A knowledge-based system for reactor selection

Computers and Chemical Engineering 24 (2000) 1781 – 1801 www.elsevier.com/locate/compchemeng

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 and derives a set of strategic notions that define a profile for the most desirable reactor type for the chemical conversion process. Then it uses explicit knowledge about technical reactors to select the reactor that is best suited. This knowledge encompasses technical and practical aspects of reactors. The system is specially geared to incorporate the practical knowledge of the engineers in industry. The fact that reactor selection is a creative task is honoured by the system. The selection problems do not proceed along a fixed pattern, but are allowed to develop in their own way. The system supports a ‘what-if’ type of reasoning such that consequences of different assumptions can be explored. We demonstrate its use in a detailed example. A prototype system has been implemented in prolog. © 2000 Elsevier Science Ltd. All rights reserved. Keywords: Reactor selection; Knowledge-based system; Knowledge-based reasoning; Knowledge acquisition

1. Introduction

1.1. Reactor selection Reactor selection addresses the problem of finding the best reactor(s) for a chemical process. The selection process begins with a large number of reactors and moves into the direction of a small number of reactors: the preferred ones. The initial set of reactors comprises technical pieces of equipment, for example, a multitubular fixed bed; a bubble column; and a circulating fluidised bed. Our objective is to develop a knowledgebased system (KBS) that supports the selection of these technical reactors. The reasoning of the system is based on explicit knowledge concerning the reactor engineering domain. As a result, the system is easy to maintain, easy to extend and valuable in the sense that it supports explanation of its reasoning process, the last providing fundamental insight in the chemical process for which a reactor is selected. The system leaves open the search space for possible solutions as long as possible, thereby avoiding early elimination of feasible possibilities and exploiting multiple criteria. The system is different from * Corresponding author. Fax: +31-20-5256896. E-mail address: [email protected] (W. Jansweijer).

mathematical simulation systems that are powerful in computing the results for particular design decisions, but lack this insight. It is possible, however, to integrate our KBS with mathematical systems, thereby making the power of mathematical simulations available within the KBS. This would result in a hybrid system where the mathematical model takes inputs from the KBS and provides answers that are now (in our KBS) given by the chemical engineer. We first describe the scope of the system. Then we analyse the task of making a choice for a particular reactor type, which is seen as an instantiation of a more general type of selection task. In Section 3, we describe the kind of knowledge that is needed for this task and the knowledge acquisition process. In Section 4, we describe the KBS in detail. We continue in Section 5 with a worked example and we compare our method with the READPERT system (Schembecker, Dro¨ge, Westhaus & Simmrock, 1995a,b). We conclude with a discussion of the presented method and we extrapolate to 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

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about the world we live in. When developing a KBS, the first essential step is to describe this specific area, to describe the scope of the KBS. The scope is a description of ‘the world’ the KBS is supposed to reason about. Problems outside the scope cannot be solved, the KBS has no knowledge of the world outside the scope. The scope of the reactor selection problem is largely determined by the problem definition, ‘select the best reactor for a chemical process from a set of existing reactors’. First the scope will be defined more precisely by 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 reactors that are applicable within one of the problem classes described below. The classification of reactor selection problems is based on the phases (gas, liquid, solid as a catalyst) that must be present to realise reaction. The scope 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 furnaces, for which desired behaviour addresses conversion in terms of the energy balance. 2. A desired component is meant to be produced; treating processes are not incorporated. In treating processes the objective is not to make a product; the chemical reaction is employed to achieve a separation task. 3. Special reaction domains are excluded, since they require special focusing. Examples of these are, biochemical reactions, photochemical reactions, polymerisation and reacting solids.

2. Analysis of the task of reactor selection Although it is no must that the KBS will behave as the chemical engineer does, it is of great help to study his behaviour when doing reactor selection. The system

Fig. 1. The problem classes.

that we present in this paper mimics the chemical engineer only to a certain extent, but we have learned a lot about how to do the reactor selection task by looking at experts. We studied human experts mainly for the following two reasons. In the first place experts show us one way to tackle the selection problem. They appear to work in a few steps. These smaller steps are easier to model then the full problem of reactor selection. Secondly our system should do more than just select the best reactor. It should be able to explain what it is doing and how it came to its conclusions. If there is a large difference between how the chemical engineer works and how the KBS works it would be difficult to give a clear explanation. Two strategies of the chemical engineer are extremely significant. 1. A human expert will develop a strategy while solving problems. So the chemical engineer has developed a strategy for reactor selection after solving several reactor selection problems. This strategy can be applied consciously, but it is more likely that it exists as a habit. One of the phenomena occurring during the development of such a strategy is that the sequence of reasoning processes will change. Reasoning processes using information that is not likely to change will move to the front and reasoning processes using information that is likely to change will move to the end of the problem solving process (Jansweijer, 1988). This results in a strategy in which the impact of retracting intermediate conclusions is minimised. 2. An engineer selecting a reactor will not use all his knowledge at the same time. Knowledge that rejects reactors that are totally inappropriate for a chemical process will be applied first. Knowledge discriminating between reactors that are all reasonably good solutions to the reactor selection problem, is applied later. The first strategy is reproduced using the concept of fixed and variable input. First information that is not likely to change is collected by the task collect-fixed-input. Fixed input is not likely to change since it originates from the laboratory. This task is followed by tasks that require solely fixed input. Next information that is likely to change is collected by the task collectvariable-input. This task is followed by tasks that do not exclusively use fixed input. The task collect-variable-input and subsequent tasks should be placed in aniterative cycle to allow for changes in the variable input, see Fig. 2. This represents in fact a general description of selection tasks, not specific for reactor selection. The fixed input, originating from the laboratory, consists mainly of a description of the kinetics. In

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3. Knowledge about the reactor engineering domain

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

practice there is only one task that requires solely fixed input: the task determination-of-the-profile-for-thereactor. The second strategy of the chemical engineer is utilised to design the tasks that do not exclusively use fixed input. First knowledge that rejects reactors that are totally inappropriate for the chemical process is applied, which involves hard features. This concerns features of the reactor selection problem that must be satisfied; these features cannot be compromised. They are derived and applied by a task that we have called derive-and-apply-hard-features. Here, reactors that do not satisfy the hard features are rejected. The reactors that are left after this first selection step are subjected to criteria that are more versatile in a task named applyspecific-and-soft-properties. A specific property is a reactor property that is specific for one or a few reactors, it describes a constraint. Soft properties are reactor properties that apply to all reactors; they can be compromised. The task decomposition of the top-level task, reactor selection, is shown in Fig. 3. It can be seen as an instantiation of the generic task decomposition given in Fig. 2. It is important to note the change in direction of reasoning between the task derive-and-apply-hard-features and the task apply-specific-and-soft-properties. The task derive-and-apply-hard-features reasons from the viewpoint of the problem, since a hard feature is a feature of the reactor selection problem. The task apply-specific-and-soft-properties reasons from the inverse perspective, the viewpoint of possible solutions. The specific and soft properties are reactor properties, so this part of the reasoning is driven by the properties of the reactors that are left. This is characteristic for the selection of a piece of equipment. The chemical engineer is not in the luxuriant position to make a combination of properties he likes best, but he is bound to choose a fixed combination of properties. Each piece of equipment represents such a fixed combination.

Fig. 3. The top-level task decomposition.

Selection of an appropriate reactors requires knowledge of these reactors and knowledge about their properties. In this section, we describe the structure of this knowledge and the knowledge acquisition process.

3.1. Reactors and their properties We have listed all technical reactors that are possibly relevant for each of the seven problem classes discussed in Section 1.2, resulting in seven lists of reactors. We have selected the reactors from textbooks (Trambouze, Landeghem, van, Wauquier & Marshall, 1988) and from Ullmann’s encyclopaedia (Ullmann, 1992). A few frequently used networks of reactors, that employ engineering principles that can be described by properties of, as it were, single reactors are added as well. These networks include the cascade and recycle reactor employing a high or a low recycle ratio, where the recycle can incorporate a heat exchanger. As an example a list of 20 reactor types in the problem class gas-catalyst can be found in Fig. 9. The total number of reactor types in this problem class is 30, the list presents the reactor content after the first selection step. Then we have listed, for each of these seven problem classes, all the properties of those reactors that are of physical significance. We have collected this set of properties by a knowledge acquisition procedure as described in the next section. One set of those properties was constructed for each set of reactors within one problem class. This resulted for each problem class in a matrix with along one dimension the reactors and along the other dimension reactor properties. For the properties we have made a distinction between three types of properties, hard; specific; and soft. The first set, the hard properties of a reactor, is the set of properties that are always important for each reactor, scale, heat and, if appropriate for the problem class, catalyst replacement. These properties can not be compromised, although some reactors are suited for a whole range of values for a property. Scale for a simple packed bed in the problem class gas-catalyst is an example.

3.1.1. Scale Scale is a measure for the scale at which the reactor is applicable. It describes whether a reactor is applicable at a small scale or at a very large scale. A division between small and very large scale is often treated as the choice between batch and continuous, which we could have used instead as a hard property. In many textbooks, the choice between batch or continuous is presented as the first problem to be addressed. Scale of the process allows the assignment of finer grained property values. Therefore, we use scale, which is closely related to the mode of operation. Possible values of this property are, fine; semi-fine; big; and bulk.

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3.1.2. Heat Heat 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 wall is considered, but we aim also at other techniques to control the temperature level in the reactor. Examples are, solids transport; catalyst transport involving regeneration; and evaporation. Of course these techniques only apply to certain problem classes. The property heat, however, is always important, since a reactor has to be operated at an appropriate or reasonable temperature level. If the reactor is non-adiabatic, this property can be annotated with properties of the heat transfer process such as ‘a cycle including, reaction; regeneration; and heat transfer’. 3.1.3. Catalyst replacement Catalyst replacement describes how the catalyst is supplied to, and removed from the reactor. Values on this property are, ‘continuous very-short’; ‘continuous short’; ‘continuous moderate’; and ‘discontinuous’. Selection on the basis of this property can be very restrictive, for example if the process is fluidised catalytic cracking then the rapid deactivation of the catalyst should result in a reactor which has the value ‘continuous very-short’ for catalyst replacement, which results in a riser as the only alternative. Of course catalyst replacement is applicable only in the problem classes that involve a heterogeneous catalyst. The second set of properties are the so called specific properties. These are also properties that cannot be compromised, but they are specific to certain reactors. Four examples of specific properties are. The reactor is sensitive to dust, it can only be applied when the feed streams to the reactor are free of dust. The catalyst must be attrition resistant, the reactor cannot be applied when the catalyst is not attrition resistant. The reactor is inappropriate when the catalyst cannot withstand the force in a packed bed. The reactor is inappropriate when there is no gas– liquid phase envelop at reaction temperature. Reactive distillation should not be considered when there is no phase envelop. Reactive distillation is within the scope of the system, it is defined as a reactor type for the problem classes liquid and liquid-catalyst. The classification of reactors is based on the minimum number of phases that are required for reaction (Section 4.1). A creative system should also handle the possibility of adding an extra phase in addition to this minimum. The definition of a reactive distillation column for the problem classes liquid and liquid-catalyst, shows one example of how we incorporated this. The third set of properties are the so called soft

properties. These are properties that are desirable, but that can be compromised to a certain extent. A soft property applies to every reactor in a problem class. Whereas the values on the specific properties usually are expressed nominally, the values of the soft properties generally are quantified on at least an ordinal scale. More then one value can apply, indicating that the reactor type is applicable under more then one circumstance. Four examples of soft properties are. Back-mixing of the phase in which reaction occurs. Possible values are, plug, intermediate, mixed or nota-single-flow-pattern. Development, describing whether or not the choice for this reactor will result in a great development effort. Pressure drop, a measure for the pressure drop over the reactor, possible values are, low, intermediate and high. As an example, a reactor type can possess the values low and intermediate for the soft property ‘pressure drop’, indicating that this reactor is suitable under both conditions. Back-mixing heat, the mixing behaviour with respect to the heat. Costs are addressed implicitly, for example, in case of undesired reactions, the system rejects the reactors possessing a back-mixing characteristic, that result in a poor selectivity. In this way, the raw-materials costs are incorporated indirectly. An explicit approach to costing involves design and should consider the complete flowsheet. Some reactors are useful in more than one problem class. Such reactors are repeated over the lists. For example a packed bed reactor of catalyst particles naturally 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 more than one list they are described with different sets of properties: i.e. properties required in one problem class and the properties required in the other problem class.

3.2. Knowledge acquisition The matrices of reactors and properties have to be filled with appropriate values. Part of this information originates from the common textbooks concerning reactor engineering. Another source of information is the domain expert, whose knowledge is extracted by interviews. Two interview techniques are used, concept sorting and questionnaires. We approached several companies for these interviews, resulting in sessions at three different business enterprises. At only one company we succeeded to get a sufficient level of co-operation and matrices where constructed for three problem classes, ‘gas’; ‘gas-catalyst’; and ‘liquid-catalyst’.

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Concept sorting is a well known technique with proven effectiveness for the systematic acquisition of properties and values with an existing set of concepts. It is developed in cognitive psychology (Shadbolt & Burton, 1995). The expert is given a pile of cards. Each of these cards gives a description of one reactor type. The cards are shuffled and the expert is asked to sort the cards into a number of piles according to some characteristic that he considers important. Then the expert is asked on the basis of which dimension the sort was made and what the piles represent. This is repeated until the expert does not know any new way to sort the cards. For example, it is conceivable that the expert sorts on the basis of the reactor property back-mixing and makes various piles including a plug-flow pile and a mixed-flow pile. This reveals back-mixing as a relevant reactor property and the values plug-flow and mixed-flow as possible values for this attribute. When the expert was confronted with the results of this knowledge acquisition process he was amazed by the results and said, ‘somehow you manage to obtain for millions worth of information but I could not exactly grasp what that information is’. Interviews are time consuming and the amount of time that an expert has available is limited, so there is an urge for a time efficient way to gather information. Questionnaires meet this requirement to a certain extent. Therefore, after a while, we have constructed questionnaires on the basis of the outcomes (i.e. the elicited properties and values) of the initial concept sorting task. Each questionnaire represented one reactor and consisted of two parts, a description and a picture of the reactor to prevent possible misunderstandings about which reactor was meant, and secondly a list with the properties that had to be given a value. Definitions of these reactor properties, together with a list of values from which the answer should be picked, were presented on a separate card. Questionnaires have the important advantage that they can be filled out by the expert independently and spread out over a number of days. Our expert provided a total of 759 property– value pairs, being the sum of all the entries in the matrices of the three problem classes.

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Table 1 Overview hard properties G Scale Heat Catalyst replacement

L

GL



LL

GC

LC

GLC









We can extrapolate the knowledge for the three investigated problem classes to the other ones and construct questionnaires for the other problem classes by analogy. This can be done for properties as well as for their possible values. The next examples illustrate the principle. 1. The property back-mixing applies to the problem class ‘gas’. If we try to use it by analogy for the other problem classes we will find the following. It can be maintained for the problem classes ‘liquid’, ‘gas-catalyst’ and ‘liquid-catalyst’. However, it will be rejected for the problem classes ‘gas–liquid’, ‘liquid–liquid’ and ‘gas–liquid-catalyst’ since these problem classes are not exclusively concerned with back-mixing. The property back-mixing plus contacting 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 problem classes. The problem class ‘gas-catalyst’ incorporates temperature control by catalyst transport, which cannot be copied to other problem classes. This idea leads to the properties that we expect to be important for the other four problem classes. Tables 1–3 give an overview. A black dot denotes that a property applies to a problem class. The tables contain the actual properties as obtained from our expert for the problem classes ‘gas’, ‘gas-catalyst’ and ‘liquid-catalyst’. For the other problem classes, we have presented the expected properties. The actual covering of the other four problem classes with actual property value pairs — i.e. the filling of the matrices — requires an estimated 500 additional judge-

Table 2 Overview specific properties G Sensitivity to dust Catalyst attrition resistant Force in packed bed Thermal recycle catalyst Temperature rise high Temperature rise small Gas–liquid envelop

L





GL

LL

GC

LC

GLC







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Table 3 Overview soft properties

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

G

L

GL

LL













ments by an expert. Currently, we have not done this knowledge acquisition exercise. Our knowledge base about reactor properties is founded 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 the KBS for reactor selection insofar as we want to demonstrate and test the ideas that we have developed for the reactor selection task.

3.3. Knowledge about chemical components The physical properties of the components involved in the selection problem such as their molecular weights must be known. In the current prototype system this is just a simple database that contains all the relevant properties. In a full system, this part could be replaced by a module assessing data from the usual (commercially available) property sources.

4. The knowledge-based system

GC

LC







scope of the KBS as sketched in Section 1.2. The classification of a reactor selection problem is based on the phases (gas, liquid, solid as a catalyst) that must be present to realise the reaction. A more precise definition is, the classification of reactor selection problems is based on the minimum number of phases that must be present to achieve conversion. The phase in which a reaction occurs should always be present and of course the heterogeneous catalyst must be present when required. However, as an additional requirement, it should be possible to obtain a reasonable level of conversion. For example, if a gaseous component is only sparsely soluble in a liquid phase where reaction occurs it is impossible to achieve a reasonable level of conversion without the presence of a gas phase. So, the minimum number of phases for this case is one higher. This shows that the minimum number of phases is also dependent on solubility and stoichiometry of the components involved in the reaction network. A problem class with two fluid phases should only be chosen if this is required from the perspective of solubility and stoichiometry.

4.1. Collect the fixed input As a first step the chemical engineer has to provide all basic information that is needed for the reactor selection problem. This includes among others, the problem class; the chemical components involved in the process; the objective of the process and the reactions with their properties. The chemical engineer can use various sources for this information such as, information obtained from experimental work, from literature or company owned knowledge sources.

4.1.1. The problem class The class of the reactor selection problem is an important one. The selection of a reactor is impossible without knowledge of the problem class. The problem class also determines whether the problem is within the

GLC

Fig. 4. Example of input specification.

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4.1.2. Components and objecti6e The chemical components involved in the process and the objective of the process must be known. The chemical components involved in the process are simply the components that exist, a set of relevant components. The objective of the process is described by two sub-sets, one set containing the components in the feed streams to the overall process and one set containing the desired product(s) of the process. 4.1.3. Reactions and reaction rates The chemical engineer has to provide descriptions of all reactions that might occur in the chemical process with their reaction kinetics. This description encompasses. 1. The reversibility; is the reaction reversible or irreversible. 2. The stoichiometry. 3. The heat of reaction. 4. A rate equation of the forward reaction; reaction orders for the components involved and an activation energy. 5. A similar rate equation of the backward reaction. Of course this rate equation is only required for reversible 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 input The following example is taken from a kinetic paper (Li, Wang & Chang, 1993). The input for this reactor 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 set of strategic notions, a kind of profile for the reactor. Examples of strategic notions are, a qualitative notion of the desired operating temperature and certain notions for the phase in which reaction occurs, such as (by example), mixing/staging, selective removal of a component, high or low concentration of a component and a contacting strategy between the phases, co-/countercurrent. The strategic notions can only be derived, when it is possible to make a desired product, given, feed components, desired products and reactions. In addition the performance criteria, conversion and selectivity, should be unambiguously defined, for this reason we introduced small reaction patterns (SRPs). The input specification given below contains two reactants and two desired products, that can be used to generate performance definitions, this results in four ways to

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view the reaction network, four SRPs. One of these SRPs is: phenol(reference

component)

(+ methanol)

R1

“ o-cresol(product)(+ H2O) We will use this SRP in the worked example of Section 5. In (Jacobs & Jansweijer, 2000) we describe a five-step knowledge-based method for the automatic derivation of a reactor profile, including a detailed description of the SRPs.

4.3. Collect 6ariable input The chemical engineer has to provide the variable input. The variable input is the information about the feed streams to the reactor, the phase of each feed stream, as well as the flow rate and mole fractions of the components in each of the feed streams. In order to provide this the chemical engineer has to interpret the information from the flow sheet. Sometimes the engineer can only come near to a good initial estimate. Anyhow he is supported by the strategic notions derived in the previous task, determine-a-profile-of-thedesired-reactor (Section 4.2). This knowledge directs the chemical engineer to a proper selection of, for instance, desirable concentrations of feed components. Since at this stage the system has knowledge about the problem class and the reactions, it can perform some consistency checks on the variable input provided. For example the phases of the feed streams should match the known problem class and each of the known feed streams has to be covered.

4.4. Deri6e and apply hard features of the process Now the system has enough information to start the selection of a reactor. This is done by sieving out reactors that are less promising according to known properties of these reactors. The initial list of reactors is the list that belongs to the problem class of the chemical process (Section 3.1). So this is the first selection made. The selection process continues with this large number of possible reactors within this problem class and in a number of steps reactors are rejected on the basis of what is known about the required reactor processes. This continues until one or a few reactors are left over, or until all information about the desired reactor processes has been used. The system makes a distinction between, in the first place, so called hard features; properties of the reactor and the process that have to meet by necessity. Secondly the system applies properties specific to certain reactor types and finally it applies properties that can be compromised: so called soft properties. The latter two will be discussed in the

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next section (Section 4.5). We will first discuss the application of the hard features. The hard features of the chemical process are described in Section 3.1, scale; heat; and catalyst replacement. The values on these properties of the processes are derived from what has become known about the process, 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) as well as from information obtained by additional questions to the user. The properties can be multi-valued. For example, when a process requires cooling, several cooling agents might be suitable and when a process allows adiabatic operation it might be advantageous to use a non-adiabatic reactor although it is not a necessity for the process. So in this case the hard feature heat will at least contain the values adiabatic and non-adiabatic. The inference is a data abstraction step (Clancey, 1985). An example of a knowledge rule is, the hard feature heat has the value adiabatic when, (1) cooling is not necessary and (2) heating is not necessary (definitional abstraction): IF THEN

cooling-not necessary, heating-not-necessary hard-feature-heat (adiabatic)

(1) (2)

Fig. 5. Abstraction and sieving.

exists for a reactor having the value ‘very high production’. A reactor can have multiple values for a hard property. For example, a reactor that is applicable at any scale will possess the whole range of values, from ‘very low production’ to ‘very high production’. This represents the fact that the reactor is not sensitive to the scale of the process. Only one of these values has to match to satisfy the hard feature scale. The order in which the hard features are applied is, scale; heat; and; last, catalyst replacement when this is appropriate. The set of reactors that is left after application of the hard features will be subjected to more versatile selection criteria by the next task, applyspecific-and-soft-properties.

4.5. Apply specific and soft properties A second example is, the hard feature catalyst replacement has the value continuous regeneration of the catalyst using a very short residence time for the catalyst when, (1) the catalyst deactivation time is obtained; and (2) the deactivation time is shorter than 10 s (qualitative abstraction): IF

THEN

deactivation-time-catalyst (DTC) DTCB10 hard-feature-catalyst-deactivation (continuous (veryshort)

(1) (2)

These property values are used to make a coarse initial selection by rejecting reactors that are certainly inappropriate. A reactor is rejected if the hard feature of the process and the hard property of the reactor do not match. The inference is a match and sieve step. See Fig. 5 This selection of an initial set of reactors is best explained by an example. Suppose the production of a few tons a year is required. The task derive-hard-features does the abstraction step, resulting in the value ‘very low production’ for the hard feature scale. A match exists for a reactor that has the value ‘very low production’ for the hard property scale. No match

After the crude selection by application of hard properties more versatile selection criteria are used, specific properties; and soft properties. A specific property describes a constraint. These constraints cannot be compromised, they are strictly required. Specific properties do not apply to every reactor in a problem class, they are specific for one or a few reactors. Examples of specific properties are given in Section 3.1. A soft property describes a reactor property that does not have the character of a constraint that must be satisfied, on the contrary, a compromise is possible. A less desired value for one soft property can be compensated by outstanding values for other soft properties. The method that we have developed for the application of specific and soft properties of the process is designed with the following in mind. 1. The method is flexible. It is able to handle all selection problems, no matter which, or how many reactors are left. 2. It is possible to follow the progress of the selection process and to try alternatives. 3. Not every selection problem can be solved on the sole basis of the information and knowledge available, which results in an impasse. Impasses are recognised.

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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 that compares two reactors in which a conclusion is drawn by interpretation of results obtained from rigorous mathematical modelling, we could include this knowledge in the method. We can even integrate our system with a mathematical simulation environment that computes outcomes of certain design decisions that are now asked to the chemical engineer. This opens the way to a hybrid system involving a reasoning process providing fundamental insight in the chemical process and design calculations providing quantitative results but no fundamental insight. The method works on a choice matrix. The choice matrix represents the reactors that are left and the reactor properties that can be used for selection. In general not every reactor property can be used, only those properties that have discriminative power are accepted in the choice matrix. A specific property has discriminative power when it does not apply to every reactor in the choice matrix and has not already been applied. A soft property has this power when it does not have the same set of values for every reactor in the choice matrix. An example of the choice matrix is given below, see Table 4. A black dot denotes that a specific property applies to a reactor and an upper case letter represents a property value. For example, reactor 1 possesses two specific properties Sp1 and Sp3, and soft property two of this reactor (So2) contains the property values D and E. Progress of the selection process after rejection of one or 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 preserved. So, the set of reactor properties useful for selection is not fixed in advance. And on top of it, this set changes as the selection process progresses. The selection process can be described as the sequential construction of new reduced choice matrices by matching properties of reactors to the desired chemical process, until a matrix results with only one reactor, or with some reactors but no more properties to be apTable 4 Example of the choice matrix Specific properties

Reactor1 Reactor2 Reactor3

Sp1

Sp2





Soft properties Sp3

So1

So2

So3



A B C

D, E D, E E

F F, G G



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Fig. 6. The search space.

plied. This process can be seen as a search through a search space where the top node stands for the initial choice matrix that contains all reactors that are left after the task apply-hard-features. This search space is expanded through selection steps that can be made and ends where final outcomes are found (Rich & Knight, 1991). This search space is examined with a search strategy that prefers selection steps that are as selective as possible and that can be made without intervention from the chemical engineer (such as a question that is being asked). It stops when it has come to an answer, but the chemical engineer has full control to let the system explore other parts of the search space. He can, for instance, select any node that contains more than one reactor and select a possible selection step. Such a selection step can consist of an intervention from the chemical engineer where he provides new information, or makes an assumption or where he ‘manually’ (for possibly private reasons) eliminates one of the reactors. Such new information and such assumptions are valid only for that part of the search-space that springs from the choice matrix where they are actually made. This makes a ‘what-will-happen-if’ type of reasoning possible. In general only a small part of the complete search space is searched through. The searched part is represented (or administered) as a search tree. The leaves of this tree stand for possible solutions (choice matrices with one or some reactors and all properties applied). The paths through the tree represent the rationalisations for the answers found. For instance Fig. 6 represents the search space at a moment where nodes 4, 5 and 6 all represent possible solutions. Tracing back and reading the labels along the arrows back to node 1 from a particular end-node gives a description of how this conclusion was reached.

4.5.1. Selection steps The search tree is expanded by applying selection steps. The selection steps refer to either specific or soft properties and both types of steps can be applied with or without additional questions to the chemical engineer using the system. Furthermore, a step allowing ‘manual’ elimination of a reactor is possible, (see above).

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Specific properties describe constraints that cannot be compromised. Therefore, they can be used to eliminate reactors. Selection based on specific properties goes in three steps. 1. A specific property is taken from the choice matrix. 2. The question whether or not this property is satisfied is answered. For a silent step, this involves only available information. In the other case additional questions are allowed. 3. The specific property is used to create a new choice matrix. Reactors that do not comply with the specific property are eliminated, resulting in lesser rows. If all reactors satisfy the property the number of rows in the choice matrix remains unchanged, but the number of columns changes in all cases since, once a specific property has been applied, it loses its discriminative power.

expresses the goodness of the fit between a value on a reactor property and a strategic notion from the task determine-a-profile-of-the-desired-reactor, is expressed by a number. In our definition lower numbers stand for a better fit. The actual range is unimportant; the numbers are used only to rank reactors on this property. The knowledge used in this ranking process has the following characteristics. 1. Ranking knowledge often refers to a combination of, on the one hand, a value on a soft property of a reactor and on the other hand a strategic notion as found in the profile for the desired reactor as described in Section 4.2. 2. Ranking knowledge is not exhaustive, it is not always possible to find a ranking for each soft property in the choice matrix.

The property that is taken from the list (step 1, above) is initially, in silent mode when steps are tried without asking questions to the chemical engineer, the first property found in the list. And the next property is tried automatically when this property can not be proven (step 2). In non-silent mode, when questions are allowed, the chemical engineer chooses the property from the list or refuses the question, in which case the selection step fails. Later, the chemical engineer can reconsider his choice when the strategy enters the ‘user driven’ level, see the search strategy below (see Section 4.5.2). Soft properties do not have the character of a constraint that must be satisfied, they can be compromised. A reactor doing badly on one property can be preferred over others if it performs outstandingly on other properties. Each property represents just one possible view at the reactor selection problem. Hence soft properties cannot be dealt with in isolation; all soft properties in the choice matrix are taken into account. This may result in conflicts or impasses, meaning that one reactor is preferred for one reason, while another one is preferred for another, different reason. Impasses have to be dealt with in a special manner, discussed later. The soft properties are used in a ranking process, we rank the reactors in the choice matrix. Ranking is done for each soft property, by judging for each reactor the desirability of its score on this property for the chemical process under study. So, for example, if a reactor has scored the value plug flow on the property back-mixing of gas, while the task determine-a-profile-of-the-desiredreactor resulted in a profile including the strategic notion, ‘plug-flow of the gas phase’, then this reactor is desired from the perspective of the property back-mixing of gas. The score of a reactor on a property is a result from the knowledge acquisition process (Section 3.2). The desirability, which in this example

4.5.1.1. Strict predominance. If there is no conflict, i.e. if the ranking of reactors on the soft properties results in one strictly best, or one strictly worst reactor, then we can simply choose the best or eliminate the worst reactors. This is done in the following two-step way. 1. The soft properties that represent an important effect are identified, all soft properties in the choice matrix are potentially important. The important properties are identified in a three step process. 1.1. Identify the useful soft properties, these are properties for which a ranking is found and that do not have the same level of desirability (expressed by a number) for every reactor in the choice matrix. Non-discriminative properties are ignored. 1.2. Inform the chemical engineer about the softproperties for which no ranking is found. This can happen, due to the fact that ranking knowledge is not exhaustive. It is the chemical engineer’s choice whether or not the selection step is pursued. 1.3. Obtain important properties. The soft properties that represent an important effect are isolated from the useful properties. For the time being every useful property is considered important. But this can be (and probably needs to be) refined (Section 5.3). 2. The important properties are used to create a new choice matrix. The principle of strict predominance is applied to obtain this matrix (Russell & Norvig, 1995). Strict predominance describes two situations, best from any perspective; and worst from any perspective. An example, using two properties is shown in Fig. 7. In the first example one reactor is ‘strictly best’, while in the second example one reactor is ‘strictly worst’ from any perspective.

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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 properties and there is no ‘strictly best’ or ‘strictly worst’ reactor, then we cannot simply eliminate reactors, there is an impasse. In this case, we divide the reactor selection problem into two (or more) sub-problems, resulting in two (or several) ways in which the principle of strict predominance is denied. Each of such a sub-problem represents a viewpoint where one soft property is assumed to be more important than the others. This splitting involves the following steps. Strict predominance fails; there is a real impasse. The system ‘proves’ that there is not one strictly best or worst reactor. The important properties (see 1.3 above) are used to split the choice matrix into several new matrices, each matrix representing a sub-problem with a set of reactors belonging to a particular point of view, a best reactor and all reactors behind. A splitting example is shown in Fig. 8. Note that it is common for some reactors to end-up in more sub-sets. After a selection step that results in a division of the original problem into some new sub-problems, all those sub-problems are searched through independently.

4.5.2. The search strategy Progress through the search space depends on the strategy for proposing selection steps. We have the opinion that first the system should guide the user to a solution and next the user should be allowed to investigate alternatives. This is realised by a strategy, which contains a ‘guiding’ level and a ‘user driven’ level. The ‘user driven’ level is subordinate to the ‘guiding’ level, it is only used when the ‘guiding’ level fails to propose a step. The ‘guiding’ level largely determines the behaviour of the system. We have used the following strategy 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. Elimination reduces the number of reactors, it is an effective step for reactor selection. Splitting does not reduce the number of reactors. The sub-problems are smaller but the total number of reactors is not reduced. 3. It is preferred to try a step without questions to the chemical engineer. This minimises interaction with the user and also the number of assumptions that have to be made. 4. Each selection step is tried only once. Thus the selection process at the ‘guiding’ level is finite. A survey of all steps is provided in Table 5. It also reflects the sequence in which steps are proposed by the ‘guiding’ level, for a selection problem that can be represented by a choice matrix containing both specific and 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 the system about why it reached this conclusion and to try alternatives. Explanation about the reasons for the elected reactor are generated from the search path. Alternatives can be tried. To do so the chemical engineer chooses the node and the selection step to be applied on this node. In this way the consequences of alternative steps as well as the consequences of different information on steps that have already been tried can

Fig. 8. Splitting strategy.

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Table 5 Survey of steps Step name

Principle

Eliminate-on-the-basis-of-specific-properties-without-questions Eliminate-on-the-basis-of-soft-properties-without-questions Eliminate-on-the-basis-of-specific-properties-with-questions Eliminate-on-the-basis-of-soft-properties-with-questions Eliminate-a-reactor-manually Split-on-the-basis-of-soft-properties-without-questions Split-on-the-basis-of-soft-properties-with-questions

Check constraint Strict predominance Check constraint Strict predominance Chemical engineer interferes No strict predominance No strict predominance

be investigated. At the ‘user driven’ level, the chemical engineer can choose any node containing more than one reactor and any step that applies to this node. The selection process stops when the user decides that he does not want to investigate any other alternative. Each of the end-nodes describes a solution that is acceptable. A solution can consist of one or a few reactors. In the last case, the system could not find a reason to prefer one reactor above another one with the information given. Further refinement of the answer in those cases could probably (but not for sure) be obtained by answering more questions posed by the system.

methanol and H2O in the molar ratio 1:6:1. The total flow is 100 mole/s. These values should be regarded as an initial guess, since the variable input is subject to change. The property data encompasses only the molecular weights of the components involved.

5. An example of the selection process In this section, we describe the behaviour of the system in the context of a concrete example, the manufacture of ortho-cresol and 2,6-dimethylphenol (2,6-DMP) by catalytic vapour-phase methylation of phenol. First the necessary input is collected, and then, secondly, this information is used to select a reactor.

5.1. Input and determination-of-the-profile-for-thereactor The necessary input: problem class, components, reaction 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 the desired reactor, according to the method described in (Jacobs & Jansweijer, 2000). The reaction network can be viewed in four ways, four SRPs are derived (Section 4.2). The chemical engineer has to choose one of these SRPs at the moment the system requires results from the task determine-a-profile-of-the-desired-reactor, when the system ranks the soft properties. We will not present the whole list of strategic notions for each of the four SRPs, instead the notions of the chosen SRP will be discussed at the moment the SRP is selected. The variable input is concerned with the feed stream to the reactor. The composition of the feed stream is obtained from the kinetic paper, it contains phenol,

5.2. The reactor selection process The problem class of this example — gas-catalyst — possesses three hard features, scale; heat; and catalyst replacement. Scale, the system derives the value of ‘semi-fine’ for the hard feature scale. It estimates the production in tons per year based on the variable input and the SRPs. The value of ‘semi-fine’ comes from the characteristic rule, ‘the scale is semi-fine when the production is larger than 5000 and smaller than 100 000 tons/year’. Heat, deducing the hard feature heat requires a value for the adiabatic temperature change. The kinetic paper comprises results of adiabatic reactor simulations, the maximum temperature rise is 67 K at a feed temperature of 673 K and the minimum temperature rise is 6.7 K at a feed temperature of 743 K. The average value is supplied. For the hard feature heat a set of values are obtained. 1. Regenerative, the adiabatic temperature change is positive, so regenerative heat-exchange can be applied. 2. Adiabatic, both cooling and heating are not necessary. The adiabatic temperature change is smaller than the temperature interval described by the temperature window. 3. Non-adiabatic with the use of one of the following heat transfer media, flue gas; water vapour; molten metal; carbonate melt or nitrate melt. Although cooling and heating are not essential a non-adiabatic reactor type can be applied. Having control over the temperature level in the reactor might be advantageous. The given heat transfer media cover at least half of the temperature window. 4. Catalyst transport, the catalyst is circulating, which involves just reaction or reaction in combination with regeneration and/or heat-exchange. Combining reaction with regeneration and/or

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

heat-exchange provides a means to control the temperature level. Circulation involving just reaction, is the adiabatic variant. Catalyst replacement, the hard feature catalyst replacement requires information concerning the deactivation time of the catalyst. The chemical engineer provides the presumption that the deactivation time of the catalyst is 1 year. This results in the values discontinuous (replacement easy or difficult). The proof encompasses the rule, ‘catalyst replacement has the value discontinuous when the deactivation time is longer than 5 days’.

reactors rejected are the co-current moving bed and the cross-current moving bed reactor. The energy balance for these reactors relies on transport of solids not involving circulation, so again there is no matching value. Catalyst replacement, on the basis of the hard feature catalyst replacement, possessing the values discontinuous replacement easy and discontinuous replacement difficult, seven reactors are rejected. These reactors involve continuous catalyst regeneration, such as a ‘dilute phase riser with regeneration’ and a ‘circulating fluidised bed with regeneration’.

The initial set of reactors for the problem class gas-catalyst incorporates 30 reactors. The abovederived features of the chemical process make the following coarse selection possible. Scale, the value semi-fine for the hard feature scale is not selective. Every reactor in the problem class gas-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 the hard feature heat. The first reactor is named ‘fixed bed reactor with heating or cooling elements’, this reactor relies on boiling water as a cooling agent. The hard feature heat does not include the value non-adiabatic-boiling-water, so no match is found. The other

This coarse selection rejects ten reactor types. The 20 remaining reactor types are subjected to the more versatile selection methods, discussed in Section 4.5. These 20 reactors form the starting set, named node1 in the following. Four steps are tried without interaction with the chemical engineer, see Table 6. The steps are proposed and tried in the order in which they are listed, as explained below. Fig. 9 presents the resulting selection path. “ Eliminate-on-the-basis-of-specific-properties-without-questions, tried on node1. The adiabatic temperature rise is greater than 15 K, which is a necessity for feed to effluent heat-exchange. The correspond-

Table 6 Steps tried without interaction Selection step name

Tried on node

Result

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

Node1 Node2 Node3 Node3

node2 node3 Failure Failure

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

“

“

ing specific property is satisfied, hence no reactors are eliminated. Although no reactors are eliminated the consequence of this step is the elimination of this selection criterion. Eliminate-on-the-basis-of-specific-properties-without-questions, tried on node2. The reverse flow reactor, applying the principle of direct heat-exchange, is inappropriate when the adiabatic temperature rise is greater than 15 K. The specific property is not satisfied so the ‘reverse flow reactor’ is eliminated. The consequence of this step is elimination of a reactor as well as elimination of a selection criterion. Eliminate-on-the-basis-of-specific-properties-without-questions, tried on node3. This step fails, checking whether or not a specific property is satisfied requires additional information from the chemical engineer, which is initially not an option. This holds for all specific properties that are left in the choice matrix of node3.

“

Eliminate-on-the-basis-of-soft-properties-withoutquestions, tried on node3. This step fails since some soft properties in node3 can not be ranked. Because the system is in its initial, non-interactive mode, it can not ask the chemical engineer permission to go ahead with only a few rankings available.

The explanations of the two successful steps given above are generated by interpretation of the proof trees of these steps, whereas the explanations of the two failed steps are based on an understanding of how the system works. Fig. 9 shows the successful steps so far. In this search tree, we show only the successful paths and not the failing ones (such as those leading to failure, starting from node3). The system, however, administers those trials such that these failing selection steps are not tried again later. The system has run out of possibilities to go ahead without interaction with the user (see Table 5). It goes-

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ahead in question mode. In the selection process that follows, proceeding from node3, 46 selection steps are considered by the system. Only ten of these are successful. Fig. 10 shows the resulting selection path. For the sake of clarity all failing steps are not shown, nor discussed in what follows. Selection steps leading to a dead end are explored by the system since it investigates all alternatives in a systematic way. “ Step node3 “node4, eliminate-on-the-basis-of-specific-properties-with-questions. The chemical engineer is asked to choose a specific property that the system should deal with next. Four specific properties are available addressing the following problems, sensitivity to dust; attrition resistance catalyst; force in packed bed; and thermal cycle catalyst. We assume that the engineer chooses sensitivity to dust. Assume he specifies that the feed stream is free of dust, so the specific property is satisfied and no reactors are eliminated. “ Step node4 “ node5, eliminate-on-the-basis-of-softproperties-with-questions.The selection step starts with ranking, as all selection steps based on soft properties do. This leads to the following questions. 1. The chemical engineer is asked to specify a SRP to focus on. We assume that he selects the SRP describing the production of o-cresol from phenol: phenol(reference

component)

( +methanol)

R1

“ o-cresol(product)( +H2O) This SRP was classified as series. The temperature strategy for this SRP is, low temperature for both; the case that the reactor content is mixed; and the case that the reactor content is staged. The derived backmixing strategy was, plug-flow of the gas phase and selective removal of o-cresol from the gas phase (strategies resulting from the task determine-a-profileof-the-desired-reactor). The details of the classification of SRPs and of the derivation of strategies are discussed in Jacobs and Jansweijer (2000). 2. We assume that a question concerning the residence time of the gas is not answered by the engineer, so the selection step must be solved without this information. “ The choice matrix contains nine soft properties. For three properties a ranking is found, back-mixing gas; development; and pressure drop. Ranking of the properties development and pressure drop is very simple. A low development effort is preferred, as are the lower values for the pressure drop. Ranking of the property back-mixing gas uses the strategic notions derived for the SRP that has been chosen. The back-mixing strategy for this SRP comes with the strategic notion plug-flow of the gas phase, so reactors approaching plug-flow are preferred. The rankings of the other six properties remain unknown, back-mixing heat; residence time gas; catalyst sizes and shapes; external transfer catalyst; catalyst volume fraction;

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and catalyst residence time distribution. This conclusion is only really sound for back-mixing heat and residence time gas. The other properties remain unknown simply because no ranking knowledge has been supplied. None of the properties for which a ranking is found possesses the same number of desirability for every reactor, so they are all useful. The chemical engineer is informed about the unknown properties and we assume that he decides that the selection step should be tried anyway. One reactor appears to be the strictly predominant, the monolith reactor. Note that this conclusion is reached on the basis of only three properties, back-mixing gas; development; and pressure drop. The system now has found an answer, the monolith reactor. The chemical engineer can continue to use the system to explore other possible solutions. For the example we assume that he chooses to go back to node4 but this time to answer the question about residence time that he previously did not want to answer. This leads to the following. “ Step node4 “ node6 and node7 and node8, split-on-thebasis-of-soft-properties-with-questions.The selection step again starts with ranking. Again the SRP describing the production of o-cresol from phenol is chosen, but in contrast to the previous situation the question concerning the residence time is not refused, a value of 90 s is specified. A ranking is found for the following properties, back-mixing gas; development and pressure drop as before; and residence time gas based on the information supplied on this item. Ranking of the residence time gas involves the following values, very-short; short; moderate; and long, each value representing a time interval. The specified residence time fits within the interval moderate, so ‘moderate’ is preferred. The SRP that is chosen has been earlier classified as ‘series’. This means that the residence time has to be not too long. Therefore, the value ‘long’ is considered worst and ‘short’ is preferred above ‘veryshort’. The chemical engineer is informed that five properties remain unknown, but we assume that the selection step is pursued. The choice matrix, node4, is split in three sub-matrices, node6, node7 and node8, using the properties, back-mixing gas (B); development (D); pressure drop (P); and residence time gas (R), see Table 7. The numbers stand for the ranking of properties, lower numbers representing preferred values and the black dots indicate that a reactor applies to a node. Table 7 is a four-dimensional instance of a splitting step as shown in Fig. 8, which explains the splitting step using a two-dimensional example. “ Step node8 “ node9, eliminate-on-the-basis-of-soft-

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Table 7 Step node4 “node6 and node7 and node8, no strict predominance Reactors in node4

Ranking B

D

New nodes P

R

6

7

8

1 2 3

Simple fixed bed reactor, adiabatic Fixed bed reactor with combustion zone Radial flow reactor, out in

1 1 1

1 1 2

3 3 2

1 1 1



6 7 10

Multitubular fixed bed reactor Tubular reformer Fluidised bed reactor, adiabatic bubbling

1 1 6

1 1 2

3 3 2

1 1 1



12 13 14

Simple fixed bed reactor with large recycle, non-adiabatic Simple fixed bed reactor with external feed to effluent heat exchange Multitubular fixed bed reactor with internal feed to effluent heat exchange

4 1 1

1 1 2

3 3 3

1 1 1



18 19 20

Simple fixed bed reactor with large recycle, adiabatic Monolith reactor. Circulating fluidised bed, non-adiabatic

4 1 5

1 1 2

3 1 2

1 2 2

21 22

Simple fixed bed reactor with small recycle, adiabatic Fluidised bed reactor, non-adiabatic bubbling

2 6

1 2

3 2

1 1



25 27

Simple fixed bed reactor with small recycle, non-adiabatic Radial flow reactor, in out

2 1

1 2

3 2

1 1



28

Dilute phase riser, adiabatic

2

2

3

2







29

Dilute phase riser, non-adiabatic

2

2

3

2







30

Circulating fluidised bed, adiabatic

5

2

2

2



properties-with-questions. The property development is not accepted in the choice matrix of node8, because it does not have discriminative power, since all reactors in node8 require a great development effort. So the matrix reduces from nine to eight soft properties. A ranking is found for the properties, back-mixing gas; pressure drop; and residence time gas. The chemical engineer is informed that five properties remain 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-softproperties-with-questions. A ranking is found for the following properties, back-mixing gas; development; pressure drop; and residence time gas. The property pressure drop is not useful, since the same number of desirability is assigned to each reactor in the choice matrix of node7. The chemical engineer is informed

Table 8 Step node8 “node9, strict predominance best Reactors in node8

Ranking B

3 10 14 20 22 27 28 29 30

Radial flow reactor, out in Fluidised bed reactor, adiabatic bubbling Multitubular fixed bed reactor with internal feed to effluent heat exchange Circulating fluidised bed, non-adiabatic Fluidised bed reactor, non-adiabatic bubbling Radial flow reactor, in out Dilute phase riser, adiabatic Dilute phase riser, non-adiabatic Circulating fluidised bed, adiabatic

1 6 1 5 6 1 2 2 5

Node9 P 2 2 3 2 2 2 3 3 2

R 1 1 1 2 1 1 2 2 2





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Table 9 Step node7 “node10, strict predominance best Reactors in node7

Ranking B

1 2 6 7 12 13 14 18 21 25 28 29

“

Simple fixed bed reactor, adiabatic Fixed bed reactor with combustion zone Multitubular fixed bed reactor Tubular reformer Simple fixed bed reactor with large recycle, non-adiabatic Simple fixed bed reactor with external feed to effluent heat exchange Multitubular fixed bed reactor with internal feed to effluent heat exchange Simple fixed bed reactor with large recycle, adiabatic Simple fixed bed reactor with small recycle, adiabatic Simple fixed bed reactor with small recycle, non-adiabatic Dilute phase riser, adiabatic Dilute phase riser, non-adiabatic

that five properties remain unknown. Again we assume that the selection step is pursued using, backmixing gas (B); development (D); and residence time gas (R). See Table 9. Step node6 “ node11, eliminate-on-the-basis-of-softproperties-with-questions. A ranking is found for the following properties, back-mixing gas; development; pressure drop; and residence time gas. The property residence time gas is not useful, since the same number of desirability is assigned to each reactor in the choice matrix of node6. The chemical engineer is informed that five properties remain unknown. We continue the example by assuming that the selection step is pursued using, back-mixing gas (B); development (D); and pressure drop (P). See Table 10.

The system now has found new answers in the nodes 9, 10 and 11. Node9 contains the reactor types ‘radial flow out in’ and ‘radial flow in out’. Node10 contains five reactors, ‘simple fixed bed, adiabatic’; ‘fixed bed with combustion zone’; ‘multitubular fixed bed’; a ‘tubular reformer’; and a ‘simple fixed bed with external feed to effluent heat exchange’. Node11 leads to the same outcome as was found before, the ‘monolith reactor’.

Node10 D

R

1 1 1 1 1 1 2 1 1 1 2 2

1 1 1 1 1 1 1 1 1 1 2 2

1 1 1 1 4 1 1 4 2 2 2 2



Suppose the chemical engineer regards the monolith reactor to be an inferior solution and he wants to see what happens in case this reactor is excluded in advance. In order to investigate this, he goes back to node4 and eliminates there the monolith reactor as one of the possible reactor types. This leads to the new branch in the selection tree. “ Step node4 “ node12, eliminate-a-reactor-manually. The chemical engineer uses the ‘manual’ elimination step, to eliminate the monolith reactor. “ Step node12 “ node13 and node14, split-on-the-basisof-soft-properties-with-questions. The SRP describing the production of o-cresol from phenol has been chosen, but the question concerning the residence time is refused, so the selection step must be solved without this information. A ranking is found for the following properties, back-mixing gas; development; and pressure drop. The chemical engineer is informed that six properties remain unknown, but the selection step is pursued. The choice matrix, node12, is split in two sub-matrices, node13 and node14, using the properties, back-mixing gas (B); development (D); and pressure drop (P), see Table 11. “ Step node14 “ node15, eliminate-on-the-basis-of-softproperties-with-questions. This step is almost equal

Table 10 Step node6 “node11, strict predominance best Reactors in node6

Ranking B

19 20 28 29 30

Monolith reactor Circulating fluidised bed, non-adiabatic Dilute phase riser, adiabatic Dilute phase riser, non-adiabatic Circulating fluidised bed, adiabatic

1 5 2 2 5

Node11 D 1 2 2 2 2

P 1 2 3 3 2



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Table 11 Step node12 “ node13 and node14, no strict predominance Reactors in node12

Ranking B

1 2 3 6 7 10 12 13 14 18 20 21 22 25 27 28 29 30

Simple fixed bed reactor, adiabatic Fixed bed reactor with combustion zone Radial flow reactor, out in Multitubular fixed bed reactor Tubular reformer Fluidised bed reactor, adiabatic bubbling Simple fixed bed reactor with large recycle, non-adiabatic Simple fixed bed reactor with external feed to effluent heat exchange Multitubular fixed bed reactor with internal feed to effluent heat exchange Simple fixed bed reactor with large recycle, adiabatic Circulating fluidised bed, non-adiabatic Simple fixed bed reactor with small recycle, adiabatic Fluidised bed reactor, non-adiabatic bubbling Simple fixed bed reactor with small recycle, non-adiabatic Radial flow reactor, in out Dilute phase riser, adiabatic Dilute phase riser, non-adiabatic Circulating fluidised bed, adiabatic

to the earlier presented step from node8 to node9. The difference is that now we find rankings only for the properties back-mixing gas and pressure drop. The selection step, however, results in the same reactors as present in node9 (Table 8), ‘radial flow out in’ and ‘radial flow in out’. The system continues to investigate the other possibility that was left after the previous splitting step that resulted in node13. “ Step node13 “node16, eliminate-on-the-basis-of-softproperties-with-questions. This step is almost equal to 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 every possible reactor in this node has the same desirability. The chemical engineer is informed that six properties remain unknown, but the selection step is pursued based on the properties, back-mixing gas; and development. The system finds five possible reactor 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 the system, he gets presented the gradually expanding search tree. The selection steps are displayed one at a time. All intermediate states are shown, which gives the selection process a self-explanatory quality. In this article, we have presented only two of the intermediate search states, the intermediate state in Fig. 9 and the

1 1 1 1 1 6 4 1 1 4 5 2 6 2 1 2 2 5

New nodes

D 1 1 2 1 1 2 1 1 2 1 2 1 2 1 2 2 2 2

P 3 3 2 3 3 2 3 3 3 3 2 3 2 3 2 3 3 2

13

14



state at this moment in Fig. 10. The other states can easily be inferred if one knows that nodes with a higher number are added later in time. To summarise; the system solves the selection problem represented by node4 in three different ways. After that the system has found the ‘monolith reactor’ in node5 the chemical engineer exploits the opportunities offered by the system to explore other paths to see what happens. In this example the chemical engineer has applied the following ‘what-if’ scheme. 1. Step node4 “ node5, specify an SRP but refuse the question concerning the residence time, resulting in one reactor in node5. 2. Step node4 “ node6 and node7 and node8, specify the same SRP and provide a residence time, resulting in the nodes 9, 10 and 11, each with one or a few possible reactor types. 3. Step node4 “ node12, eliminate from the contents of node4 the monolith reactor. Next, supply the same information as in step node4 “ node5, resulting in step node12 “ node13 and node14, and finally in

Fig. 11. The modules in READPERT.

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nodes 15 and 16, each with some possible reactor types. The solution node11 contains only the monolith reactor and the contents of the nodes 10 and 16 are identical, as are the contents of the nodes 9 and 15. The chemical engineer can draw the following conclusion from this ‘what-if’ scheme, deleting the monolith reactor on the basis of my own preference, also eliminates residence time as an important consideration.

5.3. Discussion of the selection example The example shows that the task derive-and-applyhard-features is capable of making a coarse selection. In this case ten reactors are rejected, but in general the number depends heavily upon the severity of the hard features derived for the problem considered. Examples of more restrictive hard features are given in Jacobs, 1998. In the current implementation of the system, all soft properties that are useful are considered important as well. This could be improved. Adding knowledge that weights the useful soft properties relative to one another enables the system to do selection steps based on a smaller number of soft properties. A smaller number of soft properties will result in more strict-predominance steps and less impasse steps, since the chance of a property disturbing strict-predominance is reduced. This will also change the current situation, that a splitting step is always followed by elimination steps. Also it will enable to foresee the result of the elimination steps, by examination of the property ranking, used in the preceding splitting step. We anticipate that the weighting knowledge will be quite complex. Therefore, we like to point out that the system is already advantageous when this knowledge is reduced to a single question to the chemical engineer in which he specifies the properties that he considers to be important, for the next step to be tried. The chemical engineer can explore several ‘what-if’ scenario’s each representing different sequences of important properties and see if and how the result is sensitive to this.

5.4. A comparison to READPERT In this section, a comparison to READPERT is made, which is also a system for advice concerning the selection of technical reactors. In READPERT, the whole task reactor-selection is divided into four subproblems, general-reactor-type, operating-conditions, heat-transfer-equipment and technical-reactor, which can mostly be solved independent from each other. These sub-problems are collected into four different modules, see Fig. 11.

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The modules are described below. 1. The first module deriving the general reactor type, should not be interpreted as a module which proposes a concrete technical piece of equipment, instead it denotes a specific type of back-mixing behaviour (Dro¨ge, Schembecker, Westhaus & Simmrock, 1994). 2. The module operating-conditions provides recommendations for the most important operating conditions of the reactor, involving parameters as, temperature profile within the reactor; need for recycle streams; qualitative temperature levels at beginning and end of the reactor; qualitative concentration levels for the reactants; need for inerts; etc. 3. The module heat-transfer-equipment addresses the problem of heat transfer, involving a choice between a wide range of different possibilities and equipment types. A three step procedure is followed. 3.1. Check which kind of equipment is suitable for the problem. The following example is provided, helical coils cannot be used in an agitated vessel if the reaction mixture has a high viscosity and the tendency to encrust. 3.2. Calculate the heat flows for each of the elements being taken into consideration, using short-cut calculations. An element may be used when its maximum flow is higher than needed. 3.3. Select the best elements among the remaining heat transfer possibilities. Equipment costs are considered to allow some estimate of a proper choice. 4. The module technical-reactor tries to determine a suitable technical reactor. An appropriate technical reactor has to satisfy the proposals developed in the previous modules, as well as further criteria of technical relevance. The technical reactors are classified according to the phase of reaction. The phase of reaction is used as a constraint, which reduces the number of solutions drastically (Dro¨ge et al., 1994). The division into four modules is the READPERT counterpart of our top-level task decomposition presented in Fig. 3. The differences will be discussed below. 1. In READPERT, Levenspiel type of reasoning is split, it is allocated to the modules general-reactortype and operating-conditions. Our KBS has only one sub-task that is devoted to this type of reasoning, the task determine-a-profile-of-the-desired-reactor. Thus, we adopt an approach that is closer to that of the chemical engineer, who perceives Levenspiel type of reasoning as a single cluster. Deviations in the problem-solving strategies between the chemical engineer and the KBS are undesired because they will hamper the provision of a clear explanation.

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2. In READPERT, one module addresses the problem of heat transfer, and another module tries to determine a suitable technical reactor. In our KBS the heat transfer problem is not assigned to a separate sub-task. The assignment of different modules to the heat transfer problem and the technical reactor is controversial, since the heat transfer problem and the technical reactor are tightly connected. 3. In READPERT, the choice of the technical reactor is assigned to one module, the module technical-reactor. In our KBS this choice is assigned to the tasks, derive-hard-features, apply-hard-features and apply-specific-and-soft-properties. So, when it comes to the technical reactor, the task decomposition in our KBS is more refined. As a rule, the task decomposition should continue up to a level that allows the user of the system to understand what is happening, that is, at least, distinguishing various aspects, as reflected by notions like, hard features, soft properties, etc. This is critical, since we are dealing with the type of system for which the explanation of the answer is equally important as the answer itself. The literature on READPERT (Dro¨ge et al., 1994; Schembecker et al., 1995a,b) does not provide an extensive explanation of the module technical-reactor, which confirms a too coarse task decomposition. Apart from the differences in the task decomposition, the following similarities and contrasts are observed. 1. The module general-reactor-type does not pay attention to different perspectives. Our KBS generates all perspectives: the SRPs, (Jacobs & Jansweijer, 2000). 2. The module general-reactor-type addresses two constraints of technical nature in addition to Levenspiel type of reasoning, namely, phase of reaction; and mode of operation. The task determine-a-profile-ofthe-desired-reactor in our KBS is exclusively concerned with Levenspiel type of reasoning. Results from Levenspiel type of reasoning represent what is desired, whereas the phase of reaction and mode of operation represent what is required. So in READPERT distinctively different types of knowledge that leads to conclusions with equally different characteristics, are associated in one module. 3. The technical constraint phase of reaction, is the READPERT equivalent of consulting the set of reactors appropriate for the problem class (Section 4.4). 4. The technical constraint mode of operation, is the READPERT equivalent of the hard feature scale (Section 3.1). 5. The module heat-transfer-equipment, can be interpreted as the READPERT equivalent of the hard feature heat (Section 3.1). The steps (b) and (c) of the module heat-transfer-equipment are not covered

by the hard feature heat, since they incline towards design. 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 property that is concerned with a high viscosity and the tendency to encrust. Naturally, this is only of importance, when the level of detail in the grid of the problem class liquid allows individual representation of an agitated vessel having a helical coil. 7. The classification of technical reactors according to the phase of reaction, is the READPERT equivalent of the problem classes. It is not stressed that this classification should be based on the minimum number of phases, and consequently a particular class will not encompass reactors possessing an extra phase in addition to this minimum.

6. Discussion and conclusion This paper presented a knowledge-based method for reactor selection. The method selects a reactor based on explicit knowledge concerning reactor engineering and is inspired on the problem solving strategies of the chemical engineer in industry. The selection method that is used can be seen as an instantiation of a more general selection task. The method has, in variant instantiations, a potential for other selection problems. The knowledge that is used in the reactor selection process is for a large part acquired through interviews with an expert in industry. Although the current system is based on the knowledge of only one expert, we have shown that the approach works. However, further work is needed to validate this knowledge and to extend the scope of the system to other problem classes. A possible improvement of the system is its integration with a system that computes results based on rigorous mathematical modelling. In such a hybrid system we have both, an explicit reasoning process providing fundamental insight in the chemical process and a mathematical method providing precise quantitative results but no fundamental insight. The knowledge-based approach has the capability to derive results based on partial and qualitative information (Jacobs & Jansweijer, 2000). Therefore, it can be used at the early stage of conceptual design, which allow swift feedback to the laboratory, indicating directions for experimental design. Some engineers try to postpone the choice of the reactor as long as possible. They regard this choice as the most predominant decision in process development and consequently they try to postpone the decision, to have the advantage of maximum information. We did not intend the system to be used in this manner, but it

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supports this kind of behaviour as well, because alternative branches can be created. The engineers who like to postpone, only have to interpret these branches as possible selection paths, whereas engineers who want to select look for efficient selection steps. Both groups can obtain useful information from a session with the system. The KBS knows how to do a selection task in general. First it collects the fixed givens and applies them. Then it loops through a sequence of collecting additional more dynamic givens that are applied until no more givens can be used or until the user is satisfied with the result (Fig. 2). This paper describes the reactor selection task as an instance of this general selection task. If we provided the system with the appropriate domain knowledge, it could be used for other equipment selection problems such as, limiting ourselves to chemical process industry, pumps; compressors; heatexchangers; valves; measuring devices; column-packing; and tray-types, etc. These problems are all less challenging than the reactor selection problem and we expect their solution to be simpler.

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(1994). Heuristisch-numerisches Beratungssystem fu¨r die Reaktorauswahl bei der Verfahrensplanung. Chemical Ingineering Techniques, 66 (8), 1043 – 1050. Jacobs, R. (1998). A knowledge-based system for reactor selection. Ph.D. thesis, University of Amsterdam. Jacobs, R., & Jansweijer, W. N. H. (2000). A knowledge-based method for the automatic derivation of reactor strategies. Computers & Chemical Engineering, 24 (8), 1803 – 1813. Jansweijer, W. N. H. (1988). PDP: an artificial intelligence approach to problem solving and learning-by-doing in a semantically rich domain. Ph.D. thesis, University of Amsterdam. Li, K., Wang, I., & Chang, K. (1993). Methylation of phenol to 2,6-dimethylphenol on a manganese oxide catalyst. Industrial & Engineering Chemistry Research, 32, 1007 – 1011. Rich, E., & Knight, K. (1991). Artificial intelligence. New York: McGraw-Hill. Russell, S., & Norvig, P. (1995). Artificial intelligence: a modern approach. Englewood Cliffs, NJ: Prentice Hall. Schembecker, G., Dro¨ge, T., Westhaus, U., & Simmrock, K. H. (1995a). A heuristic – numeric consulting system for the choice of chemical reactors. American Institute of Chemical Engineers Symposium Series, 91, 336 – 339. Schembecker, G., Dro¨ge, T., Westhaus, U., & Simmrock, K. H. (1995b). READPERT — development, selection and design of chemical reactors. Chemical Engineering & Processing, 34, 317– 322. Shadbolt, N. R., & Burton, A. M. (1995). Knowledge elicitation: a systematic approach. In J. R. Wilson, & E. Corlett, E6aluation of human work: a practical ergonomics methodology (2nd ed.). London: Taylor & Francis. Trambouze, P., Landeghem, P., van, H., Wauquier, J. P., & Marshall, N. (1988). Chemical reactors: design, engineering and operation. Paris: E´ditions Technip. Ullmann’s encyclopedia of industrial chemistry, vol. B4 (1992). Deerfield Beach, FL, USA: VCH Publishers.