Developments in computer-aided dryer selection (Baker, Lababidi).pdf

DRYING TECHNOLOGY, 19(8), 1851–1873 (2001)

DEVELOPMENTS IN

COMPUTER-AIDED

DRYER SELECTION

C. G. J. Baker and H. M. S. Lababidi

Chemical Engineering Department, Kuwait University,

P. O. Box 5969 – Safat, 13060 Kuwait

ABSTRACT

This paper describes recent advances in the development of

a fuzzy expert system for food dryer selection. An earlier

version, which was restricted to batch dryers, has now

been extended to include continuous dryers. The modular

approach originally proposed by the present authors was

adopted. The current implementation of the system includes

three knowledge bases: the mode (batch-continuous) selector,

the batch-dryer selector, and the continuous-dryer selector. A

blackboard architecture was used to facilitate full data inter-

change between the three knowledge bases. A user interface

and a scheduler were developed to automate the system.

Examples of ancillary programs (design, costing, help, appli-

cations) have also been developed. Satisfactory predictions

were obtained using the selection algorithm. Typical examples

are presented in case studies.

Key Words: Dryer selection; Expert systems; Fuzzy logic;

System development; Continuous dryers; Food

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BAKER AND LABABIDI

INTRODUCTION

Dryer selection involves an interplay between a relatively large number

of factors, both technical and economic. The chosen dryer must primarily be

capable of performing the required duty in terms of moisture removal,

throughput, and feed-handling capability. In certain applications, such as

in the drying of biomaterials, for example, it may also be required to develop

other desirable product-quality features – e.g. functionality, taste, and color.

These stringent technical requirements should naturally be met at the lowest

possible cost.

Until recently, its complexity has restricted dryer selection to

‘‘experts’’. However, computer-based tools are now available that can be

employed to at least partially de-skill this process. Most are based on con-

ventional expert systems (see Nevenkin and Chavdarov, 1992; Kemp and

Bahu, 1995; Kemp, 1998; Matasov et al., 1998). Other workers, such as

Kraslawski et al. (1999) have employed case-based reasoning as an alter-

native approach.

Baker and Lababidi (1998) presented their initial ideas on the features

that should be incorporated into a computerized dryer selection algorithm.

They proposed the use of a modular fuzzy expert system for this purpose

and outlined a knowledge base that could be used for the selection of batch

dryers for foodstuﬀs. This aspect of the work was further developed and

reﬁned and was subsequently published elsewhere (Lababidi and Baker,

1999: Baker and Lababidi, 2000). The key objectives of the work described

in the present paper are:

1. To design a continuous-dryer selection algorithm for food

products.

2. To integrate the new knowledge base with the earlier batch-dryer

selection algorithm.

3. To develop a variety of ‘‘foreign’’ programs that will provide the

user with additional information to aid the selection process.

4. To automate the selection process and to provide a user interface

to aid the data input and output processes.

COMPUTER-BASED DRYER

SELECTION ALGORITHMS

Baker and Lababidi (2000) described a structured technique for dryer

selection. The approach is iterative and involves the following steps: draw-

ing up the process speciﬁcations, making a preliminary selection, planning

COMPUTER-AIDED DRYER SELECTION

1853

and conducting bench-scale tests, making an economic comparison of

alternatives, conducting pilot-scale trials, and, ﬁnally, selecting the most

appropriate dryer type. As noted by these authors, the principal value of

computer-based algorithms is in the preliminary selection stage in which it is

necessary to screen a relatively large number of dryer types and sub-types.

They cannot, however, provide a substitute for bench- or pilot-scale tests,

which can yield vital information not only on the drying kinetics but also

on the materials handling characteristics, which, in practice, are equally

important.

Computer-based methods that can be used for dryer selection include

expert systems, fuzzy logic, and neural networks. In the case of expert

systems, the selection decisions are based on a series of rules formulated

by the specialist. These are generally quantitative and inﬂexible. Combining

fuzzy logic with an expert system results in a more ﬂexible knowledge base

reasoning system, in which the selection qualiﬁers are represented as linguis-

tic variables (e.g. temperature¼high, low, very low, as opposed to numeric

values). Internally the system transforms the knowledge into fuzzy repre-

sentation, performs the reasoning process, and ﬁnally translates the results

into the appropriate output format. Neural networks vaguely resemble the

vastly more complicated networks of neurons present in the human brain.

They can be ‘‘trained’’ to develop explicit relationships between input and

output variables and update these relationships on the basis of experience.

After an initial assessment of the problem, Baker and Lababidi (2000) con-

cluded that a fuzzy expert system (Zadeh, 1965; Dubois and Prade, 1980)

would provide the most appropriate platform for their selection algorithm.

Their choice was based on the following arguments. Firstly, the selec-

tion process relies on variables that are often diGcult to deﬁne quantita-

tively. An obvious example is cohesiveness of the feed. For practical

purposes, it is quite adequate to deﬁne this on a scale of zero (not sticky)

to one (very sticky). At a later date, it would be relatively easy to convert a

scientiﬁc measurement of cohesiveness to an appropriate value on the 0–1

scale for selection purposes. Secondly, as dryer selection is not an exact

science, precise numerical rules are unlikely to be appropriate. Boolean out-

comes (True or False) are rarely adequate. Rather, the terms Possibly or

Probably are more appropriate.

STRUCTURE OF FUZZY EXPERT SYSTEMS

For dryer selection, fuzzy expert systems oﬀer several advantages

over their conventional counterparts. Principal amongst these are their

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BAKER AND LABABIDI

ability to handle ill-deﬁned parameters and to deal with the large number of

uncertainties involved in the reasoning process.

A fuzzy expert system consists of two principal components: the

knowledge base and the inference engine. The knowledge base contains

the logic upon which a speciﬁc decision by the human expert is based.

The inference engine uses the facts and rules in the knowledge base to

arrive at a conclusion.

Baker and Lababidi (2000) described the structure of fuzzy knowledge

bases in some detail. Brieﬂy, they consist of a number of rules formulated as

If-Then statements:

IF condition (hypothesis, antecedent), THEN conclusion (consequent)

It typically states that if we know a fact (the condition, hypothesis or ante-

cedent) then we can infer or, or derive, another fact called a conclusion (or

consequent). A hypothetical example of a rule is as follows.

IF:

Exposure temperature is Low

THEN:

Freeze Dryer Conﬁdence¼0.5

and Horizontal Agitated Dryer Conﬁdence¼1

Here, ‘‘Exposure temperature’’ is a variable whose value is deﬁned by a

linguistic term (e.g. Very Low, Low, Medium, etc.). Fuzzy expert systems

deﬁne a linguistic term as a fuzzy set and evaluate the conﬁdence with which

the variable belongs to this set. This is expressed by a value in the range zero

to one, which is determined by a membership function. This must be deﬁned

by the expert for each fuzziﬁed variable employed in the knowledge base.

Typical examples of membership functions are illustrated in Figure 1; the

numerical values of g ij employed in this study were summarized by Baker

and Lababidi (2000). Note that any given variable can belong to more than

one set.

Referring again to the hypothetical rule, the terms ‘‘Freeze Dryer’’ and

‘‘Horizontal Agitated Dryer’’ are known as goals. The inference engine

evaluates each rule to determine whether the condition (or conditions) is

satisﬁed. If it is, the rule will be ‘‘ﬁred’’ and the inference engine proceeds to

assign an appropriate conﬁdence to each goal. This is determined by com-

bining the conﬁdences associated with each condition with the conﬁdences

of the conclusions. Fuzzy expert systems diﬀer in this respect from their

conventional counterparts, which only take the conﬁdence associated with

each goal into account.

COMPUTER-AIDED DRYER SELECTION

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Figure 1. Examples of typical membership functions.

The inference engine tests each rule to determine which conditions are

met. In this manner, it produces a set of multiple recommendations arran-

ged in order of likelihood based on the certainty with which the rules are

satisﬁed.

STRUCTURE OF THE KNOWLEDGE BASES

Baker and Lababidi (2000) argued that a series of modular knowledge

bases was likely to oﬀer advantages in terms of improved accuracy,

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