,

AN ARTIFICiAL INTELLIGENCE APPROACH TO THE SYNTHESIS OF A MASS EXCHANGER NETWORK FOR HAZARDOUS WASTE

MINIMIZATION AND TREATMENT

Y. L. Huang, Y. W. Huangt, and L. T. Fan*

Laboratory for Artificial Intelligence in Process Engineering Department of Chemical Engineering

Kansas State University Manhattan, Kansas 66506

September 1, 1989

ABSTRACT

Separation involving mass exchange of chemical species is almost always a key step

for hazardous waste minimization and treatment. This is accomplished best through a

network of mass exchangers performing various separation or mass exchange operations,

such as distillation, absorption, adsorption, extraction, filtration, and sieving. The present

work proposes a systematic approach based on the methodology of Artificial Intelligence

(AI) to synthesize a mass exchanger nehvork (MEN). The resultant M E N not only has the

lowest possible total cost, attained through minimization of both the amount of mass

separating agents and the number of process units, but also is highly controllable.

Also with Odin Corporation, Manhattan, Kansas

* Author to whom correspondence should be addressed

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INTRODUCTION

Separation is involved in nearly all types of process plants, including those for waste

treatment. Furthermore, i t is an important operation for minimizing the generation of

hazardous waste or for recycling of by-products from the process plants. Among the

numerous separation or mass exchange operations and processes, the better known ones are

distillation, absorption, extraction, supercritical extraction, ion exchange, adsorption,

leaching, reverse osmosis, ultrafiltration, dialysis, filtration, sedimentation, and sieving.

Seldom can any operation or process by itself optimally perform separation of all chemical

species under varying conditions. More often than not, two or more processes are needed

to attain the optimality. This gives rise to the notion of a mass exchanger network (MEN).

The first and most important phase in the design of a MEN is the synthesis of its

configuration. El-Halwagi and Manousiouthakis (1988; 1989) have proposed an approach

based on mixed-integer linear programming to the synthesis of a cost-effective MEN. The

network generated by their approach is expected to recover waste chemical species to the

maximum extent possible with the lowest possible cost. While it is highly desirable to take

into account an additional criterion or criteria in sapthesizing a MEN to render it robust

and readily controllable, to do so is extremely difficult because: (1) the process data

necessary for the design are almost always imprecise, incomplete, and indefinite; and (2)

the synthesis relies heavily on the designer’s experience. These factors tend to restrict the

applicability of purely algorithmic techniques in the synthesis phase.

Artificial Intelligence (AI) techniques in large part resort to heuristics and

non-deterministic logics (see, e.g., Rich, 1983). AI is concerned with mimicking human

intelligence on a computer, primarily with non-numeric processes that frequently can be

complex, uncertain, and ambiguous. It enables us to comprehend interrelationships among

the presented facts in such a way as to guide actions towards a desired goal. The present

work proposes a systematic approach based on AI methodology for synthesizing a MEN for

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the treatment of hazardous waste materials or recovery of useful components from these

waste materials.

MASS EXCHANGER NETWORK SYNTHESIS

A mass exchanger resorts to a mass separating agent (MSA) or agents, such as

solvents and adsorbents, to effect separation. A MEN consists of a number of mass

exchangers in which mass transfer occurs among process streams. A MEN synthesis has

been stated as follows (El-Halwagi and Manousiouthakis, 1988; 1989): i

Given a set R of rich process streams, a set L of lean process streams,

and a set E of auxiliary lean external stream? synthesize a MEN that can

potentially transfer certain species from the rich streams to the lean streams at

minimum total cost.

The source and target compositions, and flowrate of each stream are given at normal

conditions. To synthesize a MEN, the following basic assumptions are generally imposed:

a. The required separation duties are based on the exchange of a certain key

component.

b. Mixing of different streams and stream recycling are not allowed.

c. In the range of compositions involved, the equilibrium relation governing the

distribution of the key component between a rich stream and a lean stream is linear and

independent of the presence of other soluble components in the rich stream.

It is all but impossible to totally prevent disturbances and variations to occur in the

source compositions and flowrates of streams in any chemical plant; this is particularly true

for a waste treatment plant. Besides, the target compositions of streams may need to be

controlled with different degrees of precision. Consequently, the MEN synthesis should

strive to attain not only the minimum total cost, but also the superior control performance.

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ARTIFICIAL INTELLIGENCE APPROACH

The present approach for MEN synthesis comprises two parts. One is knowledge

representation, and the other knowledge manipulation.

Knowledge Representation

Two classes of knowledge are required in MEN synthesis; these are the

first-principle knowledge and heuristic knowledge. The former, considered as "deep

knowledge", includes mass balance, equilibrium relation, basic laws of thermodynamics,

etc., while the latter, considered as "shallow knowledge", mainly is the manifestation of the

designer's experiences in the form of heuristics. Three sets of heuristic rules and one

heuristic strategy have been generated in the present work. These are match-end selection

rules, stream elimination strategy, stream match selection rules, and backtracking reduction

rule.

Match-end selection. Figure 1 depicts a pair of streams to be matched. The source

composition, YS, of stream R is to be reduced to its target composition, Yt, through mass

exchange with stream L whose source composition, Xs, is to be increased to its target

composition, %. The left-hand side of each stream, Le., the inlet of stream R or the outlet

of stream L, is termed as the lean end, while the right-hand side, Le., the outlet of stream R

or the inlet of stream L, is termed as the rich end. The composition at the rich end of any

stream is always higher that at the lean end. With the equilibrium relationship given, a

match can be made at any location along the two streams. This leads to numerous options

for the match mode and thus a vast solution space. To reduce it, the following two heuristic

rules are generated.

a. Rich-end match rule

Match the rich end of the residual of a rich stream with the rich end of the

residuczl of a lean stream

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b. Lean-end match rule

Match the lean end of the residual of a rich stream with the lean end of

the residual of a lean stream

Figure 2 illustrates hvo matches made according to these two rules.

Stream elimination. Although the above two rules drastically reduce the possible

match modes, the resultant solution space remains unmanageably large. The mass

exchanged between the two streams can be any value up to the maximum allowed mass

load. This renders the number of mass transfer units to exceed the minimum predicted at

the preanalysis stage of the synthesis. To curtail the solution space and capital cost, at least

one of the two streams should be eliminated through a match; in other words, no more than

one stream should remain after a mass exchanger is placed between them. Hence, we have

the following heuristic strategy.

c. Stream elimination strategy

Let each mutch between two streams eliminate at least one stream

Stream match selection. Usually, a wide spectrum of matches is feasible among a set

of rich and lean streams. The two heuristic rules stated below facilitate an intelligent

selection of feasible matches, thereby rapidly leading to the generation of an initial

structure of a MEN with a reasonably low capital cost.

d. Less-interconnection rule

Avoid matching a stream, whose target composition is to be controlled

precisely, with a stream, whose source composition andlor mass flowrate is

likely to experience intemiveflutmtiom.

e. Cost-effectiveness rule

Always select a match so that the size of the resultant mass exchanger is

substantially different from that of the mass exchanger generated in the

preceding match

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Backtracking reduction. The reduction in the cost of an individual mass exchanger

does not necessarily lead to the lowering of the overall cost of the network. It depends on

the total mass transfer area and its distribution among the mass transfer units. The

following heuristic rule, termed as the leaving-end-unmatched rule, accelerates the

generation of a desirable MEN by reducing the number of backtracking.

f. Leaving-end-unmatched rule

Select a m c h so that the rich end of a rich stream or the lean end of a

lean stream remuins unmutched as long as the match is not detrimental to the

stability of the synthesized network and does not increase the total cost.

Figure 3 illustrates two types of matches suggested by this rule.

Knowledge Manipulation

The knowledge extracted and formalized thus far deals with ways to attain the

minimum utility, minimum mass transfer units, and superior control performance.

However, it is imperative that a strategy for stepwise synthesis be developed; in other

words, the knowledge represented in the preceding section should be well organized. A

systematic strategy for knowledge manipulation is proposed as follows:

a. Identify the location of the pinch point and determine the minimum utility

required for the problem. It is not necessary to set the minimum units as the target as long

as the stream elimination strategy is adhered to in the network invention.

b. Divide the problem into two parts, one above and one below the pinch point.

c. Synthesize separately each part of the problem by following the guideline

delineated below in d and e.

d. List all the match candidates for each part according to the stream elimination

strategy subject to the equilibrium-line and operating-line constraints. Any feasible match

can be placed only at either the rich end or the lean end of a pair of streams according to

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the match-end selection rules.

e. Select, in the stream matching step, a match from a list of match candidates

according to three rules, namely less-interconnection rule, cost-effectiveness rule, and

leaving-end-unmatched rule. Note that the stream elimination strategy must be adhered to

for each match.

f. Combine the two synthesized parts if the pinch point is not at either end of the

stream pair.

g. M o w the structure of the resultant network if necessary.

EXAMPLE

Several examples have been solved by the present approach. The example

illustrated here is based on the problem posed by El-Halwagi and Manousiouthakis (1988).

Nevertheless, its complexity is magnified by considering that the feed compositions and

flowrate are disturbed and requiring that the target compositions of streams be controlled

within certain degrees of precision. The problem is stated as follows:

In a chemical plant, two multicomponent gas streams, R1 and R2 , are fed to a

catalytic reactor. The existence of a certain species A in a concentration higher than 2.5%

w/w in the feed streams causes a rapid deactivation of the catalyst. It is necessary,

therefore, to reduce the concentration of A in R1 and R2 from 11.5% whv and 10% w h ,

respectively, to 2.5% w h . Two liquid streams, L1 and L2, can be exploited for the selective

absorption of A from the two feedstreams, R1 and R2.

Table 1 lists the pertinent data for all the process streams. In the range of composi-

tions involved, the equilibrium solubility of A in stream L1, XI, and that in stream Q, x~

are linearly related to the composition of a rich feed stream, y, respectively, as

y = 0.8x1 + 0.002

and

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y = 0 . 5 x2

The auxiliary external adsorbent, L3, is available to supplement L1 and L-, - in accomplishing

the required separation duty. This externally supplied adsorbing agent, L3, contains 1%

w/w of species A, and its outlet content of component A should not exceed 8% w/w. The

adsorption isotherm for component A on stream L3 in the range of operating compositions

is given by

y = 0.2x3

The feed compositions of streams R1 and L1 are appreciably disturbed, and the

target compositions of streams R2 and L2 need be controlled precisely. This necessitates

that the streams be matched carefully to prevent undesirable disturbances from propagating

through the network of mass exchangers. The initial grid diagram of the problem is given in

Figure 4. In the figure, the intensity of disturbance is indicated by the number of solid

circles "0'"s. The greater the number of circles, the more intensive the disturbance. The

degree of precision required in controlling the target composition of a stream is indicated

by the number of triangles "A"'s. The greater the number of triangles at the outlet of a

stream, the higher the degree of precision required.

The pinch point of the problem is located where the compositions of the first

and second rich streams, y1 and y2, and that of the first lean stream, xi, are 0.05, and the

composition of the second lean stream, x2, is 0.09; these values correspond to the so-called

interval boundary compositions. The minimum number of mass transfer units is

Umin = N - 1 = 5 - 1 = 4

where N is the total number of streams including utilities (see, e.g., L i d o f f gt A,, 1982).

The minimum utility required is 0.043 kg/s.

The resultant MEN is illustrated in Figure 5. This solution attains the goal of Umin,

Le., it needs four mass transfer units; however, the utility consumed is equal to 0.072 kg/s,

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which is larger than the minimum utility necessary. Nevertheless, the network can be

controlled with relative ease because the severe disturbances originating from the inlets of

streams R1 and L1 can not reach the outlets of streams L2 and R2, respectively, as

demonstrated in Figure 6. It is, therefore, an effective solution.

CONCLUDING REMARKS

An Artificial Intelligence approach is proposed to synthesize a mass exchanger

network; it aims at minimizing the amount of mass separation agent and the number of

mass transfer units or exchangers, and at achieving superior control performance. This is

essential especially for a process receiving feeds with continually varying flowrates and

compositions, e.g., a waste treatment process. In future work, network evolution strategy

will be developed, which will become the foundation of a knowledge-based system to be

implemented on an object-oriented multi-paradigin programming environment, namely,

KEE (Knowledge Engineering Environment).

ACKNOWLEDGEMENT

Although the research described in this article has been funded in part by the United

States Environmental Protection Agency under assistance agreement R-815709 to the

Hazardous Substance Research Center for U.S. EPA Regions 7 and 8 with headquarters at

Kansas State University, it has not been subjected to the Agency’s peer and administrative

review and therefore may not necessarily reflect the views of the Agency and no official

endorsement should be inferred.

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REFERENCES

El-Halwagi, M. M. and V. Manousiouthakis, "Automatic Synthesis of Mass Exchange Networks," paper 80b, AIChE Annual Meeting, Washington D. C., Nov. 27 - Dec. 2 (1988).

El-Halwagi, M. M. and V. Manousiouthakis, "Synthesis of Mass Exchange Networks," AIChE J., 35,1233-1244 (1989).

Linnhoff, B., D. W. Townsend, D. Boland, G. F. Hewitt, B. E. A. Thomas, A R. Guy, R. H. Marsland, 3. R. Flower, J. C. Hill, J. A. Turner, and D. A. Reay, User Guide on Process Integration for the Efficient Use of Energy,' The Institute of Chemical Engineers, London, UK, 1982.

Rich, E., Artificial Intelligence, McGraw-Hill, New York, NY (1983).

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Figure 1. A pairof richand leanstreamto be matched.

(a) Rich-end match

R i

(b) Lean-end match

Figure 2. Stream match options according to match-end selection rules.

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C

r

W Match Type - a

C

W

Match Type - b (WR > WL 1

C: Concentration of key compoment in a stream

W - Mass-exchange load of a rich stream

W - Mass-exchange load of a leanstream R

i

Figure 3. Two types of stream matches.

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Table 1. Data f o r t h e Process S t r e a m of t h e Example

S t r e a m M i Y i S Y i t (kg/s)

1-3 0.115 0 . 0 2 5

R2 1.5 0.100 0.025

R 1

~

R i c h S t r e a m s

Stream M j X j S X j t (kg/s)

L1 2 . 5 0.05 0 - 110 L2 0.5 0 .035 0,120

L3 Q 0.010 0.080

1 Lean Streams 1

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R 2 (0-007s) fi (0.025) +

Figure 4. Initialgrid diagramfortheexample.

(0.100) (1 -5)

1 5

(0 -007 5)

(0.1 5)

1 I 043 (0.030)

AA (0.029,

@O(O.OM) L1 d

- [0-045]

El d

(0.100) 0

(0.110) A

Figure 5. Solutionof theexample by the Alapproach.

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(0.100)

(0.110) A - @-' I [0.045]

[0.075]

14- (0.0425) 9(0-035)

: [0.038]

Figure6. Disturbance propagationthroughthe network.

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