AZEOPERT: 공비물형성 예측을 위한 지식베이스 전문가시스템

김 영 재

선문대학교 화학공학부

AZEOPERT: a Knowledge-based Expert System

for the Prediction of Azeotrope Formation

Young-Jae Kim

Division of Chemical Engineering, College of Engineering, Sun Moon University, Asansi 337-840, Korea

Introduction

In recent years, research in the field of artificial intelligence(AI) has had many important successes. Among the most significant of these has been the development of powerful new computer systems known as "expert" or "knowledge-based" systems. The considerable development in the artificial intelligence could afford the use of expert system as an auxiliary tool in the field of chemical engineering.

Most of the process synthesis studies in the field of separation have been concerned with multicomponent ideal mixtures, because there is no difficulty in predicting which components can be made to come off the top of a column and which can be made to come off the bottom. However, this process synthesis may be complicated by forming of azeotropes due to nonidealities in the mixture. These azeotropes can make a given separation impossible by ordinary distillation. These components must be separated by using the special separation processes such as azeotropic distillation or extractive distillation, when azeotrope forming components are encountered. Therefore, an in formation of occurrence of azeotropes in a mixture is one of the basic and most important thermodynamic data to produce a judicious design for the separation step.

In order to predict the binary and ternary azeotrope formation between components in mixtures and the types of formed azeotropes, a hierarchical knowledge-based expert system, called AZEOPERT was constructed. The AZEOPERT knowledge base can be largely divided into the knowledge base for the prediction of binary azeotrope formation and the knowledge base for the prediction of ternary azeotrope formation. Each of the knowledge bases consists of the different knowledge levels, arranged in a hierarchical structure.

AZEOPERT may be a useful tool for many chemical engineering activities, especially such as process synthesis and when selecting the environment-friendly alternative cleaning solvents to CFC solvents, an entrainer for azeotropic distillation and an appropriate solvent for extractive distillation or extraction.

Azeotropy

Azeotrope, from the Greek "constant boiling", means literally that the vapor boiling from a liquid has the same composition as the liquid. According to the Gibbs-Konovalov theorem an azeotrope exhibits a maximum or minimum boiling point relative to the boiling point of surrounding mixture compositions at an azeotrope point. Whether or not a binary mixture is azeotropic depends essentially upon following two factors:

- the degree of nonideality of mixture

- the difference in boiling points between the two pure components.

The nonideality of a mixture depends largely on the intermolecular forces of the components such as dispersion forces, dipole-dipole interactions, dipole-induced dipole interactions, and hydrogen bonding, of which latter is the most important effect on azeotrope formation. The closer the boiling points of the two components are, the more likely they will be azeotropic; the more ideal the solution of the two components is, the less likely they will form an azeotropic system. However, a mixture of close-boiling components may form an azeotrope despite only slight deviations from ideal liquid solutions and a mixture of wide boiling components may not exhibit an azeotrope even though they form a very non-ideal fluid mixture.

Although significant progress in azeotropy basically depends upon the development of molecular physics, the degree of nonideality in mixtures can be predicted with reasonable accuracy with the classification of components on the basis of the capabilities of hydrogen bonding of the certain groups and the series of homologues without the accurate informations of intermolecular forces.

Berg has classified liquids into five groups according to their hydrogen bonding capabilities and has satisfactorily predicted the degree of nonideality in a mixture. Swietoslawski and other workers have predicted the series of azeotropes on the basis of the series of homologues. However, only with the prediction of the degree of nonideality in a mixture azeotrope formation can be only qualitatively predicted.

A more accurate prediction can be achieved by taking account of the difference in boiling points between the two pure components. This boiling point difference effect on azeotropy can be well explained with the "azeotropic range" concept that was introduced by Swietoslawski and has been developed by Malesinki and Yoshimoto on the general assumption that the components form a regular solution.

AZEOPERT, a Knowledge-based Expert System

The knowledge base of AZEOPERT is largely divided into the knowledge base for binary azeotropes and for ternary azeotropes. The knowledge base for binary azeotropes is organized into five hierarchical levels, divided according to the different kinds of abstraction principles. That is, it is organized into a hierarchy with the top level (fifth level) concerning the prediction of the types of the binary azeotropes based on the compiled heuristic knowledge and the estimation of azeotrope temperature and concentration, the fourth level associated with generic model-based heuristic knowledge, the third level associated with group-oriented compiled heuristic knowledge, and the second level associated with component-specific compiled heuristic knowledge. In addition, as the bottom level (first level) the azeotropic data bank, which can be regarded as the lowest level of abstraction of domain knowledge, is integrated into the knowledge base. Figure 1 shows how the domain knowledge is organized into knowledge bases for binary azeotropic prediction.

5. level : The prediction of the types of the binary azeotropes based on compiled               heuristic knowledge and the estimation of azeotrope temperature and                 concentration

4. level : The prediction of binary azeotrope formation with model-based heuristic               knowledge associated with the azeotropic range derived from the regular              solution theory between different classes that are divided by the strength             of hydrogen bonding

3. level : The prediction of binary azeotrope formation with group-oriented compiled             heuristic knowledge associated with the azeotropic range between different            series of homologues

2. level : The prediction of binary azeotrope formation with component-specific                  compiled heuristic knowledge associated with the azeotropic range between             a certain component and a series of homologues

knowledge base for binary azeotropic prediction

Figure 1 Hierarchical knowledge structure for binary azeotrope prediction

1. level :  Azeotropic data bank for binary azeotropes

The knowledge base for ternary azeotropes is structured into a hierarchy with the top level associated with model-based knowledge and the second level associated with component-specific compiled heuristic knowledge. And the ternary azeotropic data bank is integrated into the knowledge base as the first level.

In the case of the prediction of binary azeotrope formation AZEOPERT begins with the examination of the lowest level, the binary azeotropic data bank, in order to confirm whether there are already binary azeotropic data for the consulted binary system in the binary azeotropic data bank. If there are no binary azeotropic data for the consulted binary system, AZEOPERT proceeds on to the next higher level, the level of component-specific compiled knowledge to make a predictive reasoning. It will proceed on to the next higher level of knowledge until the prediction succeeds. In this manner, AZEOPERT can predict the occurrence of azeotrope formation in a wide variety of mixtures regardless of known azeotropic systems or unknown azeotropic systems.

The group-oriented heuristic rules in the third level in Fig. 1 have been formulated within this contribution on the basis of the series of homologues and the azeoptropic ranges between the series of homologues that have been estimated by the extensive case studies with the azeotropic data bank which is implemented with the Foxbase data base system. For example, the heuristic rules for the system ketones/sulfides shown in Fig. 2 were formulated from the case studies with the azeotropic data bank.

Figure 2 The group-oriented heuristic rules for ketones and sulfides

Rule : Group_356

Two components K1 and K2 form an azeotrope,

if component K1 belongs to the ketones

and if component K2 belongs to the sulfides

and if T1[□C] is the normal boiling point of component K1

and if T2[□C] is the normal boiling point of component K2

and if the difference of the normal boiling points(T2-T1) is □ -5.0 □C

and if the difference of the normal boiling points(T2-T1) is □ 6.0 □C.

Rule : Group_356.1

Two components K1 and K2 do not form an azeotrope,

if component K1 belongs to the ketones

and if component K2 belongs to the sulfides

and if T1[□C] is the normal boiling point of component K1

and if T2[□C] is the normal boiling point of component K2

and if the difference of the normal boiling points(T2-T1) is □ -13.0 □C

or the difference of the normal boiling points(T2-T1) is □ 11.0 □C.

Conclusions

The knowledge-based expert system AZEOPERT for the prediction of azeotrope formation in a binary or ternary mixture presented in this work is an intelligent means for the solution of complex azeotropic problems; thus, it serves as a useful aid to chemical engineers, especially in process synthesis domain. AZEOPERT makes full use of the diverse types of knowledge available in the azeotropy domain for efficient and powerful problem-solving. AZEOPERT flexibly draws a reasoning with the integration of compiled knowledge derived from the generalization of a large number of case studies with the azeotropic data bank and model-based knowledge derived from deep knowledge. In AZEOPERT the following important issues have been treated and achieved to a large degree; hybridity of knowledge representation and problem-solving methodology, multiple levels of knowledge abstraction, modularity, the integration of a data bank into the knowledge base via file-transfer, the integration of an algorithmic method, and an interface to access files written in other programming language such as Fortran.

References

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2. Horsley, L. H., Azeotropic Data-III, Advances in Chemical Series, No. 116,          American Chemical society, Washington, D. C. (1973).

3. Malesinski, W., Azeotropy, other theoretical problems of vapor - liquid              equilibrium, PWN-Polish scientific publishers, Warszawa (1965).

4. Swietoslawski, W., Bulletin De L'academie, Polonaise Des Sciences, A, 19, 29       (1950).

5. Yoshimoto, T. and Y. I. Mashiko, Studies on Azeotropic Mixtures I, Bull. Chem.     Soc. Japan, Vol. 29, No. 9, p. 990 (1956).