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Pure Component Property Estimation: Models & Databases
Computer Aided Property Estimation for Process and Product Design G.M. Kontogeorgis and R. Gani (Editors) © 2004 Elsevier B.V. All rights reserved.
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Chapter 3: Pure Component Property Estimation: Models & Databases Jorge Marrero and Rafiqul Gani 3.1 INTRODUCTION Pure component properties are needed for many process and product design calculations. They may be needed to study the behavior of the product (such as the solubility of drug in water), behavior of a chemical under the conditions of operation of a process (heat of vaporization of a refrigerant or process fluid in a closed cycle), dimensioning of equipment (density of the chemical in a tank), the physical state of the product (melting point and/or boiling point to identify solid, liquid or vapor state) and many more. For the estimation of mixture properties also, the pure component properties are employed in different mixture property models. For example, the well-known SRK equation of state employs the critical properties while an ideal mixing model for liquid density employ only the pure component liquid densities of each chemical species present in the mixture. The objective of this chapter is to provide the reader with a set of pure component property models for a corresponding set of frequently used properties in process-product design. These models have been tested and evaluated against a wide range of chemical species by the authors. A good collection of pure component property models can also be found in many specialized property estimation books, journal papers, commercial software and databases. It is beyond the scope of this chapter to name all the references as well as methods. The calculation methods outlined in each section of this chapter should provide some guidance in terms of the important steps related to estimation of a pure component property.
3.2 MODELS FOR PRIMARY PROPERTIES As described in Chapter 1, primary properties are classified as those, which can usually be determined only from the molecular structural information and have a single unique value. In this chapter, only a set of primary properties that are needed for a wide range of processproduct design calculations are presented through one property estimation approach. This does not mean that the property models used below are the best or have the highest accuracy. These models are, however, frequently used and their details, including the model parameter tables, are readily available. The property model presented below can be classified as an additive method using a group-contribution^ approach. The property estimation methods will be highlighted through the molecular structure of Glycine (CAS No. 000056-40-6). Only the estimated primary and the secondary properties of
46 Glycine are given. When the estimated value for a secondary property for Glycine is not given, it means that not all the dependent properties are available. When experimental data for the property is available, it is also given. The chemical formula, group assignment and 3D molecular structure is given in Figure 1.
Compound Smiles: NCC(=0)0 Compound Formula: C2H5N02 Molecular Weight (g/mol): 75.07 1 st-order groups 1 COOH 1 CH2NH2
1
2"^-order Groups CHm(NHn)-COOH Figure 1: Molecular structural details for Glycine
3.2.1
Primary Property Models
All the properties Hsted below are only functions of the molecular structural information described in terms of first-order, second-order and third-order groups. Note that all molecules must be completely described by first-order groups and may or may not have second- and third-order groups. The estimation steps are as follows: 1. 2. 3. 4.
Identify the groups (first-, and if necessary, second- and third-order groups) Determine how many groups of each type are needed to represent the molecule Retrieve the parameters from the model parameter tables for the property of interest Sum the contributions and use the corresponding property model fimction
Properties & Models The following property models have been proposed by Marrero and Gani [1, 2]. In each case, the sunmiation terms having the following expression Contribution of first-order groups, Sum.Groups.I = Zi Hi Ci for i = 1, NCi Contribution of second-order groups, Sum.Groups.II = Ej nij Dj for j = 1, NC2 Contribution of third-order groups, Sum.Groups.II! = Ek Ok Ek for k = 1, NC3
(1) (2) (3)
In the above equations, ni, mj, Ok are the number of first-, second- and third-order groups of types i, j and k, respectively. Ci, Dj and ER are the contributions for the selected property for first-, second- and third-order groups of types i, j and k, respectively. NCi, NC2 and NC3 are the total numbers of different types of first-, second- and third-order groups representing the molecule. Critical temperature. K Tc = 231.239*log(Sum.Groups.I + Sum.Groups.II + Sum. Groups.Ill)
(4)
47 Glycine: 1028.0 K (experimental: 1028) Critical pressure, bar Pc = l/(Sum.Groups.I + Sum.Groups.II + Sum.Groups.III + 0.108998)^ + 5.9827
(5)
Glycine: = 67.4 bar (experimental: 67.4) Critical volume, cm^/mol Vc = 7.95 + Sum.Groups.I + Sum.Groups.II + Sum.Groups.III
(6)
Glycine: = 234.01 cmVmol (experimental: 234.0) Normal melting point, K Tn,= 147.450*log(Sum.Groups.I + Sum.Groups.II + Sum.Groups.III)
(7)
Glycine: = 535.63 K (experimental: 535.15) Normal boiling point, K Tb = 222.543 *log(Sum.Groups.I + Sum.Groups.II + Sum.Groups.III)
(8)
Glycine: = 710.97 K Standard Gibbs free energy of formation, kJ/mol Gf = -34.967 + Sum.Groups.I + Sum.Groups.II + Sum.Groups.III
(9)
Glycine: -300.1 kJ/mol (experimental: -300.1) Standard Enthalpy of formation at 298 K, kJ/mol Ht = 5.549 + Sum.Groups.I + Sum.Groups.II + Sum.Groups.III
(10)
Glycine: -388.49 kJ/mol (experimental: -392.1) Enthalpy of vaporization at 298 K, kJ/mol Hv = 11.733 + Sum.Groups.I + Sum.Groups.II + Sum.Groups.III Enthalpy of vaporization at TH, kJ/mol Hvb = a + Sum.Groups.I + Sum.Groups.II + Sum.Groups.III
(11) (12)
Glycine: = 43.0 kJ/mol Heatoffusionat298K, kJ/mol Hfus = -2.806 + Sum.Groups.I + Sum.Groups.II + Sum.Groups.III Glycine: 28.4 kJ/mol (experimental: 28.4)
(13)
48 3.3 MODELS FOR SECONDARY PROPERTIES As defined in Chapter 1, secondary properties are those that cannot be expUcitly calculated only from structural information and usually require the knowledge of other properties. Most of these models have been derived from the principle of corresponding states, although, a number of empirical correlations also exist. There is available, a wide range of methods for prediction of secondary properties. Many books and handbooks provide methods for calculating these properties. Poling et al. [3] provides a good collection of many of the needed methods. Horvath [4] also provides a large number of methods for primary as well as secondary properties. In this section, a set of pure component properties that have a single value is listed together with a corresponding property estimation method. 3.2.1 Secondary Property Models The following steps may be followed in the estimation of pure component secondary properties. 1. For the secondary property of interest, select an estimation method 2. For the selected estimation method, identify the properties (data) needed to use the model and verify the application range of the method in terms of chemical species (type) 3. Retrieve from a database or predict the necessary properties (to be used as input) 4. Calculate the property through the selected method For the secondary properties listed below, the following properties are needed as input. All these properties are also defined below. For each property, first the generic form of the equation in terms of dependency on other properties/variables is given, followed by the method for calculation, the model equations, and finally, the calculated value for the chemical used as an example. Hfos (kJ/mol), Tb (K), Tc (K), Pc (bar), Vc (cmVmol), SoW (MPa^-^), 5s (MPa^-^), UD, D„, (debye), p'(bar), Mw (g/mol), Ws (mg/L) Properties & Models For each property, the name of the property, the representation of the property in terms of its dependence on other properties, the method used and the equations involved are presented. Heat of Vaporization at Th, Hvb= f(Tb, T^ Pc) Method: Correlation (Equation 7-11.5 in Reid et. aL [5]) tr = Tb/Tc X = 0.37691 - 0.37306*tr+ 0.15075/(Pc*tr^) Y = (0.4343*log(Pc) - 0.69431+ 0.89584*tr)/X
49 Hvb = Tb*0.008314*Y (14) Not recommended for Glycine Pitzer's Acentric Factor, oo = f(Tb, Tc, Pc) Method: Lee-Kesler Correlation (2-3.4 in Reid et aL [5])) / Constantinou & Gani [6] e = Tb/Tc a =- -log(Pc*0.98692327) - 5.92714 + 6.09648/6 + 1.28862*log(e) - 0.169347'^e^ P = 15.2518 - 15.6875/e - 13.472l*log(e) + 0.43577*9^ G) = a / p (15) Glycine: 0.747 Lee-Kessler 0.673 Constantinou & Gani [6] Critical Compressibility Factor, Zc = f(Tc, Pc, Vc) Method: Theoretical (Equation) Definition Zc = (Pc''Vc)/(83.14=^Tc) (16) Glycine: 0.185 Liquid Volume at Tj2, Vb =f(Vc), cm^/mol Method: Tyn and Calus Correlation (3-10.1 in Reid et. aL [5]) Vb = 0.285*Vc^-^^^ (17) Glycine: 86.5 cmVmol Liquid Volume at 298 K, Vm = f(Tc, Pc, o), cmVmol Method: Rackett Modified Correlation tr=1.0-298.15/Tc Zra = 0.29056-0.08775*G) tfunc- 1 +(l-tr) V n , = (83.14*Tc*Zra "'''yPc
(18) Refractive Index, no = f(Solpar) Method: Correlation [4] no = (0.48872*Solpar+5.55)/9.55 (19) Glycine: 1.8 Molar Refraction, Rm = f(nD, Vm) Method: Correlation [4] Rm = (((nD)'-l)*V„,nOOO)/((nD)' + 2) (20) Surface Tension at 298 K, a = f(Solpar, Vm), dyne/cm Method: Correlation [4]
50 a = 0.01707'^(SolparrnVmr (21) Entropy of Fusion, Sfus = f(Hfus, Tn,), J/(mol*K) Method: Theoretical (Equation) Definition Sfus=1000*Hfus/Tm
(22)
Glycine: 53.07 J/(moPK) Closed Flash Temperature, Tfc = f(GCcG, Tb), K Method: Constantinou and Gani [6] Tfc = -2.03'=^(Sum.Groups.IcG) + 0,659'^Tb + 20.00
(23)
Open Flash Temperature, Tfo = f(GCcG, Tb), K Method: Constantinou and Gani [6] Tfo = 3.63*(Sum.Groups.IcG) + 0.409*Tb + 88.43
(24)
Glycine: 414 K Hansen Dispersive Solubility Parameter, 5s = f(GCcG, Vm), MPa^^ Method: Constantinou and Gani [6] 5s = (Sum.Groups.IcG)/Vm
(25)
Glycine: 17.74 MPa^^ Hansen Polar Solubility Parameter, 5P = f(GCrG, Vm). MPa^^ Method: Constantinou and Gani [6] 5p = [(Sum.Groups.IcG)' ']/Vm (26) Glycine: 12.16 MPa^0.5 Hansen Hydrogen Bonding Solubility Parameter, 5HB = f(GCcG, Vm), MPa0.5 Method: Constantinou and Gani [6] 5HB = [(Sum.Groups.IcG)A/^m]'^' (27) Glycine: 17.38 MPa^^ Dipole Moment, Dm = f(6s, Vm), debye Method: Correlation [4] Dm = 0.02670*6s*(Vmf' (28) Dielectric Constant, DE = F(Solpar, HD, Dm) Method: Correlation [4] IfnD< 0.001, DE = (nD)'
51 Else, DE = (Solpar*0.48871-7.5)/0.22 (29) Henry Constant of a gas in water at 298 K. Hhenry = (p\298), Mw, Ws), bar*mVmol Method: Theoretical (Equation) Definition Hhenry = p ' ( 2 9 8 ) * M w / W s
(30)
3.3,1 Secondary Properties modeled as Primary Property For a number of secondary of secondary properties, it is sometimes possible to model them as primary properties. That is, it is possible to predict the property only as a function of the molecular structural information. Recently, Marrero and Gani [2] have developed models for Octanol-water partition coefficients. Solubility of a chemical in water at 298 K, and the Hildebrand solubility parameter. Also, the method of Martin and Young [7] for the measure of toxicity in terms of 50% mortality of Fathead Minnow after 96 hours of exposure has been adapted to the Marrero and Gani method. As in the case of primary properties listed in section 3.2, the prediction of the following properties also follow the same steps Octanol-water partition coefficient (LogKow) LogKow = A + Sum.Groups.I + Sum.Groups.II + Sum.Groups.III
(31)
Glycine: -3.41 (experimental:-3.21) Water Solubility. Ws, Log(mg/L) LogWs = A + Sum.Groups.I + Sum.Groups.II + Sum.Groups.III Glycine: 5.41 (experimental: 5.39)
(32)
Hildebrand solubility parameter at 298 K, Solpar, M(Pa) Solpar = A + Sum.Groups.I + Sum.Groups.II + Sum.Groups.III (33)
Glycine: 23.9 MPa^^ Acute Toxicity r96-h LC50) to Fathead Minnow. mol/L -Log(LC50) = Sum.Groups.I (34) Glycine: 2.82
3.4 FUNCTIONAL PROPERTIES As defined in Chapter 1, pure component functional properties are those that depend on the specific value of temperature and/or pressure. Most prediction methods employ a suitable equation of state, the principle of corresponding states or a specially fitted correlation. In this
52 section, a set of functional properties and a corresponding property model is presented. Note that as in secondary properties, functional properties may also require other properties as input data. Note also that many temperature dependent functional properties are available in databases where the coefficients for the correlation of each property and chemical are stored. These correlation functions are discussed in section 3.4 of this chapter. The following steps may be employed in the estimation of functional properties. 1. For the property of interest, select an appropriate property model. 2. Verify the applicability of the model in terms of chemical species as well as the temperature (and/or pressure) limit of the method. 3. Retrieve or estimate the necessary properties to be used as input data 4. Calculate the property of interest at the condition (temperature and/or pressure) of interest using the selected method 3.4.1 Properties & Models For each property, the name of the property, the functional dependence, the units of measure, the method and the model equations are presented. Diffusion coefficient of component at infinite dilution in water. Dab = f(Vb, Tb, T) cmVs Method: Modified Tyn & Calus Correlation (11-9.5 in Reid et. al [5]) X = exp(-24.71 + 4209/T + 0.04527*T - 0.00003376'^T^) for 273.15 < T < 643.15 Dab = 0.01955/[(Vb)^-^^^]*(T/X) (34) See also Chapter 9 for other prediction methods. Liquid Density, GL = f(Tc, Pc, co, T), g/cm^ Method: Modified Rackett correlation (3-11.10 in Reid et. al. [5]) Zra = 0.29056-0.08775*03 Tfonc = 1 + (1-T/Tc)^'^^^^^ for T/Tc < 0.9 CJL = (83.14*Tc*(Zra)'^'""')/Pc
(35) Thermal Conductivity, Tcon = f(Tb, Tc, T, Mw) W/m*K Method: Correlation (10-9.5 in Reid et. al [5]) Tr = T/Tc Tbr=Tb/Tc Tcon = [l.ll/[(Mw)^-^]*(3 + 20*(l-Tr)^-^^^^)]/[(3 + 20*(1-Tbr)^-^^^^)] (36) forTr<0.9;Tbr<0.9 Vapor Pressure, pS = f(Pc, Tc, T, co) bar Method: Modified SRK EOS or any appropriate equation of state See Chapter 5 for details
53
Enthalpy of Vaporization, Hv(T) = f(Tc, T, co) kJ/mol Method: Correlation (7-9.5 in Reid et aL [5]) tr=1.0-T/Tc W = ((D - 0.21)/0.25 for (0.2 < tr < 0.9) Ri = ^.'b^lH^™ - lA(flH^-^^^ - 1151\H}'^^^ + 59.634*tr + 36.009^^^ - 14.606*tr^ R2 = -0.133*tr^-^^^ - 28.215nr^-^^^ - 82.958*tr^^^^ + 99.000*tr+ 19.105*tr^ - 2.796*tr^ Hv(T) = (Ri + W*R2)*Te*0.008314 (37) Hildebrand Solubility Parameter, 8h(T) = f(Hv(T), Vn,(T), T) MPaVS Method: Theoretical (Equation) Definition 5h(T) = [(1000*Hv(T) - 8.314*T)/Vna(T])^^ (38) 3.5 DATABASES Databases relevant to property estimation for process-product design are collections of experimental pure component and mixture properties. In many cases, these databases also include coefficients for correlations of functional properties. A large variety of databases having a wide variety of pure component and mixture property data and their correlation function coefficients can be found, among others, on the internet, as commercial database services and as part of non-commercial software (usually from academia). In this section, only a non-commercial database is discussed together with references for some of the wellknown databases. 3.5.1
The CAPEC Database^
The CAPEC database^ contains information on 13000 compounds and on 40 pure component properties of different types (primary, secondary and functional), on 9 mixture properties, classification of compounds according to molecular structure, data on molecular structural representation in terms of groups and special solvent-solubility data. Compound Classification For each compound, molecular structural description in terms of SMILES string and the UNIFAC groups (first-order and higher-orders) are available and the compounds may be identified through their chemical name, formula or CAS number. The compounds are classified in terms of nine main categories: normal fluid, polar associating, polar nonassociating, multifunctional (with respect to groups), water, polymer, electrolyte, steroid and amino acid. Each main category is further divided into sub-categories, for example, steroids are further divided in terms of adrenal corticosteroids, androgens & anabolic steroids, estrogens, progestogens, and cholesterols. Figure 2 illustrates the classification of compounds, highlighting the sub-categories under polar associating compounds.
54 1. Normal Fluids 2. Polar Non-Associating Compounds 3. Polar Associating Compounds a. Organic i. Alcohols ii. Hydroperoxides iii. Amines (& Imines) iv. Acids V. Oximes vi. Nitriles vii. Sulfonic Acids viii. Isocyanates ix. Isothiocyanates X. Oxides xi. Phosphoric Acid (including Phosphorous & Phosphoric) b. Inorganic c. Inorganic Polar Associating 4. Multifunctional Grouped Molecules 5. Water 6. Polymers 7. Electrolytes 8. Steroids 9. Aminoacids Figure 2: The 9 main categories for the classification of chemicals in the CAPEC database Pure Component Data When available, the following pure component properties are given for each compound: Primary Property
Secondary Property
Functional Property
Molecular weight, critical temperature, critical pressure, critical volume, normal boiling point, normal melting point, heat of fusion at 298 K, heat of combustion at 298 K, ideal gas enthalpy at 298 K, ideal gas entropy at 298 K, ideal gas Gibbs energy at 298 K, liquid volume at normal boiling point, Hildebrand solubility parameter at 298 K, van der Waals surface area, van der Waals volume Critical compressibility factor, triple point temperature, triple point pressure, acentric factor, flash-point temperature, radius of gyration, dipole moment, refractive index, dielectric constant Vapor pressure, solid density, liquid density, solid heat capacity, liquid heat capacity, ideal gas heat capacity, liquid viscosity, vapor viscosity, liquid thermal conductivity, vapor thermal conductivity, surface tension and second virial coefficients
55
Mixture Data When available, the following mixture data can be found in the database. Binary Mixture Property (41000 data points) Ternary Mixture Property (10000 data points)
VLE, LLE, SLE, infinite dilution activity coefficients, heats of mixing, partial molar heats of mixing at infinite dilution, excess Gibbs energy, Henry's law constants, and mutual solubilities VLE, LLE, SLE, VLLE, heats of mixing
Special Data This class contains 2769 solubility data points consisting of solubility values and temperatures for 1374 binary mixtures involving 202 solutes (having molecular weights greater than 94 g/mol and having 4 < carbon atoms < 40) and 162 different types of solvents. In addition, to the solubility data, a list of 80 well-known solvents together with their solubility indicators (decomposes, miscible, insoluble, slightly soluble, soluble and very soluble) for most of the 13000 compounds in the database is also available. Search Engine An advanced search engine is available in the CAPEC database to identify compounds with a variety of search specifications. Two examples of search are given below. A. Find all compounds that are soluble in ethanol, having a boiling point > 300 K and a melting point < 250 K. B. Find all compounds having a boiling point > 300 K, a melting point < 250 K and the Hildebrand solubility parameter between 25 and 27 MPa°^. 3.5.2 References for Databases In this section, a few of the well-known databases found on the internet are listed below in Table 1, while references where useful data can be found are given in Table 2. Table 1: List of well-known databases Name API TECH Database CambridgeSoft ChemFinder
Address & Comments Pure component, petroleum characterization, etc. http://www.epcon.com Searchable data and hyperlink index for thousands of compounds - the ideal starting point for internet "data-mining" http://chemfinder.cambridgesoft.com/
56 Table 1 continued CRC Handbook of Chemistry and Physics DECHEMA Chemistry Data Series DETHERM DIPPR Electrolytes GPSA Data Book
lUPAC-NIST SDS Knoval Science and Engineering Resources PDB PPDS TAPP TheNISTWebbook
Library Network Database (http://www.hbcpnetbase.com/) A 15 volume data collection
Comprehensive collection of thermophysical and mixture properties data, includes Dortmund DDE and ELDAR DDE http://www.dechema.de/f-infsys-e.htm7englisch/dbMain.htm Critically evaluated thermophysical data http://www.aiche.org/dippr/vision.htm IVC-SEP database for properties of electrolyte systems www.ivc-sep.kt.dtu.dk/databank GPSA Engineering Data Eook -section 23 (physical properties) & 24 (thermodynamic properties) http://www.gasprocessors.com Solubility Data Series http://www.unileoben.ac.at/-eschedor Library Network Database (International Critical Tables, Polymers -Property Database, Handbook of Thermodynamic and Physical Properties of Chemical Compounds, etc.) Protein Data Eank - Processing and distribution of 3-D biological and macromolecular structural data http://pdb.ccdc.cam.ac.uk/pdb/ Physical Properties Data Service http://www.tds-tds.com/fs_ppds.htm Thermochemical and Physical Properties Database http://www.chempute.com/tapp.htm An excellent source of physical and chemical data http://webbook.nist.gov
3.6 CONCLUSIONS Pure component properties are needed in the solution of various types of process-product design problems as well as input in many models for estimation of mixture properties. Usually, they are stored (experimental data) in databases, at least, the single value properties and the temperature dependent functional properties. The problem, however, is that even though the database may contain thousands of compounds, not all data is available for all the listed compounds. Also, in process-product design, new chemicals may be synthesized, which would not be present in the database. For this reason, property models for estimation of pure component properties are needed. In this respect, the chapter provides the reader a
57 quick guide in terms of the most commonly used pure component properties and a representative set of property models. Table 2: References for data Biochemistry & Biotechnology Drugs- Phase diagrams Octanol-water partition coefficients Polymer Data Solubility data Solubility data Water infinite dilution activity coefficients
Thermodynamic data for biochemistry and biotechnology, Hans-Jurgen Hinz, Editor, Springer-Verlag, 1986 J. Phys Chem Res Data, 1999, 28(4), 889-930 J. Phys Chem Res Data, 1989, 18(3) 1111-1229 Polymer DIPPR 881 Project High &Danner, 1992 Barton Handbook, CRC Press 1990 J. Marrero & J. Abildskov, Solubility and realted properties of large complex molecules. Part 1, Chemistry Data Series, Vol XV, DECHEMA, 2003 Voutsas & Tassios, Ind Eng Chem Res, 1996, 35, 1438 supporting material
REFERENCES 1. J. Marrero, R. Gani, Fluid Phase Equilibria, 183-184 (2001)183. 2. J. Marrero, R. Gani, Industrial Engineering & Chemistry Research, 41 (2002) 6623. 3. B. E. Poling, J. M. Prausnitz, J. P. O'Connell, "The Properties of Gases and Liquids", McGraw-Hill, New York, 5th Edition, 2000. 4. A. L. Horvath, "Molecular Design", Elsevier, Amsterdam, The Netherlands, 1992. 5. R. Reid, J. M. Prausnitz, B. E. Poling, "The Properties of Gases and Liquids", McGraw-Hill, New York, 4th Edition, 1987. 6. L. C. Constantinou,R. Gani,AIChEJ,40(1994)1697 7. T. M. Martin, D. M. Young, Chem, Res. Toxicol., 14 (2001), 1378 8. T. L. Nielsen, J. Abildskov, P. M. Harper, I. Papaeconomou, R. Gani, J. Chem Eng Data, 46 (2001) 1041.
45
Chapter 3: Pure Component Property Estimation: Models & Databases Jorge Marrero and Rafiqul Gani 3.1 INTRODUCTION Pure component properties are needed for many process and product design calculations. They may be needed to study the behavior of the product (such as the solubility of drug in water), behavior of a chemical under the conditions of operation of a process (heat of vaporization of a refrigerant or process fluid in a closed cycle), dimensioning of equipment (density of the chemical in a tank), the physical state of the product (melting point and/or boiling point to identify solid, liquid or vapor state) and many more. For the estimation of mixture properties also, the pure component properties are employed in different mixture property models. For example, the well-known SRK equation of state employs the critical properties while an ideal mixing model for liquid density employ only the pure component liquid densities of each chemical species present in the mixture. The objective of this chapter is to provide the reader with a set of pure component property models for a corresponding set of frequently used properties in process-product design. These models have been tested and evaluated against a wide range of chemical species by the authors. A good collection of pure component property models can also be found in many specialized property estimation books, journal papers, commercial software and databases. It is beyond the scope of this chapter to name all the references as well as methods. The calculation methods outlined in each section of this chapter should provide some guidance in terms of the important steps related to estimation of a pure component property.
3.2 MODELS FOR PRIMARY PROPERTIES As described in Chapter 1, primary properties are classified as those, which can usually be determined only from the molecular structural information and have a single unique value. In this chapter, only a set of primary properties that are needed for a wide range of processproduct design calculations are presented through one property estimation approach. This does not mean that the property models used below are the best or have the highest accuracy. These models are, however, frequently used and their details, including the model parameter tables, are readily available. The property model presented below can be classified as an additive method using a group-contribution^ approach. The property estimation methods will be highlighted through the molecular structure of Glycine (CAS No. 000056-40-6). Only the estimated primary and the secondary properties of
46 Glycine are given. When the estimated value for a secondary property for Glycine is not given, it means that not all the dependent properties are available. When experimental data for the property is available, it is also given. The chemical formula, group assignment and 3D molecular structure is given in Figure 1.
Compound Smiles: NCC(=0)0 Compound Formula: C2H5N02 Molecular Weight (g/mol): 75.07 1 st-order groups 1 COOH 1 CH2NH2
1
2"^-order Groups CHm(NHn)-COOH Figure 1: Molecular structural details for Glycine
3.2.1
Primary Property Models
All the properties Hsted below are only functions of the molecular structural information described in terms of first-order, second-order and third-order groups. Note that all molecules must be completely described by first-order groups and may or may not have second- and third-order groups. The estimation steps are as follows: 1. 2. 3. 4.
Identify the groups (first-, and if necessary, second- and third-order groups) Determine how many groups of each type are needed to represent the molecule Retrieve the parameters from the model parameter tables for the property of interest Sum the contributions and use the corresponding property model fimction
Properties & Models The following property models have been proposed by Marrero and Gani [1, 2]. In each case, the sunmiation terms having the following expression Contribution of first-order groups, Sum.Groups.I = Zi Hi Ci for i = 1, NCi Contribution of second-order groups, Sum.Groups.II = Ej nij Dj for j = 1, NC2 Contribution of third-order groups, Sum.Groups.II! = Ek Ok Ek for k = 1, NC3
(1) (2) (3)
In the above equations, ni, mj, Ok are the number of first-, second- and third-order groups of types i, j and k, respectively. Ci, Dj and ER are the contributions for the selected property for first-, second- and third-order groups of types i, j and k, respectively. NCi, NC2 and NC3 are the total numbers of different types of first-, second- and third-order groups representing the molecule. Critical temperature. K Tc = 231.239*log(Sum.Groups.I + Sum.Groups.II + Sum. Groups.Ill)
(4)
47 Glycine: 1028.0 K (experimental: 1028) Critical pressure, bar Pc = l/(Sum.Groups.I + Sum.Groups.II + Sum.Groups.III + 0.108998)^ + 5.9827
(5)
Glycine: = 67.4 bar (experimental: 67.4) Critical volume, cm^/mol Vc = 7.95 + Sum.Groups.I + Sum.Groups.II + Sum.Groups.III
(6)
Glycine: = 234.01 cmVmol (experimental: 234.0) Normal melting point, K Tn,= 147.450*log(Sum.Groups.I + Sum.Groups.II + Sum.Groups.III)
(7)
Glycine: = 535.63 K (experimental: 535.15) Normal boiling point, K Tb = 222.543 *log(Sum.Groups.I + Sum.Groups.II + Sum.Groups.III)
(8)
Glycine: = 710.97 K Standard Gibbs free energy of formation, kJ/mol Gf = -34.967 + Sum.Groups.I + Sum.Groups.II + Sum.Groups.III
(9)
Glycine: -300.1 kJ/mol (experimental: -300.1) Standard Enthalpy of formation at 298 K, kJ/mol Ht = 5.549 + Sum.Groups.I + Sum.Groups.II + Sum.Groups.III
(10)
Glycine: -388.49 kJ/mol (experimental: -392.1) Enthalpy of vaporization at 298 K, kJ/mol Hv = 11.733 + Sum.Groups.I + Sum.Groups.II + Sum.Groups.III Enthalpy of vaporization at TH, kJ/mol Hvb = a + Sum.Groups.I + Sum.Groups.II + Sum.Groups.III
(11) (12)
Glycine: = 43.0 kJ/mol Heatoffusionat298K, kJ/mol Hfus = -2.806 + Sum.Groups.I + Sum.Groups.II + Sum.Groups.III Glycine: 28.4 kJ/mol (experimental: 28.4)
(13)
48 3.3 MODELS FOR SECONDARY PROPERTIES As defined in Chapter 1, secondary properties are those that cannot be expUcitly calculated only from structural information and usually require the knowledge of other properties. Most of these models have been derived from the principle of corresponding states, although, a number of empirical correlations also exist. There is available, a wide range of methods for prediction of secondary properties. Many books and handbooks provide methods for calculating these properties. Poling et al. [3] provides a good collection of many of the needed methods. Horvath [4] also provides a large number of methods for primary as well as secondary properties. In this section, a set of pure component properties that have a single value is listed together with a corresponding property estimation method. 3.2.1 Secondary Property Models The following steps may be followed in the estimation of pure component secondary properties. 1. For the secondary property of interest, select an estimation method 2. For the selected estimation method, identify the properties (data) needed to use the model and verify the application range of the method in terms of chemical species (type) 3. Retrieve from a database or predict the necessary properties (to be used as input) 4. Calculate the property through the selected method For the secondary properties listed below, the following properties are needed as input. All these properties are also defined below. For each property, first the generic form of the equation in terms of dependency on other properties/variables is given, followed by the method for calculation, the model equations, and finally, the calculated value for the chemical used as an example. Hfos (kJ/mol), Tb (K), Tc (K), Pc (bar), Vc (cmVmol), SoW (MPa^-^), 5s (MPa^-^), UD, D„, (debye), p'(bar), Mw (g/mol), Ws (mg/L) Properties & Models For each property, the name of the property, the representation of the property in terms of its dependence on other properties, the method used and the equations involved are presented. Heat of Vaporization at Th, Hvb= f(Tb, T^ Pc) Method: Correlation (Equation 7-11.5 in Reid et. aL [5]) tr = Tb/Tc X = 0.37691 - 0.37306*tr+ 0.15075/(Pc*tr^) Y = (0.4343*log(Pc) - 0.69431+ 0.89584*tr)/X
49 Hvb = Tb*0.008314*Y (14) Not recommended for Glycine Pitzer's Acentric Factor, oo = f(Tb, Tc, Pc) Method: Lee-Kesler Correlation (2-3.4 in Reid et aL [5])) / Constantinou & Gani [6] e = Tb/Tc a =- -log(Pc*0.98692327) - 5.92714 + 6.09648/6 + 1.28862*log(e) - 0.169347'^e^ P = 15.2518 - 15.6875/e - 13.472l*log(e) + 0.43577*9^ G) = a / p (15) Glycine: 0.747 Lee-Kessler 0.673 Constantinou & Gani [6] Critical Compressibility Factor, Zc = f(Tc, Pc, Vc) Method: Theoretical (Equation) Definition Zc = (Pc''Vc)/(83.14=^Tc) (16) Glycine: 0.185 Liquid Volume at Tj2, Vb =f(Vc), cm^/mol Method: Tyn and Calus Correlation (3-10.1 in Reid et. aL [5]) Vb = 0.285*Vc^-^^^ (17) Glycine: 86.5 cmVmol Liquid Volume at 298 K, Vm = f(Tc, Pc, o), cmVmol Method: Rackett Modified Correlation tr=1.0-298.15/Tc Zra = 0.29056-0.08775*G) tfunc- 1 +(l-tr) V n , = (83.14*Tc*Zra "'''yPc
(18) Refractive Index, no = f(Solpar) Method: Correlation [4] no = (0.48872*Solpar+5.55)/9.55 (19) Glycine: 1.8 Molar Refraction, Rm = f(nD, Vm) Method: Correlation [4] Rm = (((nD)'-l)*V„,nOOO)/((nD)' + 2) (20) Surface Tension at 298 K, a = f(Solpar, Vm), dyne/cm Method: Correlation [4]
50 a = 0.01707'^(SolparrnVmr (21) Entropy of Fusion, Sfus = f(Hfus, Tn,), J/(mol*K) Method: Theoretical (Equation) Definition Sfus=1000*Hfus/Tm
(22)
Glycine: 53.07 J/(moPK) Closed Flash Temperature, Tfc = f(GCcG, Tb), K Method: Constantinou and Gani [6] Tfc = -2.03'=^(Sum.Groups.IcG) + 0,659'^Tb + 20.00
(23)
Open Flash Temperature, Tfo = f(GCcG, Tb), K Method: Constantinou and Gani [6] Tfo = 3.63*(Sum.Groups.IcG) + 0.409*Tb + 88.43
(24)
Glycine: 414 K Hansen Dispersive Solubility Parameter, 5s = f(GCcG, Vm), MPa^^ Method: Constantinou and Gani [6] 5s = (Sum.Groups.IcG)/Vm
(25)
Glycine: 17.74 MPa^^ Hansen Polar Solubility Parameter, 5P = f(GCrG, Vm). MPa^^ Method: Constantinou and Gani [6] 5p = [(Sum.Groups.IcG)' ']/Vm (26) Glycine: 12.16 MPa^0.5 Hansen Hydrogen Bonding Solubility Parameter, 5HB = f(GCcG, Vm), MPa0.5 Method: Constantinou and Gani [6] 5HB = [(Sum.Groups.IcG)A/^m]'^' (27) Glycine: 17.38 MPa^^ Dipole Moment, Dm = f(6s, Vm), debye Method: Correlation [4] Dm = 0.02670*6s*(Vmf' (28) Dielectric Constant, DE = F(Solpar, HD, Dm) Method: Correlation [4] IfnD< 0.001, DE = (nD)'
51 Else, DE = (Solpar*0.48871-7.5)/0.22 (29) Henry Constant of a gas in water at 298 K. Hhenry = (p\298), Mw, Ws), bar*mVmol Method: Theoretical (Equation) Definition Hhenry = p ' ( 2 9 8 ) * M w / W s
(30)
3.3,1 Secondary Properties modeled as Primary Property For a number of secondary of secondary properties, it is sometimes possible to model them as primary properties. That is, it is possible to predict the property only as a function of the molecular structural information. Recently, Marrero and Gani [2] have developed models for Octanol-water partition coefficients. Solubility of a chemical in water at 298 K, and the Hildebrand solubility parameter. Also, the method of Martin and Young [7] for the measure of toxicity in terms of 50% mortality of Fathead Minnow after 96 hours of exposure has been adapted to the Marrero and Gani method. As in the case of primary properties listed in section 3.2, the prediction of the following properties also follow the same steps Octanol-water partition coefficient (LogKow) LogKow = A + Sum.Groups.I + Sum.Groups.II + Sum.Groups.III
(31)
Glycine: -3.41 (experimental:-3.21) Water Solubility. Ws, Log(mg/L) LogWs = A + Sum.Groups.I + Sum.Groups.II + Sum.Groups.III Glycine: 5.41 (experimental: 5.39)
(32)
Hildebrand solubility parameter at 298 K, Solpar, M(Pa) Solpar = A + Sum.Groups.I + Sum.Groups.II + Sum.Groups.III (33)
Glycine: 23.9 MPa^^ Acute Toxicity r96-h LC50) to Fathead Minnow. mol/L -Log(LC50) = Sum.Groups.I (34) Glycine: 2.82
3.4 FUNCTIONAL PROPERTIES As defined in Chapter 1, pure component functional properties are those that depend on the specific value of temperature and/or pressure. Most prediction methods employ a suitable equation of state, the principle of corresponding states or a specially fitted correlation. In this
52 section, a set of functional properties and a corresponding property model is presented. Note that as in secondary properties, functional properties may also require other properties as input data. Note also that many temperature dependent functional properties are available in databases where the coefficients for the correlation of each property and chemical are stored. These correlation functions are discussed in section 3.4 of this chapter. The following steps may be employed in the estimation of functional properties. 1. For the property of interest, select an appropriate property model. 2. Verify the applicability of the model in terms of chemical species as well as the temperature (and/or pressure) limit of the method. 3. Retrieve or estimate the necessary properties to be used as input data 4. Calculate the property of interest at the condition (temperature and/or pressure) of interest using the selected method 3.4.1 Properties & Models For each property, the name of the property, the functional dependence, the units of measure, the method and the model equations are presented. Diffusion coefficient of component at infinite dilution in water. Dab = f(Vb, Tb, T) cmVs Method: Modified Tyn & Calus Correlation (11-9.5 in Reid et. al [5]) X = exp(-24.71 + 4209/T + 0.04527*T - 0.00003376'^T^) for 273.15 < T < 643.15 Dab = 0.01955/[(Vb)^-^^^]*(T/X) (34) See also Chapter 9 for other prediction methods. Liquid Density, GL = f(Tc, Pc, co, T), g/cm^ Method: Modified Rackett correlation (3-11.10 in Reid et. al. [5]) Zra = 0.29056-0.08775*03 Tfonc = 1 + (1-T/Tc)^'^^^^^ for T/Tc < 0.9 CJL = (83.14*Tc*(Zra)'^'""')/Pc
(35) Thermal Conductivity, Tcon = f(Tb, Tc, T, Mw) W/m*K Method: Correlation (10-9.5 in Reid et. al [5]) Tr = T/Tc Tbr=Tb/Tc Tcon = [l.ll/[(Mw)^-^]*(3 + 20*(l-Tr)^-^^^^)]/[(3 + 20*(1-Tbr)^-^^^^)] (36) forTr<0.9;Tbr<0.9 Vapor Pressure, pS = f(Pc, Tc, T, co) bar Method: Modified SRK EOS or any appropriate equation of state See Chapter 5 for details
53
Enthalpy of Vaporization, Hv(T) = f(Tc, T, co) kJ/mol Method: Correlation (7-9.5 in Reid et aL [5]) tr=1.0-T/Tc W = ((D - 0.21)/0.25 for (0.2 < tr < 0.9) Ri = ^.'b^lH^™ - lA(flH^-^^^ - 1151\H}'^^^ + 59.634*tr + 36.009^^^ - 14.606*tr^ R2 = -0.133*tr^-^^^ - 28.215nr^-^^^ - 82.958*tr^^^^ + 99.000*tr+ 19.105*tr^ - 2.796*tr^ Hv(T) = (Ri + W*R2)*Te*0.008314 (37) Hildebrand Solubility Parameter, 8h(T) = f(Hv(T), Vn,(T), T) MPaVS Method: Theoretical (Equation) Definition 5h(T) = [(1000*Hv(T) - 8.314*T)/Vna(T])^^ (38) 3.5 DATABASES Databases relevant to property estimation for process-product design are collections of experimental pure component and mixture properties. In many cases, these databases also include coefficients for correlations of functional properties. A large variety of databases having a wide variety of pure component and mixture property data and their correlation function coefficients can be found, among others, on the internet, as commercial database services and as part of non-commercial software (usually from academia). In this section, only a non-commercial database is discussed together with references for some of the wellknown databases. 3.5.1
The CAPEC Database^
The CAPEC database^ contains information on 13000 compounds and on 40 pure component properties of different types (primary, secondary and functional), on 9 mixture properties, classification of compounds according to molecular structure, data on molecular structural representation in terms of groups and special solvent-solubility data. Compound Classification For each compound, molecular structural description in terms of SMILES string and the UNIFAC groups (first-order and higher-orders) are available and the compounds may be identified through their chemical name, formula or CAS number. The compounds are classified in terms of nine main categories: normal fluid, polar associating, polar nonassociating, multifunctional (with respect to groups), water, polymer, electrolyte, steroid and amino acid. Each main category is further divided into sub-categories, for example, steroids are further divided in terms of adrenal corticosteroids, androgens & anabolic steroids, estrogens, progestogens, and cholesterols. Figure 2 illustrates the classification of compounds, highlighting the sub-categories under polar associating compounds.
54 1. Normal Fluids 2. Polar Non-Associating Compounds 3. Polar Associating Compounds a. Organic i. Alcohols ii. Hydroperoxides iii. Amines (& Imines) iv. Acids V. Oximes vi. Nitriles vii. Sulfonic Acids viii. Isocyanates ix. Isothiocyanates X. Oxides xi. Phosphoric Acid (including Phosphorous & Phosphoric) b. Inorganic c. Inorganic Polar Associating 4. Multifunctional Grouped Molecules 5. Water 6. Polymers 7. Electrolytes 8. Steroids 9. Aminoacids Figure 2: The 9 main categories for the classification of chemicals in the CAPEC database Pure Component Data When available, the following pure component properties are given for each compound: Primary Property
Secondary Property
Functional Property
Molecular weight, critical temperature, critical pressure, critical volume, normal boiling point, normal melting point, heat of fusion at 298 K, heat of combustion at 298 K, ideal gas enthalpy at 298 K, ideal gas entropy at 298 K, ideal gas Gibbs energy at 298 K, liquid volume at normal boiling point, Hildebrand solubility parameter at 298 K, van der Waals surface area, van der Waals volume Critical compressibility factor, triple point temperature, triple point pressure, acentric factor, flash-point temperature, radius of gyration, dipole moment, refractive index, dielectric constant Vapor pressure, solid density, liquid density, solid heat capacity, liquid heat capacity, ideal gas heat capacity, liquid viscosity, vapor viscosity, liquid thermal conductivity, vapor thermal conductivity, surface tension and second virial coefficients
55
Mixture Data When available, the following mixture data can be found in the database. Binary Mixture Property (41000 data points) Ternary Mixture Property (10000 data points)
VLE, LLE, SLE, infinite dilution activity coefficients, heats of mixing, partial molar heats of mixing at infinite dilution, excess Gibbs energy, Henry's law constants, and mutual solubilities VLE, LLE, SLE, VLLE, heats of mixing
Special Data This class contains 2769 solubility data points consisting of solubility values and temperatures for 1374 binary mixtures involving 202 solutes (having molecular weights greater than 94 g/mol and having 4 < carbon atoms < 40) and 162 different types of solvents. In addition, to the solubility data, a list of 80 well-known solvents together with their solubility indicators (decomposes, miscible, insoluble, slightly soluble, soluble and very soluble) for most of the 13000 compounds in the database is also available. Search Engine An advanced search engine is available in the CAPEC database to identify compounds with a variety of search specifications. Two examples of search are given below. A. Find all compounds that are soluble in ethanol, having a boiling point > 300 K and a melting point < 250 K. B. Find all compounds having a boiling point > 300 K, a melting point < 250 K and the Hildebrand solubility parameter between 25 and 27 MPa°^. 3.5.2 References for Databases In this section, a few of the well-known databases found on the internet are listed below in Table 1, while references where useful data can be found are given in Table 2. Table 1: List of well-known databases Name API TECH Database CambridgeSoft ChemFinder
Address & Comments Pure component, petroleum characterization, etc. http://www.epcon.com Searchable data and hyperlink index for thousands of compounds - the ideal starting point for internet "data-mining" http://chemfinder.cambridgesoft.com/
56 Table 1 continued CRC Handbook of Chemistry and Physics DECHEMA Chemistry Data Series DETHERM DIPPR Electrolytes GPSA Data Book
lUPAC-NIST SDS Knoval Science and Engineering Resources PDB PPDS TAPP TheNISTWebbook
Library Network Database (http://www.hbcpnetbase.com/) A 15 volume data collection
Comprehensive collection of thermophysical and mixture properties data, includes Dortmund DDE and ELDAR DDE http://www.dechema.de/f-infsys-e.htm7englisch/dbMain.htm Critically evaluated thermophysical data http://www.aiche.org/dippr/vision.htm IVC-SEP database for properties of electrolyte systems www.ivc-sep.kt.dtu.dk/databank GPSA Engineering Data Eook -section 23 (physical properties) & 24 (thermodynamic properties) http://www.gasprocessors.com Solubility Data Series http://www.unileoben.ac.at/-eschedor Library Network Database (International Critical Tables, Polymers -Property Database, Handbook of Thermodynamic and Physical Properties of Chemical Compounds, etc.) Protein Data Eank - Processing and distribution of 3-D biological and macromolecular structural data http://pdb.ccdc.cam.ac.uk/pdb/ Physical Properties Data Service http://www.tds-tds.com/fs_ppds.htm Thermochemical and Physical Properties Database http://www.chempute.com/tapp.htm An excellent source of physical and chemical data http://webbook.nist.gov
3.6 CONCLUSIONS Pure component properties are needed in the solution of various types of process-product design problems as well as input in many models for estimation of mixture properties. Usually, they are stored (experimental data) in databases, at least, the single value properties and the temperature dependent functional properties. The problem, however, is that even though the database may contain thousands of compounds, not all data is available for all the listed compounds. Also, in process-product design, new chemicals may be synthesized, which would not be present in the database. For this reason, property models for estimation of pure component properties are needed. In this respect, the chapter provides the reader a
57 quick guide in terms of the most commonly used pure component properties and a representative set of property models. Table 2: References for data Biochemistry & Biotechnology Drugs- Phase diagrams Octanol-water partition coefficients Polymer Data Solubility data Solubility data Water infinite dilution activity coefficients
Thermodynamic data for biochemistry and biotechnology, Hans-Jurgen Hinz, Editor, Springer-Verlag, 1986 J. Phys Chem Res Data, 1999, 28(4), 889-930 J. Phys Chem Res Data, 1989, 18(3) 1111-1229 Polymer DIPPR 881 Project High &Danner, 1992 Barton Handbook, CRC Press 1990 J. Marrero & J. Abildskov, Solubility and realted properties of large complex molecules. Part 1, Chemistry Data Series, Vol XV, DECHEMA, 2003 Voutsas & Tassios, Ind Eng Chem Res, 1996, 35, 1438 supporting material
REFERENCES 1. J. Marrero, R. Gani, Fluid Phase Equilibria, 183-184 (2001)183. 2. J. Marrero, R. Gani, Industrial Engineering & Chemistry Research, 41 (2002) 6623. 3. B. E. Poling, J. M. Prausnitz, J. P. O'Connell, "The Properties of Gases and Liquids", McGraw-Hill, New York, 5th Edition, 2000. 4. A. L. Horvath, "Molecular Design", Elsevier, Amsterdam, The Netherlands, 1992. 5. R. Reid, J. M. Prausnitz, B. E. Poling, "The Properties of Gases and Liquids", McGraw-Hill, New York, 4th Edition, 1987. 6. L. C. Constantinou,R. Gani,AIChEJ,40(1994)1697 7. T. M. Martin, D. M. Young, Chem, Res. Toxicol., 14 (2001), 1378 8. T. L. Nielsen, J. Abildskov, P. M. Harper, I. Papaeconomou, R. Gani, J. Chem Eng Data, 46 (2001) 1041.