A knowledge-based method for the automatic derivation of reactor strategies

Computers and Chemical Engineering 24 (2000) 1803–1813

A knowledge-based method for the automatic derivation of reactorstrategies

Ralph Jacobs, Wouter Jansweijer *Social Sciences Department, Uni6ersity of Amsterdam, Roetersstaat 15, 1018WB Amsterdam, The Netherlands

Received 9 July 1999; received in revised form 27 March 2000; accepted 28 March 2000

Abstract

We describe a five-step knowledge-based method for the analysis of reaction networks. We have implemented a prototypesystem that realises this method. The system contains well-known concepts from the field of chemical engineering and reactorengineering and introduces a number of new concepts. The method derives a temperature and a back-mixing strategy for thereactor to optimise conversion and selectivity. We demonstrate its applicability with four different examples. We argue that thismethod is better than current approaches such as the geometric or attainable region approach and optimisation of complexreactor networks, mainly because the system allows inspection of its reasoning and of the knowledge used to reach its conclusionsand because the decisions are presented in a form that is suitable for reactor selection. The presented method is part of a morecomprehensive knowledge-based system (KBS) that does the complete reactor selection task. The prototype system is implementedin prolog. © 2000 Elsevier Science Ltd. All rights reserved.

Keywords: Reactor selection; Reaction network analysis; Knowledge-based system; Knowledge-based reasoning

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1. Introduction

Reactor selection addresses the problem of findingthe best reactor(s) for a chemical process. The selectionprocess begins with a large number of reactors andmoves into the direction of a small number of reactors,the preferred ones. The initial set of reactors comprisestechnical pieces of equipment, for example: a multi-tubular fixed bed, a bubble column and a circulatingfluidised bed. Our objective is to develop a knowledge-based system (KBS) that supports the selection of thesetechnical reactors (see Jacobs & Jansweijer, 2000).KBSs have the important property that their reasoningis based on explicit knowledge, which make these sys-tems easy to maintain, easy to extend and valuable inthe sense that they support explanation of their reason-ing process. An important step in this knowledge-basedselection process is the analysis of the reaction network.It results in a temperature and a back-mixing strategy,which form a profile for the reactor. The profile for thereactor is a rough description of what is desired. It is

well-suited to reason about technical pieces of equip-ment, or more specifically to rank reactor properties.So, the result from analysis of the reaction network canbe used in reactor selection. The reactor network, re-sulting from the geometric or attainable region ap-proach (Glasser, Hildebrandt & Crowe, 1987) oroptimisation of complex reactor networks (Kokossis &Floudas, 1990) cannot be used directly for selection oftechnical reactors. This type of result requires addi-tional (human) interpretation to accommodate the re-sult to a reasoning process, so these approaches are lesssuited as part of a KBS.

1.1. Input for analysis of the reaction network

The task of the analysis of the reaction networkrequires a set of inputs and givens. It is assumed thatthe following information is in hand:

The problem class, a description of the phases (gas,liquid, solid as a catalyst) that need to be presentedin the system.The components, components that exist, componentsin the feed streams to the overall process and desiredproducts.

* Corresponding author. Fax: +31-20-5256896.E-mail address: [email protected] (W. Jansweijer).

0098-1354/00/$ - see front matter © 2000 Elsevier Science Ltd. All rights reserved.PII: S 0 0 9 8 -1354 (00 )00500 -7

R. Jacobs, W. Jansweijer / Computers and Chemical Engineering 24 (2000) 1803–18131804

Reaction descriptions, reaction type (reversible, irre-versible), reactants and products, heat of reaction,activation energies and reaction orders.

In our reactor selection KBS (Jacobs & Jansweijer,2000) these data are given by the chemical engineer whocollects them from various sources such as laboratorydata, literature, experimental work and companyowned knowledge sources.

Fig. 1 gives an example of a complete specification ofthe data that are input for the analysis of the reactionnetwork. It is taken from an example from the READ-PERT system (Schembecker, Droge, Westhaus &Simmrock, 1995a,b), a system which is also used foradvice concerning the selection of technical reactors. Itshould be noted that the specification in Fig. 1 is notexactly identical to the specification of the READ-PERT example, due to differences between theprograms.

2. From laboratory data towards the profile for thereactor

The derivation of a profile for the reactor is done bya five-step procedure. First we identify all relevant andimportant reaction patterns (steps 1 and 2), which aresplit in so-called small reaction patterns (SRPs, step 3).Then each SRP is classified in the categories of Leven-spiel (1972) (step 4). Finally this information is used toderive temperature and back-mixing strategies (step 5).

2.1. Find all reaction patterns: step 1

The objective of a chemical process is to make de-sired products from components in the feed streams tothe overall process. A reaction pattern describes how

this objective can be met. It connects components in thefeed streams to a desired product. Such a reactionpattern consists in the simplest case of one single reac-tion when the reactants of the reaction are componentsin the feed stream and a product of the reaction is thedesired product, or of a chain of reactions when inter-mediate products are generated. More complex patternsdo also exist. For instance, a reaction depleting morethan one component may rely upon other reactions toproduce these components. Or a component may bedepleted by several reactions that are all essential tomake the desired product. This implies that the reactionpattern can branch, in both the counter-reaction-wiseand the reaction-wise direction. So, in the general case,a reaction pattern is a graph. We will, for the sake offurther processing, represent such graphs as treesbranching in the counter-reaction-wise direction, dupli-cating components in the feed streams and intermediateproducts when they appear in more than one branch.Reaction patterns have a desired product at the rootand feed components at the leaves, Fig. 2 shows thesimplest example of a reaction pattern that branches.

Hence, the first step is finding all reaction patterns,given the input specification as described in Fig. 1. Thiscan be just one pattern, but several patterns are alsopossible when there is more than one desired product orwhen there are several routes to one product. A reac-tion pattern is found by a backward search procedurethrough the given reaction network, the list of reac-tions. It starts from the desired product, the initialstate, and adds a reaction from the list of reactions thatproduces this product. Next, the reactants of this reac-tion become the desired products for which, in a recur-sive manner, reaction patterns have to be found. Abranch of the reaction pattern terminates when thedesired product belongs to a feed stream, the final state.The list of reactions used in the above procedure, the

Fig. 1. Example of input specification.

Fig. 2. A branched reaction pattern.

R. Jacobs, W. Jansweijer / Computers and Chemical Engineering 24 (2000) 1803–1813 1805

Fig. 3. A reaction network which is a graph.

operators, is not exactly identical to the reactions in theinput specification. Reversible reactions are representedby both of their possible directions. Therefore, eitherdirection can be used during the generation of thereaction pattern.

Since, in the general case, reaction networks aregraphs; it is possible to become trapped in loops and togenerate solutions that are unrealistic. Fig. 3 shows areaction network for which this happens.

Component A is the component in the feed streamand component F is the desired product. An infinitenumber of unrealistic reaction patterns can be gener-ated; A(feed)�B�C�E�B�D�E�F(product) beingjust one example. Therefore, the backward search pro-cedure rejects reactions that have been used before,either in the same or in the opposite direction. It alsorejects products that appeared before. With these re-strictions on the operators, and an exhaustive search wegenerate the complete set of all reaction patterns thatare possibly relevant. The relevant reaction patternsderived from Fig. 3 are: A(feed)�B�C�E�F(product),A(feed)�B�E�F(product) and A(feed)�B�D�E�F(product).

The description above, including: initial state, opera-tors, final state and constraints on the operators, consti-tutes a state space definition (Russell & Norvig, 1995).This principled representation and the exhaustivesearch procedure ensure a complete list of all reactionpatterns. The READPERT system probably doessomething similar.

2.2. Eliminate reaction patterns: step 2

The second step allows the chemical engineer toeliminate interactively some of the reaction patternsthat are found during the previous step. The describedgeneration procedure suppresses unrealistic reactionpatterns, but not every one of the found reactionpatterns is necessarily of equal importance. Reactionpatterns depleting a component, that is only present intrace amounts, is probably unimportant and should beeliminated. The result of this task is a set of significantreaction patterns.

2.3. Split reaction patterns: step 3

The profile for the reactor can only be derived when

the performance criteria, conversion and selectivity, areunambiguously defined. These performance criteria arebased on a single reference component. However, areaction pattern can consume several feed components,resulting in ambiguity of the performance definition.For example, both component A and B in Fig. 2 can beused as reference component. Therefore we introducethe notion of SRPs. In an SRP all branches deplete thesame component, which will be the reference compo-nent, while the other branches are ignored. SRPs arefound by pruning the ancestor significant reaction pat-terns to trees that have precisely one reference compo-nent. A complete set of SRPs covers all possiblereference components for every significant reaction pat-tern. The SRPs generated from the reaction patterndepicted in Fig. 2 are: A(reference component)�C(+D)�E(product) and B(reference component)�D(+C)�E(product).

2.4. Classify small reaction patterns: step 4

Each individual SRP is classified as being one of’‘simple’, ‘series’, ‘parallel’ or ‘series–parallel’ using theclassical nomenclature of Levenspiel (1972). This isdone on the basis of the reactions that are attached tothe SRP. Four different attachments are possible, butthere can be more than one attachment to one singleSRP:1. an attached reaction depletes the desired product;2. an attached reaction depletes a reactant;3. an attached reaction produces the desired product;4. an attached reaction produces a reactant.

Only the first two of these possible attachments areused for classification of the SRP since we are con-cerned about negative effects. The first situation, wherea reaction causes the negative effect of depletion of thedesired product, is generally named a series reaction.The second situation, where an attached reaction isresponsible for the negative effect of depletion of a

Table 1Classification rules for SRPs

Series reactionsNo series reactions

SeriesNo parallel reactions SimpleParallel reactions Parallel Series–parallel

have generated other reaction patterns and as a conse-quence other SRPs represent these positive effects.


reactant, is generally named a parallel reaction. Thesetwo properties classify an SRP completely, see Table 1.

A more precise definition of series and parallel reac-tions respecting reversible reactions are:1. A series reaction of an SRP is an attached reaction

that depletes the desired product of the SRP, whilenot belonging to the SRP itself (since it is onlyattached to it). If the desired product is depleted bythe reverse direction of a reaction that belongs tothe SRP, it is not counted as an attached reaction.

2. A parallel reaction of an SRP is a reaction thatdepletes the reference component or an intermediateproduct of the SRP, while not belonging to the SRPitself (since it is only attached to it). If an intermedi-ate product is depleted by the reverse direction of areaction that belongs to the SRP, it is not countedas an attached reaction.

This definition satisfies the important criterion thatall attached series and parallel reactions sort an effecton the selectivity, while using the regular selectivitydefinition based on the reference component and thedesired product of the SRP.

(1)IF back-mixing-problem (SRP, simple),(2)classification-srp (SRP, series, – SE–REAC),(3)reacting-phase (PHASE)

back-mixing-strategy (SRP, mixing-phase (PHASE, plugflow))THEN

IF back-mixing-problem (SRP – 13, simple),classification-srp (SRP – 13, series, – ),reacting-phase (liquid)

THEN back-mixing-strategy (SRP – 13, mixing-phase (liquid, plugflow))

It should be noted that positive effects, production ofa component in the SRP by an attached reaction (cases3 and 4, above), are not ignored. In case of positiveeffects, the previous step ‘find-all-reaction-patterns’ willhave generated other reaction patterns and as a conse-quence other SRPs represent these positive effects.

2.5. Deri6e the profile for the reactor: step 5

The next step is the determination of the profile forthe reactor. Two strategies are of importance: a strategyfor the temperature level in the reactor, the temperaturestrategy, and a strategy for the concentration level/profile in the reactor, named the back-mixing strategy.These strategies are decided for each one of the previ-ously derived and classified SRPs individually. Laterthis set of (sometimes perhaps conflicting) profiles will

be used for the selection of the most appropriate reac-tor (see Section 4, and Jacobs & Jansweijer, 2000). Thederivation of suitable strategies is based on a knowl-edge base which is a compilation of two types ofknowledge, (1) the knowledge and expertise about reac-tor engineering; and (2) knowledge that addresses thecontext in which the knowledge and expertise men-tioned can be used. The knowledge about reactor engi-neering is taken from general textbooks (among others,Levenspiel, 1972) and formalised as a set of rules in theformat IF Bset of conditions \ THENBcon-clusion \ . The rules contain logical variables de-noted by uppercase letters. During their use theybecome instantiated with concepts from the context oftheir application. Variables for which the instantiationis of no importance to the rule are denoted with anunderscore or a string starting with an underscore (cf.prolog).

An example of a rule is, IF (1) the type of theback-mixing problem is simple and (2) the SRP isclassified as ‘series’ and (3) the phase in which reactionoccurs is identified THEN the back-mixing strategycontains the notion plug-flow of the phase in whichreaction occurs.

During its use it becomes instantiated into, forinstance:

Currently the knowledge base contains about 500rules. Both the temperature strategy and the back-mix-ing strategy are derived by exhaustive search throughthese rules, for each SRP individually. Since the SRPsare directed towards the production of a desired com-ponent, the scope is limited to reactor selection prob-lems for production processes. Treating processes andconversion processes addressing desired behaviour interms of the energy balance are excluded.

2.5.1. Deri6ing a temperature strategyFor each of the previously classified SRPs we derive

the temperature strategy. This involves a qualitativestatement about the desired temperature within thereactor, which is derived for each one of the followingtwo possible situations:1. the phase in which reaction occurs is mixed;2. the phase in which reaction occurs is staged.


The qualitative statement for the temperature strat-egy can be ‘as high as possible’, ‘as low as possible’,‘from low to high’ (only for staged) etc. The strategy isderived using the SRP, the other SRPs that are presentand the properties of both the reactions in the SRP andthe reactions attached to the SRP, such as, reversibility,heat of reaction and activation energy.

For SRPs classified as simple the objective is alwaysmaximum conversion. Because we have adopted aknowledge-based approach, this can be interpreted asboth a strategy for maximum conversion given a fixedreactor volume and a strategy for minimum reactorvolume given a fixed conversion. The objective forselectivity problems, represented by SRPs classified asseries, parallel or series–parallel, is more complex. Forselectivity problems the objective is always maximumselectivity, since selectivity is considered to be moreimportant than conversion, but there is a restriction.The objective will be maximum conversion when theselectivity problem is indifferent. For example, temper-ature has no effect on selectivity when the activationenergies of all reactions requiring comparison are equal.

classification-srp (SRP, series-parallel, SE–REAC, – PAR–REAC),IF (1)(2)selectivity-not-T-sensitive (SRP, all parallel),(3)no-single-T-strategy (SRP, all-series),

selectivity-not-T-sensitive (SRP, most-undesirable-series), (4)selectivity-T-sensitive (SRP, undesirable-series), (5)

(6)last-reaction (SRP, DESIRED–REAC),(7)undesirable-reactions (SRP, SE–REAC, UNDESIRABLE–REAC),(8)compare-e-act (DESIRED–REAC, UNDESIRABLE–REAC, highest)

temperature-strategy (SRP, mixed (highest))THEN

In this case the temperature strategy for maximumconversion is derived.

Sometimes reasoning about activation energies leadsto conflicting situations. For example, when compari-son of a desired reaction and an undesired reactionsuggests a high temperature and comparison of anotherpair of desired and undesired reactions suggests a lowtemperature. In these situations the strategic notionoriginating from the most important pair of reactions isfollowed. The most important pair of reactions is thepair that involves the undesired reaction that is rankedas most undesirable. The ranking follows from a clas-sification of the undesired reactions. The classificationinvolves three classes. In rising order of undesirabilitythey are:

neutral, the undesired reaction is a desired reaction inanother SRP;undesirable, the undesired reaction is no member ofanother SRP, but the reaction and all reactions thatare connected indirectly are reversible;most undesirable, the undesired reaction does not fitinto one of the classes described above.

This classification does not guarantee that all confl-icts are solved. It is possible that several undesiredreactions are ranked as most undesirable and that thesereactions give rise to a conflicting situation. In thissituation the proposed temperature strategy will bein-between.

An example of a rule is: IF (1) the SRP is classifiedas ‘series–parallel’ and (2) selectivity is not temperaturesensitive from the perspective of all parallel reactionsand (3) there is a conflicting situation from the perspec-tive of all series reactions and (4) selectivity is nottemperature sensitive from the perspective of the seriesreactions that are ranked as most undesirable and (5)selectivity is temperature sensitive from the perspectiveof the series reactions that are ranked as undesirableand (6) the last reaction of the SRP, producing thedesired product, is identified and (7) a list of seriesreactions that are ranked as undesirable is obtained and(8) comparison of the activation energies of the lastreaction of the SRP and the series reactions ranked asundesirable results in a high temperature THEN thetemperature in the reactor should be as high as possible.

The knowledge base contains a total of about 200rules concerning temperature strategy.

2.5.2. Deri6ing a back-mixing strategyWe also derive a back-mixing strategy for each of the

previously classified SRPs. The back-mixing strategyinvolves a qualitative statement about the concentra-tion levels in the reactor as well as some other points.Currently supported strategies are:1. mixing of the phase in which the reaction occurs;2. selective removal of a component from the phase in

which the reaction occurs;3. high concentration of a component in the phase in

which the reaction occurs;4. low concentration of a component in the phase in

which the reaction occurs;5. the concentration of a component in the phase in

which the reaction occurs is unknown;6. contacting between the phases counter-current.

These strategies are formulated as general as possi-ble. They subsume more specific strategies like, forexample a high operating pressure in case of a gas


phase reaction, being a simple instance of strategy 3,and staged injection of reactants, being a more detailedsolution of strategy 4. The level of detail in thesestrategies reflects the detail needed for a complete reac-tor selection task. More detailed strategies can beadded when needed.

We derive the back-mixing strategy using the SRPtogether with the other SRPs that are present and theproperties of both the reactions in the SRP and thereactions attached to the SRP. In this case these arereactants, products, reversibility, reaction orders andthe phase in which a component is supplied.

The knowledge, addressing the context in whichLevenspiel type of reasoning takes place, is almostidentical to that of the temperature strategy. A fewadditional rules are needed to restrict counter-currentcontacting to reactor selection problems in which twofluid phases are present. This is done by introduction ofthe concept ‘type of the back-mixing problem’, whichhas the value ‘simple’ in case of selection problemsinvolving only one fluid phase and ‘complex’ in case oftwo fluid phases.

An example of a rule for derivation of a back-mixingstrategy has been given in Section 2.5. Another exampleof a rule is: IF (1) the type of the back-mixing problemis complex and (2) the SRP is classified as ‘parallel’ and(3) selectivity cannot be influenced by adjusting concen-tration levels and (4) the last reaction of the SRP,producing the desired product, is identified and (5) thelast reaction is reversible and (6) a product of this lastreaction is identified and (7) the product is not arequired component and (8) the phase in which reactionoccurs is identified THEN the back-mixing strategycontains the notion remove component P selectivelyfrom the phase in which reaction occurs.

IF back-mixing-problem (SRP, complex), (1)(2)classification-srp (SRP, parallel, – PAR–REAC),

selectivity-not-concentration-sensitive (SRP, all-parallel-reac), (3)(4)last-reaction (SRP, REAC),(5)reversible-reaction (REAC),(6)product-of-reaction (REAC, PROD),(7)not-required-component (SRP, PROD),(8)reacting-phase (PHASE)

THEN back-mixing-strategy (SRP, remove-selective (SRP, PHASE, PROD))

In total the knowledge base contains about 300 rulesfor the derivation of the back-mixing strategy.

3. Examples of the derivation of the profile for thereactor

We present four examples. The first two examplespresent generic problems, a conversion problem and aselectivity problem. The third example presents the

‘Van de Vusse’ problem, which has been chosen be-cause it is also used by Glasser et al. (1987) and byKokossis and Floudas (1990). The last example is areproduction of an example from the READPERTsystem.

3.1. A con6ersion example

3.1.1. Input laboratory data

Problem class: gas–liquidFeed components: A in liquid and B in gasDesired product: C

Reaction 1: A+B�R1

C+DHeat of reaction: 1×104 J/moleForward:Activation energy: 9×104 J/mole. Reaction orders:A=1, B=1Backward:Activation energy: 8×104 J/mole. Reaction orders:C=1, D=1

3.1.1.1. Step 1: find all reaction patterns. One reactionpattern is found:

A(feed)+B(feed) �R1forward

C(product)(+D)

3.1.1.2. Step 3: split reaction patterns. The reactionpattern derived in the previous step depletes two feedcomponents, which results in two SRPs, one with refer-ence component A and one with reference componentB :

SRP-1: A(reference component)(+B) �R1forward

C(product) (+D)

SRP-2: B(reference component)(+A) �R1forward

C(product) (+D)

3.1.1.3. Step 4: classify small reaction patterns. TheSRPs are both classified as simple, since there areno reactions attached that deplete a reactant or thedesired product, no series and no parallel reactions arefound.


3.1.1.4. Step 5: deri6e the profile for the reactor.First we derive the temperature strategies for boththe SRPs; the SRPs have the same strategy:

desired temperature mixed, highest;desired temperature staged, highest.

This is logical since reaction R1,forward is endother-mic: high temperature shifts the equilibrium in thedesired direction, the rule applying for the casemixed is shown below, the rule for staging isidentical in this case:

IF classification-srp (SRP, simple)last-reaction (SRP, REAC)reversible (REAC)endothermic (REAC)temperature-strategy (SRP, mixed (highest))THEN

For the SRP with reference component A the follow-ing back-mixing strategy is derived:

plug-flow of the liquid phase;high concentration of component B in the liquidphase;remove component C selectively from the liquidphase;remove component D selectively from the liquidphase;contacting counter-current.

The strategy of the SRP with reference component Bis identical, except that a high concentration of compo-nent A in the liquid phase is desired, instead of a highconcentration of component B.

The liquid phase is significant because reaction occursin the liquid phase, which is concluded from the problemclass. Plug-flow is desired since there are no intermedi-ates or products that possess auto-catalytic properties.High concentration and selective removal help to shiftthe equilibrium in the desired direction. Counter-currentcontacting has the same objective. Under plug-flow, theproduct concentration in the liquid will be highest at theend of the reactor. The equilibrium is shifted in thedesired direction when this liquid is exposed to fresh gas,which can be realised by counter-current contacting.

3.2. A selecti6ity example


Problem class: GasA and BFeed components:E and GDesired products:

Reaction 1: A�R1

CActivation energy: 8×104 J/mole. Reaction order: A=1

Reaction 2: B�R2

D

Activation energy: 8×104 J/mole. Reaction order: B=1

C�R3

EReaction 3:Activation energy: 5×104 J/mole. Reaction order: C=1

C�R4

FReaction 4:Activation energy: 9×104 J/mole. Reaction order: C=1

Reaction 5: C+D�R5

GActivation energy: 8×104 J/mole. Reaction orders: C=1,D=1

3.2.1.1. Step 1: find all reaction patterns. Two reactionpatterns are found, one consisting of two reactions inseries, the other being a tree:

A(feed)�R1

C�R3

E(product)

A(feed)�R1

C

C + D�R5

G(product)

B(feed)�R2

D

3.2.1.2. Step 3: split reaction patterns. The first reactionpattern depletes only one component; the reaction pat-tern and the SRP are similar. The second reactionpattern depletes two components; this results in twoSRPs, so the total number of SRPs is 3:

SRP-1: A(reference component)�R1

C�R3

E(product)


C (+D)�R5

G(product)

SRP-3: B(reference component)�R2

D (+C)�R5

G(product)

3.2.1.3. Step 4: classify small reaction patterns. The firstand the second SRP are classified as parallel. The firstSRP has the parallel reactions R4 and R5 attached to it.The second SRP has the parallel reactions R3 and R4

attached. The third SRP is classified as simple, there areno reactions that sort an effect on the selectivity withrespect to reference component B and desired productG.

3.2.1.4. Step 5: deri6e the profile for the reactor. Thestrategies of the second SRP will be discussed. Thisselectivity example is included to show an analysis


involving the classification of undesired reactions (neu-tral, undesirable, most-undesirable). The second SRP isillustrative for this.

The temperature strategy for the second SRP is:desired temperature mixed, lowest;desired temperature staged, unknown.

The first result, for mixed operation, is derived asfollows. The two attached parallel reactions R3 and R4

compete with the desired reaction R5. Comparing withthe activation energies of reaction R3 and R5 results ina high temperature, but comparison of the activationenergies of reaction R4 and R5 results in a low temper-ature, so there is a conflict. This conflict is resolved byfollowing the strategy of the most important pair ofreactions. Reaction R3 is classified as ‘neutral’, since itis a desired reaction in SRP-1, and reaction R4 isclassified as ‘most-undesirable’, the reaction is irre-versible and does not occur in any of the other SRPs. So,the most important pair of reactions is R4 and R5, andthe desired temperature mixed is lowest.

The temperature strategy for staged operation isunknown. For mixed operation, it is apparent that allpairs of desired and undesired reactions should becombined in a single analysis, for staged operation thisis not the case, which results in a mindful answer in caseof a conflict. The SRP is a tree, branching in thecounter-reaction-wise direction; therefore it is intricate toseparate reactions in case of staging, according to theprinciple earlier or later in time. Special rules handlingthe case that the tree degenerates to a chain of reactionsare at this moment not included in the program.

The back-mixing strategy for the second SRP is:High concentration of component D in the gas phase.

This strategy is derived as follows. The two parallelreactions R3 and R4 are competing with the desiredreaction R5. Comparing with the reaction orders ofreaction R3 and R5 as well as R4 and R5 lead to the sameconclusion, high concentration of D in the gas phase.This example shows that reasoning about reaction ordersis not confined to the common reactant, shared by adesired and an undesired reaction, D is no reactant ineither R3 or R4. The analysis involves all components; theorder 0 is used when no reaction order for a componentin a specific reaction is supplied.

3.3. The ‘Van de Vusse’ example

First the knowledge-based approach to the ‘Van deVusse’ problem is described; next it is compared with thedesign/synthesis approaches.


liquidProblem class

AFeed component:BDesired product:A�

R1BReaction 1:

Activation energy: 8×104 J/mole.Reaction order: A=1

B�R2

CReaction 2:Activation energy: 8×104 J/mole.Reaction order: B=1

Reaction 3: 2A�R3

DActivation energy: 8×104 J/mole.Reaction order: A=2

3.3.2. The knowledge-based approach

3.3.2.1. Step 1: find all reaction patterns. Only onereaction patterns is found:

A(feed)�R1

B(product)

3.3.2.2. Step 3: split reaction patterns. The reactionpattern depletes only one component; the reaction pat-tern and the SRP are identical:


B(product)

3.3.2.3. Step 4: classify small reaction patterns. The SRPis classified as series–parallel, reaction R2 is the seriesreaction and reaction R3 is the parallel reaction of theSRP.

3.3.2.4. Step 5: deri6e the profile for the reactor. Thefollowing temperature strategy is derived:

desired temperature mixed, highest;desired temperature staged, highest.

Selectivity is not sensitive to the temperature, so thetemperature strategy corresponding to highest conver-sion is obtained.

The back-mixing strategy contains three notions:plug-flow of the liquid phase;low concentration of component A in the liquid phase;remove component B selectively from the liquid phase.

Plug-flow is desired because of the series reaction, alow concentration of component A suppresses the paral-lel reaction. Selective removal of component B inhibitsthe series reaction.

3.3.3. Comparison to design/synthesis approachesWe compare with our approach with design/synthesis

approaches: the geometric or attainable region approach(Glasser et al., 1987) and the optimisation of complexreactor networks (Kokossis & Floudas, 1990). Althoughthere are new developments in this respect we havechosen these two intentionally, since they will allow us


Table 2Comparison to design/synthesis approaches

Knowledge-based approachDesign/synthesis approaches

A complete specification of the kinetics is always required1 Also results on the basis of partial and/or qualitative informationThe components present in the feed streams to the overall processThe feed stream to the reactor system must be specified2must be knownThe program generates all possible perspectives: the SRPs3 An objective function has to be supplied

4 The results are reactor networks, for the ‘Van de Vusse’ problem: The results are strategic notions, for the ‘Van de Vusse’ problem:CSTR+PFR or a single PFR, depending on assumptions made plug-flow, low concentration of A and selective removal of Bfor either the kinetics or the feed concentration of A

The proof-tree contributes to a fundamental understanding of theNo rationalisation of the answer5processEarly in process development, see 1 and 26 Later in process development, see 1 and 2

to contrast our approach in its most fundamental way.It is assumed that both methods need no introduction.A comparison is given in Table 2. Most differences areself-explanatory, but the entries 3 and 4 need an expla-nation.

Analysis-of-the-reaction-network supports the for-mulation of the problem; the problem is viewed fromevery perspective, since all SRPs are generated. Design/synthesis approaches do not support the formulation ofthe problem, which here is up to the chemical engineer,who has to provide an objective function. The chemicalengineer is usually preoccupied with a particular per-spective, in which case the decision concerning theperspective remains implicit. The task analysis-of-the-reaction-network provides the chemical engineer with acomplete set of perspectives. At an appropriate point inthe selection process, the chemical engineer is invited tochoose a perspective, by choosing an SRP. This resultsin an explicit decision as a distinct part of the selectionprocess. So analysis-of-the-reaction-network allows theintegration of one of the predominant decisions inprocess design, perhaps the most predominant, in anelegant manner.

Analysis-of-the-reaction-network results in strategicnotions, which can be put into use in the selectionprocess. Design/synthesis approaches result in a net-work consisting of CSTRs and PFRs and values for thereactor volumes and the flow rates. This result cannotbe used directly for selection of technical reactors, so anintermediate state is reached, requiring interpretationby the chemical engineer.

3.4. The READPERT example

The following example is taken from the developersof READPERT (Schembecker et al., 1995a,b).

3.4.1. Input laboratory dataThe input was presented in Fig. 1. We have, however,

quantified the reaction orders and the activation ener-gies that were described qualitatively only in the origi-nal example.

3.4.1.1. Step 1: find all reaction patterns. One reactionpattern is found:

C2H4(feed) + Cl2(feed)�R1

CH2Cl�CH2Cl(product)

3.4.1.2. Step 3: split reaction patterns. The reactionpattern has two feed components. Therefore there aretwo SRPs, one for each reference component:

SRP-1: C2H4(reference component)(+Cl2)

�R1


SRP-2: Cl2(reference component)(+C2H4)

�R1


3.4.1.3. Step 4: classify small reaction patterns. TheSRP-1 is classified as series. It has reaction R2 attachedthat depletes the desired product CH2Cl�CH2Cl. SRP-2is classified as ‘series–parallel’ since it has, besides thisseries reaction R2, also the reactions R2 and R3 asparallel reactions depleting the component Cl2.

3.4.1.4. Step 5: deri6e the profile for the reactor. Thetemperature strategy for both SRPs is similar:

desired temperature mixed, lowest;desired temperature staged, lowest.

Comparison of the activation energies, for all pairs ofdesired and undesired reactions, always results in a lowtemperature.

For the back-mixing strategy for SRP-1 the followingis advised:

plug-flow of the liquid phase;remove CH2Cl�CH2Cl selectively from the liquidphase.

Both plug-flow and selective removal ofCH2Cl�CH2Cl are desired because of the series reactionR2.

The back-mixing strategy for SRP-2 contains a thirdstrategic notion in addition to those of SRP-1:

low concentration of Cl2 in the liquid phase.


The first and second notions are identical becauseboth SRPs share the same series reaction R2. The thirdnotion, low concentration of Cl2, suppresses the parallelreactions R2 and R3.

The READPERT system comes with a back-mixingstrategy that is completely opposite to the strategyadvised by our method, ‘plug-flow of the liquid phase’.READPERT advises as a general reactor type the‘continuous stirred tank’, which realises precisely theopposite. The literature about READPERT is not de-tailed enough to trace-back the reason for thisdiscrepancy.

4. Using the profile for the reactor in reactor selection

Reactor selection addresses the problem of findingthe best reactor for a chemical process. The profile forthe reactor derived above describes what is desired fromthe viewpoint of the reaction network, the kinetics andthe objective of the process. They provide a decisivecontext for the selection of the most profitable reactor.A grid of technical reactors and reactor properties takesa central place in our framework for reactor selection(Jacobs & Jansweijer, 2000). This grid contains severaltypes of properties. Some have the character that theyneed to be satisfied. Others can be compromised, inwhich case selection is based on ranking of the proper-ties. The profile for the reactor is used to find a rankingfor the property back-mixing. However, this is notalways possible, the profile might contain too littleinformation to make such a ranking, in which case theboundaries of the knowledge-based approach arereached, necessitating the incorporation of design calcu-lations into the selection process.

The profile for the reactor presents an importantcontext for the chemical engineer for the further designprocess. For instance, when he has to specify the com-positions of the feed streams to the reactor, he willprovide better initial guesses when he knows from thisanalysis about the desirability of high or low concentra-tions of components.

Of course the reactor profile suggested by differentSRPs may be contradictory. Elsewhere we describe asystem for the selection of the most appropriate reactor(Jacobs & Jansweijer, 2000). This system allows a‘what-if’ type of exploration. Thus the chemical engi-neer can examine the consequences of choosing anotherSRP as the most important one.

5. Discussion and conclusion

The approach presented in this study describes aknowledge-based method for the automatic derivationof strategies for reactor selection. The method starts

with an input specification, see Fig. 1, and derives allpossible perspectives from which the reaction networkcan be viewed, the SRPs. Next, a temperature and aback-mixing strategy are derived for each SRP individ-ually. This temperature and back-mixing strategy formthe profile for the reactor, which can be used in reactorselection.

The most significant contributions of our approachto the problem of finding a reactor for a chemicalprocess are the following:

We have introduced the notion of SRPs. After thethird step in our described method we have reachedan intermediate state, in which the ways to obtain thedesired products are known and ambiguities concern-ing performance definitions are dealt with. The ma-chine has reached a level of understanding that iscomparable to that of the chemical engineer in thefollowing situation. The chemical engineer is readinga prototypical example in chapter 7 or 8 of Leven-spiel (1972), he understands the problem being posedand consequently, he has a sense of purpose. So theresult of the task make-small-reaction-patterns is im-plicit in the problem descriptions given by Leven-spiel, which is legitimate, since his objective is toprovide examples to explain his theory. However, thetask analysis-of-the-reaction-network is different, theprofile for the reactor should be derived from theinput specification, which is data, it does not includean understanding of the problem being posed. So asense of purpose needs to be generated.The method is particularly useful in the early stage ofconceptual process design, in situations where only alimited amount of data is available. It does not needinformation of the complete kinetics to reach results.It produces results that can be used directly in selec-tion of technical reactors. The chemical engineer doesnot have to interfere, in order to extract the engineer-ing principle that is implicitly represented in a reactornetwork resulting from design/synthesis approaches.The method contributes to a fundamental under-standing of the process. Since our method is knowl-edge-based we can query our system about itsconclusions. The system constructs a trace of thereasoning process that can be used to answer ques-tions about ‘how’ and ‘why’ it reached its judgement.This strengthens the chemical engineer in his accept-ing or critiquing the presented reactor strategies.Some of the used knowledge rules are based on firstprinciples of the domain, things that are undeniablytrue. The chemical engineer will find it self-evident toaccept conclusions based on those rules. However,some of the knowledge in the system is more ‘rule-of-the-thumb’ like (heuristic rules). The possibility toask the system about its decision’s background al-lows the chemical engineer to judge the soundness ofthe conclusions of the system on the basis of the faith


he has in the knowledge rules used. With our systemthe chemical engineer is in a better position than he iswith methods that are not knowledge-based. Thesemethods can only be trusted as a whole.The method offers a basis for a hybrid system forreactor selection. The profile for the reactor is usedfor the ranking of reactor properties. When theprofile contains too little information to make such aranking, the boundaries of the purely knowledge-based approach are reached. In this situation a hy-brid system should change to reactor selection byexamination of design results for the various reac-tors.The method can be used to add extra functionality tosystems used for optimisation of complex reactornetworks (MINLP). The results of step 3: split reac-tion patterns and step 4: classify SRPs, can be usedfor an automated derivation of objective functions.The result of step 5: derive the profile for the reactor,is useful to generate an initial guess for the reactornetwork super-structure.

We have taken the knowledge that is used by thesystem from standard textbooks on chemical engineer-ing and reactor engineering and formalised this knowl-edge in about 500 rules. The test of the system on fourexample problems was successful. This strengthens ourbelief in the completeness and the correctness of our

representation for what we have found in these text-books.

An experimental prototype of the system is imple-mented in prolog and is part of a more comprehensivesystem that does selection of the most appropriatetechnical reactor. The system runs under UNIX andunder Windows-95/98/NT.

References

Glasser, D., Hildebrandt, D., & Crowe, C. (1987). A geometricapproach to steady flow reactors: the attainable region and opti-mization in concentration space. Industrial & Engineering Chem-istry Research, 26, 1803–1810.

Jacobs, R., & Jansweijer, W.N.H. (2000). A knowledge-based systemfor reactor selection. Computers & Chemical Engineering, 24(8),1781–1801.

Kokossis, A. C., & Floudas, C. A. (1990). Optimization of complexreactor networks-I. Isothermal operation. Chemical EngineeringScience, 45, 595–614.

Levenspiel, O. (1972). Chemical Reaction Engineering. New York:Wiley.

Russell, S., & Norvig, P. (1995). Artificial intelligence: a modernapproach. Englewood Cliffs, NJ: Prentice Hall.

Schembecker, G., Droge, T., Westhaus, U., & Simmrock, K. H.(1995a). A heuristic-numeric consulting system for the choice ofchemical reactors. American Institute of Chemical Engineers Sym-posium Series, 91, 336–339.

Schembecker, G., Droge, T., Westhaus, U., & Simmrock, K. H.(1995b). READPERT-development, selection and design of chem-ical reactors. Chemical Engineering & Processing, 34, 317–322.

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A knowledge-based method for the automatic derivation of reactor strategies