Computers &em. Engng, Vol. 17, No. 7, pp. 717-738, 1993 Printed in Great Britain. All rights reserved

0098-I 354/93 S6.00 + 0.00 copyright 8 1993 Pergamon Press Ltd

A KNOWLEDGE INTENSIVE METHODOLOGY FOR THERMODYNAMIC CHOICES

P. K. PARANJAPE’ and A. P. KUDCHADKER’J ‘Computer Aided Design Centre, Indian Institute of Technology, Bombay-400076, India

*Chemical Engineering Department, Indian Institute of Technology, Bombay-400076, India

(Received 5 April 199I;final revision received 3 June 1992; received for publication 4 September 1992)

Abstract-A knowledge intensive approach has been developed for choosing an appropriate thermodyn- amic correlation for a given process calculation. The limitations of a typical nonexuert. and the streneth and the responsibilities of a domain expert (or a knowledge-based -ixpert systeh) have been cle&y identified. A prototype expert system CHOCOVALE (CHoice of COrrelation for V&or-Liauid Equilibrium computations) which -&sumes the responsibility of making most of the necessaj intelli&nt judgments, has been developed for making an appropriate choice of a vapor-liquid equilibrium computation methodology. The advice of CHOCOVALE has been compared to that given by thermodyn- amic experts for more than 50 test mixtures at different process conditions. The results indicate that CHOCOVALE is a competent advisor.

1. INTRODUCTION

The utility of knowledge-based systems for solving a variety of problems in the domain of chemical engin- eering is currently a well appreciated fact. In the recent past, a lot of research effort was expended in exploring the suitability of the knowledge-based ap- proach in solving problems such as fault diagnosis, process design, network synthesis and selection of material or methodology.

The emphasis of the work has so far been on developing prototype systems. Some of the examples are HEATEX (Grimes et al., 1982) which aids the user in the synthesis of a heat exchanger network that minimizes energy requirements; FALCON (Dhurjati et al., 1987), or a prototype system based on CSRL language (Shum et al., 1988) which carry out fault diagnosis; DECADE (Banares-Alcantara, 1984), which aids the user in catalyst selection; and CON- PHYDE (Banares-Alcantara et al., 1985), which pro- vides advice on the choice of a proper correlation for estimating vapor-liquid equilibrium (VLE) con- ditions. The idea of DESIGN-KIT, a tool kit that allows designers to move about all the subtasks without interrupting their train of thought or impos- ing unnecessary bookkeeping burdens, is also being developed with the aid of AI methodologies (Stephanopoulos, 1987). The process of development of a prototype system allows one to assess the utility of KBES for solving that problem, and to examine the suitability of various knowledge structures for capturing the domain knowledge.

The earlier knowledge-based systems employed a predominantly heuristic approach. Recent works

have explored the model-based approach (Rich and Venkatasubramanian, 1987; Dalle Molle et al., 1988; Kramer, 1987) and the first principles approach (Grantham and Ungar, 1990) for a more generalized and robust representation of domain knowledge. The use of neural networks has also been explored (Hoskins and Himmelblau, 1988; Venkatasubrama- nian et al., 1990; Venkatasubramanian and Chan, 1989). The emphasis of these and other similar studies has been on the development or assessment of a generalized methodology that has some desirable characteristics for knowledge elicitation in a particu- lar domain.

There are other important issues associated with the development of knowledge-based systems. Today, experts are taking an increasing interest in the build- ing of knowledge-based systems with the help of expert system shells or building tools. In this context, the central issue is of making the domain knowledge explicit in such a way as to achieve a maximal problem solving capability within any knowledge representation formalism. Birky and McAvoy (1990) have addressed this issue in the context of control system design.

The present study attempts to focus attention on the domain-specific issues with respect to the domain of thermodynamic choices. In particular it proposes a knowledge intensive set of rules for addressing the problem of providing intelligent advice in the context of choosing an appropriate correlation for making vapor-liquid equilibrium (VLE) computations.

Accurate estimation of physical and thermodyn- amic properties is essential for the analysis and

717

718 P. K. PARANJAPE and A. P. KUDCHALXER

simulation of chemical process plants. Various meth- odologies are available for estimation of any given property. Making an appropriate choice is important. In the case of VLE computations, a wrong choice of methodology could lead to major undesirable wnse- quences such as inefficient design, overestimation of plant costs and even plant failure in some cases (Zudkevitch, 1980; Martin, 1964). Various factors, such as the nature of the compounds, the nature of the process and the availability of the experimental data determine the applicability of any methodology for a particular case. It can be argued that most of the limitations of a given available methodology can be traced to its inadequate mathematical form. Each mixture at a given process condition demands a certain degree of mathematical rigor for its proper characterization. Each methodology can satisfy these demands to a certain extent. A proper match must be established for an appropriate choice. Although a vast amount of information about the applicability of different methodologies is available (Prausnitz, 1977; Prausnitz et al., 1980, 1986; Walas, 1985; Gray, 1979; Chao and Robinson, 1979; Storvik and Sandler, 1977; Fredenslund et al., 1980; Martin, 1979), a formal approach towards making a proper choice has not been developed.

An entirely numerical approach is usually unsuit- able for arriving at a proper choice. Typically, the advice of an “expert”, who uses his experience and knowledge for skilful manipulation of concepts and symbols, is sought for arriving at a proper choice of methodology.

The present work attempts to identify some concepts that are useful for arriving at good de- cisions on property options. The idea is to develop some pointers towards examination of the knowledge component that is associated with an expert’s meth- odology. A subset of thermodynamic properties, namely the VLE conditions has been chosen. All the ideas have been developed with respect to the process of choosing a proper correlation for esti- mation of VLE conditions. Similar ideas would apply to estimation of other thermodynamic pro- perties. No emphasis, however, has been placed on developing a generalized model of an expert behavior.

The first attempt at tackling this problem that has been reported in the literature has resulted in the development of a prototype expert system CON- PHYDE (CONsultant for PHYsical property DE- &ions-Banares-Alcantara et al., 1985). Although CONPHYDE was developed without any significant amount of input from the domain experts, it amply illustrated the utility of the knowledge-based expert systems, and captured a good deal of information

concerning the applicability of different correlations. It, however, left a number of important decisions (for example, the judgment regarding the nature of the mixture) to the user thus necessitating an intelligent input.

Gani and O’Connell (1989) have used tables of selection indices in their work. The various indices along with rules automatically classify the mixtures and process conditions and examine the applicability of different correlations. The classification schemes used by them are somewhat inflexible (the use of absolute pressure for classifying process conditions for example); and explicit a priori judgments regard- ing the behavior of a mixture have not been at- tempted.

The present work attempts to probe deeper into the “knowledge component” of the problem solution. It contrasts information against domain knowledge in the context of thermodynamic correlations. It clearly identifies the limitations of a typical nonexpert, and the strength and the responsibilities of a domain expert (or a knowledge-based expert system). Based on this analysis, the possibility of making intelligent judgments starting from a minimal amount of infor- mation that a typical user is likely to be able to provide has been explored. A knowledge-intensive set of rules for classifying mixtures and process con- ditions has been suggested.

A prototype expert system CHOCOVALE, which assumes the responsibility of making most of the necessary intelligent judgments, has been developed. CHOCOVALE was developed on an IBM PC com- patible in the rule-based language 0PS5 -t . It consists of nearly 210 0PS5 + rules. The advice of CHOCO- VALE has been compared to that given by thermo- dynamic experts for more than 50 test mixtures at different process conditions. The results indicate that CHOCOVALE is a competent advisor.

2. THERMODYNAMICS BACKGROUND

No single methodology of VLE computations can adequately account for all the types of mixtures and the process conditions. A large number of different correlations, each having a different range of applica- bility, are commonly employed. In this section, a brief review of the merits and demerits of the different models that are widely used to estimate VLE con- ditions for mixtures of organic compounds is pre- sented. The corresponding mathematical expressions are available in literature. Correlations that cater to inorganic compounds or electrolytes have not been included.

Out of the commonly used models, equations of state (EOS) cater to both liquid and vapor phases

A knowledge intensive methodology for thermodynamic choices 719

for mixtures of relatively simple molecules. Activity coefficient models are generally used for predicting the liquid phase behavior of mixtures of polar sub- stances.

2.1. Uniform approach

The uniform approach is based upon the use of an EOS for both the vapor and liquid phases. An EOS gives the pressure-volume+temperature relationship of a pure component or a mixture. Whenever poss- ible, it is advisable to employ the uniform approach for following reasons:

1.

2. 3.

4.

5.

It does not require specification of extra func- tions or variables. Continuity at the critical point is guaranteed. Most of the thermodynamic properties needed could be obtained from the same model. Presence of noncondensable gases does not cause any problems. Consistency is inherent in the uniform ap- proach.

Most of these advantages become more and more significant as the pressure of the system increases and in the vicinity of critical conditions.

Many classifications of the EOS have been pro- posed in the literature (Fredenslund et al., 1980).

The main criteria for classification have been the basis of an EOS and the computational aspects. Cubic EOS which express pressure as a cubic function of molar volume are simple, easy to use, and quite accurate except in the vicinity of the critical con- ditions (Martin, 1979). As opposed to these, EOS such as the Benedict-Webb-Rubin equation of state (BWR) are accurate but complex (Starling, 1973).

Complexity necessitates iterative volume calcu- lations and renders estimation of parameters more difficult. If the number of characteristic constants in an EOS is high, more experimental data are required for their estimation. These EOS are not very reliable for extrapolation. The Lee-Kesler-Plocker (LKP) EOS is a generalized form of BWR EOS that is applicable to hydrocarbons, associated gases and some mixtures containing water (Plocker et al., 1978). It is an EOS based on the three-parameter corresponding-states theory and gives accurate re- sults even for mixtures that consist of compounds of widely differing sizes. Since the LKP EOS does not need any characteristic constants, it can be classified as a predictive equation. For mixtures, LICP needs binary parameters and in the case of some mixtures, correlations of binary parameters with critical constants are available and give accurate results.

2.2. Liquid model approaih

As the uniform approach is not suitable for polar compounds, a dual approach that incorporates ac- tivity coefficient models to account for liquid phase nonideality is needed. The activity coefficient is ex- pressed as a function of temperature and the liquid composition in most of these models. The pressure dependence of the activity coefficient is assumed to be negligible. For this reason, as the pressure of the system increases, the use of EOS with a uniform approach becomes more promising.

Raoult’s law which is applicable to ideal liquid mixtures is the simplest activity coefficient model with a constant value 1.0 of the activity coefficient. De- pending upon whether the value of the activity co- efficient is greater than or less than 1.0, the mixture is said to have positive or negative deviations from ideality. The form of certain activity coefficient models prevents them from accounting for both types of deviations. Extrema in the activity coefficient vs composition curve, nonsymmetrical deviations from ideality in a given mixture (positive deviations for a high concentration of one component and negative deviations for a high concentration of other) and dependence of activity coefficient values on tempera- ture and pressure are some other features that an appropriate liquid model must account for in the case that any or all of them are present in a given mixture.

There are some other important issues associated with the activity coefficient equations. In addition to the VLE conditions, such an equation should ideally be able to predict the liquid phase immiscibility if it exists. This requirement places certain mathematical restrictions on the model equation. Surprisingly, not all the common activity coefficient models can predict the phase splitting behavior. The regular solution model and Margules equation are some such examples. The Wilson equation, which works very well for even some highly nonideal mixtures, is also unable to predict the liquid phase immiscibility. In addition to this reason, there are other mathematical inadequacies that limit the applicability of some liquid models.

Models such as UNIFAC contain no adjustable parameters. UNIFAC computes activity coefficients based on the functional groups that constitute a mixture. It is thus predictive in nature and is an excellent method when experimental data are not available. It can predict immiscibility. Extensive tab- ulations of the values of the group contributions are available.

In solutions of gases, one pure component just does not exist as a liquid at the mixture temperature and pressure. Standard state reference fugacity

720 P. K. PAIUNJAPE and A. P. KUDCHA~KER

cannot be accurately estimated for such components. Henry’s law is used in such cases.

2.3. Common factors that govern the applicability

From the preceding discussion, it is clear that the main factors that determine the applicability of vari- ous methods include the temperature and pressure ranges, the mixture type, the mathematical form of the correlation and the availability of data.

In addition to the above-mentioned factors, certain general rules may be used for determining the appli- cability. Complex equations are generally not suitable if extrapolations are necessary. Empirical equations should not be used for extrapolation.

These factors have been used for determining the scheme of knowledge representation of CHOCO- VALE. A detailed discussion about the correlation of these factors with the process needs follows later.

A summary of general characteristics of different methodologies in the Attribute-Value format of 0PS5 + is given in Table 1. The exact temperature range in which each methodology is applicable, and the width of the range it can handle are some more important characteristics that are associated with each methodology. The temperature and pressure ranges could be in terms of absolute or reduced values. Additional knowledge about the applicability of these correlations is encoded in terms of rules of OPSS+. A master list of correlations considered by CHOCOVALE is given in Appendix A.

3. ANALYSIS OF EXPERTISE

The performance of an expert in solving problems in his domain of expertise is distinctly superior to that of a novice. The analysis of expertise must therefore begin with the analysis of performance. In particular,

the manifestation of superiority and its causes must be investigated. One can begin with the specification of the problem which warrants an expert’s assistance.

3.1. The problem specification

The typical scenario that warrants the help of an expert could be as follows. A process engineer wants to design a chemical plant unit; or perhaps the debottlenecking of an existing plant is intended. property values for pure components or mixtures are required to be estimated. The expert is consulted for finding out the best methodology of estimating the required property values. The information that is likely to be available is:

1. The approximate temperature and pressure ranges of the process.

2. The components that form the mixture. 3. Experimental data on some of the desired prop-

erties.

The expert would be expected to provide guidance on the choice of the methodology, the expected accuracy of the recommended correlation and the likely prob- lems to be encountered.

In the case of the problem of an accurate esti- mation of VLE conditions, the expert should indicate the proper approach to be adopted and the corre- lations to be used both for the vapor and liquid phases.

3.2. The strength of an expert

Each correlation has some strengths and weak- nesses. It can be argued that most of the limitations of a given available methodology can be traced to its inadequate mathematical form. Each mixture of a given process condition demands a certain degree of mathematical rigor for its proper characterization. Each methodology can satisfy these demands to a

Table 1. Attribu(c-value table for VLE methodologies (Paranjape, 1989)

Name

Margules-2-suffix Margules-3-suffix van Laar Regular solution 2-parameter-Wilson I-parameter-Wilson 2-parameter-NRTL 3-parameter-NRTL l-parameter-NRTL UNIQUAC UNIFAC ASOG R-K S-R-K P-R L-K-P BWRS C-S P-T Henry’s law

Model

Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid EOS EOS EOS EOS EOS Liquid EOS Liauid

Parameters Basis Calculations Type Class

1 Empir. Simple Fit. Wohl-cxp 2 Empir. Simple Fit. Wohl-exp 2 Empir. Simple Fit. Wohl-exp

Theor. Simple PE. 2 Sem-Tb. Medium Fit.

: Sem-Th. Medium Fit. Sem-Th. Medium Fit.

3 Sam-Th. Medium Fit. 1 Sem-Th. Medium Fit. 2 Sem-Th. Medium Fit.

Sem-Th. Complex Pre. Grp Cont. Sem-Th. Complex Pm Grp Cont.

I Empir. Medium Fit. Cubic I Empir. Medium Fit. Cubic 1 Empir. Medium Fit. Cubic

f Theor. Complex Fit. BWR Theor. Complex Fit. BWR Empir. Simple PI-e. Reg soln

2 Empir. Medium Fit. Cubic Em&r. Simde Fit. HCtXY

A knowledge intensive methodology for thermodynamic choices 721

certain extent. A proper match must be established for an appropriate choice.

Based on these assumptions, certain requirements of an expert’s performance may be identified. Thus, an expert must have a complete knowledge about the strengths and weaknesses of different methodologies; he must be able to gauge the behavior of a given mixture a priori; he must be able to identify the accuracy requirements of the process and finally, he must choose a proper correlation that satisfies the requirements of a given problem to the best possible degree. It is also desirable that an expert should be able to explain his reasoning and offer a justification for his results.

A novice may not be able to classify the mixture in a proper way, Whether a liquid mixture exhibits positive deviations from ideality or not is perhaps the kind of judgment that can only be made by an expert. Similarly, by looking at the experimental data that are available, only an expert can decide whether extrapolation is needed when VLE con- ditions for a particular process are to be estimated. A common process engineer is usually not familiar with all the available correlations and their applicability. These are the main reasons why expert advice is sought.

In addition to the above-mentioned points, an expert may have some other strengths. Because of his vast experience in using different correlations, he may be aware of certain unexpected strengths or short- comings of some correlations (Gray, 1979). He may be aware of some novel ways of using some method- ologies. A KBES may be strengthened by incorporat- ing any such rules.

3.3. Information us judgment

As has been pointed out earlier, a lot of infor- mation about the merits and demerits of different correlations is available in the literature. In order to make good use of the available information for guiding the choice of a proper correlation, certain judgments must be made and making such judgments intelligently is perhaps the main strength of an expert.

The regular solution theory, for example, predicts only positive deviations from ideality, that is, it can only yield values of activity coefficients that are greater than or equal to 1.0. If one has to determine the applicability of the regular solution model to a particular mixture, one must know whether negative deviations from ideality are expected in the case of that mixture. The question to be probed here is whether such a priori judgment about the likely behavior of a mixture is possible.

Similar information is available about the applica- bility of different correlations in various situations

such as at supercritical conditions, near critical point, in the presence or absence of data, at low, moderate or high pressure, for associating com- pounds, for large molecules, for process calculations wherein only qualitative results are required, and so on. The judgment regarding the existence of one or more of these conditions given the process par- ameters and the compounds concerned must be made by an expert.

3.4. The responsibility of an expert (or a K3ES)

An expert may make such judgments purely on the basis of his experience. On the other hand, he may use certain judicious classificatory schemes to arrive at a good qualitative estimate of the behavior of a mix- ture. In any case, an expert must have some method of arriving at these decisions if his advice is to be of any consequence.

Any classification scheme that the expert may employ will be based upon some concepts that ac- count for the observed tendencies of the participating compounds. Compounds could be classified as non- polar, slightly polar, highly polar and so on, for example. The classification is solely the expert’s (or in case of a KBES, computer’s) prerogative. It would be unreasonable to expect a novice (a common process engineer) to carry out the .classification.

The expert must also have good knowledge about the availability of data. Almost all the correlations of thermodynamic properties require certain basic data on each pure component involved. These basic data include properties such as molecular weight, critical constants, acentric factor, solubility parameter and normal boiling point. If a proper classification of process conditions in terms of their proximity to the critical condition (a point which places possibly the most stringent demands on the form of the corre- lation for its proper characterization) is to be made, these basic data are absolutely necessary. A common process engineer may or may not be able to supply all of these data. An expert must be able to make the best of the available data. A KBES should ideally have a data bank that stores these basic data.

In the case of the problem of estimation of VLE conditions, experimental data on equilibrium con- ditions must be analyzed. A process engineer may or may not have any data. An expert should know whether data are available for the system of interest; if not, whether data on a similar system could be used gainfully (it could be a case of a judicious use of a group contribution method); and if any extrapol- ations are required or not. The issue of extrapolation influences the choice because fundamental corre- lations are more suitable in that case. A KBES is ideally suited for providing data analysis. A KBES

722 P. K. PARANJAPE and A. P. KUDCHADICER

could be supported by a comprehensive data bank for this purpose.

To summarize, an expert is primarily needed be- cause a common process engineer cannot by himself choose the best correlation. Information about the process is provided by the process engineer. Infor- mation about the applicability of different corre- lations and the ability to make judgments about the behavior of the mixture at given process conditions must come from an expert.

4. CRITERIA FOR CHOOSING AN APPROPRIATE METHODOLOGY

Judicious thermodynamic decisions are based upon good data, information and knowledge. Information can be collected and compiled; data can be collected or experimentally measured, but then it has to be tested for consistency, screened and compiled; while knowledge must be distilled from available infor- mation or evolved (typically, by an expert, by making use of his experience in the field concerned).

4.1. The nature of qualitative estimates

In order to understand the different possible quali- tative descriptions, a basis for all such descriptions must be established. In the case of pure gases for example, the Ideal Gas Law is taken to be the basis. Deviations from the Ideal Gas Law are then used to describe other results.

In the case of mixtures, the following criteria are normally used to describe their behavior in qualitat- ive terms:

1. Quasi-ideal against highly nonideal mixtures. 2. Deviations from ideality in the context of

Raoult’s law (> 1: positive; < 1: negative). 3. Symmetric vs asymmetric mixtures based on

size differences. 4. Near-critical mixture based on the proximity to

the critical point. 5. Presence of supercritical components.

These are some of the wmmon terminologies that are used to describe different mixtures. Items 1, 2 and 3 point towards some kind of a classitication of mix- tures and participating components, and items 4 and 5 point towards a classification of process conditions.

4.2. CIassiJication of components

Compounds are often described as nonpolar, slightly polar or highly polar, that is, in terms of the polarity they exhibit. Dipole moment is a good indicator of the polarity of a compound_ Yet there are polar compounds that have zero dipole moment, carbon dioxide for example. Moreover, though po-

larity is a convenient term to use, it does not usually tell anything about the behavior of a mixture in terms of its deviation from the ideal liquid solution behav- ior, the characteristic that is so important from the point of view of the applicability of a given liquid model.

Table 2 gives the infinite dilution activity co- efficients of some binary mixtures of methyl acetate, which in itself is a moderately polar compound (the value of dipole moment for each compound is also given in one of the columns of Table 2 as a quick polarity reckoner). A value (of infinite dilution ac- tivity coefficient) close to 1 indicates that the mixture is almost ideal. It can be seen that the two compounds that form comparatively ideal liquid mixtures with methyl acetate are acetone, and ethyl acetate both of which are polar in nature. On the other hand, cyclo- hexane, which has a dipole moment of only 0.3 units gives rise to a far more nonideal liquid mixture; and chloroform and dichloroethylene form mixtures that exhibit negative deviations from ideality.

A much more useful classification has been suggested by Ewe11 et al. (1944). It is known that those compounds that can form or break hydrogen bonds almost always form mixtures that deviate significantly from ideality. H-bond formation leads to negative deviations from ideality and H-bond cleav- age leads to positive deviations from ideality in the liquid mixtures. Table 3 presents a brief analysis of the different possible compound classes. Table 4 presents a summary of likely interactions and gives a qualitative estimate of the likely deviations from ideality. The classification scheme in Table 3 covers only the organic compounds and the nonelectrolytes.

Information about the type of functional groups present in a compound is likely to be available with the user and can as such bc used to classify a compound as per the scheme in Table 4, CHOCO- VALE uses this classification.

Other factors such as the size and shape of the participating components also play an important role in determining the extent to which mixtures deviate from ideality. A quantitative characterization of the influence of these factors however is not the issue at

Table 2. Infinite dilution activity coefficients for binary mixtures of methvl acetate II) (Paraniaoe. 19891

Compound

Acetone

Dipole moment (debye)’

2.9 r;” Y2”

1.037 1.102 Ethyl acetate 1.9 Jknzene 0.0 Cyclohexane 0.3 Methanol 1.7 Chloroform 1.1 Dichloroethylene 1.8 Methyl acetate 1.7

’ 1 debye = 3.162 x 1Omu (J.m’)‘.‘.

I .072 1.201 I .43 1.53 5.032 3.734 3.403 3.330 0.833 0.618 1.604 0.804

- -

A knowledge intensive methodology for thermodynamic choices 723

Table 3. Classification of pure components (Ewe11 et al., 1944)

Class Description Examples

I

II

III

IV

V

Molecules capable of forming 3-D networks of strong H-bonds Other molecules containing both active hydrogen atoms and donor atoms (0, N and F)

Molecules containing donor atoms but no active hydrogen

Molecules containing active hydrogen but no donor atoms that have two or three chlorine atoms on the same carbon atom as a hydrogen atom, or one chlorine atom on the same carbon atom and one or more chlorine atoms on adjacent carbon atoms All other molecules having neither active hydrogen nor donor atoms

Water, glycols, glycerol, amino-alcohols, hydroxylamines, hydroxy-acids, polyphenols and amides Alcohols. acids, phenols, primary and secondary amines, oximes, nitro and nitrile compounds with z-hydrogen atoms, ammonia, hydra&e, hydrogen Rouride and hydrogen cyanide Ethers, ketones, aldehydes, esters, tertiary amines (including pyridine type.), and nitro and nitrile compounds without a-hydrogen atoms CHCI,, CH,Cl,, CH,CHCI,, CH,CICH,Cl, CH, CICHClCH, Cl and CH, CICHCl,

Hydrocarbons, carbon disulfide, sulfides, mercaptans and halo-hydrocarbons not in Class IV

hand as the chosen correlation is ultimately expected to fulfil that purpose.

4.3. Classification of mixtures

Table 4 gives one of the ways of classifying mix- tures and estimating the type of deviations from ideality. A more discriminating classification that takes into account the applicability of different liquid models in terms of the knowledge gathered from their regular usage and evaluation is available (Malik, 1988). Table 5 presents the classification. Table 6 gives the expected accuracy of each of the liquid models for different types of mixtures. Table 4 rep- resents a classification that is more fundamental in nature while Table 5 presents an application-oriented classification. Both have been used in CHOCO- VALE.

If Table 3 is used for the classification of pure components and Table 4 is used for making a quali- tative estimate of the mixture behavior, it is possible to predict that the mixture of chloroform (or dichloroethylene) and methyl acetate would exhibit negative deviations from ideality; and a mixture of methyl acetate and ethyl acetate would not deviate much from ideality.

4.4. Classification of process conditions

For any EOS model, its applicability in the vicinity of the critical point is a crucial matter. At higher pressures, the use of EOS with a uniform approach is more desirable in order to account for the pressure effects on liquid. (Most of the common activity coefficient models neglect the effect of pressure on the value of the activity coefficient.) These observations suggest that a pressure-based classification of process conditions would be useful.

The following scheme has been used in CHOCO- VALE:

1.

2.

3.

4.

For ideal or quasi-ideal mixtures, up to 3 bar constitutes low pressure. For highly nonideal mixtures, up to 0.5 bar is low pressure. For all mixtures, a pressure value of up to 5 bar represents moderate pressure. The mixture is in the vicinity of the critical point if there exists a component (with moderate concentration) for which the reduced tempera-

ture (T,,, /T,,,,) is between 0.93 and 1.07. The number 0.93 is chosen for two reasons.

Prausnitz et al. (1980) suggest a reduced tem-

Table 4. Classification of mixtures based on molecular interactions causing devi- ations from Raoult’s law (Ewell el al., 1944)

Classes

III + IV III + III III + v IVfIV 1v+v v+v 1+1

II + II III + III III + II III + III III + IV (frequently limited solubility) III + IV III + v III + v

Hydrogen bond formation

H-bonds formation only No H-bonds involved

H-bonds broken and formed

H-bonds broken and formed, but dissociation of Class I or II is a more important effect

H-bonds broken only

Type of deviation

Always negative Quasi-ideal; always positive or ideal

Usually positive, but some negative

Always positive

Always positive

P. K. PARANJAPE and A. P. KWXXUXER 724

5.

6.

Table 5. Classilication of mixtures based on applicability of liquid models (Malik, 1988)

Components

Group Present Absent A S Exam&s

I

II

III

IV

V

VI

VII

VIII

IX

X

XI

XII

Class v

Hydrocarbons Fluorocarbons Class III Class v

Class III Class I Nitrocompound or nitrile compound Class III compounds Class III Class IV Class I/II Saturated hydrocarbons Class I/II Unsaturated hydrocarbons Class I/II Class III Class I/II Class N Water Class III Water -_ -.__

Flourocarbons _ Class I-IV Class I-IV -

Class I, II, IV Nitro compounds Nitrile compounds Class I, II, IV -

Class I, II, IV

Class I, II -

Class III, N + Unsaturated hydrocarbons Class II, N +

+

+

Associated compounds

+

+

-

-

_

-

-

-I-

-

-

L

H

+

+

Etenzene-pentatte CyclohexaneCCl, C,F,&,H,.

Ethyl acetate-hexane Acetone-Ccl,

Nitromethane-hexane AcetonitrileCCl,

Nitromethaneacetone Acetone-acetonitrile CHCIs-acetone CH, Cl,-sthyl acetate Methanol-hexane EthanoCcyclohexane

Methanol-benzene EthanoCCCl,

Methanol-nitromethane Ethanol-acetone Methanol-CHCI, Ethanol-pyridine Water-acetone

Water-ethanol Class I/II

A: association; S: salvation; H: high; L: low.

ponents is assigned. This is useful for judging the applicability of Henry’s law.

7. If the process condition cannot be classified as any of the above, it is high-pressure.

perature of 0.93 as a demarcation point for reference fugacity calculations. Secondly, the suggested limit for using the revised Chao-Seader method for some mixtures is also a reduced temperature of 0.93 (Henley and Seader, 1981). For a value of T, that is between

4.5. Wide temperature range

0.93 and 1.0, the LKF’ equation, which is sup- posed to be very accurate even near the critical point, also runs into some computational difficulties. The number 1.07 is chosen for sym- metry about the critical point. If T, > I.07 for any component present in mod- erate concentration, it is a supercritical con- dition. If T, > 1.07 for a component with low concen- The variation of the activity coefficient with respect tration, the condition of supercritical com- to temperature is proportional to the excess enthalpy

Most of the above-mentioned equations use the binary interaction parameters for fitting mixture data. In some equations such as PR, UNIQUAC and LKP, these binary parameters are not very dependent on the mixture temperature. The applicability of such equations covers a wider temperature range than those equations for which binary parameters are temperature sensitive.

Table 6. Expected accuracy of liquid models (Malik, 1988)

Group number

Name of the model I II III IV V VI VII VIII IX X XI XII

Margules z-suffix VG F-G G P-F G F-G P F F-G P-F P F Margules 3-suffix E GVG VG G VG G-VG F G G G F-G G “an Laar E VG VG G-VG VG VG F-G G G-VG G F-G G Regular solution VG P-F G P-F

: P P

: F-G P P P

Wilson 2-parameter E VG E VG-E VG E E VG VG VG Wilson l-parameter E G VG F VG VG F G VG F F G NRTL I-parameter E VG E VG E VG VG E E VG VG VG NRTL 3-parameter E VG E VG-E E VG VG E E VG-E VG VG NRTL 1 -parameter E G VG F-G VG VG

FG G VG G

UNIQUAC E VG E VG-E E VG E E E GG ;G

Quantitative estimate vs accuracy in terms of average deviation y x 1000. E=Rxcallent <IO; VG=very good (20; G= good ~40; F=fair ~80; P=poor ~80.

A knowledge intensive methodology for thermodynamic choices 725

Table 7. Sensitivity of caIculations to data error (Malik, 1988)

property

Pr- calculation

Critical properties

PVT density

vapor pressure VLE

Specific heat enthalpy

Kinetic data Entropy

Reaction Distillation absorption Heat exchange Fluid flow compression expansion Heat-mass balances Energy audit

Low

Average Average

High Average Average

Low AVWag.Z

Average High Low Sometimes high

High Average Low High Average Low

Average

Averas Average

Average - Low

AWIagt

Average High

High High High

High Average

Sometim- high - Sometimes high Low

- Average Low - Sometimes high High

of mixing. This should suggest that the excess en- thalpy value, if available would provide a good pointer to the “width” of the temperature range. In the absence of such a value, a default width can be employed to judge the “wideness” of temperature range.

4.6. Extrapolation of data

Extrapolation of data is an important aspect of all data analysis. Extrapolation involves using the avail- able experimental data in a particular range of con- ditions for making predictions at a different set of conditions. Data on a set of compounds can also be used to predict properties of other similar compounds (using group contribution methods). In the case of VLE data, it is not easy to decide whether accurate extrapolation is possible or not.

Most of the activity coefficient models do not account for the effect of pressure. In many cases, the effect is negligible. VLE data at one value of pressure can therefore safely be used to determine activity coefficient values at some other pressure as long as pressure is not high. Similarly, data in a given temperature range may be used to predict activity coefficient values at other temperatures if the chosen correlation accounts for the temperature dependence in an adequate manner.

The extrapolation with respect to composition follows a slightly different rule. The maximum devi- ation from ideality of the value of the activity co- efficient of a component occurs when that component is present in extreme dilution. The commonly used term for such a value is the “infinite dilution activity coefficient”. If data at infinite dilution are available, those can be safely used to predict the values of activity coefficients at other compositions; however, if data in some middle concentration range are avail- able, the accuracy of extrapolation depends on the correlation chosen and the reliability of such an extrapolation may still be questionable.

4.7. Complexity, accuracy and usage

Complex equations would normally be expected to be more accurate. Using some of the ideas that have

been outlined in the preceding discussion, situations wherein simple equations are good enough could be identified. Sometimes, highly accurate results may not be needed because the process sensitivity to the VLE computations may not be very high or because the error associated with some other models may be the limiting factor in overall analysis.

Complexity also has some associated problems. If the methodology is going to be used in an iterative calculation process (such as a simulation of a distilla- tion column), the time consumed may be an import- ant parameter. Some interesting results have been given (Mathias and Benson, 1986) regarding the time intensive nature of various methodologies.

Table 7 gives the sensitivity of various processes to VLE and other predictions (Malik, 1988). Table 8 gives a rough estimate of the usage of VLE compu- tations in a given process study. If the VLE calcu- lations are to be used for the design or analysis of a distillation column, accurate estimates of the separ- ation factors or K-values (y/x; the ratio of the mole fraction of a component in the vapor phase to its mole fraction in the liquid phase) must be provided. Some other process calculation may not require accurate VLE estimates. The important fact that needs to be emphasized here is that the accuracy demands of each process are likely to be different and similarly the usage of VLE computations in a process calculation may vary.

Tables 7 and 8 only provide an overall (and perhaps a tentative) estimate of the requirements of some typical process calculations. A complete KBES should perhaps incorporate a number of questions related to each process calculation which allow it to identify and pinpoint the specific accuracy needs of the process. Tables 7 and 8 have been included in this

Table 8. Usage of VLE computations (Paranjape. 1989)

PKNXSS Number of VLE comnutations

Reaction Distillation-absorption Heat transfer Fluid flow Heat-mass balances Energy audit

Low High Low Low Medium Medium

726

work as pointers to edge-based schemes.

4.8. Miscibility

P. K. PARANJAPE and A. P. KUDCHADKER

some (possible) refined knowl- Table 10. Wcightings assigned to important criteria (Paranjape. 1989)

Criterion Method Points

Process conditions EOS 3 near or above critical High-pressure pr-s EOS Variable l-3

Low-pressure process Depends on P

Liquid models 3 Polar compounds present Liquid models 5 Data unavailable Predictive 5 Data available Fitting Commonly used methods?

Variable up to 3

Accuracy at specific up to 3

Method conditions

up to 2 SpeCiflC

Inapplicable to certain Method up to -2 components specific Advantage of simple Simple modes up to 2 calculations

The fact that the phenomenon of miscibility eludes some of the otherwise accurate equations such as the Wilson equation indicates that a qualitative esti- mation of immiscibility is a difficult task. Two guide- lines, however, are available for making a rough estimate. The first comes from Table 4 and is based upon the classification of mixtures. The second guide- line uses the value of the solubility parameter. Martin (1975) has deduced some criteria for immiscibility for symmetrical binary mixtures. A rough estimate indi- cates that those compounds whose solubility par- ameters differ widely are likely to exhibit immiscibility in the liquid phase. Table 9 gives a list of some binary mixtures which exhibit immiscibility. The value of the solubility parameter for each com- ponent is also given.

In the preceding discussion, a number of ideas that make use of information and data to form important judgments have been outlined. CHOCOVALE uses these ideas. The classification of components, mix- tures and process conditions is done according to the schemes presented in this section. The weightings assigned to different criteria are given in Table 10. Simple rules about miscibility, wide temperature range, complexity, accuracy and usage have been employed in the present scheme. As far as the need for extrapolation of data is concerned, the user has to determine whether extrapotations would be required or not. CHOCOVALE accepts this as part of its input. Part of the knowledge is encoded in terms of the Attribute-Value format in the OPS5+ (refer to Table 1 for an example) scheme and part is encoded in terms of rules.

5. THE CHOCOVALE KBES

CHOCOVALE (CHOice of Correlation for VA- por-Liquid Equilibria) is a prototype expert system that has been developed in the production system language OPS5+. CHOCOVALE uses a minimal amount of information that a typical user is likely to be easily able to supply about a mixture; uses a data bank to obtain certain basic data for the mixture;

Table 9. Pairs of immiscible liquids and their solubility parameter values (Paranjape, 1989)

Compound I 6,. Compound 2 6,

1 Ethylbenzcnc 8.8 Ethylene glycol 16.6 2 n-Heptane 7.4 Furfural 12.0 3 n-Hcptane 7.4 Aniline 11.5 4 Toluene 8.9 Ethylene glycol 16.6 5 Di-k+butylene 7.9 Triethylcne glycol 12.7

‘(cal/mr~‘)~.’ = 2045.5 (J/mJ)o,J.

sBecause of this rule, the Patel-Teja equation which is a relatively recent proposal (Pate1 and Teja, 1982) is assessed conservatively, because of lack of evidence. The UNIFAC method similarly gets more weighting than the ASOG method.

prompts for any more required data that may not be available in the bank and that a user might be able to supply; and attempts to classify the mixture and the process conditions on the basis of the components in the mixture by using If-Then rules. CHOCOVALE also attempts to infer about the required accuracy and speed of calculations by using rules about the sensitivity of the process to VLE calculations and the amount of usage of VLE calculations in the process simulation. CHOCOVALE adopts a two-stage ap- proach. It generates a list of four appropriate corre- lations for the given process calculations in the first stage and follows up with a closer examination of the shortlisted correlations in the second stage. CHOCO- VALE can be used directly for examining the suit- ability of a given correlation if desired.

During a consultation session, CHOCOVALE asks for the relevant information about the problem as enumerated in 5.1; gives a detailed chart of its activity, provides a transparent trace of its reasoning, and gives an explanation about the questions asked (if it is desired).

The present section briefly describes the implemen- tation of CHOCOVALE.

5.1. Input information for CHOCOVALE

CHOCOVALE needs the following input infor- mation:

1. The list of compounds that are present in the process and the concentration (high/ average/low) of each compound.

2. The maximum and minimum temperature of the process.

3. The maximum pressure of the process. 4. Information about the availability of data. 5. Whether extrapolations are needed.

CHOCOVALE requires basic data on pure com- ponents for making its estimates. These data are

A knowledge intensive methodology for thermodynamic choices 727

normally obtained from the data bank that supports CHOCOVALE. Basic data such as the molecular weight, critical constants, solubility parameter and acentric factor are obtained from this data bank which has data on more than 150 different organic compounds_ The type of compound is also included as a field in the compound record. If the compound data in a given case are not available in the bank, CHOCOVALE prompts for the missing data and infers the type of the compound by asking specific questions. CHOCOVALE uses these data to formu- late estimates about the critical temperature of the mixture, to judge whether the mixture is asymmetri- cal, and to detect the presence of large molecules.

5.2. Two-stage processing of correlations

Since the choice of one or more correlation out of a set and the examination of the suitability of a given single correlation arc two different activi- ties, CHOCOVALE handles them in two stages, designated as correlation-selection and correlation- evaluation. In the first stage of processing (correlation-selection), general rules that apply to a group of correlations are utilized.

The second stage of processing (correlation- evaluation) is necessary because the temperature and pressure range of the applicability of each correlation must be specifically checked. In addition, if some components are inimical to a particular method, this can also be checked in the second stage.

CHOCOVALE starts with a set of 20 correlations in the correlation selection stage. Four out of these 20 are shortlisted at the end of first stage based on the grading points assigned. One or more of these corre- lations may get rejected in the evaluation stage. In all the cases that were examined in this work, at least 2 of the 4 shortlisted correlations were found to be appropriate. Thus the number four out of the original 20 seems to be a good choice for the shortlisting step.

A close examination of temperature range, press- ure range, unsuitability for certain components in the mixture and accuracy is carried out in the evaluation stage. Points are assigned for each of these criteria. A correlation is appropriate if it gets 15 or more points in the evaluation stage. CHOCOVALE does not recommend a correlation if it gets less than 15 points. Thus the correlation selection step only does a pre- liminary screening and the evaluation step determines the appropriateness. Examples of some of the short- listed correlations getting rejected in the evaluation step are presented in Appendix B. It must also be pointed out here that CHOCOVALE only finds out appropriate correlations; it does not attempt to select or define the “best”.

Each rule adds or subtracts grading points assigned to different correlations. At the end of the first stage, the total points given to 4 correlations that obtained maximum points are printed. Reasons for assigning grade points are given in the messages and after the set of 4 correlations, a list of good and bad points of any correlation can be obtained. Details of point assignments are given later in this section; however, it must be emphasized here that the assignments of points is made only for the sake of convenience of comparison. The messages about the likely difficulties of using a correlation are more important as far as the applicability is concerned.

5.3. Zmplementarion in OPS5-t

CHOCOVALE has been developed in OPS5+, a production system language. OPS5+ employs the Object-AttributeValue triplets (along with pro- duction rules to manipulate the values) as its knowl- edge representation scheme. The choice of OPS5+ for implementing CHOCOVALE is purely arbitrary. The concepts employed for making intelligent judg- ments are the main strength of CHOCOVALE. As such, it could well have been implemented in any other language without any loss of capability.

CHOCOVALE operates in a goal-driven mode. In each stage, namely, correlation-selection and correlation-evaluation, it sets up the main goal to begin with. It then proceeds in the depth-first mode as explained below:

A.

B.

C.

It creates subgoals for the most recent goal if that goal has any subgoal. If the most recent goal has no subgoals associ- ated with it, all the activities associated with that goal are completed. These activities cover all the computations, classifications and evalu- ations. After Step B, CHOCOVALE proceeds to con- sider the next goal, that is, either the next goal which has the same parent goal as the last completed goal, or the parent goal of the last completed goal.

Starting from one of the top goals, namely correlation-selection or correlation-evaluation, CHOCOVALE employs a tangled hierarchy of sub- goals to carry out thermodynamic judgments in the correct order. During a consultation session, CHOCOVALE displays the current goal that it is pursuing. The goal tree for correlation-selection is given in Fig. 1.

5.4. Weightings of dtflerent criteria

Because of the strength of the inference rules that

CHOCOVALE employs, it is possible to use a simple

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A knowledge intensive methodology for thermodynamic choices 729

scheme of weightings to assess the suitability of a correlation. This leads to a highly transparent knowl- edge-base. CHOCOVALE assigns numbers to indi- cate the relative importance of different criteria. The sum total of these numbers may be taken to be a convenient indicator of the applicability of a method. The weightings of different criteria are given in Table 10. In addition to the criteria mentioned in Table 10, points are assigned for other more specific criteria.

5.5. Assessment of correlations

For the assessment of a correlation, the points that it gets based on different criteria are just added up. The goal trees are so structured that the classification and assessment activities are handled in proper order. In case some of the correlations are just not appli- cable in a particular case, CHOCOVALE omits them from consideration at an early stage. CHOCOVALE thus omits Margules equation if the mixture has more than two components.

6. RESULTS

CHOCOVALE was consulted for advice on many different mixtures. Some representative systems and the prescriptions of CHOCOVALE are presented in this section. These systems have been selected with an idea of testing the performance of CHOCOVALE in diverse situations. For the sake of brevity, only the relevant portions of the advice given by CHOCO- VALE in a typical consultation session are repro- duced here along with some comments. Comments are given inside square brackets. The input to the CHOCOVALE (responses to the questions asked) is underlined. The major portion of the text is the CHOCOVALE output.

6.1. A sample of CHOCOVALE interaction

phis section depicts the style of CHOCOVALE. The message displayed before asking a question, the choices given for formulation of the reply and the justification for the question are some of the salient features of CHOCOVALE interaction with the user.]

I shall attempt to give you some suggestions for Choosing an appropriate correlation for evaluation of vapor-liquid equilibria.

I try to base my conclusions on minimal infor- mation. I look for most of the needed data in my data bank. You only have to furnish certain basic information on the process and the components of the mixture.

I shall try to make my thinking transparent and in phase I, come out with four most suitable corre- lations.

In Phase II, I shall make some specific checks for a given method.

-Now for the first phase-

key in the name of the process calculation that you wish to make.

Choose from the following: reaction distillation-absorption heat-exchange fluid-flow heat-mass-balances energy-audit

why *The purpose of my existence is to provide you with some assistance in choosing appropriate cor- relations for estimating thermodynamic properties of substances for use in process calculations. This version of the advisor considers 6 different types of process calculations that chemical engineers have to perform frequently. Sensitivity of different processes to the errors in estimation of thermo- dynamic properties are different. The choice of correlation is many times dictated by this sensi- tivity *

key in the name of the process calculation that you wish to make.

Choose from the following: reaction distillation-absorption heat-exchange fluid-flow heat-mass-balances energy-audit

reaction For classifying the process conditions a rough idea about the temperature and pressure ranges covered by the process is required. The next few questions enquire about these.

key in the approximate minimum temperature of the process:

** reaction ** in degree C.

why * Different correlations are applicable in different ranges. Therefore, it is important to define the range of application before making any choice. *

key in the approximate minimum temperature of the process:

** reaction ** in degree C.

70 -

key in the approximate maximum temperature of the process:

*+ reaction ** in degree C.

730 P. K. PARANJAPE and A. P. KUDCSLWKER

90 -

key in the approximate maximum pressure of the process:

** reaction ** in bar.

compound o-cresol is a phenol l from data bank*

key in the type of the compound

* 3-methylpentanol +

choose one of the following:

hydrocarbon aromatic ether sulphide amine amide alcohol oxide

fluorocarbon organic-acid ketone sulphur-compound nitrile nitrogen-compound phenol inorganic

other-halocarbon ester aldehyde mercaptan nitro-compound hydrazine oxime

glycol other

0.53

. . You need to do VLE calculations for process reaction

, . Its temperature range is 343-363 K . . Its maximum pressure is 0.53 bar

Let us now look at the mixture

Key in the name of compound 1:

0 -cresol

. . Compound number 1 is o-cresol.

key in the approximate concentration of the com- pound * o-cresol * in the mixture:

Choose from:

high

high medium low

You have keyed in data for 1 compounds so far. Are there any more compounds in the mixture? key iny or n:

E key in the name of compound number 2:

3-methylpentanol

. . Compound number 2 is 3-methylpentanol

key in the approximate concentration of the com- pound: * 3-methylpentanol * in the mixture.

Choose from:

high medium low

You have keyed in data for 2 compounds so far. Are there any more compounds in the mixture? key in y or n:

n _ . . I have found data for o cresol in the data bank . .

If you do not know the type, key in u or unknown.

alcohol

Is the compound * 3-methylpentanol * a amino-al- cohol? answer y or n:

n -

Is the compound * 3-methylpentanol * an hydroxy- acid? answer y or n:

n

[ CHOCOVALE proceeds to ask for some more basic data (on 3-methylpentanol since the data bank does not have any) and then processes the available infor- mation. ]

6.2. Correlation selection

r]The system of interest is a ternary mixture com- prising of methane, n-butane and n -decane. The process temperature is in the range 130 to 140°C. Maximum pressure is 30 bar]

. . methane is a class-5 compound . . Nonpolar compound that cannot participate in H-bond formation. .

. . butane is a class-5 compound . . Nonpolar compound that cannot participate in H-bond formation. . . . decane is a class-5 compound . . Nonpolar compound that cannot participate in H-bond formation . .

. . nonpolar compounds. No fluorocarbon. . Hence mixture is quasi-ideal. Type is group I . .

. . I am deleting Margules model from consider- ation because of presence of 3 or more com- ponents.

A knowledge intensive methodology for thermodynamic choices 731

. _ Compound methane present in high concen- 0.0145 in the vapor phase mole fraction estimation. tration . . Experimental data from Reamer and Sage (1951).] Process temperature much above its critical , . Hence process condition is supercritical. .

. . Process conditions near or above critical . . Equations of state with unified approach more suitable . . 3 estimation-points to patel-teja method . .

6.4. An example of a mixture of polar compounds

[The following example illustrates the prescriptions of CHOCOVALE for a mixture of polar com- ponents. The process conditions and the type of the components involved are markedly different from the conditions in the previous example. ]

. . What follows is a list of 4 correlations that are most appropriate for the system of your interest according to my estimate, and their relative appli- cability in terms of points. .

. . You need to do VLE calculations for process energy-audit;

I gave 16 points to lee-kesler-plocker method. I gave 16 points to peng-robinson method. I gave 16 points to soave-redlich-kwong method. I gave 15 points to redlich-kwong method.

. . Its temperature range is 328 to 363 K;

. . Its maximum pressure is 1.01 bar;

. . Compound number 1 is water;

. . Compound number 2 is acetone;

6.3. Correlation evaluation . . I shall now try to examine van Laar correlation

more closely.

. . I shall now try to examine the lee-kesler-plocker correlation more closely.

. . The range of applicability of the van Laar corre- lation covers the process temperature range.

. . The range of applicability of the lee-kesler- plocker correlation covers the process temperature range.

. . water is a class-l compound . . Strongly associated because of H-bond for- mation. .

. . The range of applicability of the lee-kesler-plocker correlation covers the process pressure range.

. . acetone is a class-3 compound. . There are donor atoms but no active H for H-bond formation . .

. . Making a preliminary assignment of 2 points. Shall now consider whether lee-kesler-plocker can predict the type of deviations that the mixture is expected to have, and whether the process is sensi- tive to errors in prediction.

. Presence of water and a donor compound _ . No class-4 compound with active H . .

Hence mixture type is group XI . . Expected deviations from ideality usually-positive.

. . Making a preliminary assignment of 2 points. Would now consider if the correlation is too complex to use based upon the usage of VLE calculations in the process.

. . Pressure less than 5 bar but not low enough. . Condition moderate pressure . .

. . Process distillation-absorption requires large amount of VLE calculations. lee-kesler-plocker is highly complex-not wholly suitable. A simpler correlation if applicable could reduce computation time.

. . Some data are available. lee-kesler-plocker is appropriate as it does not have too many fitting parameters.

. . Large difference in values of solubility parameter

. . immiscibility likely . .

[This is an erroneous estimate; however it is an error on the conservative side. In general, immiscibility is difficult to estimate. Though such an overestimation of immiscibility downgrades correlations such as Wilson even where it is applicable, it ensures that Wilson will not be recommended when it is not applicable. ]

. . lee-kesler-plocker method got 17 points out of a total of 20 evaluation points. More than 15 points indicate good applicability. I have tried to indicate its weak points to you.

. . Large data available. van Laar is a fitting corre- lation with 2 numbers of parameters. Is quite appropriate.

[A sample calculation of bubble pressure at 138°C with the LKP equation shows a maximum error of

. . van Laar method got 17 points out of a total of 20 evaluation points. More than 15 points indicate good applicability. I have tried to indicate its weak points to you.

732 P. K. PARANJAPE and A. P. KUDCHADKKR

[ The published results of the van Laar equation show an excellent fit to the water-acetone VLE data (Gmehling er al., 1977). ]

6.5. An example of unfavorable recommendation

[Following is a portion of CHOCOVALE rec- ommendations for a binary system of chloroform and acetone. The applicability of the regular solution model to this system is under consideration. This example illustrates that CHOCOVALE can point out the drawbacks of a given correlation. ]

..VLE calculations. process : distillation- absorption . . Temperature range is 333-343 K . . Maximum pressure is 1 .Ol bar

. . Compound number 1 is acetone

. _ Compound number 2 is trichloromethane

compound trichloromethane is a chlorocarbon-with-active-hydrogen

* from data bank *

compound acetone is a ketone * from data bank *

. . I shall now try to examine regular-solution corre lation more closely.

. . The range of applicability of regular-solution

correlation covers the process temperature range.

. . acetone is a class-3 compound . . There are donor atoms but no active H for H-bond formation . .

. . trichloromethane is a class-4 compound. . There are active H atoms but no donor for H-bond formation . . . . Mixture of more than one polar compound. Presence of electron donors . . and active H atoms.

H-bond formation. Negative deviation from ideality . . Hence mixture type is group VI.. . . Pressure less than 5 bar but not low enough . . Condition moderate pressure . .

. _ regular-solution cannot predict negative devi- ations . .

. . regular-solution method got 13 points out of a total of 20 evaluation points. More than 15 points indicate good applicability. I have tried to indicate its weak points to you.

[The infinite dilution activity coefficient value of chloroform in acetone is less than 0.5 whereas the one predicted by the regular solution model is greater than 1.0.1

The preceding consultations indicate that CHOCO- VALE is capable of drawing sensible inferences from a very small amount of information about the process concerned. The prescriptions of CHOCOVALE are almost always good. Use of CHOCOVALE for many different mixtures has indicated that the predictions are never unreasonable. More examples of CHOCO- VALE prescriptions are given in Table 11 and Appendix B.

It must be emphasized here that CHOCOVALE is not a complete system. The erroneous estimate of the immiscibility of the water-acetone binary is an example of CHOCOVALE’s fragility and incom- pleteness. The primary goal of CHOCOVALE is to form qualitative estimates. The accurate quantitative predictions of the behavior of a mixture are expected to be given by the chosen correlation. The extent to which a KBES can indulge in improving its qualitat- ive estimates will be dictated by the accuracy of its prescriptions, and there will always be some scope for improving these.

components

Ethane Butane Hexane 2-Methylpcntane Methanol 2-Methylpropane Methylpropene Butane 1 -Butenc Octane Ethylbenzene Octane Ethylbenzene Methanol Ditihylamine Acetone Ethyl ether water Chlorine

Table 11. CHOCOVALE prescriptions (Paranjape, 1989)

T (“C) P CHOCOVALE Range (mmHg) Process Data prescriptions

-38:36 5200 Distillatior+ Some L-K-P absorption P-R

45:60 760 Distillation- SOllIe 2-parameter-NRTL absorption UNIQUAC

100:110 16,CQO Energy N0II.Z C-S audit P-R

120: 125 760 Distillation- None c-s absorption Regular solution

120:125 760 Distillatiori- some L-K-P absorption P-R

57:68 730 Heat Some 2-parameter-Wilson exchange UNIQUAC

15:30 450 Distillation- Large 3-parameter-NRTL absorption 2-parameter-Wilson

30:40 760 Reaction Some Henry’s law P-T

A knowledge intensive methodology for thermodynamic choices 733

7. CONCLUSIONS

In this work, some ideas have been developed for formalizing the procedure of making thermodynamic choices using a knowledge-intensive methodology. Starting with a typical problem specification, the limitations of a novice and the strength and responsi- bilities of a domain expert have been identified.

An enquiry into the knowledge underlying the procedure of making appropriate thermodynamic choices has been conducted in order to find out ways of making intelligent judgments. A prototype knowl- edge-based expert system CHOCOVALE has been developed for making the choice of a correlation for VLE computations using some of these ideas. Results indicate that CHOCOVALE is a competent advisor.

CHOCOVALE is, however, only a prototype ex- pert system. A more complete and commercially useful system would need to have a much wider range of application. It should cater for electrolytes and inorganic compounds, and it should be able to make choices for other thermodynamic properties also.

With the support of a comprehensive data bank, it would also be possible to increase the depth of inferences in CHOCOVALE. In particular, rules to judge the need for extrapolation could be added easily. Rules for classifying concentration ranges should be employed. More discriminating rules for judging the presence of immiscibility are also necess- ary. Future work would be directed towards some of these issues.

The main strength of CHOCOVALE appears to be its ability to make good judgments using a minimum amount of information about the process. CHOCO- VALE is thus a true prototype knowledge-based expert system.

REFERENCES

Banares-Alcantara R., DECADE-Design Expert for CA& alyst DEvelopment. Proposal for Doctoral Research Pro- ject, Camegi&Mellon University (1984).

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APPENDIX A

List of Correlations in CHOCOVALE Knowledge Base

Equations of state

LeeKesler-Plocker BWRS Soave-Redlich-Kwong Redlich-Kwong Peng-Robinson Patek-Teja An EOS that can cater to polar

components.

Liquid activity coeBcient models @ymmetricai convention)

Margules-3-suffix Margules-2-suffix Models suitable for binary

mixtures. van Laar J UNIFAC group contribution method. ASGG Group contribution method. UNIQUAC - Regular solution 2-parameter-Wilson 3-parameter-Wilson Models applicable to

multicomponent mixtures as well as binaries.

1-parameter-NRTL 2-parameter-NRTL 3-parameter-NRTL

Activity coeficient model with asymmetrical convention

Henry’s Law.

Empirical mode[

Chao-Seader.

APPENDIX B

CHOCOVALE Results

T range Max P Estimated Evaluated System (“Cl (mmHg) Correlation points points

Methane -90: -85 23,000 c-s 16 20 Ethane P-R 16 1.5 Distillation-absorption L-K-P 16 15 No mixture data available S-R-K 14

Methane -90: -85 23,000 L-K-P 16 Ethane P-R :: 16 Distillation-absorption S-R-K 15 15 Large data available R-K 14 No extrapolations needed

Methane -90: -85 23,000 P-R 14 17 Ethane S-R-K 14 16 Fluid flow R-K 14 16 Large data available L-K-P 13 No extrapolations needed

--continued

System

A knowledge intensive methodology for thermodynamic choices

APPENDIX B-confinued

T range Max P Estimated

(“Cl (mmHg) Correlation points

735

Evaluated points

Ethane -l5:-IO 13,000 L-K-P P-R S-R-K R-K

Propane Distillation-absorption Large data available

Ethane Propane Distillation-absorption No data available

Ethane Butane Distillation-absorption Some data available Extrapolations necessary

n-Dccane Propane Fluid flow Large data available No extrapolations needed

I-Butene Propane Distillation-absorption Large data available Extrapolations needed

1-Butene Butane Fluid flow Some data available No extrapolations needed

Benzene 1,3-Butadiene Distillation-absorption Some data available Extrapolations needed

Benzene Cyclopentane Fluid flow No data available

Cyclohexane Cyclohexene Distillation-absorption Large data available No extrapolations needed

I-Butene Furfural: high concentratio Energy-audit Large data available Extrapolations needed

n -Pentane Acetone Distillation-absorption Some data available No extrapolations needed

n -hexane Cyclohexene Fluid-flow Some data available Extrapolations needed

n-Octane p-Cresol Distillation-absorption No data available

-38:36

13,000

5200

70:80 15,500

70:85 21,000

30:40 3200

20: 30 650

48:80

80:90

90:125 5000

33:50

50:60 600

126: 190 760

760

760

760

11 11 II 10

16 17 17

C-S Regular solution S-R-K P-R

15

;A 10

20 14 16

L-K-P 18 P-R 18 S-R-K 14 R-K 14

17 15 14

P-R 10 S-R-K 10 R-K 10 c-s 10

18 18 18

L-K-P 17 P-R 17 S-R-K 15 R-K 15

16 14 13

P-R 9 S-R-K 9 R-K 9 L-K-P 8

19 19 19

2-parameter-Wilson 11 16 UNIQUAC 11 16 2-parameter-NRTL 11 16 c-s 11 17

c-s 16 20 Regular solution 15 20 UNIFAC 14 20 ASOG 10 18

3-parameter-NRTL 12 2-parameter-Wilson 11 UNIQUAC 11 2-parameter-NRTL 11

19 18 18

van Laar 14 16 3-suffix-Margules 14 16 UNIQUAC 14 16 3-parameter-NRTL 13 17

2-parameter-Wilson 13 UNIQUAC 13 2-parameter-NRTL 13 3-suffix-Margules 12

18 18 18

3-suffix-Margules 12 van Laar 12 2-parameter-NRTL Ii I-parameter-NRTL 11

17 17 17

UNIFAC 16 Regular solution 15 ASOG 14 UNIQUAC 13

14 15 12 11

-continued

736 P. K. PARANJAPE and A. P. KUDCHADRER

APPENDIX B-continued

system T range

(“Cl

124:137 n-Octane Propionic acid Fluid flow Large data available No extrapolations needed

Max P (mmHg)

750

Correlation

3-suffix-Margules van Laar 3-parameter-NRTL 2-s&ix-Margules

Estimated points

12 12 12 11

Evaluated points

19 19 20

Phenol Methylcyclohexane Fluid flow Large data available Extrapolations needed

Benzene Phenol Energy audit Some data available No extrapolations necessary

Benzene Aniline Distillation-absorption Some data Extrapolations required

2-Butanone Toluene Fluid flow No data available

Acetone Ethyl ether Distillation-absorption Large data No extrapolations necessary

Ethanol Ethyl ether Energy audit Large data Extrapolations needed

Acetone Ethanol Fluid flow Some data No extrapolations necessary

Acetone Acetic acid Distillation-absorption No data available

Methanol MEK Fluid flow Large data No extrapolations necessary

Methanol Acetic acid Distillation-absorption Large data Extrapolations required

Methanol Diethylamine Heat exchange Some data No extrapolations necessary

UNIQUAC 15 3-parameter-NRTL 14 2-parameter-Wilson 13 2-parameter-NRTL 13

16 17 16

van Laar 14 19 Margules-3-suffix 14 19 Margules-ZsutEx 14 19 UNIQUAC 12 19

UNIQUAC 15 2-parameter-Wilson 13 2-parameter-NRTL 13 Margules-2-suffix 12

16 16 16

UNIFAC 16 Regular solution IS ASOG 14 UNIQUAC 11

3-parameter-NRTL 14 2-parameter-Wilson 13 UNIQUAC 13 2-parameter-NRTL 13

20 20 18 17

19 18

::

van Laar 14 17 Margules-3-suffix 14 17 3-parameter-NRTL 13 18 UNIQUAC 12 18

Margules-2-suflix 12 19 Margules-3-suffix 12 19 van Laar 12 19 2-parameter-Wilson 11 19

UNIFAC 16 19 Regular solution 15 20 ASOG 14 17 UNIQUAC 13 16

Margules-3-sutTix van Laar 3-parameter-NRTL 2-parameter-NRTL

12 12 12 11

15 14 13 13

:; 12 11

19 19 20 19

UNIQUAC 3-parameter-NRTL t-parameter-Wilson 2-parameter-NRTL

16 17 16 16

2-parameter-Wilson UNIQUAC 2-parameter-NRTL Margules-3-s&%x

19 19 19 19

-continued

100: 150 760

60:80 550

80: 151 760

79:111 760

15:30 450

20:45 530

57:76 760

59:llO

64:76

760

760

68:112 700

57:68 730

System

A knowledge intensive methodology for thermodynamic choices

APPENDIX honthed

T range Max P Estimated (“Cl &mHgI Correlation points

737

Evaluated points

Acetone Chlorobenzene Distillation-absorption Some data Extrapolations necessary

Ethanol Chloroform Heat-exchange No data available

58 : 126 760

Ethanol Chloroform Heat exchange Some data No extrapolations necessary

Water Acetaldehyde Distillation-absorption Large data Extrapolations needed

SD* Methyl acetate Fluid flow Large data No extrapolations necessary

Methane Ethane Distillation-absorption Some data Extrapolations necessary

Methane Ethylene Fluid flow Some data No extrapolations

Methane n-Butane Distillation-absorption

70:94

70:94

100: 150

25:50 650

140:-110

25:75 200,000

Methane iso-Pentane Fluid flow Large data Extrapolations needed

Methane n-Nonane Distillation-absorption Large data No extrapolations necessary

Propane iso-Pentane Fluid flow Some data Extrapolations necessary

Methane Nitrogen Distillation-absorption Some data No extrapolations necessary

-2O:lO

70:140

0:50

150: 170

-150: -85

760

760

3700

10,000

82,000

52,000

93,000

28,000

30,000

UNIQUAC 15 2-parameter-Wilson 13 2-parameter-NRTL 13 Margules-3-sufhx 12

16 16

t4

UNIFAC ASQG 2-parameter-NRTL 3-parameter-NRTL

UNIQUAC 2-parameter-NRTL Margules-3-suffix van Laar

13

:: 10

20

:!: 17

12 12 11 11

19 19 19 19

UNIQUAC 15 16 3-parameter-NRTL 14 17 2-parameter-NRTL 13 16 Margules-3-suffix 12 16

Henry’s law 13 17 Pate1 teja 8 16 c-s 8 17

L-K-P P-R S-R-K BWRS

:: :z 11 16 11 <IO

P-R 17 18 L-K-P 16 19 R-K 15 17 SR-K 15 17

P-R 16 16 BWRS 16 15 L-K-P 16 15 S-R-K 14 16 P-R 16 16 L-K-P 15 18 R-K 14 16 S-R-K 14 16

L-K-P P-R S-R-K R-K

16 17 17 17

P-R R-K S-R-K L-K-P

16 15 15 19

L-K-P P-R BWRS S-R-K

17 17 15 14

15 15 15 14

19 17

::

17 17 15 16

--confinued

738

System

P. K. PARANJAPE and A. P. KUDCHADKJZR

APPENDIX P-ontinued

T range Max P Estimated Evaluated (“Cl (mmHg1 Correlation points points

Nitrogen Carbon dioxide Fluid flow No data available *Unsuitable

Methane Carbon dioxide Distillation-absorption Large data Extrapolations necessary

Methane Carbon dioxide Hydrogen sulfide Fluid ftow Large data No extrapolations needed

Water Dichloromethane Distillation-absorption Some data Extrapolations needed

Water Acetonitrile Energy audit Some data No extrapolations

Water Ethanol Distillation-absorption No data available

Water Ethanol Benzene Heat exchange Large data No extrapolations

Water Ethyl acetate Butyl acetate Distillation-absorption Large data over full range

Water Diethylamine Fluid flow Some data Extrapolations necessary

Water Methanol 1 -Propanol Distillation-absorption Some data over full range

Acetone Chloroform Toluene Energy audit. No data available

Benzene 0: <40 Ethylene Fluid flow Large data No extrapolations

Acetone 0:40 Ethylene Distillation-absorption Some data No extrapolations

- 30:o 60,000

- 50: -35 36,000

38:73 750

75: 86 760

78:95 760

65:77 760

60:80 760

38:58 800

66:86

60:90

760

760

760

760

0: 15 110,000 L-K-P 13 17 c-s 10 13’ R-K 10 15 S-R-K 10 16

L-K-P 13 16 P-R 11 15 S-R-K 8 15 R-K 8 12

L-K-P 9 18 P-R 8 18 c-s 8 12

UNIQUAC 15 16 2-parameter-NRTL 13 16 Margules-3-suffix 12 17 van Laar 12 17

van Laar 14 19 Margules-S-s&ix 14 19 UNIQUAC 12 19 2-parameter-NRTL 12 19

UNIQUAC 14 19 ASOG 12 17 2-parameter-NRTL 11 16 3-parameter-NRTL 11 16

3-parameter-NRTL 13 20 UNIQUAC 12 19 2-parameter-NRTL 12 19 I-parameter-NRTL 9 18

3-parameter-NRTL 14 19 UNIQUAC 13 18 2-parameter-NRTL 13 18 I-parameter-NRTL 10 16

Margules-3-s& 12 17 van Laar 12 17 2-parameter-NRTL 11 17 I-parameter-NRTL 11 17

UNIQUAC 2-parameter-NRTL I-parameter-NRTL 3-parameter-NRTL

13 18 13 18

:: ::

UNIFAC 17 20 ASOG 15 18

P-R 13 18 L.-K-P 12 18 R-K 11 18 S-R-K 11 18

Henry’s law 16 18 UNIQUAC 15 11 2-parameter-Wilson 13 11

C-S = Chao-Seader; P-R = Peng-Robinson; L-K-P = Lee-Kesler-Plocker; R-K = Redlich-Kwong.

S-R-K = Soave-Redlich-Kwong;