an expert system architectural framework for engineering selection

Pergamon PIhS0952-1976(97)00028-6

Engng Applic. Artif. Intell. Vol. 10, No. 4, pp. 357-367, 1997 © 1997 Elsevier Science Ltd

Printed in Great Britain. All rights reserved 0952-1976/97 $17.00 + 0.00

Contributed Paper

An Expert System Architectural Framework for Engineering Selection

CHRISTINE CHAN University of Regina, Canada

PATRICK LAU University of Regina, Canada

(Received November 1996)

A generic architectural framework for constructing flexible expert systems for engineering selection is proposed in this paper. A two-tiered framework is suggested to mirror the two steps of elimination and refinement in selection tasks. The top layer consists of reverse rules for implementing the elimination of inapplicable alternatives, and the bottom layer consists of multiple-criteria decision-making (MCDM) models for explicitly modeling the decision-making processes in selection. Some MCDM models which involve user preferences or weights are incorporated in the framework because the selection task involves user preferences or weights assigned to decision parameters. The framework has been applied to the domain of solvent selection for carbon dioxide removal processes, and a prototype advisory system based on the framework has been developed. The proposed framework contributes to the field of engineering selection because it enables the explicit representation of selection knowledge, and the formal modeling of user preferences.

© 1997 Elsevier Science Ltd. All rights reserved

1. INTRODUCTION

Providing intelligent decision support for engineering design tasks is a key to enhancing productivity in the process industries. However, design tasks are difficult and not easily amenable to automation, largely because they are based on heuristics and their solution spaces are open- ended. In design, the elements of the designed artifact are not constrained to come from a predefined set; instead, they are subject only to the constraints of the manufacturing methods and the characteristics of the raw materials. Hence, the knowledge for general design tasks is open-ended. Configuration and selection are both special cases of design. The knowledge necessary for configuration tasks is more bounded, and deals with defining and characterizing the set of possible parts, which are designed so that they can be combined systematically and would cover the desired range of possible functions (Stefik, 1995). Selection, on the other hand, involves making a choice within a predefined or existing enumerated set of alternatives which can be

Correspondence should be sent to: Christine Chan, Dept of Computer Science/Energy Informatics Laboratory, University of Regina, Regina, SASK, Canada $4S 0A2 [[email protected]].

expanded. Hence, the solution space for selection is similar to that for configuration, and is more bounded than that for design. In contrast to configuration however, selection does not involve the systematic combination of components, but simply choosing one or a few from a set of available alternatives to ensure that a desired function can be accomplished.

In this paper, an architectural framework for constructing intelligent decision-support systems for the selection task within engineering design is proposed. Some examples of selection in process design include the selection of activation systems in oil production (Hoffmann and Valentin, 1987), parameter selection in metal cutting (Malakooti and Deviprasad, 1989), and the selection of a solvent for acidic gas removal (Gao, 1996; Chan and Tontiwachwuthikul, 1995). Selection of engineering processes is a design task that involves ,a two-step process: (i) selection of the appropriate process through iterative applications of heuristics, and (ii) refinement of the selection by means of mathematical modeling or optimization techniques. When the process has been selected, then the physical components involved in it are appropriately sized. Automated support for the selection task is needed, and both rule-based and

357

358 CHRISTINE CHAN and PATRICK LAU: ENGINEERING SELECTION

procedural-based systems have been used to support the process. The problem being examined here is that of solvent selection for carbon dioxide (COz) removal processes, a process crucial to the design of carbon dioxide absorbers. Section 2 presents some background on the problem domain and a brief review of two advisory systems that have been implemented for the problem: the "Solvent Selection Advisory System" (SSAS~) (Chan and Tontiwachwuthikul, 1995) which employs a rule-based representation, and the "Solvent Knowledge-Based System" (SOLKB) (Gao, 1996) which employs a procedural-based representation. The weaknesses in a rule-based representation are described in Section 3, and the disadvantages of a procedural-based representation in Section 4. In Section 5, an architectural framework for constructing selection advisory systems is proposed, which overcomes the weaknesses discussed in the previous two sections. Section 6 is a discussion of the pros and cons of the proposed framework, Section 7 briefly describes an implemented application of the framework which demonstrates its feasibility, and Section 8 provides a conclusion.

2. B A C K G R O U N D

2.1. Problem domain: solvent selection for the carbon dioxide removal processes

This work focuses on a specific design problem in the chemical engineering discipline of solvent selection for carbon dioxide removal processes. Carbon dioxide (CO2) removal from industrial gas treatment units is an important issue, given current environmental concerns about global warming and the greenhouse effect. A large amount of CO2 is being produced from the combustion of fossil fuel, including coal, oil, and natural gas. Under pressure from government regulatory agencies, the oil and gas industry is attempting to reduce the emission of CO2 by removing the acid gas before it is discharged into the atmosphere.

COz removal from gases is a process of acid gas removal generally referred to as "gas treating". Many industrial processes require acid gas removal, e.g. hydrogen manufacture, ammonia manufacture, natural gas purification, coal gasification, refinery fuel gas treating, and ethylene manufacture.

A typical flow sheet of an industrial acid gas removal unit utilizing a chemical solution is shown in Fig. 1. The sour gas, which enters the unit through an inlet separator where entrained liquid and solid particulates are removed, flows from the bottom of the absorber upwards against a counter- current stream of the lean solution. The acid gases are absorbed and the treated gas leaves from the top of the absorber.

The acid gas "loaded" (or "rich") solution flows from the bottom of the absorber and passes through the lean-rich heat exchanger, where it is heated by the hot, in-coming lean solution. It then enters the top of the regenerator. In some cases, a flash tank is installed upstream of the heat exchanger to desorb some of the acid gases by letting down the pressure of the rich stream.

Upon entry into the regenerator, some of the absorbed acid gases are flashed. The solution then flows downward against a counter-current flow of vapour generated in the reboiler. The stripping vapour removes most of the remaining acid gases from the rich stream. The overhead mixture passes through a condenser where most of the vapour is condensed. The acid gases are separated from the condensate in a separator, and the condensate is returned to the top of the regenerator as reflux. The lean solution, which leaves the bottom of the regenerator, exchanges heat with the rich solution in the lean-rich heat exchanger, and then passes through a cooler to return to the top of the absorber.

Generally, a gas-treating unit is integrated into a larger process. In CO2 removal, particularly in connection with ammonia manufacture, the separated CO2 may become the feedstock for making urea, or it may be recovered and sold to beverage companies for the manufacture of carbonated soda pop, or to oil and gas companies to be injected into the ground for enhancing the recovery of oil and gas.

In a solvent acid gas removal system, up to 70% of the plant investment is directly associated with the magnitude of the solvent circulation rate (Astarita et al., 1983). This has a direct impact on the size of the absorption tower, the piping, the circulation pumps, and size of the regenerator facilities. Therefore, selecting a suitable solvent for the process is one of the most important steps in designing the separation facilities.

The essential elements of the solvent selection methodology for the CO2 removal processes are shown in Fig. 2. Starting from the top, the definition of a process is provided by two elements: the raw or feed gas characteristics (such as composition, pressure, etc.) and the product or treated gas specifications (i.e. the process requirements). Firstly, the partial pressure of CO2 in the feed gas determines the solvent circulation rate. In an absorber, the feed gas contacts the rich solvent at the bottom of the column. At a minimum, therefore, there must be sufficient solvent to absorb the CO2 in the lean solvent. Otherwise, there will not be a positive driving force for absorption at the absorber top. At this point, if more than one applicable solvent is available, further selection criteria such as heat supply, corrosiveness of the solvent, etc. are applied for choosing the most suitable solvent.

Developing decision support systems for the preliminary selection is being addressed in this paper (see Fig. 2). The decision support tool developed can be used as a teaching tool for chemical engineering students in hydrocarbon processing and process design courses. This system may also be used by design and process engineers who are designing a new gas processing plant or modifying an existing gas-treatment facility. Unlike diagnostic tasks, in which sensors gather data from each relevant piece of equipment and each datum is checked according to the range of acceptable or normal values to determine if a fault exists, in a design problem, normality in property values is assumed. For example, the composition of the raw gas is assumed to contain between 0 to 50% of CO2: less than 0% means no absorption of the acidic gas is needed, while for

CHRISTINE C H A N and PATRICK LAU: ENGINEERING SELECTION 359

sweet

\

/ \

sour gas

\ /

l iqu id-separator -1

*t

i I i .......... ' absorber

f low-p ipe

cooler

heat exchan

acid

condenser

reflux drum

pump

regenerator

: . . . . . . . . . . I o°

pump

l iquid-separatorJ I I J II . fuel reboi ler

/ reclaimer

flash tank

pump

\

pump make up tank

Fig. 1. A typical flowsheet of an industrial acid gas removal unit utilizing chemical solution.

I 'ss I I Transfer Trace Wasted-heat Impurities

Integration k,, ~ / ~ . L ~ emoval

Partial Pre in RawFe i d f l i t I ~ / . . . .

Fig. 2. Solvent-selection methodology for CO, removal processes. L~I 10:4-C


Table I. The constraints for selecting a solvent for CO., removal (Gao, 1996)

Primary constraints Secondary constraints Optional constraints

CO,_ partial pressure in treated gas Corrosion Patent CO, partial pressure in raw gas Degradation Trace impurities

Hydrocarbon solubility Wasted-heat integration

higher than 50% of COz, solvent absorption is no longer the optimal process. Similarly, the loading of the chosen solvent(s) is assumed to fall within the normal range of 0.1 to 0.15 moles of CO2 per mole of absorbent, and its temperature is assumed to be between 25 and 75°C.

2.2. Solvent Selection Advisory System (SSASI) (Chan and Tontiwachwuthikul, 1995)

The Solvent Selection Advisory System (SSAS0 is a prototype rule-based expert system written in G2 ~. SSASt aids users in selecting solvents for the carbon dioxide removal process which takes place in an absorption column or an absorber, by suggesting the solvent to be used, based on a set of user specifications.

The development of the conceptual model for SSAS1 was based on a diagram in which the domain was divided into 13 regions. For each region an inference structure is con- structed to represent expert knowledge; then production rules are created, based on the inference structures. A sample production rule in SSAS~ is as follows:

If reg9 and avail-sur-heat is no and solvent-loss is no and corrosion is no then inform the operator that "Attention: the recommended solvent is secondary amine

(certain~ factor 100%)"

The system provides recommendations and explanations to the user. However, it does not allow the addition of new solvent alternatives in the knowledge base. This is a problem because new solvents can be formulated and become available in the market, which means the solution space for the selection task needs to be updated periodically. SSAS~ does not support this updating.

2.3. Solvent Knowledge-Based System (SOLKB) (Gao, 1996)

SOLKB is a solvent-selection knowledge-based system which was also developed on G2. The objective in the project was to develop a solvent-selection system that would allow the addition of new solvents to the knowledge base. However, unlike most expert systems, SOLKB does not have any production rules. Instead, it uses only procedures to control the selection process, which has been separated into four steps:

(1) Find the solvent(s) that satisfies a task. (2) Find the solvent(s) that satisfies the acid gas partial

pressure in treated gas.

(3) From the solvent(s) found in step 1, find the one(s) that satisfies the acid gas partial pressure in the raw gas.

(4) From the solvent(s) found in step 2, find the one(s) that has lower corrosion, lower hydrocarbon solubility, and then adjust the choice with the expert's subjective evaluation.

Similar to the development of SSAS~, the development of SOLKB involved defining the decision parameters. These are divided into three groups of constraints: the primary constraints, secondary constraints, and optional constraints. Primary constraints are the hard constraints, and a solvent is considered workable only if the primary constraints can be completely satisfied. Secondary constraints are soft constraints, and are less important, while optional constraints have little effect on the selection process but serve as a reference in SOLKB. The constraints for selecting a solvent implemented in SOLKB are listed in Table 1.

SOLKB also uses suitability functions for primary and secondary constraints; these are developed based on linear approximation. For instance, the suitability of a solvent in terms of hydrocarbon solubility is given by the suitability function shown in Fig. 3.

Using linear approximation, the degree of suitability in terms of hydrocarbon solubility is mapped to a range of 0-100, and the number indicating the degree of suitability is obtained from the graph as shown in Fig. 3. To evaluate the likelihood of one solvent being the optimal choice, SOLKB uses the summation of suitability values as

100

'R t~

g~

H 100

The Degree of Hydrocarbon Solubility

Trademark of Gensym Corp. USA. Fig. 3. Hydrocarbon suitability.

CHRISTINE CHAN and PATRICK LAU: ENGINEERING SELECTION 361

measured by the suitability functions. Hence, SOLKB uses both suitability functions and procedures for providing data- level analysis; new solvents or decision alternatives can be added to the system.

and diagnostic systems. However, it is a serious problem for selection systems in which new decision alternatives frequently become available in the market, and the solution space needs to be expanded.

3. WEAKNESSES OF A RULE-BASED REPRESENTATION

3.1. Inflexibility

Representing the knowledge in production rules has enhanced the modularity of the system. However, the rule- based representation of SSASI does not allow the simple addition of new decision alternatives to the selection system (Gao, 1996). The problem can be illustrated by the following example:

Given a solvent selection advisory system with only one heuristic rule:

Rule 1: IF corrosion = high THEN physical solvent,

if a new heuristic rule Rule 2 needs to be added into the system, where

Rule 2: IF energy consumption = high THEN chemical solvent,

the addition is straightforward because Rule 2 and Rule 1 are independent, and the modularity of heuristic rules guarantees the simplicity of this type of addition. However, it would not be a simple task to add a newly invented solvent to the solvent selection advisory system, especially when the characteristics of the new solvent are similar to those of the physical solvent. In this case it is not clear whether the decision part in Rule 1 should be substituted with the new solvent, or Rule 1 should be left unmodified. Before the addition of a new solvent to the knowledge base, a thorough examination of all the rules relevant to a physical solvent needs to be conducted. Then a decision has to be made as to whether to modify the rules in the knowledge base so as to accommodate the addition of Rule 2. If a system consists of several hundred rules relevant to a particular solvent, the review process can become tedious. In other words, in a selection expert system, heuristic rules are modular, but decision alternatives are not. The inflexibility arises because decision alternative (B) is directly associated with the decision parameter (A) in a production rule such as "IF A THEN B". Implicitly, "IF A THEN B" asserts that B is preferable over any other alternative, given situation A. This affects the independence or locality of a decision alternative. For instance, Rule 1: IF corrosion =

high THEN physical solvent implicitly states that the physical solvent is the best alternative, given the condition that pressure and corrosion are high, regardless of whether a new solvent becomes available or not. Therefore, "IF A THEN B" causes problems when the knowledge base of a selection advisory system needs to be updated. This kind of inflexibility may be acceptable for systems with fixed numbers of decision alternatives, such as control systems

3.2. Implicitness of selection knowledge

A second problem in a rule-based representation of the selection task is that it cannot explicitly represent the two types of selection knowledge. It has been recognized that the process of selection involves two steps: (1) elimination of inapplicable alternatives, and (2) refinement of the applicable alternatives. Hoffmann and Valentin (1987), Gao (1996), Myint and Tabucanon (1994) Beiter et al. (1991), Gardone and Ragade (1990), Venkatasubramanian (1988), and Shema et al. (1989) describe the two types of knowledge as "hard" and "soft" constraints. Hard constraints constitute constraints with which feasible alternatives should be consistent, and soft constraints are helpful but not essential. Typically, hard constraints specify objective requirements for the choice, while soft constraints contribute to the subjective refinement of the selection. The two types of knowledge have different characteristics, and assume different roles in the selection process. However, this difference cannot be represented by using production rules alone. For example, the rule in Fig. 4 explicitly asserts that, given the situation defined in the antecedent of the rule (where "n" stands for "no"), the expert's choice will be secondary amine.

While the above rule is conceptually sound, it does not explicitly represent elimination knowledge (i.e. hard constraints) versus refinement knowledge (i.e. soft constraints), and the two types of knowledge cannot be extracted from it. Therefore, from the rule represented in Fig. 4, one cannot ask questions such as, "Is physical solvent not applicable for the given situation?", "Is hybrid solvent not applicable for the given situation?", and "Why is physical solvent less applicable than secondary amine in the given situation?". It may be that physical solvent has been eliminated in the first phase of the selection process, or that physical solvent is applicable but less appropriate compared to secondary amine. Explicit representation of the two types of knowledge would provide information to answer the above questions.

3.3. Lack of representation of the relative importance of decision parameters

A third problem in the rule-based representation is that it cannot represent the relative importance among the decision parameters involved in the selection process. As discussed

/freg9 and avaff-sur-heat U n a~t solvent-la~ is n tarl corrosion is n

then i~orm the openv, ar that

"Attention: the r e ~ d solvent is secondary tarane "

Fig. 4. A sample selection rule (Chan and Tontiwachwuthikul, 1995).


earlier, selection involves the two processes of elimination and refinement. Often in the elimination process, solid rules on hard constraints can be formulated. For example, in the solvent-selection domain, if the partial pressure value of CO2 in raw gas is above 80 psi, then hybrid solvents are not applicable. This is true, regardless of the situation or users involved. In contrast, soft constraints, which are user- or situation-dependent, are often involved in the refinement or evaluation process. In the solvent-selection task, the partial pressure indices constitute hard constraints, and are the most important parameters in the elimination process. In the second refinement process, the expert assigns a relative importance or weight to the decision parameter according to the situation and the expert's subjective viewpoint. In other words, corrosion may be more important than other decision parameters in one situation, while energy consumption may be more important in another. Therefore, the representation of relative importance in the decision parameters is required for refinement.

In the case of selecting plastics for design, for example, both Beiter et al. (1991) and Venkatasubramanian (1988) claimed that they needed to weigh the properties of the alternatives, i.e. the decision parameters, in importance relative to the design before a reasonable judgement could be made. Similarly, for selecting a laboratory reactor, Hanratty et al. (1992) declared that the weight or importance of performance factors (decision parameters) such as mass transfer limitations, iso-thermality, catalyst deactiva- tion, data analysis, and cost are essential to the decision process. Hence, the need for representing the relative importance of attributes or decision parameters is well documented. However, this type of knowledge is not reflected in a production rule such as "IF A~ and A2 THEN B", where Aj and A2 are two decision parameters. For example, in the rule IF corrosive and energy consuming THEN solvent X, the two decision parameters of corrosion and energy consumption assume equal weight, and the system cannot determine which is more important from this representation.

4. WEAKNESSES OF A PROCEDURAL-BASED REPRESENTATION

To overcome the problem of inflexibility of SSASt (Chan and Tontiwachwuthikul, 1995), procedures and linearity- based suitability functions are used to evaluate the suitability of a solvent, given user specifications in SOLKB (Gao, 1996). SOLKB allows the knowledge base to be updated with new decision alternatives, and shows that a procedural representation can overcome the problem of inflexibility implicit in the rule-based representation of SSAS~. However, the implementation approach of SOLKB has two problems. First, heuristic knowledge on the domain, such as whether some solvents are suitable for specific tasks, and control strategies are both represented in the system's procedures. This approach ignores a fundamental principle of expert-system construction, which requires the separation of domain knowledge from control strategies.

Secondly, decision processes are modeled with evaluation functions based upon linear approximations and summation methods. Neither is sufficiently accurate for modeling decision-making processes in the refinement phase, which is event-driven and relatively subjective in nature. Moreover, the functions are created in an ad hoc manner which decreases the reliability of the system. More formal modeling functions should be adopted to increase the accuracy of the decision-modeling process. Although it is widely documented that a representation of the relative importance in decision parameters is essential in the refinement process, Gao's implementation ignores this consideration.

5. AN ARCHITECTURAL F R A M E W O R K FOR ENGINEERING SELECTION

A two-tiered architectural framework for representing the engineering selection task, which includes the two sub- processes of elimination and refinement, can overcome the inadequacies of a procedural-based or purely rule-based approach. The top layer of the selection framework consists of reverse rules for eliminating inapplicable alternatives, while in the bottom layer, Multiple-Criteria Decision Making (MCDM) models can be used to support the refinement process (see Fig. 5).

The advantage of this new framework is two-fold. First, the knowledge in the two phases of the selection process can be explicitly represented. Second, the relative importance of

y El imina t ion P r o c e s s

using "IF A T H E N not B" to ensure only useful a l te rna t ives can be

passed into the eva luat ion layer

R e f i n e m e n t P r o c e s s makes j u d g e m e n t based on

M C D M models

Fig. 5. A two-tiered framework for selection systems.


decision parameters can be represented for the refinement process. Before explaining the benefits, the two-tiered framework will be presented as follows.

5.1. Top layer: reverse rule

In the top layer of the architectural framework, the reverse rule from CAPS (Venkatasubramanian, 1988), a rule-based expert system for the selection of plastics, and object technology (Kaindl, 1994; Taylor, 1992; Brian, 1992; Ellis, 1991; Stefik and Bobrow, 1986) are adopted. CAPS is encoded in OPS5 and LISP, and a forward chaining search strategy is used. CAPS (Venkatasubramanian, 1988) does not recommend a specific plastic given a situation, but rather uses reverse rules to indicate the inappropriateness of decision alternatives. Rules in the "IF A then ~B" format are used to describe domain knowledge, where A is a set of decision parameters and B is a decision alternative. For example, the following is a rule in English whose function is to eliminate useless alternatives,

IF There is an active goal to compare colorability The desired colorability is unlimited There is a group that does not have unlimited

colorability

THEN Remove that group.

Since rules in the system only state what is not workable given the conditions, instead of assuming that the choice in the consequent of the rule is preferable, given the conditions in the premise, decision alternatives can be easily added to the system. A drawback of the reverse rule approach is that a large number of rules is needed to identify the best solution. This problem will be discussed later. The reverse rule implemented in CAPS is conceptually similar to the idea of elimination in the first phase of the selection process. Both the reverse rule and elimination use "constraints" to discard unsuitable alternatives. This approach agrees with Chandrasekaran's observation that, "the design problem is formally a search problem in a very large space for objects that satisfy multiple constraints", and that "what is needed to make design practical are strategies that radically shrink the search space" (Chandrasekaran, 1995). Reverse rules can be used as constraints to reduce the solution or decision alternative space, thereby increasing the efficiency of the selection process. Hence, "reverse rules" in fact foster the independence or locality of decision alternatives. They also enhance the explicit representation of the elimination and refinement knowledge, which is discussed as follows.

5.2. Bottom layer: M C D M models

In the bottom layer of the framework, multiple-criteria decision making or MCDM models (Hwang and Yoon, 1981) are used. Many engineering problems are multiple- criteria decision making (MCDM) problems; for example, the problem of power plant siting (Hobbs, 1980), selection of activation systems in oil production (Hoffmann and

Valentin, 1987), selection of industrial robots (Gardone and Ragade, 1990), parameter selection in metal cutting (Mala- kooti and Deviprasad, 1989), and selection of laboratory reactors (Hanratty et al., 1992). Before a decision can be made in these problems, the effects and consequences of that decision must be analyzed from multiple perspectives. To further complicate the problem, conflicts may also arise between some of the criteria. Multiple-criteria decision methods developed from the multiple-criteria decision making (MCDM) research area are useful for dealing with this type of problem.

MCDM research focuses on the problem of selecting a suitable or best solution for a problem involving multiple attributes and/or objectives from the pool of alternatives (Tabucanon, 1988). To model decision processes in humans, both compensatory and non-compensatory models have been developed. In compensatory models, advantages from one dimension can be traded for, or are restricted by, advantages from another dimension. In non-compensatory models, on the other hand, advantages from one dimension cannot be traded for another. Different problems require different models, and the selection of an appropriate MCDM model is not a simple process. Procedures and classification taxonomies for multiple-attribute decision making are available to facilitate model selection (Hwang and Yoon, 1981). Users need to understand the properties of the models before a suitable model can be chosen for a specific situation. For example, weighting models which involve a representation of subjective judgement are appropriate for representing user preferences and the relative importance of solvent characteristics. Two sample models from this group and their application to the solvent- selection task are presented as follows.

5.2.1. Two sample multiple-criteria decision-making models

There are many weighting models in the "cardinal preference of attribute given" group (Hwang and Yoon, 1981). Compromise programming (CP) by Zeleny (1973) and cooperative game theory (CGT) by Szidarovszky et al. (1978) are two examples that involve weights in their models. Both are suitable for the refinement process within engineering selection, because both involve the representation of information on attributes and users' preferences or weights in the decision process. If the feasible solution closest to the best solution is needed, and information on attributes of the decision alternative is given, one can use CP. In the CP model, the distance from the feasible point f, to the ideal point f*, is represented as D(x), which is described by the following formula:

D(x)= ~ ,f;/-.L ail - I

where oti is the weight of the ith criterion, ~ is the ith element of the ideal point, f.,, is the ith element of the worst alternative obtainable for criterion i andf.x is the ith element of the alternative x. In contrast, the CGT model searches for


the most suitable solution, which maximizes the geometric Table 4. Modeling with CGT

distance from the worst point to the point defined by the f, f2 fa user. The geometric distance g(x) is defined by the following formula, I(f,.~-f~..)l 0 1 2

lot2., - f2, . ) l 2 1 0 10%., -Z.,,)l 0 1 2

g ( x ) = l~ g a - - f , w I '~' CGT 0 1 0 i=1

f ,=decision alternative x.

where oe i is the weight of the ith criterion, f.w is the ith element of the worst alternative obtainable for criterion i, and f. , is the ith element of the alternative x (Gershon, 1984). As can be seen in the CGT model, the geometric distance is measured by the term f..< -f.w; hence, g(x) of any decision alternative x is zero if there exists any f.~ equal to f,,. Therefore, CGT discards any decision alternative that has even one of its attributes exhibiting worst-case characteristics. Since this modeling approach measures the actual against the worst rather than the ideal case, it is more "conservative" than CP, and would be suitable for choosing an alternative with a high-risk factor.

To illustrate how MCDM models can be used in modeling the refinement process within engineering selection tasks, consider the example of three decision alternatives in the solution space or decision set S= {f~ f2f~ }, as shown in Table 2.

The best point f* would be given by an aggregation of all the "best" characteristics that the decision alternatives in S possess, and the worst point, fw would be given by an aggregation of all the "worst" characteristics that the decision alternatives in S possess.

To evaluate the decision alternatives using CP, the result shown in Table 3 is obtained, assuming that all attributes are equally important, i.e. ai= 1.

Since CP measures the distance from a feasible point to the ideal one, the shorter the distance, the better the decision alternative. Hence, f3 would be the most suitable choice among the three options.

To evaluate the decision alternatives using CGT, the result shown in Table 4 is obtained, assuming that all attributes are equally important, i.e. ai= 1.

Table 2. Matrix of decision alternatives

f~ f2 f , f* f~

Attribute I ~ . , ) I 2 3 3 1 Attribute 2 (f:.~) 6 5 4 6 4 Attribute 3 0%.,) 7 8 9 9 7

f , = decision alternative x, f* = ideal point or best point, f~ = worst point.

Table 3. Modeling with CP

f, f2 f~

10'7 -f,.,)/~ -f,.,,)M 1 0.5 o I~ -f2.,.)/~ - fz , . ) I 0 0.5 1 l(l~.~ -f,.~)l(l~3 -A.,,)l I 0.5 0 CP 2 1.5 I

f ,=decis ion alternative x.

Since CGT excludes alternatives that possess any attribute equal to the worst value from the solution set, alternatives fj and f3 are discarded. Hence, f2 would be the most suitable among the three options. It is observed from the above examples that CGT is more "conservative" than CP, and provides a "safer" solution, in that an alternative that possesses a worst-case attribute is discarded. Therefore, it can be seen that CP is a preferred modeling approach for choosing an alternative that is closer to an ideal, while CGT is preferable for generating a "safe" solution. Each model is designed for a specific purpose, and the selection of a suitable model is very important in multiple-criteria decision making. In addition to compromise programming (CP) and cooperative game theory (CGT), many other models are available for modeling various decision processes. Some well-known examples include ELECTRA and TOPSIS; for a detailed discussion of MCDM models, see (Hwang and Yoon, 1981).

6. STRENGTHS AND WEAKNESSES OF THE PROPOSED FRAMEWORK

A major advantage of the proposed two-tiered architectural framework is that it succeeds in explicitly representing the two types of selection knowledge, a task in which the rule-based representation of "IF A THEN B" has failed. In this framework, "IF A THEN ~B" rules are used for representing elimination knowledge, and MCDM models for representing refinement knowledge. In this way, knowledge related to each phase of the selection process can be explicitly represented and easily extracted from the system. For example, given the rule "IF partial pressure <500 then ~ physical solvent", the user can infer that physical solvent is not workable when the partial pressure is too low. Furthermore, the reason for the elimination of physical solvent can be derived from the reverse rule: "physical solvent" is eliminated because "partial pressure <500". In SSASII the reason for removal is displayed to the user, as shown in Fig. 6.

The MCDM models enable explicit comparison of the alternatives in the refinement step. For example, to explain why alternative B~ is better than alternative B2, the relative suitabilities of the solvents generated by the MCDM models can be shown. In English the suitabilities may be as follows: "The overall suitability of Br is 0.8, and the overall suitability of B2 is 0.5". In SSAS., the suitability indices are generated as shown in Fig. 7.

However, a problem with reverse rules is that redundan- cies in the rules become inevitable. For instance, given a


I Why Not Screen

METHANOL is eliminated because it cannot fulfi l l the decision criteria: product partial pressure

U oK II I I cLAs" Q . . . . . . . E

Fig. 8. Classification of decision alternatives.

Fig. 6. Explanation Screen from SSAS..

problem with seven decision choices, D1 to D7, with D1 and D2 being suitable for a given situation, the if-then production rule formalism requires only two rules to describe the situation: RULE 1: IF A THEN D1, RULE 2: IF A THEN D2. Using the reverse-rule formalism, five rules are needed: RULE 1: IFA THEN-~D3, RULE 2: IFA THEN ~D4, RULE 3: IF A THEN-~D5, RULE 4: IF A THEN ~D6, RULE 5: IFA THEN--,D7. In SSAS~, a sample reverse rule is implemented as I f raw _pp <80 then remove (hybrid solvent, "raw partial pressure"). A classification which is supported by the object hierarchy in G2 is adopted to reduce the number of rules required. Given the seven options, D1 to D7 in the above example, DI and D2 can be classified into a new class named C1, and D3 to D7 into another group named C2 (see Fig. 8).

Hence, only one rule "IF A THEN ~C2" is needed to represent the information that D3 to D7 are not applicable. This substantially reduces the number of rules, since without classification, five rules are required. The classification approach is applicable to the solvent-selection domain because solvents can be classified into a solvent hierarchy. In S S A S H, the solvents have been classified into the five

I Suggestion and Explanation I

SSAS has the following suggestions (solvent on the top of the list has higher preference):

To show a complete applicable solvent list, click

Index: I 0.289

107t61

Solvent: MDEA-SULFOLANE MEA MDEA DEA

IWHY NOT I ~ ICONTINUEI

Fig. 7. Recommendation Screen from SSAS H.

groups of aqueous amine, promote hot carbonate, inhibited concentrated amine, physical solvent, and hybrid solvent.

A second advantage in the proposed architectural framework is that users can use the modeling functions provided in MCDM models for evaluating decision alternatives, thereby increasing the accuracy and reliability of the decision-making process. In SSASH, the user can choose among the three models of Simple Additive Weighting (SAW), Compromise Programming (CP), and Co-operative Game Theory (CGT) for modeling the refinement process. The system calculates indices for each of the applicable solvents using the chosen model, and ranks them according to the indices. In this way, comparison of the solvents can be easily performed. If deemed necessary in the future, other MCDM models can be added to the system. This approach enables users to formalize the decision-modeling process and eliminate ad hoc functions such as the suitability function in SOLKB (Gao, 1996), or the scoring function in HyperQ (Beiter et al., 1991). With the help of MCDM models, the relative importance of the decision parameters can be formally represented for the refinement process.

7. AN APPLICATION OF THE PROPOSED FRAMEWORK

To demonstrate the feasibility of the proposed two-tiered architectural framework, an application of it has been implemented in a new prototype solvent-selection advisory system SSAS.. G2 is chosen as the implementation platform because it supports the development of an object class hierarchy and a graphical user interface, and provides an inference mechanism. In the following brief discussion, G2 features are italicized.

The implemented framework consists of reverse rules, object technology, and MCDM models. In SSASH, reverse rules are implemented using a combination of rules and methods, while MCDM models are implemented with procedures. The dynamic model of SSASH consists of the four modules of (1) pre-process, (2) elimination, (3) refinement, and (4) recommendation. The pre-process module initializes system variables, and allows users to input partial pressure values for raw gas and treated gas. The elimination process eliminates inapplicable solvents by invoking reverse rules. The refinement process refines the selection by using MCDM models. In SSASH, only the three


MCDM models of simple additive weighting (SAW), compromise programming, and co-operative game theory have been implemented, and the user can choose which model to use. Finally, recommendations are displayed to users. A detailed discussion of the implementation of the two-tiered framework will be reported in future publica-

tions.

8. CONCLUSION

This work has been motivated by a need to address the

inadequacies of a purely rule-based or procedural-based representation of the selection task. Specifically, the follow-

ing problems in the rule-based representation are noted:

• inability to update the decision alternatives in a knowl-

edge base because a decision alternative is directly associated with decision parameters in the rule

• representation of the two types of selection knowledge, i.e. elimination and refinement knowledge, are not explicit in the antecedents of rules

• the relative importance of decision parameters is ignored

because all decision parameters in the antecedent of a rule

are assumed to be equally important.

To solve these problems, a two-tiered architectural framework which combines reverse rules, object technology, and MCDM models has been proposed. The top layer of the framework consists of reverse rules and object technology. Reverse rules are local, and allow the flexible addition of new information or decision alternatives to the system; a classification hierarchy inherent in object technology enables a reduction in the number of rules that the reverse rule formalism introduces. Since reverse rules do not specify which is the solution, and do not allow the representation of the relative importance of decision parameters, MCDM models are used for supporting the refinement process in the bottom layer of the framework. The benefits of using the two-tiered framework includes: (1) addition of new decision alternatives is possible and, in fact, easy, (2) the elimination and refinement knowledge are clearly separated and explicitly represented, and (3) representation of the relative importance of decision parameters can be achieved with the MCDM models. The proposed framework is generic, and can be applied to other

engineering selection tasks.

Acknowledgements--The generous support of the Natural Sciences and Engineering Research Council of Canada is gratefully acknowledged. We would also like to thank Dr. P. Tontiwachwuthikul, A. Veawab and A. Aroonwilas of the Faculty of Engineering, University of Regina, for enlightening discussions about the solvent selection domain.

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AUTHORS' BIOGRAPHIES

Christine Chan received M.Sc. degrees in Computer Science and Management Information Systems from the Department of Computer Science and the Faculty of Commerce and Business Administration of the University of British Columbia in 1986 and 1988. In 1992, she received a Ph.D. degree in the interdisciplinary studies of Computer Science, Philosophy, and Psychology from Simon Fraser University of Canada. In 1993, she joined the University of Regina, where she is now an Associate Professor in the Department of


Computer Science. Her research interests include industrial applications of Artificial Intelligence, object-oriented methodologies for knowledge-based systems development, and knowledge acquisition. She has published more than 70 refereed papers, and is a member of the AAAI, the IEEE Computer Society, the ACM, and the Canadian Information Processing Society (CIPS). She has been an invited speaker at the IEEE Computer Society and the Instrument Society of America (ISA). Patrick Lau received B.Sc. and M.Sc. degrees in Computer Science from the University of Regina in 1994 and 1996, respectively. His research interests include the construction of intelligent systems, uncertainty reasoning, and industrial applications of Artificial Intelligence. He is currently working as a software engineer in Hong Kong.

an expert system architectural framework for engineering selection

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