy is sufficient for most practical applications. Such use of molecular simulations as computer models is particularly suitable for avoiding measurements involving hazardous or unstable compounds (or even compounds which may not be easily isolated) or other cases where property measurements may be difficult or require special safety procedures. In Figure 16, we show a ranking of how molecular simulations may be useful in the future as a tool for prediction of physical properties. This figure was developed after preparing a list of physical property needs familiar to us, for nearly all the physical properties. An assessment was then carried out in terms of the importance of these physical properties and their needs versus the state of the art in molecular simulations. The x-axis of this figure shows the relevance of molecular simulation as compared to experimental measurements whereas the y-axis shows the importance of physical properties to chemical process development and design. The top-right corner of this figure shows the properties for which molecular simulations are likely to be most useful as a tool in property predictions. Criteria for Successful Industrial Use of Molecular Theory-Based Property Models While the use of molecular theory-based property models is promising, some caveats must be taken into account when developing such models. While the technological advantage provided by such models is important, ease of applying these models will also be a crucial factor in their usage. For example, equations of state are preferable to direct use of perturbation theory. Further, parameters for these models should be easily correlated to known or measurable physical properties


for several classes of compounds. An example is the Boston-Mathias70 version of the Redlich-Kwong-Soave equation of state which has been well-parametrized through the critical constants of fluids. The combined ease of use with readily available parameters (critical constants) has made this equation popular in industrial use. This has occurred despite the general limitations of the van der Waals-type equations of state as discussed earlier. On the other hand, and as pointed out earlier, molecular theories are well-developed for interfacial properties and yet are not utilized in practice due to difficulty of use by those who are not experts in methods of statistical mechanics. Trends in Experimental Measurements Data measurement of physical properties and phase equilibria for process design is being made more difficult by the need to study components that contain many functional groups, react (decompose), and form complexes. There is an increasing need to measure data at extreme conditions using short residence times. Simultaneous measurement of kinetic and thermodynamic data is increasingly more common. To meet these needs, increased automation and use of better analytical chemistry tools is essential. Faster turnaround times are important and some of the nontraditional applications described earlier will also have an impact on measurement methods. Several contributors to the recent Forum 2000-Fluid Properties for New Technologies-Connecting Virtual Reality with Physical Reality71 note the decrease of academic research programs in experimental measurement of physical properties and phase equilibria data.72 This is perhaps due to the perception that experimental measurements are no longer needed and, therefore, are no longer a significant research area. However, in addition to its immediate short-term use, measured data are (1) the raw material from which rapid, easily used, and cost-effective estimation methods are derived and (2) the ultimate validation of molecular simulation prediction methods. There is a necessary balance between theory, molecular simulation, and experiment as shown earlier in Figure 11. Despite the (alarming) trend just noted, there are many recent examples of new or improved experimental techniques for measurement of physical properties and phase equilibria. A small sample includes the following: use of spectroscopy for direct examination of the vapor phase in VLE experiments;73-76 measurement of ppm levels in PTxy recirculating equilibrium stills77-79 (see Figure 17); automated VLE apparatus;80 short residence time critical-point measurement;81,82 improved ebulliometer analysis and design;83,84 simultaneous phase and chemical equilibria (and kinetics!);85-87 and phase equilibria via calorimetry.88 Below are some specific suggestions for industrially relevant physical property and phase equilibria measurement projects updated from a previous list:89 (1) Any property measurements on “mixed-moiety” (multifunctional) chemicalssOne important example is monoethanolamine (2-aminoethanol). MEA properties cannot be estimated by any of the common homologous series methodologies. Another industrially important class of mixed moiety compounds is the huge family of glycol ethers, for example, 2-ethoxyethanol and 2-(2ethoxyethoxy)ethanol.

Figure 17. Recirculating PTxy equilibrium still data at the ppm level for dilute glutaraldehyde in water at 319 K. The slope gives a direct measurement of Henry’s Law constant. Experimental data are from ref 77.

(2) Measurements on model mixtures over wide ranges of temperature and pressuresMost phase equilibria and thermophysical property measurements are restricted to a few properties over a limited range of T and P. Most useful to molecular simulators and the producers of estimation methods would be data on say, GE, HE, CPE, VE, transport, and electromagnetic properties on a few carefully chosen mixtures that represent the variety of intermolecular interactions encountered in “real” design problems. Another way to give these suggestions is, what should be the “steam-table equivalent” for mixtures? We suggest water + ethylene glycol. (3) Data measured at other than 25 °CsAn example of this problem is the many VE measurement papers that are now reported, made easy by the advent of commercially available vibrating-tube densimeters. Although measurements over the range 10-80 °C are easy with this instrument, most mixture VE data are still only reported at 25 °C, as if they were still being measured with pycnometers! This prevents derivation of the theoretically important temperature dependence and connection to SE from these mixture VE data. (4) Simultaneous physical and chemical equilibria datasThis is a general topic that would benefit from more attention by experimenters, simulators, and theoreticians: the analysis, measurement, and characterization of simultaneous chemical reaction equilibria and phase equilibria. This topic is rarely covered in engineering education, ignored in many experiments, and is not available as an option in many process simulator applications (or available as only an approximation). Jensen and Datta82 point out, “It is, indeed, surprising that although the problem of determination of liquidphase reaction equilibria from gas-phase thermodynamic data is common, it is not the stuff of textbooks yet.” (5) Liquid-phase heat capacity datasMeasurement of liquid-phase heat capacity to (1-3% for typical roomtemperature chemicals is straightforward with a differential scanning calorimeter (DSC). Yet few data by this method are reported. (6) Environmental phase equilibria datasData for octanol-water partition coefficients, Henry’s Law constants, and water solubilities (particularly as a function


of temperature) are limited. More measurements would improve group-contribution estimation methods and simulation methods for these properties. (7) Process safety datasFew systematically measured flash-point temperature and autoignition temperature data exist. We conclude this section by pointing out the necessity for data quality measures (meta-data) when reporting experimental measurements. These measures would at least include the following: (a) uncertainty estimates on all measured and derived quantities, (b) details on the origin and purity of the chemicals studied, (c) uncertainty limits on thermometers, pressure transducers, and other instrumentation, (d) description of the date and method of calibration of apparatus, (e) specification of the temperature scale used, and (f) comparisons with previous work. Necessity for and use of data quality measures is discussed in detail elsewhere.90,91 Final Thoughts Physical property research continues to be important to chemical industry. However, needs change, and these changes set the directions for physical properties research and usage. Our challenge is to meet these changing needs. Importance of physical properties to the chemical industry can only be understated, and we would like to leave the reader with one final thought. Recently, our management pointed out to us that “ease of convergence alone is not the criteria of success of chemical process simulations. It is important to have property models that are robust and meet the quality needs of the company.” Acknowledgment Many co-workers and peers provided material for this article. We thank Prof. Keshawa P. Shukla, Prof. Sohail Murad, and Dr. Andre Anderko for discussions and for providing figures for this paper. We also thank many co-workers at The Dow Chemical Company: Mr. Auleen Ghosh (of Rice University while visiting The Dow Chemical Company as a summer intern in 2002), Dr. Prasanna Jog, Dr. Sabine Enders, and Dr. Tyler Thompson for providing electronic versions of the figures from their works. We thank Prof. Keith Gubbins for discussions on molecular theory, Prof. James M. Haile for discussions on molecular simulations, Mr. J. Bruce Powers for discussions of flammability, and Dr. Ashok Chakrabarti for discussions on reactive relief designrelated property needs. S.G. thanks Profs. Abraham Lenhoff, Harvey Blanch, and Man Mohan Sharma for discussions on biotechnology and bioseparations and Profs. Walter Chapman, Theo de Loos, and Maciej Radosz for discussions of polymer phase equilibria. The authors have enjoyed many conversations during the past 2 years with Prof. C. A. Eckert on the topics of physical property estimation, modeling, and measurements and are privileged to contribute this paper for the celebration of his 65th birthday. Literature Cited (1) Fair, J. R. Advanced Process Engineering; AIChE Monograph Series; American Institute of Chemical Engineers: New York, 1980; no. 76. (2) Whiting, W. B. Effect of Uncertainties in Thermodynamic Data and Models on Process Calculations. J. Chem. Eng. Data 1996, 41, 935-941.

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Received for review February 24, 2003 Revised manuscript received May 19, 2003 Accepted May 20, 2003 IE030170V

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