A knowledge-based system for reactor selection

Computers and Chemical Engineering 24 (2000) 1781–1801

A knowledge-based system for reactor selection

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

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

Abstract

We present a knowledge-based system for reactor selection. First the system makes an analysis of the reaction network andderives a set of strategic notions that define a profile for the most desirable reactor type for the chemical conversion process. Thenit uses explicit knowledge about technical reactors to select the reactor that is best suited. This knowledge encompasses technicaland practical aspects of reactors. The system is specially geared to incorporate the practical knowledge of the engineers inindustry. The fact that reactor selection is a creative task is honoured by the system. The selection problems do not proceed alonga fixed pattern, but are allowed to develop in their own way. The system supports a ‘what-if’ type of reasoning such thatconsequences of different assumptions can be explored. We demonstrate its use in a detailed example. A prototype system hasbeen implemented in prolog. © 2000 Elsevier Science Ltd. All rights reserved.

Keywords: Reactor selection; Knowledge-based system; Knowledge-based reasoning; Knowledge acquisition

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1. Introduction

1.1. Reactor selection

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. The reasoning of the system is basedon explicit knowledge concerning the reactor engineer-ing domain. As a result, the system is easy to maintain,easy to extend and valuable in the sense that it supportsexplanation of its reasoning process, the last providingfundamental insight in the chemical process for which areactor is selected. The system leaves open the searchspace for possible solutions as long as possible, therebyavoiding early elimination of feasible possibilities andexploiting multiple criteria. The system is different from

mathematical simulation systems that are powerful incomputing the results for particular design decisions,but lack this insight. It is possible, however, to integrateour KBS with mathematical systems, thereby makingthe power of mathematical simulations available withinthe KBS. This would result in a hybrid system wherethe mathematical model takes inputs from the KBS andprovides answers that are now (in our KBS) given bythe chemical engineer.

We first describe the scope of the system. Then weanalyse the task of making a choice for a particularreactor type, which is seen as an instantiation of a moregeneral type of selection task. In Section 3, we describethe kind of knowledge that is needed for this task andthe knowledge acquisition process. In Section 4, wedescribe the KBS in detail. We continue in Section 5with a worked example and we compare our methodwith the READPERT system (Schembecker, Droge,Westhaus & Simmrock, 1995a,b). We conclude with adiscussion of the presented method and we extrapolateto selection tasks in general.

1.2. Scope of the system

A KBS has the ability to reason about a specific area,which is tiny in comparison to the knowledge we have

* 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 )00499 -3

R. Jacobs, W. Jansweijer / Computers and Chemical Engineering 24 (2000) 1781–18011782

that we present in this paper mimics the chemicalengineer only to a certain extent, but we have learned alot about how to do the reactor selection task bylooking at experts. We studied human experts mainlyfor the following two reasons. In the first place expertsshow us one way to tackle the selection problem. Theyappear to work in a few steps. These smaller steps areeasier to model then the full problem of reactor selec-tion. Secondly our system should do more than justselect the best reactor. It should be able to explain whatit is doing and how it came to its conclusions. If thereis a large difference between how the chemical engineerworks and how the KBS works it would be difficult togive a clear explanation.

Two strategies of the chemical engineer are extremelysignificant.1. A human expert will develop a strategy while solv-

ing problems. So the chemical engineer has devel-oped a strategy for reactor selection after solvingseveral reactor selection problems. This strategy canbe applied consciously, but it is more likely that itexists as a habit. One of the phenomena occurringduring the development of such a strategy is that thesequence of reasoning processes will change. Rea-soning processes using information that is not likelyto change will move to the front and reasoningprocesses using information that is likely to changewill move to the end of the problem solving process(Jansweijer, 1988). This results in a strategy in whichthe impact of retracting intermediate conclusions isminimised.

2. An engineer selecting a reactor will not use all hisknowledge at the same time. Knowledge that rejectsreactors that are totally inappropriate for a chemicalprocess will be applied first. Knowledge discriminat-ing between reactors that are all reasonably goodsolutions to the reactor selection problem, is appliedlater.

The first strategy is reproduced using the concept offixed and variable input. First information that is notlikely to change is collected by the task collect-fixed-in-put. Fixed input is not likely to change since it origi-nates from the laboratory. This task is followed bytasks that require solely fixed input. Next informationthat is likely to change is collected by the task collect-variable-input. This task is followed by tasks that donot exclusively use fixed input. The task collect-vari-able-input and subsequent tasks should be placed inaniterative cycle to allow for changes in the variableinput, see Fig. 2. This represents in fact a generaldescription of selection tasks, not specific for reactorselection.

The fixed input, originating from the laboratory,consists mainly of a description of the kinetics. In

about the world we live in. When developing a KBS,the first essential step is to describe this specific area, todescribe the scope of the KBS. The scope is a descrip-tion of ‘the world’ the KBS is supposed to reasonabout. Problems outside the scope cannot be solved, theKBS has no knowledge of the world outside the scope.

The scope of the reactor selection problem is largelydetermined by the problem definition, ‘select the bestreactor for a chemical process from a set of existingreactors’. First the scope will be defined more preciselyby explaining the content of the set of existing reactors.After that three additional restrictions are introduced,to further reduce the scope.

The set of existing reactors consists of all reactorsthat are applicable within one of the problem classesdescribed below. The classification of reactor selectionproblems is based on the phases (gas, liquid, solid as acatalyst) that must be present to realise reaction. Thescope encompasses seven problem classes, see Fig. 1.

The additional restrictions are.1. Desired behaviour addresses conversion in terms of

the mass balance. This excludes for example fur-naces, for which desired behaviour addresses con-version in terms of the energy balance.

2. A desired component is meant to be produced;treating processes are not incorporated. In treatingprocesses the objective is not to make a product; thechemical reaction is employed to achieve a separa-tion task.

3. Special reaction domains are excluded, since theyrequire special focusing. Examples of these are, bio-chemical reactions, photochemical reactions, poly-merisation and reacting solids.

2. Analysis of the task of reactor selection

Although it is no must that the KBS will behave asthe chemical engineer does, it is of great help to studyhis behaviour when doing reactor selection. The system

Fig. 1. The problem classes.

R. Jacobs, W. Jansweijer / Computers and Chemical Engineering 24 (2000) 1781–1801 1783

Fig. 2. The relation between task decomposition and input.

3. Knowledge about the reactor engineering domain

Selection of an appropriate reactors requires knowl-edge of these reactors and knowledge about their prop-erties. In this section, we describe the structure of thisknowledge and the knowledge acquisition process.

3.1. Reactors and their properties

We have listed all technical reactors that are possiblyrelevant for each of the seven problem classes discussedin Section 1.2, resulting in seven lists of reactors. Wehave selected the reactors from textbooks (Trambouze,Landeghem, van, Wauquier & Marshall, 1988) andfrom Ullmann’s encyclopaedia (Ullmann, 1992). A fewfrequently used networks of reactors, that employ engi-neering principles that can be described by propertiesof, as it were, single reactors are added as well. Thesenetworks include the cascade and recycle reactor em-ploying a high or a low recycle ratio, where the recyclecan incorporate a heat exchanger. As an example a listof 20 reactor types in the problem class gas-catalyst canbe found in Fig. 9. The total number of reactor types inthis problem class is 30, the list presents the reactorcontent after the first selection step.

Then we have listed, for each of these seven problemclasses, all the properties of those reactors that are ofphysical significance. We have collected this set ofproperties by a knowledge acquisition procedure asdescribed in the next section. One set of those proper-ties was constructed for each set of reactors within oneproblem class. This resulted for each problem class in amatrix with along one dimension the reactors and alongthe other dimension reactor properties. For the proper-ties we have made a distinction between three types ofproperties, hard; specific; and soft. The first set, thehard properties of a reactor, is the set of properties thatare always important for each reactor, scale, heat and,if appropriate for the problem class, catalyst replace-ment. These properties can not be compromised, al-though some reactors are suited for a whole range ofvalues for a property. Scale for a simple packed bed inthe problem class gas-catalyst is an example.

3.1.1. ScaleScale is a measure for the scale at which the reactor

is applicable. It describes whether a reactor is applica-ble at a small scale or at a very large scale. A divisionbetween small and very large scale is often treated asthe choice between batch and continuous, which wecould have used instead as a hard property. In manytextbooks, the choice between batch or continuous ispresented as the first problem to be addressed. Scale ofthe process allows the assignment of finer grained prop-erty values. Therefore, we use scale, which is closelyrelated to the mode of operation. Possible values of thisproperty are, fine; semi-fine; big; and bulk.

practice there is only one task that requires solely fixedinput: the task determination-of-the-profile-for-the-reactor.

The second strategy of the chemical engineer isutilised to design the tasks that do not exclusively usefixed input. First knowledge that rejects reactors thatare totally inappropriate for the chemical process isapplied, which involves hard features. This concernsfeatures of the reactor selection problem that must besatisfied; these features cannot be compromised. Theyare derived and applied by a task that we have calledderive-and-apply-hard-features. Here, reactors that donot satisfy the hard features are rejected. The reactorsthat are left after this first selection step are subjected tocriteria that are more versatile in a task named apply-specific-and-soft-properties. A specific property is a re-actor property that is specific for one or a few reactors,it describes a constraint. Soft properties are reactorproperties that apply to all reactors; they can be com-promised. The task decomposition of the top-level task,reactor selection, is shown in Fig. 3. It can be seen asan instantiation of the generic task decomposition givenin Fig. 2.

It is important to note the change in direction ofreasoning between the task derive-and-apply-hard-fea-tures and the task apply-specific-and-soft-properties.The task derive-and-apply-hard-features reasons fromthe viewpoint of the problem, since a hard feature is afeature of the reactor selection problem. The task ap-ply-specific-and-soft-properties reasons from the inverseperspective, the viewpoint of possible solutions. Thespecific and soft properties are reactor properties, sothis part of the reasoning is driven by the properties ofthe reactors that are left. This is characteristic for theselection of a piece of equipment. The chemical engi-neer is not in the luxuriant position to make a combina-tion of properties he likes best, but he is bound tochoose a fixed combination of properties. Each piece ofequipment represents such a fixed combination.

Fig. 3. The top-level task decomposition.


3.1.2. HeatHeat describes the heat transfer characteristics of the

reactor (or its absence in case of an adiabatic reactor).Heat transfer suggests that only transfer through a wallis considered, but we aim also at other techniques tocontrol the temperature level in the reactor. Examplesare, solids transport; catalyst transport involving regen-eration; and evaporation. Of course these techniquesonly apply to certain problem classes. The propertyheat, however, is always important, since a reactor hasto be operated at an appropriate or reasonable temper-ature level. If the reactor is non-adiabatic, this propertycan be annotated with properties of the heat transferprocess such as ‘a cycle including, reaction; regenera-tion; and heat transfer’.

3.1.3. Catalyst replacementCatalyst replacement describes how the catalyst is

supplied to, and removed from the reactor. Values onthis property are, ‘continuous very-short’; ‘continuousshort’; ‘continuous moderate’; and ‘discontinuous’. Se-lection on the basis of this property can be very restric-tive, for example if the process is fluidised catalyticcracking then the rapid deactivation of the catalystshould result in a reactor which has the value ‘continu-ous very-short’ for catalyst replacement, which resultsin a riser as the only alternative. Of course catalystreplacement is applicable only in the problem classesthat involve a heterogeneous catalyst.

The second set of properties are the so called specificproperties. These are also properties that cannot becompromised, but they are specific to certain reactors.Four examples of specific properties are.

The reactor is sensitive to dust, it can only be appliedwhen the feed streams to the reactor are free of dust.The catalyst must be attrition resistant, the reactorcannot be applied when the catalyst is not attritionresistant.The reactor is inappropriate when the catalyst cannotwithstand the force in a packed bed.The reactor is inappropriate when there is no gas–liquid phase envelop at reaction temperature. Reac-tive distillation should not be considered when thereis no phase envelop.

Reactive distillation is within the scope of the system,it is defined as a reactor type for the problem classesliquid and liquid-catalyst. The classification of reactorsis based on the minimum number of phases that arerequired for reaction (Section 4.1). A creative systemshould also handle the possibility of adding an extraphase in addition to this minimum. The definition of areactive distillation column for the problem classesliquid and liquid-catalyst, shows one example of howwe incorporated this.

The third set of properties are the so called soft

properties. These are properties that are desirable, butthat can be compromised to a certain extent. A softproperty applies to every reactor in a problem class.Whereas the values on the specific properties usuallyare expressed nominally, the values of the soft proper-ties generally are quantified on at least an ordinal scale.More then one value can apply, indicating that thereactor type is applicable under more then one circum-stance. Four examples of soft properties are.

Back-mixing of the phase in which reaction occurs.Possible values are, plug, intermediate, mixed or not-a-single-flow-pattern.Development, describing whether or not the choicefor this reactor will result in a great developmenteffort.Pressure drop, a measure for the pressure drop overthe reactor, possible values are, low, intermediate andhigh. As an example, a reactor type can possess thevalues low and intermediate for the soft property‘pressure drop’, indicating that this reactor is suitableunder both conditions.Back-mixing heat, the mixing behaviour with respectto the heat.

Costs are addressed implicitly, for example, in case ofundesired reactions, the system rejects the reactors pos-sessing a back-mixing characteristic, that result in apoor selectivity. In this way, the raw-materials costs areincorporated indirectly. An explicit approach to costinginvolves design and should consider the completeflowsheet.

Some reactors are useful in more than one problemclass. Such reactors are repeated over the lists. Forexample a packed bed reactor of catalyst particlesnaturally appears in the problem class ‘gas-catalyst’,but it also appears in the list for the problem-class ‘gas’when the particles are inert. If reactors appear in morethan one list they are described with different sets ofproperties: i.e. properties required in one problem classand the properties required in the other problem class.

3.2. Knowledge acquisition

The matrices of reactors and properties have to befilled with appropriate values. Part of this informationoriginates from the common textbooks concerning reac-tor engineering. Another source of information is thedomain expert, whose knowledge is extracted by inter-views. Two interview techniques are used, concept sort-ing and questionnaires.

We approached several companies for these inter-views, resulting in sessions at three different businessenterprises. At only one company we succeeded to get asufficient level of co-operation and matrices where con-structed for three problem classes, ‘gas’; ‘gas-catalyst’;and ‘liquid-catalyst’.


Concept sorting is a well known technique withproven effectiveness for the systematic acquisition ofproperties and values with an existing set of concepts. Itis developed in cognitive psychology (Shadbolt & Bur-ton, 1995). The expert is given a pile of cards. Each ofthese cards gives a description of one reactor type. Thecards are shuffled and the expert is asked to sort thecards into a number of piles according to some charac-teristic that he considers important. Then the expert isasked on the basis of which dimension the sort wasmade and what the piles represent. This is repeateduntil the expert does not know any new way to sort thecards. For example, it is conceivable that the expertsorts on the basis of the reactor property back-mixingand makes various piles including a plug-flow pile anda mixed-flow pile. This reveals back-mixing as a rele-vant reactor property and the values plug-flow andmixed-flow as possible values for this attribute. Whenthe expert was confronted with the results of thisknowledge acquisition process he was amazed by theresults and said, ‘somehow you manage to obtain formillions worth of information but I could not exactlygrasp what that information is’.

Interviews are time consuming and the amount oftime that an expert has available is limited, so there isan urge for a time efficient way to gather information.Questionnaires meet this requirement to a certain ex-tent. Therefore, after a while, we have constructedquestionnaires on the basis of the outcomes (i.e. theelicited properties and values) of the initial conceptsorting task. Each questionnaire represented one reac-tor and consisted of two parts, a description and apicture of the reactor to prevent possible misunder-standings about which reactor was meant, and secondlya list with the properties that had to be given a value.Definitions of these reactor properties, together with alist of values from which the answer should be picked,were presented on a separate card. Questionnaires havethe important advantage that they can be filled out bythe expert independently and spread out over a numberof days. Our expert provided a total of 759 property–value pairs, being the sum of all the entries in thematrices of the three problem classes.

Table 1Overview hard properties

GLCLL GC LCGLLG

Scale Heat Catalyst replacement

We can extrapolate the knowledge for the threeinvestigated problem classes to the other ones andconstruct questionnaires for the other problem classesby analogy. This can be done for properties as well asfor their possible values. The next examples illustratethe principle.

1. The property back-mixing applies to the problemclass ‘gas’. If we try to use it by analogy for theother problem classes we will find the following. Itcan be maintained for the problem classes ‘liquid’,‘gas-catalyst’ and ‘liquid-catalyst’. However, it willbe rejected for the problem classes ‘gas–liquid’,‘liquid–liquid’ and ‘gas–liquid-catalyst’ since theseproblem classes are not exclusively concerned withback-mixing. The property back-mixing plus con-tacting seems more appropriate for these cases.

2. Every problem class comprises the property heat.The problem class ‘gas’ contains the property value‘adiabatic’ which value is copied to all other prob-lem classes. The problem class ‘gas-catalyst’ incor-porates temperature control by catalyst transport,which cannot be copied to other problem classes.

This idea leads to the properties that we expect to beimportant for the other four problem classes. Tables1–3 give an overview. A black dot denotes that aproperty applies to a problem class. The tables containthe actual properties as obtained from our expert forthe problem classes ‘gas’, ‘gas-catalyst’ and ‘liquid-cata-lyst’. For the other problem classes, we have presentedthe expected properties.

The actual covering of the other four problem classeswith actual property value pairs — i.e. the filling of thematrices — requires an estimated 500 additional judge-

Table 2Overview specific properties

G L GL LL GC LC GLC

Sensitivity to dust Catalyst attrition resistant Force in packed bed Thermal recycle catalystTemperature rise high

Temperature rise small Gas–liquid envelop


Table 3Overview soft properties

GLCG LCGCLLGLL

Back-mixing Development

Pressure drop Back-mixing heat Residence time

Catalyst sizes and shapes External transfer catalyst

Catalyst volume fraction Catalyst RTD

Back-mixing plus contacting Transfer resistance

ments by an expert. Currently, we have not done thisknowledge acquisition exercise.

Our knowledge base about reactor properties isfounded on the knowledge of only one expert.Therefore, its truth still needs to be assessed.Nevertheless, it is safe to use this knowledge base in theKBS for reactor selection insofar as we want todemonstrate and test the ideas that we have developedfor the reactor selection task.

3.3. Knowledge about chemical components

The physical properties of the components involvedin the selection problem such as their molecular weightsmust be known. In the current prototype system this isjust a simple database that contains all the relevantproperties. In a full system, this part could be replacedby a module assessing data from the usual(commercially available) property sources.

4. The knowledge-based system

4.1. Collect the fixed input

As a first step the chemical engineer has to provideall basic information that is needed for the reactorselection problem. This includes among others, theproblem class; the chemical components involved in theprocess; the objective of the process and the reactionswith their properties. The chemical engineer can usevarious sources for this information such as, informa-tion obtained from experimental work, from literatureor company owned knowledge sources.

4.1.1. The problem classThe class of the reactor selection problem is an

important one. The selection of a reactor is impossiblewithout knowledge of the problem class. The problemclass also determines whether the problem is within the

scope of the KBS as sketched in Section 1.2. Theclassification of a reactor selection problem is based onthe phases (gas, liquid, solid as a catalyst) that must bepresent to realise the reaction. A more precise definitionis, the classification of reactor selection problems isbased on the minimum number of phases that must bepresent to achieve conversion. The phase in which areaction occurs should always be present and of coursethe heterogeneous catalyst must be present when re-quired. However, as an additional requirement, itshould be possible to obtain a reasonable level ofconversion. For example, if a gaseous component isonly sparsely soluble in a liquid phase where reactionoccurs it is impossible to achieve a reasonable level ofconversion without the presence of a gas phase. So, theminimum number of phases for this case is one higher.This shows that the minimum number of phases is alsodependent on solubility and stoichiometry of the com-ponents involved in the reaction network. A problemclass with two fluid phases should only be chosen if thisis required from the perspective of solubility andstoichiometry.

Fig. 4. Example of input specification.


4.1.2. Components and objecti6eThe chemical components involved in the process

and the objective of the process must be known. Thechemical components involved in the process are simplythe components that exist, a set of relevant compo-nents. The objective of the process is described by twosub-sets, one set containing the components in the feedstreams to the overall process and one set containingthe desired product(s) of the process.

4.1.3. Reactions and reaction ratesThe chemical engineer has to provide descriptions of

all reactions that might occur in the chemical processwith their reaction kinetics. This descriptionencompasses.

1. The reversibility; is the reaction reversible orirreversible.

2. The stoichiometry.3. The heat of reaction.4. A rate equation of the forward reaction; reaction

orders for the components involved and an activa-tion energy.

5. A similar rate equation of the backward reaction. Ofcourse this rate equation is only required for re-versible reactions.

Furthermore a temperature window has to be given;i.e. the range in which the kinetics are valid.

4.1.4. Example of a full specification of inputThe following example is taken from a kinetic paper

(Li, Wang & Chang, 1993). The input for thisreactor selection problem is specified as the following,Fig. 4.

4.2. Determine a profile of the desired reactor

On the basis of the specified input we can derive a setof strategic notions, a kind of profile for the reactor.Examples of strategic notions are, a qualitative notionof the desired operating temperature and certain no-tions for the phase in which reaction occurs, such as (byexample), mixing/staging, selective removal of a compo-nent, high or low concentration of a component and acontacting strategy between the phases, co-/counter-current. The strategic notions can only be derived,when it is possible to make a desired product, given,feed components, desired products and reactions. Inaddition the performance criteria, conversion and selec-tivity, should be unambiguously defined, for this reasonwe introduced small reaction patterns (SRPs). The in-put specification given below contains two reactantsand two desired products, that can be used to generateperformance definitions, this results in four ways to

view the reaction network, four SRPs. One of theseSRPs is:

phenol(reference component)(+methanol)

�R1

o-cresol(product)(+H2O)

We will use this SRP in the worked example ofSection 5. In (Jacobs & Jansweijer, 2000) we describe afive-step knowledge-based method for the automaticderivation of a reactor profile, including a detaileddescription of the SRPs.

4.3. Collect 6ariable input

The chemical engineer has to provide the variableinput. The variable input is the information about thefeed streams to the reactor, the phase of each feedstream, as well as the flow rate and mole fractions ofthe components in each of the feed streams. In order toprovide this the chemical engineer has to interpret theinformation from the flow sheet. Sometimes the engi-neer can only come near to a good initial estimate.Anyhow he is supported by the strategic notionsderived in the previous task, determine-a-profile-of-the-desired-reactor (Section 4.2). This knowledge directs thechemical engineer to a proper selection of, for instance,desirable concentrations of feed components. Since atthis stage the system has knowledge about the problemclass and the reactions, it can perform some consistencychecks on the variable input provided. For example thephases of the feed streams should match the knownproblem class and each of the known feed streams hasto be covered.

4.4. Deri6e and apply hard features of the process

Now the system has enough information to start theselection of a reactor. This is done by sieving outreactors that are less promising according to knownproperties of these reactors. The initial list of reactors isthe list that belongs to the problem class of the chemi-cal process (Section 3.1). So this is the first selectionmade. The selection process continues with this largenumber of possible reactors within this problem classand in a number of steps reactors are rejected on thebasis of what is known about the required reactorprocesses. This continues until one or a few reactors areleft over, or until all information about the desiredreactor processes has been used. The system makes adistinction between, in the first place, so called hardfeatures; properties of the reactor and the process thathave to meet by necessity. Secondly the system appliesproperties specific to certain reactor types and finally itapplies properties that can be compromised: so calledsoft properties. The latter two will be discussed in the


next section (Section 4.5). We will first discuss theapplication of the hard features.

The hard features of the chemical process are de-scribed in Section 3.1, scale; heat; and catalystreplacement.

The values on these properties of the processes arederived from what has become known about the pro-cess, by the previous steps, collect the fixed input(Section 4.1), determine a profile of the desired reactor(Section 4.2) and collect variable input (Section 4.3) aswell as from information obtained by additional ques-tions to the user. The properties can be multi-valued.For example, when a process requires cooling, severalcooling agents might be suitable and when a processallows adiabatic operation it might be advantageous touse a non-adiabatic reactor although it is not a neces-sity for the process. So in this case the hard feature heatwill at least contain the values adiabatic and non-adia-batic. The inference is a data abstraction step (Clancey,1985). An example of a knowledge rule is, the hardfeature heat has the value adiabatic when, (1) cooling isnot necessary and (2) heating is not necessary (defini-tional abstraction):

cooling-not necessary,IF (1)heating-not-necessary (2)hard-feature-heatTHEN(adiabatic)

A second example is, the hard feature catalyst re-placement has the value continuous regeneration of thecatalyst using a very short residence time for the cata-lyst when, (1) the catalyst deactivation time is obtained;and (2) the deactivation time is shorter than 10 s(qualitative abstraction):

deactivation-time-catalyst (1)IF(DTC)DTCB10 (2)hard-feature-catalyst-deac-THENtivation (continuous (very-short)

These property values are used to make a coarseinitial selection by rejecting reactors that are certainlyinappropriate. A reactor is rejected if the hard featureof the process and the hard property of the reactor donot match. The inference is a match and sieve step. SeeFig. 5

This selection of an initial set of reactors is bestexplained by an example. Suppose the production of afew tons a year is required. The task derive-hard-fea-tures does the abstraction step, resulting in the value‘very low production’ for the hard feature scale. Amatch exists for a reactor that has the value ‘very lowproduction’ for the hard property scale. No match

Fig. 5. Abstraction and sieving.

exists for a reactor having the value ‘very high produc-tion’. A reactor can have multiple values for a hardproperty. For example, a reactor that is applicable atany scale will possess the whole range of values, from‘very low production’ to ‘very high production’. Thisrepresents the fact that the reactor is not sensitive to thescale of the process. Only one of these values has tomatch to satisfy the hard feature scale.

The order in which the hard features are applied is,scale; heat; and; last, catalyst replacement when this isappropriate. The set of reactors that is left after appli-cation of the hard features will be subjected to moreversatile selection criteria by the next task, apply-specific-and-soft-properties.

4.5. Apply specific and soft properties

After the crude selection by application of hardproperties more versatile selection criteria are used,specific properties; and soft properties.

A specific property describes a constraint. These con-straints cannot be compromised, they are strictly re-quired. Specific properties do not apply to every reactorin a problem class, they are specific for one or a fewreactors. Examples of specific properties are given inSection 3.1.

A soft property describes a reactor property thatdoes not have the character of a constraint that must besatisfied, on the contrary, a compromise is possible. Aless desired value for one soft property can be compen-sated by outstanding values for other soft properties.

The method that we have developed for the applica-tion of specific and soft properties of the process isdesigned with the following in mind.1. The method is flexible. It is able to handle all

selection problems, no matter which, or how manyreactors are left.

2. It is possible to follow the progress of the selectionprocess and to try alternatives.

3. Not every selection problem can be solved on thesole basis of the information and knowledge avail-able, which results in an impasse. Impasses arerecognised.


4. There is a strategy for breaking impasses.5. It is possible to incorporate results from other work.

If, for example, we had available a case study thatcompares two reactors in which a conclusion isdrawn by interpretation of results obtained fromrigorous mathematical modelling, we could includethis knowledge in the method. We can even inte-grate our system with a mathematical simulationenvironment that computes outcomes of certain de-sign decisions that are now asked to the chemicalengineer. This opens the way to a hybrid systeminvolving a reasoning process providing fundamen-tal insight in the chemical process and design calcu-lations providing quantitative results but nofundamental insight.

The method works on a choice matrix. The choicematrix represents the reactors that are left and thereactor properties that can be used for selection. Ingeneral not every reactor property can be used, onlythose properties that have discriminative power areaccepted in the choice matrix. A specific property hasdiscriminative power when it does not apply to everyreactor in the choice matrix and has not already beenapplied. A soft property has this power when it doesnot have the same set of values for every reactor in thechoice matrix. An example of the choice matrix is givenbelow, see Table 4. A black dot denotes that a specificproperty applies to a reactor and an upper case letterrepresents a property value. For example, reactor 1possesses two specific properties Sp1 and Sp3, and softproperty two of this reactor (So2) contains the propertyvalues D and E.

Progress of the selection process after rejection of oneor a few reactors is represented by a new choice matrix.The properties in this new matrix are derived anew;only properties that have discriminative power are pre-served. So, the set of reactor properties useful forselection is not fixed in advance. And on top of it, thisset changes as the selection process progresses.

The selection process can be described as the sequen-tial construction of new reduced choice matrices bymatching properties of reactors to the desired chemicalprocess, until a matrix results with only one reactor, orwith some reactors but no more properties to be ap-

Fig. 6. The search space.

plied. This process can be seen as a search through asearch space where the top node stands for the initialchoice matrix that contains all reactors that are leftafter the task apply-hard-features. This search space isexpanded through selection steps that can be made andends where final outcomes are found (Rich & Knight,1991). This search space is examined with a searchstrategy that prefers selection steps that are as selectiveas possible and that can be made without interventionfrom the chemical engineer (such as a question that isbeing asked). It stops when it has come to an answer,but the chemical engineer has full control to let thesystem explore other parts of the search space. He can,for instance, select any node that contains more thanone reactor and select a possible selection step. Such aselection step can consist of an intervention from thechemical engineer where he provides new information,or makes an assumption or where he ‘manually’ (forpossibly private reasons) eliminates one of the reactors.Such new information and such assumptions are validonly for that part of the search-space that springs fromthe choice matrix where they are actually made. Thismakes a ‘what-will-happen-if’ type of reasoningpossible.

In general only a small part of the complete searchspace is searched through. The searched part is repre-sented (or administered) as a search tree. The leaves ofthis tree stand for possible solutions (choice matriceswith one or some reactors and all properties applied).The paths through the tree represent the rationalisa-tions for the answers found. For instance Fig. 6 repre-sents the search space at a moment where nodes 4, 5and 6 all represent possible solutions. Tracing back andreading the labels along the arrows back to node 1 froma particular end-node gives a description of how thisconclusion was reached.

4.5.1. Selection stepsThe search tree is expanded by applying selection

steps. The selection steps refer to either specific or softproperties and both types of steps can be applied withor without additional questions to the chemical engi-neer using the system. Furthermore, a step allowing‘manual’ elimination of a reactor is possible, (seeabove).

Table 4Example of the choice matrix

Specific properties Soft properties

Sp1 Sp2 So3So1 So2Sp3

D, EReactor1 FAD, E F, GReactor2 B

GEReactor3 C


Specific properties describe constraints that cannot becompromised. Therefore, they can be used to eliminatereactors. Selection based on specific properties goes inthree steps.1. A specific property is taken from the choice matrix.2. The question whether or not this property is sa-

tisfied is answered. For a silent step, this involvesonly available information. In the other case addi-tional questions are allowed.

3. The specific property is used to create a new choicematrix. Reactors that do not comply with the spe-cific property are eliminated, resulting in lesser rows.If all reactors satisfy the property the number ofrows in the choice matrix remains unchanged, butthe number of columns changes in all cases since,once a specific property has been applied, it loses itsdiscriminative power.

The property that is taken from the list (step 1,above) is initially, in silent mode when steps are triedwithout asking questions to the chemical engineer, thefirst property found in the list. And the next property istried automatically when this property can not beproven (step 2). In non-silent mode, when questions areallowed, the chemical engineer chooses the propertyfrom the list or refuses the question, in which case theselection step fails. Later, the chemical engineer canreconsider his choice when the strategy enters the ‘userdriven’ level, see the search strategy below (see Section4.5.2).

Soft properties do not have the character of a con-straint that must be satisfied, they can be compromised.A reactor doing badly on one property can be preferredover others if it performs outstandingly on other prop-erties. Each property represents just one possible viewat the reactor selection problem. Hence soft propertiescannot be dealt with in isolation; all soft properties inthe choice matrix are taken into account. This mayresult in conflicts or impasses, meaning that onereactor is preferred for one reason, while another one ispreferred for another, different reason. Impasseshave to be dealt with in a special manner, discussedlater.

The soft properties are used in a ranking process, werank the reactors in the choice matrix. Ranking is donefor each soft property, by judging for each reactor thedesirability of its score on this property for the chemicalprocess under study. So, for example, if a reactor hasscored the value plug flow on the property back-mixingof gas, while the task determine-a-profile-of-the-desired-reactor resulted in a profile including the strategicnotion, ‘plug-flow of the gas phase’, then this reactor isdesired from the perspective of the property back-mix-ing of gas. The score of a reactor on a property is aresult from the knowledge acquisition process(Section 3.2). The desirability, which in this example

expresses the goodness of the fit between a value on areactor property and a strategic notion from the taskdetermine-a-profile-of-the-desired-reactor, is expressedby a number. In our definition lower numbers stand fora better fit. The actual range is unimportant; thenumbers are used only to rank reactors on this prop-erty.

The knowledge used in this ranking process has thefollowing characteristics.1. Ranking knowledge often refers to a combination

of, on the one hand, a value on a soft property of areactor and on the other hand a strategic notion asfound in the profile for the desired reactor as de-scribed in Section 4.2.

2. Ranking knowledge is not exhaustive, it is not al-ways possible to find a ranking for each soft prop-erty in the choice matrix.

4.5.1.1. Strict predominance. If there is no conflict, i.e.if the ranking of reactors on the soft propertiesresults in one strictly best, or one strictly worst reactor,then we can simply choose the best or eliminate theworst reactors. This is done in the following two-stepway.1. The soft properties that represent an important

effect are identified, all soft properties in the choicematrix are potentially important. The importantproperties are identified in a three step process.1.1. Identify the useful soft properties, these are

properties for which a ranking is found andthat do not have the same level of desirability(expressed by a number) for every reactor inthe choice matrix. Non-discriminative proper-ties are ignored.

1.2. Inform the chemical engineer about the soft-properties for which no ranking is found. Thiscan happen, due to the fact that rankingknowledge is not exhaustive. It is the chemicalengineer’s choice whether or not the selectionstep is pursued.

1.3. Obtain important properties. The soft proper-ties that represent an important effect are iso-lated from the useful properties. For the timebeing every useful property is considered im-portant. But this can be (and probably needsto be) refined (Section 5.3).

2. The important properties are used to create a newchoice matrix. The principle of strict predominanceis applied to obtain this matrix (Russell & Norvig,1995). Strict predominance describes two situations,best from any perspective; and worst from anyperspective. An example, using two properties isshown in Fig. 7. In the first example one reactor is‘strictly best’, while in the second example one reac-tor is ‘strictly worst’ from any perspective.


Fig. 7. Strict predominance.

4.5.1.2. No strict predominance. If there is a conflict, i.e.if reactors are ranked differently on different soft prop-erties and there is no ‘strictly best’ or ‘strictly worst’reactor, then we cannot simply eliminate reactors, thereis an impasse. In this case, we divide the reactor selec-tion problem into two (or more) sub-problems, result-ing in two (or several) ways in which the principle ofstrict predominance is denied. Each of such a sub-prob-lem represents a viewpoint where one soft property isassumed to be more important than the others. Thissplitting involves the following steps.

Strict predominance fails; there is a real impasse. Thesystem ‘proves’ that there is not one strictly best orworst reactor.

The important properties (see 1.3 above) are used tosplit the choice matrix into several new matrices, eachmatrix representing a sub-problem with a set of reac-tors belonging to a particular point of view, a bestreactor and all reactors behind. A splitting example isshown in Fig. 8. Note that it is common for somereactors to end-up in more sub-sets.

After a selection step that results in a division of theoriginal problem into some new sub-problems, all thosesub-problems are searched through independently.

4.5.2. The search strategyProgress through the search space depends on the

strategy for proposing selection steps. We have theopinion that first the system should guide the user to asolution and next the user should be allowed to investi-gate alternatives. This is realised by a strategy, whichcontains a ‘guiding’ level and a ‘user driven’ level. The‘user driven’ level is subordinate to the ‘guiding’ level, itis only used when the ‘guiding’ level fails to propose astep. The ‘guiding’ level largely determines the be-haviour of the system. We have used the followingstrategy for the ‘guiding’ level.1. The ‘guiding’ level proposes only steps for unsolved

selection problems (nodes that are not expanded)and it has a preference for selection problems(choice matrices) which are produced last.

2. Elimination is preferred over splitting. Eliminationreduces the number of reactors, it is an effective stepfor reactor selection. Splitting does not reduce thenumber of reactors. The sub-problems are smallerbut the total number of reactors is not reduced.

3. It is preferred to try a step without questions to thechemical engineer. This minimises interaction withthe user and also the number of assumptions thathave to be made.

4. Each selection step is tried only once. Thus theselection process at the ‘guiding’ level is finite.

A survey of all steps is provided in Table 5. It alsoreflects the sequence in which steps are proposed by the‘guiding’ level, for a selection problem that can berepresented by a choice matrix containing both specificand soft properties.

After that the system has come to a conclusion (the‘guiding’ level fails to produce a further selection step),the chemical engineer has the opportunity to query thesystem about why it reached this conclusion and to tryalternatives. Explanation about the reasons for theelected reactor are generated from the search path.Alternatives can be tried. To do so the chemical engi-neer chooses the node and the selection step to beapplied on this node. In this way the consequences ofalternative steps as well as the consequences of differentinformation on steps that have already been tried can

Fig. 8. Splitting strategy.


Table 5Survey of steps

Step name Principle

Eliminate-on-the-basis-of-specific-properties-without-questions Check constraintStrict predominanceEliminate-on-the-basis-of-soft-properties-without-questionsCheck constraintEliminate-on-the-basis-of-specific-properties-with-questionsStrict predominanceEliminate-on-the-basis-of-soft-properties-with-questionsChemical engineer interferesEliminate-a-reactor-manuallyNo strict predominanceSplit-on-the-basis-of-soft-properties-without-questionsNo strict predominanceSplit-on-the-basis-of-soft-properties-with-questions

be investigated. At the ‘user driven’ level, the chemicalengineer can choose any node containing more than onereactor and any step that applies to this node. Theselection process stops when the user decides that he doesnot want to investigate any other alternative.

Each of the end-nodes describes a solution that isacceptable. A solution can consist of one or a fewreactors. In the last case, the system could not find areason to prefer one reactor above another one with theinformation given. Further refinement of the answer inthose cases could probably (but not for sure) be obtainedby answering more questions posed by the system.

5. An example of the selection process

In this section, we describe the behaviour of the systemin the context of a concrete example, the manufacture ofortho-cresol and 2,6-dimethylphenol (2,6-DMP) by cata-lytic vapour-phase methylation of phenol.

First the necessary input is collected, and then, sec-ondly, this information is used to select a reactor.

5.1. Input and determination-of-the-profile-for-the-reactor

The necessary input: problem class, components, reac-tion descriptions and a temperature window are given.Fig. 4 gives an overview of the input for this example.

This list of givens is used to derive a profile for thedesired reactor, according to the method described in(Jacobs & Jansweijer, 2000). The reaction network canbe viewed in four ways, four SRPs are derived (Section4.2). The chemical engineer has to choose one of theseSRPs at the moment the system requires results from thetask determine-a-profile-of-the-desired-reactor, when thesystem ranks the soft properties. We will not present thewhole list of strategic notions for each of the four SRPs,instead the notions of the chosen SRP will be discussedat the moment the SRP is selected.

The variable input is concerned with the feed streamto the reactor. The composition of the feed stream isobtained from the kinetic paper, it contains phenol,

methanol and H2O in the molar ratio 1:6:1. The total flowis 100 mole/s. These values should be regarded as aninitial guess, since the variable input is subject to change.

The property data encompasses only the molecularweights of the components involved.

5.2. The reactor selection process

The problem class of this example — gas-catalyst —possesses three hard features, scale; heat; and catalystreplacement.

Scale, the system derives the value of ‘semi-fine’ for thehard feature scale. It estimates the production in tonsper year based on the variable input and the SRPs. Thevalue of ‘semi-fine’ comes from the characteristic rule,‘the scale is semi-fine when the production is largerthan 5000 and smaller than 100 000 tons/year’.Heat, deducing the hard feature heat requires a valuefor the adiabatic temperature change. The kineticpaper comprises results of adiabatic reactor simula-tions, the maximum temperature rise is 67 K at a feedtemperature of 673 K and the minimum temperaturerise is 6.7 K at a feed temperature of 743 K. Theaverage value is supplied. For the hard feature heat aset of values are obtained.1. Regenerative, the adiabatic temperature change is

positive, so regenerative heat-exchange can beapplied.

2. Adiabatic, both cooling and heating are not neces-sary. The adiabatic temperature change is smallerthan the temperature interval described by thetemperature window.

3. Non-adiabatic with the use of one of the followingheat transfer media, flue gas; water vapour; moltenmetal; carbonate melt or nitrate melt. Althoughcooling and heating are not essential a non-adia-batic reactor type can be applied. Having controlover the temperature level in the reactor might beadvantageous. The given heat transfer media coverat least half of the temperature window.

4. Catalyst transport, the catalyst is circulating, whichinvolves just reaction or reaction in combinationwith regeneration and/or heat-exchange.Combining reaction with regeneration and/or


Fig. 9. The state of the selection process after trying four steps.

heat-exchange provides a means to control thetemperature level. Circulation involving just reac-tion, is the adiabatic variant.

Catalyst replacement, the hard feature catalyst re-placement requires information concerning the deac-tivation time of the catalyst. The chemical engineerprovides the presumption that the deactivation timeof the catalyst is 1 year. This results in the valuesdiscontinuous (replacement easy or difficult). Theproof encompasses the rule, ‘catalyst replacement hasthe value discontinuous when the deactivation time islonger than 5 days’.

The initial set of reactors for the problem classgas-catalyst incorporates 30 reactors. The above-derived features of the chemical process make the fol-lowing coarse selection possible.

Scale, the value semi-fine for the hard feature scale isnot selective. Every reactor in the problem classgas-catalyst can be operated at the level of the value‘semi-fine’ for the hard property scale.Heat, three reactors are rejected on the basis of thehard feature heat. The first reactor is named ‘fixedbed reactor with heating or cooling elements’, thisreactor relies on boiling water as a cooling agent. Thehard feature heat does not include the value non-adi-abatic-boiling-water, so no match is found. The other

reactors rejected are the co-current moving bed andthe cross-current moving bed reactor. The energybalance for these reactors relies on transport of solidsnot involving circulation, so again there is no match-ing value.Catalyst replacement, on the basis of the hard featurecatalyst replacement, possessing the values discontin-uous replacement easy and discontinuous replace-ment difficult, seven reactors are rejected. Thesereactors involve continuous catalyst regeneration,such as a ‘dilute phase riser with regeneration’ and a‘circulating fluidised bed with regeneration’.

This coarse selection rejects ten reactor types. The 20remaining reactor types are subjected to the more ver-satile selection methods, discussed in Section 4.5. These20 reactors form the starting set, named node1 in thefollowing.

Four steps are tried without interaction with thechemical engineer, see Table 6.

The steps are proposed and tried in the order inwhich they are listed, as explained below. Fig. 9 pre-sents the resulting selection path.� Eliminate-on-the-basis-of-specific-properties-with-

out-questions, tried on node1. The adiabatic temper-ature rise is greater than 15 K, which is a necessityfor feed to effluent heat-exchange. The correspond-

Table 6Steps tried without interaction

ResultSelection step name Tried on node

Eliminate-on-the-basis-of-specific-properties-without-questions node2Node1

Node2Eliminate-on-the-basis-of-specific-properties-without-questions node3

Node3Eliminate-on-the-basis-of-specific-properties-without-questions FailureFailureEliminate-on-the-basis-of-soft-properties-without-questions Node3


Fig. 10. The state of the selection process after 12 steps.

ing specific property is satisfied, hence no reactorsare eliminated. Although no reactors are eliminatedthe consequence of this step is the elimination of thisselection criterion.

� Eliminate-on-the-basis-of-specific-properties-with-out-questions, tried on node2. The reverse flow reac-tor, applying the principle of direct heat-exchange, isinappropriate when the adiabatic temperature rise isgreater than 15 K. The specific property is notsatisfied so the ‘reverse flow reactor’ is eliminated.The consequence of this step is elimination of areactor as well as elimination of a selection criterion.

� Eliminate-on-the-basis-of-specific-properties-with-out-questions, tried on node3. This step fails, check-ing whether or not a specific property is satisfiedrequires additional information from the chemicalengineer, which is initially not an option. This holdsfor all specific properties that are left in the choicematrix of node3.

� Eliminate-on-the-basis-of-soft-properties-without-questions, tried on node3. This step fails since somesoft properties in node3 can not be ranked. Becausethe system is in its initial, non-interactive mode, itcan not ask the chemical engineer permission to goahead with only a few rankings available.

The explanations of the two successful steps givenabove are generated by interpretation of the proof treesof these steps, whereas the explanations of the twofailed steps are based on an understanding of how thesystem works. Fig. 9 shows the successful steps so far.In this search tree, we show only the successful pathsand not the failing ones (such as those leading tofailure, starting from node3). The system, however,administers those trials such that these failing selectionsteps are not tried again later.

The system has run out of possibilities to go aheadwithout interaction with the user (see Table 5). It goes-


ahead in question mode. In the selection process thatfollows, proceeding from node3, 46 selection steps areconsidered by the system. Only ten of these are successful.Fig. 10 shows the resulting selection path. For the sakeof clarity all failing steps are not shown, nor discussedin what follows. Selection steps leading to a dead end areexplored by the system since it investigates all alternativesin a systematic way.� Step node3�node4, eliminate-on-the-basis-of-spe-

cific-properties-with-questions. The chemical engineeris asked to choose a specific property that the systemshould deal with next. Four specific properties areavailable addressing the following problems, sensitiv-ity to dust; attrition resistance catalyst; force in packedbed; and thermal cycle catalyst. We assume that theengineer chooses sensitivity to dust. Assume he spe-cifies that the feed stream is free of dust, so the specificproperty is satisfied and no reactors are eliminated.

� Step node4�node5, eliminate-on-the-basis-of-soft-properties-with-questions.The selection step startswith ranking, as all selection steps based on softproperties do. This leads to the following questions.

1. The chemical engineer is asked to specify a SRP tofocus on. We assume that he selects the SRP describ-ing the production of o-cresol from phenol:

phenol(reference component)(+methanol)

�R1

o-cresol(product)(+H2O)

This SRP was classified as series. The temperaturestrategy for this SRP is, low temperature for both; thecase that the reactor content is mixed; and the casethat the reactor content is staged. The derived back-mixing strategy was, plug-flow of the gas phase andselective removal of o-cresol from the gas phase(strategies resulting from the task determine-a-profile-of-the-desired-reactor). The details of the classifica-tion of SRPs and of the derivation of strategies arediscussed in Jacobs and Jansweijer (2000).

2. We assume that a question concerning the residencetime of the gas is not answered by the engineer, so theselection step must be solved without this information.

� The choice matrix contains nine soft properties. Forthree properties a ranking is found, back-mixing gas;development; and pressure drop. Ranking of theproperties development and pressure drop is verysimple. A low development effort is preferred, as arethe lower values for the pressure drop. Ranking of theproperty back-mixing gas uses the strategic notionsderived for the SRP that has been chosen. Theback-mixing strategy for this SRP comes with thestrategic notion plug-flow of the gas phase, so reactorsapproaching plug-flow are preferred. The rankings ofthe other six properties remain unknown, back-mixingheat; residence time gas; catalyst sizes and shapes;external transfer catalyst; catalyst volume fraction;

and catalyst residence time distribution. This conclu-sion is only really sound for back-mixing heat andresidence time gas. The other properties remain un-known simply because no ranking knowledge has beensupplied. None of the properties for which a rankingis found possesses the same number of desirability forevery reactor, so they are all useful. The chemicalengineer is informed about the unknown propertiesand we assume that he decides that the selection stepshould be tried anyway. One reactor appears to be thestrictly predominant, the monolith reactor. Note thatthis conclusion is reached on the basis of only threeproperties, back-mixing gas; development; and pres-sure drop.

The system now has found an answer, the monolithreactor. The chemical engineer can continue to use thesystem to explore other possible solutions. For theexample we assume that he chooses to go back to node4

but this time to answer the question about residence timethat he previously did not want to answer. This leads tothe following.� Step node4�node6 and node7 and node8, split-on-the-

basis-of-soft-properties-with-questions.The selectionstep again starts with ranking. Again the SRP describ-ing the production of o-cresol from phenol is chosen,but in contrast to the previous situation the questionconcerning the residence time is not refused, a valueof 90 s is specified. A ranking is found for the followingproperties, back-mixing gas; development and pres-sure drop as before; and residence time gas based onthe information supplied on this item. Ranking of theresidence time gas involves the following values,very-short; short; moderate; and long, each valuerepresenting a time interval. The specified residencetime fits within the interval moderate, so ‘moderate’is preferred. The SRP that is chosen has been earlierclassified as ‘series’. This means that the residence timehas to be not too long. Therefore, the value ‘long’ isconsidered worst and ‘short’ is preferred above ‘very-short’. The chemical engineer is informed that fiveproperties remain unknown, but we assume that theselection step is pursued. The choice matrix, node4, issplit in three sub-matrices, node6, node7 and node8,using the properties, back-mixing gas (B); develop-ment (D); pressure drop (P); and residence time gas(R), see Table 7. The numbers stand for the rankingof properties, lower numbers representing preferredvalues and the black dots indicate that a reactorapplies to a node. Table 7 is a four-dimensionalinstance of a splitting step as shown in Fig. 8, whichexplains the splitting step using a two-dimensionalexample.

� Step node8�node9, eliminate-on-the-basis-of-soft-


Table 7Step node4�node6 and node7 and node8, no strict predominance

New nodesRankingReactors in node4

B D P R 6 7 8

1 1 31 1Simple fixed bed reactor, adiabatic Fixed bed reactor with combustion zone2 1 1 3 1

1 2 2 1Radial flow reactor, out in3

1 1 36 1Multitubular fixed bed reactor 1 1 3 1Tubular reformer7

Fluidised bed reactor, adiabatic bubbling10 6 2 2 1

4 1 312 1Simple fixed bed reactor with large recycle, non-adiabatic 1 1 3 113 Simple fixed bed reactor with external feed to effluent heat exchange 1 2 3 1Multitubular fixed bed reactor with internal feed to effluent heat exchange14

4 1 318 1Simple fixed bed reactor with large recycle, adiabatic 1 1 1 2Monolith reactor. 195 2 2 220 Circulating fluidised bed, non-adiabatic

2 1 321 1Simple fixed bed reactor with small recycle, adiabatic 6 2 222 1Fluidised bed reactor, non-adiabatic bubbling

2 1 325 1Simple fixed bed reactor with small recycle, non-adiabatic 1 2 2 127 Radial flow reactor, in out

2 2 3 228 Dilute phase riser, adiabatic

2 2 3 229 Dilute phase riser, non-adiabatic

5 2 230 2 Circulating fluidised bed, adiabatic

properties-with-questions. The property developmentis not accepted in the choice matrix of node8, be-cause it does not have discriminative power, since allreactors in node8 require a great development effort.So the matrix reduces from nine to eight soft proper-ties. A ranking is found for the properties, back-mix-ing gas; pressure drop; and residence time gas. Thechemical engineer is informed that five propertiesremain unknown. We assume that the selection step

is pursued, using, back-mixing gas (B); pressure drop(P); and residence time gas (R). See Table 8.

� Step node7�node10, eliminate-on-the-basis-of-soft-properties-with-questions. A ranking is found for thefollowing properties, back-mixing gas; development;pressure drop; and residence time gas. The propertypressure drop is not useful, since the same number ofdesirability is assigned to each reactor in the choicematrix of node7. The chemical engineer is informed

Table 8Step node8�node9, strict predominance best

Reactors in node8 Ranking Node9

RB P

1 3 Radial flow reactor, out in 1 22 110 Fluidised bed reactor, adiabatic bubbling 6

13114 Multitubular fixed bed reactor with internal feed to effluent heat exchange5 2 220 Circulating fluidised bed, non-adiabatic

2 122 Fluidised bed reactor, non-adiabatic bubbling 612 127 Radial flow reactor, in out

3 228 Dilute phase riser, adiabatic 2229 Dilute phase riser, non-adiabatic 2 3

2 230 Circulating fluidised bed, adiabatic 5



Node10Reactors in node7 Ranking

B D R

1 11 1Simple fixed bed reactor, adiabatic 1 12 1Fixed bed reactor with combustion zone 1 1Multitubular fixed bed reactor 16 1 1 1 7 Tubular reformer4 1Simple fixed bed reactor with large recycle, non-adiabatic 112

Simple fixed bed reactor with external feed to effluent heat exchange13 1 1 1 Multitubular fixed bed reactor with internal feed to effluent heat exchange14 1 2 1

4 1Simple fixed bed reactor with large recycle, adiabatic 118Simple fixed bed reactor with small recycle, adiabatic21 2 1 1

2 1Simple fixed bed reactor with small recycle, non-adiabatic 1252 228 2Dilute phase riser, adiabatic2 2Dilute phase riser, non-adiabatic 229

that five properties remain unknown. Again we as-sume that the selection step is pursued using, back-mixing gas (B); development (D); and residence timegas (R). See Table 9.

� Step node6�node11, eliminate-on-the-basis-of-soft-properties-with-questions. A ranking is found for thefollowing properties, back-mixing gas; development;pressure drop; and residence time gas. The propertyresidence time gas is not useful, since the samenumber of desirability is assigned to each reactor inthe choice matrix of node6. The chemical engineer isinformed that five properties remain unknown. Wecontinue the example by assuming that the selectionstep is pursued using, back-mixing gas (B); develop-ment (D); and pressure drop (P). See Table 10.

The system now has found new answers in the nodes9, 10 and 11. Node9 contains the reactor types ‘radialflow out in’ and ‘radial flow in out’. Node10 containsfive reactors, ‘simple fixed bed, adiabatic’; ‘fixed bedwith combustion zone’; ‘multitubular fixed bed’; a‘tubular reformer’; and a ‘simple fixed bed with externalfeed to effluent heat exchange’. Node11 leads to thesame outcome as was found before, the ‘monolithreactor’.

Suppose the chemical engineer regards the monolithreactor to be an inferior solution and he wants to seewhat happens in case this reactor is excluded in ad-vance. In order to investigate this, he goes back tonode4 and eliminates there the monolith reactor as oneof the possible reactor types. This leads to the newbranch in the selection tree.� Step node4�node12, eliminate-a-reactor-manually.

The chemical engineer uses the ‘manual’ eliminationstep, to eliminate the monolith reactor.

� Step node12�node13 and node14, split-on-the-basis-of-soft-properties-with-questions. The SRP describ-ing the production of o-cresol from phenol has beenchosen, but the question concerning the residencetime is refused, so the selection step must be solvedwithout this information. A ranking is found for thefollowing properties, back-mixing gas; development;and pressure drop. The chemical engineer is in-formed that six properties remain unknown, but theselection step is pursued. The choice matrix, node12,is split in two sub-matrices, node13 and node14, usingthe properties, back-mixing gas (B); development(D); and pressure drop (P), see Table 11.

� Step node14�node15, eliminate-on-the-basis-of-soft-properties-with-questions. This step is almost equal


Ranking Node11Reactors in node6

B PD

1 19 Monolith reactor 1 122520 Circulating fluidised bed, non-adiabatic328 Dilute phase riser, adiabatic 2 2329 Dilute phase riser, non-adiabatic 2 2

2 230 Circulating fluidised bed, adiabatic 5


Table 11Step node12�node13 and node14, no strict predominance

Reactors in node12 New nodesRanking

B D P 13 14

1 11 3Simple fixed bed reactor, adiabatic 1 12 3Fixed bed reactor with combustion zone 1 2 2Radial flow reactor, out in3 1 1 3 6 Multitubular fixed bed reactor1 1 3Tubular reformer 76 2 210 Fluidised bed reactor, adiabatic bubbling 4 1 3Simple fixed bed reactor with large recycle, non-adiabatic 121 1 313 Simple fixed bed reactor with external feed to effluent heat exchange1 2 3Multitubular fixed bed reactor with internal feed to effluent heat exchange 14 4 1 318 Simple fixed bed reactor with large recycle, adiabatic5 2 2Circulating fluidised bed, non-adiabatic20 221 1Simple fixed bed reactor with small recycle, adiabatic 3 6 2 2Fluidised bed reactor, non-adiabatic bubbling22 2 1 325 Simple fixed bed reactor with small recycle, non-adiabatic1 2 2Radial flow reactor, in out27 2 2 3 28 Dilute phase riser, adiabatic2 2 3Dilute phase riser, non-adiabatic 29 5 2 230 Circulating fluidised bed, adiabatic

to the earlier presented step from node8 to node9.The difference is that now we find rankings only forthe properties back-mixing gas and pressure drop.The selection step, however, results in the samereactors as present in node9 (Table 8), ‘radial flowout in’ and ‘radial flow in out’.

The system continues to investigate the otherpossibility that was left after the previous splitting stepthat resulted in node13.� Step node13�node16, eliminate-on-the-basis-of-soft-

properties-with-questions. This step is almost equalto the earlier presented step from node7 to node10

(Table 9). The differences are that in node13

rankings are found for the following properties,back-mixing gas; development; and pressure drop.The property pressure drop is not useful, since everypossible reactor in this node has the samedesirability. The chemical engineer is informed thatsix properties remain unknown, but the selectionstep is pursued based on the properties, back-mixinggas; and development. The system finds five possiblereactor types. They are the same as found in node10

and shown in Table 9.Fig. 10 shows the complete search tree as constructed

by the system. When a chemical engineer uses thesystem, he gets presented the gradually expandingsearch tree. The selection steps are displayed one at atime. All intermediate states are shown, which gives theselection process a self-explanatory quality. In thisarticle, we have presented only two of the intermediatesearch states, the intermediate state in Fig. 9 and the

state at this moment in Fig. 10. The other states caneasily be inferred if one knows that nodes with a highernumber are added later in time.

To summarise; the system solves the selectionproblem represented by node4 in three different ways.After that the system has found the ‘monolith reactor’in node5 the chemical engineer exploits theopportunities offered by the system to explore otherpaths to see what happens. In this example the chemicalengineer has applied the following ‘what-if’ scheme.1. Step node4�node5, specify an SRP but refuse the

question concerning the residence time, resulting inone reactor in node5.

2. Step node4�node6 and node7 and node8, specify thesame SRP and provide a residence time, resulting inthe nodes 9, 10 and 11, each with one or a fewpossible reactor types.

3. Step node4�node12, eliminate from the contents ofnode4 the monolith reactor. Next, supply the sameinformation as in step node4�node5, resulting instep node12�node13 and node14, and finally in

Fig. 11. The modules in READPERT.


nodes 15 and 16, each with some possible reactortypes.

The solution node11 contains only the monolith reac-tor and the contents of the nodes 10 and 16 areidentical, as are the contents of the nodes 9 and 15. Thechemical engineer can draw the following conclusionfrom this ‘what-if’ scheme, deleting the monolith reac-tor on the basis of my own preference, also eliminatesresidence time as an important consideration.

5.3. Discussion of the selection example

The example shows that the task derive-and-apply-hard-features is capable of making a coarse selection.In this case ten reactors are rejected, but in general thenumber depends heavily upon the severity of the hardfeatures derived for the problem considered. Examplesof more restrictive hard features are given in Jacobs,1998.

In the current implementation of the system, all softproperties that are useful are considered important aswell. This could be improved. Adding knowledge thatweights the useful soft properties relative to one an-other enables the system to do selection steps based ona smaller number of soft properties. A smaller numberof soft properties will result in more strict-predomi-nance steps and less impasse steps, since the chance ofa property disturbing strict-predominance is reduced.This will also change the current situation, that asplitting step is always followed by elimination steps.Also it will enable to foresee the result of the elimina-tion steps, by examination of the property ranking,used in the preceding splitting step. We anticipate thatthe weighting knowledge will be quite complex.Therefore, we like to point out that the system isalready advantageous when this knowledge isreduced to a single question to the chemical engineer inwhich he specifies the properties that he considersto be important, for the next step to be tried. Thechemical engineer can explore several ‘what-if’ scenar-io’s each representing different sequences of importantproperties and see if and how the result is sensitive tothis.

5.4. A comparison to READPERT

In this section, a comparison to READPERT ismade, which is also a system for advice concerning theselection of technical reactors. In READPERT, thewhole task reactor-selection is divided into four sub-problems, general-reactor-type, operating-conditions,heat-transfer-equipment and technical-reactor, whichcan mostly be solved independent from each other.These sub-problems are collected into four differentmodules, see Fig. 11.

The modules are described below.1. The first module deriving the general reactor type,

should not be interpreted as a module which pro-poses a concrete technical piece of equipment, in-stead it denotes a specific type of back-mixingbehaviour (Droge, Schembecker, Westhaus &Simmrock, 1994).

2. The module operating-conditions provides recom-mendations for the most important operating condi-tions of the reactor, involving parameters as,temperature profile within the reactor; need for re-cycle streams; qualitative temperature levels at be-ginning and end of the reactor; qualitativeconcentration levels for the reactants; need for in-erts; etc.

3. The module heat-transfer-equipment addresses theproblem of heat transfer, involving a choice betweena wide range of different possibilities and equipmenttypes. A three step procedure is followed.3.1. Check which kind of equipment is suitable for

the problem. The following example is pro-vided, helical coils cannot be used in an agi-tated vessel if the reaction mixture has a highviscosity and the tendency to encrust.

3.2. Calculate the heat flows for each of the ele-ments being taken into consideration, usingshort-cut calculations. An element may be usedwhen its maximum flow is higher than needed.

3.3. Select the best elements among the remainingheat transfer possibilities. Equipment costs areconsidered to allow some estimate of a properchoice.

4. The module technical-reactor tries to determine asuitable technical reactor. An appropriate technicalreactor has to satisfy the proposals developed in theprevious modules, as well as further criteria of tech-nical relevance. The technical reactors are classifiedaccording to the phase of reaction. The phase ofreaction is used as a constraint, which reduces thenumber of solutions drastically (Droge et al., 1994).

The division into four modules is the READPERTcounterpart of our top-level task decomposition pre-sented in Fig. 3. The differences will be discussedbelow.1. In READPERT, Levenspiel type of reasoning is

split, it is allocated to the modules general-reactor-type and operating-conditions. Our KBS has onlyone sub-task that is devoted to this type of reason-ing, the task determine-a-profile-of-the-desired-reac-tor. Thus, we adopt an approach that is closer tothat of the chemical engineer, who perceives Leven-spiel type of reasoning as a single cluster. Deviationsin the problem-solving strategies between the chemi-cal engineer and the KBS are undesired becausethey will hamper the provision of a clearexplanation.


by the hard feature heat, since they incline towardsdesign.

6. The KBS counterpart of the helical coil example(step (a) of the module heat-transfer-equipment)involves a specific property. The agitated vessel,having a helical coil, will possess a specific propertythat is concerned with a high viscosity and thetendency to encrust. Naturally, this is only of im-portance, when the level of detail in the grid of theproblem class liquid allows individual representationof an agitated vessel having a helical coil.

7. The classification of technical reactors according tothe phase of reaction, is the READPERT equivalentof the problem classes. It is not stressed that thisclassification should be based on the minimum num-ber of phases, and consequently a particular classwill not encompass reactors possessing an extraphase in addition to this minimum.

6. Discussion and conclusion

This paper presented a knowledge-based method forreactor selection. The method selects a reactor based onexplicit knowledge concerning reactor engineering andis inspired on the problem solving strategies of thechemical engineer in industry. The selection methodthat is used can be seen as an instantiation of a moregeneral selection task. The method has, in variant in-stantiations, a potential for other selection problems.The knowledge that is used in the reactor selectionprocess is for a large part acquired through interviewswith an expert in industry. Although the current systemis based on the knowledge of only one expert, we haveshown that the approach works. However, further workis needed to validate this knowledge and to extend thescope of the system to other problem classes. A possibleimprovement of the system is its integration with asystem that computes results based on rigorous mathe-matical modelling. In such a hybrid system we haveboth, an explicit reasoning process providing funda-mental insight in the chemical process and a mathemat-ical method providing precise quantitative results butno fundamental insight. The knowledge-based ap-proach has the capability to derive results based onpartial and qualitative information (Jacobs & Janswei-jer, 2000). Therefore, it can be used at the early stage ofconceptual design, which allow swift feedback to thelaboratory, indicating directions for experimentaldesign.

Some engineers try to postpone the choice of thereactor as long as possible. They regard this choice asthe most predominant decision in process developmentand consequently they try to postpone the decision, tohave the advantage of maximum information. We didnot intend the system to be used in this manner, but it

2. In READPERT, one module addresses the problemof heat transfer, and another module tries to deter-mine a suitable technical reactor. In our KBS theheat transfer problem is not assigned to a separatesub-task. The assignment of different modules to theheat transfer problem and the technical reactor iscontroversial, since the heat transfer problem andthe technical reactor are tightly connected.

3. In READPERT, the choice of the technical reactoris assigned to one module, the module technical-re-actor. In our KBS this choice is assigned to thetasks, derive-hard-features, apply-hard-features andapply-specific-and-soft-properties. So, when itcomes to the technical reactor, the task decomposi-tion in our KBS is more refined. As a rule, the taskdecomposition should continue up to a level thatallows the user of the system to understand what ishappening, that is, at least, distinguishing variousaspects, as reflected by notions like, hard features,soft properties, etc. This is critical, since we aredealing with the type of system for which the expla-nation of the answer is equally important as theanswer itself. The literature on READPERT (Drogeet al., 1994; Schembecker et al., 1995a,b) does notprovide an extensive explanation of the moduletechnical-reactor, which confirms a too coarse taskdecomposition.

Apart from the differences in the task decomposition,the following similarities and contrasts are observed.1. The module general-reactor-type does not pay atten-

tion to different perspectives. Our KBS generates allperspectives: the SRPs, (Jacobs & Jansweijer, 2000).

2. The module general-reactor-type addresses two con-straints of technical nature in addition to Levenspieltype of reasoning, namely, phase of reaction; andmode of operation. The task determine-a-profile-of-the-desired-reactor in our KBS is exclusively con-cerned with Levenspiel type of reasoning. Resultsfrom Levenspiel type of reasoning represent what isdesired, whereas the phase of reaction and mode ofoperation represent what is required. So in READ-PERT distinctively different types of knowledge thatleads to conclusions with equally different charac-teristics, are associated in one module.

3. The technical constraint phase of reaction, is theREADPERT equivalent of consulting the set ofreactors appropriate for the problem class (Section4.4).

4. The technical constraint mode of operation, is theREADPERT equivalent of the hard feature scale(Section 3.1).

5. The module heat-transfer-equipment, can be inter-preted as the READPERT equivalent of the hardfeature heat (Section 3.1). The steps (b) and (c) ofthe module heat-transfer-equipment are not covered


supports this kind of behaviour as well, because alter-native branches can be created. The engineers who liketo postpone, only have to interpret these branches aspossible selection paths, whereas engineers who want toselect look for efficient selection steps. Both groups canobtain useful information from a session with the sys-tem.

The KBS knows how to do a selection task ingeneral. First it collects the fixed givens and appliesthem. Then it loops through a sequence of collectingadditional more dynamic givens that are applied untilno more givens can be used or until the user is satisfiedwith the result (Fig. 2). This paper describes the reactorselection task as an instance of this general selectiontask. If we provided the system with the appropriatedomain knowledge, it could be used for other equip-ment selection problems such as, limiting ourselves tochemical process industry, pumps; compressors; heat-exchangers; valves; measuring devices; column-packing;and tray-types, etc. These problems are all less chal-lenging than the reactor selection problem and weexpect their solution to be simpler.

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A knowledge-based system for reactor selection