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A knowledge based system for reactor selection
Computers chem. Engng Vol. 20. Suppl., pp. SI65-SI70, 1996
Pergamon
S0098-1391(96)00038-5
Copyright © 1996 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0098-1354/96 $15.00 + 0.00
A KNOWLEDGE BASED SYSTEM FOR REACTOR SELECTION RALPH JACOBS, WOUTER N. H. JANSWEIJER*, PIET IEDEMA Department of Chemical Engineering, University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV AMSTERDAM, The NETHERLANDS *Department of Social Science Informatics, University of Amsterdam, Roeterstraat 15, 1018 WB AMSTERDAM, The NETHERLANDS Abstract - In this paper a knowledge based system (KBS) for reactor selection is discussed. The objective of the KBS is to select the best reactor for a chemical process of known kinetics. The most important step in developing a KBS is to decide which tasks are needed to solve the problem. The main-task reactor selection is decomposed into sub-tasks, according to a task structure. Reactor selection is an integral part of conceptual design of a chemical process which has following consequences for the task structure. A reactor engineer will first use criteria that apply to all selection problems. Then he tries to identify the important concepts of the current selection problem. For other selection problems the set of important concepts may be totally different. It is argued that reactor selection is a creative task since no predetermined set of important concepts exists that applies to each selection problem.
INTRODUCTION The reactor selection problem can be stated as follows: Given a description of a chemical process and many types of chemical reactors, select the best reactor for the process. So reactor selection is the task of choosing the best reactor from a set of existing reactors. The description of the chemical process includes information on reaction kinetics. A reactor selection problem can be solved when it fits into a problem class known to the KBS. The problem classes are shown in Figurel; a detailed discussion will be given in the section problem classes. This defines the scope of the KBS.
Gas
I ~ E
Liquid
I~
/Gas-liquid
1Liquid-liquid l
~
l Gas-catalyst
Gas-liquid
catalyst Phase in which !11 reaction occurs.
ILILiquid-catalyst Figurel. Problem classes.
Apart from the problem classes some additional assumptions are made to make the problem tractable: • Desired behaviour addresses conversion in terms of the mass balance: a desired component is produced or an undesired component has to be removed. This excludes for example furnaces, for which desired behaviour addresses conversion in terms of the energy balance. • Special reaction domains are excluded, since they require special focusing: e.g. biochemical reactions, photochemical reactions, polymerisation and reacting solids. A computer based consulting system for reactor selection, READPERT, is already available, (Droge, et al., 1994), (Schembecker, et al., 1995a) and (Schembecker, et al., 1995b). READPERT and this work will be compared in the discussion.
KBS AND CONCEPTUAL DESIGN A reactor is an integral part of a chemical process, so the KBS must have a place in conceptual design of chemical processes. Conceptual design of chemical processes can be viewed as a process existing of layers of different complexity. See Tablel. S165
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Layer Flow-sheet Unit Laboratory
European Symposium on Computer Aided Process Engineering--6. Part A Complexity High Moderate Low
Tablel. Layers in conceptual design. The KBS selects a reactor, so it operates at the unit layer. It has to communicate with the two adjacent layers: laboratory and flow-sheet. Since the reaction kinetics are known, no changes will occur at the laboratory layer. Communication with the laboratory layer is therefore simple, input from this layer can be obtained at the start of the program and will not be subject to change. The communication with the flow-sheet layer is more complex, since in the conceptual stage the flow-sheet is still subject to change. As long as the results from reactor selection are unknown, the flow-sheet cannot be fixed, and neither the input from the flow-sheet layer required by the KBS can be fixed. This mutual dependence gives rise to an iterative cycle. Consequently, two types of input to the KBS are distinguished: • Fixed input, which does not change during the selection process, it is obtained from the laboratory layer. Tasks that require only fixed input are dealt with outside the iterative cycle. • Variable input, which may change during the selection process, it is obtained from the flow-sheet layer. Tasks requiring variable input must be placed inside the iterative cycle.
PROBLEM CLASSES The classification of reactor problems is based on the minimum number of phases that must be present to achieve conversion. The phase in which reaction occurs should always be present and naturally the heterogeneous catalyst must be present when required. However, as an additional requirement, a reasonable level of conversion should be obtainable. 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 a necessity from solubility and stoichiometric point of view. In this way the rule: "classification of reactor problems is based on the minimum number of phases", can be maintained. In our system, the determination of the problem class is not performed by the KBS, but the problem class is provided by the user. This is possible, since the reaction phase and the necessity of a heterogeneous catalyst should be derivable from the kinetic paper. Stoichiometry and solubility are already unambiguous at the laboratory layer, at this stage of conceptual design it is easy to identify the relevant problem class. Note that the scope of the KBS has been defined more strictly by the discussion above. The phases depicted in Figurel are the minimum number of phases that must be present in the reactor. The determination of the problem class is more than just providing necessary input. The user is forced to investigate whether or not the reactor selection problem is within the problem solving capabilities of the KBS. When a selection problem does not fit into one of the problem classes, the KBS should not be used. The relevant problem class identified, does not completely fix the phases that will be present in the reactor that is finally selected. On the basis of technical judgement it can be advantageous, although not strictly necessary, to add a phase in addition to the minimum number. This results in a phenomenon named changes in perspective, a reactor that is applicable in two problem classes has two perspectives. An example is given below: • Perspectivel: Packed bed of catalyst particles; the particles catalyse a gas phase reaction. Problem class: Gascatalyst. • Perspective2: Packed bed of inert particles; the particles help to create a hydrodynamic regime. Problem class: Gas.
European Symposiumon Computer Aided Process Engineering---6.Part A
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TASK STRUCTURE The reactor selection task is decomposed into sub-tasks, based on two important strategy notions: • The notion of fixed and variable input, already discussed when positioning the KBS as part of the conceptual design process. • The notion that the program eventually has to reproduce the problem solving strategies of the engineer selecting a reactor. This is not necessary but the problem solving strategy of an engineer is a good starting point. The task structure is influenced by the fact that an engineer will not use all his knowledge about reactors simultaneously. Knowledge that is generally applicable and describing constraints that must be satisfied and cannot be compromised, is applied first. These criteria apply to all selection problems. Knowledge describing constraints that must be satisfied and cannot be compromised, but being specific for one reactor is applied later. Knowledge that does not describe constraints but properties that can be compromised against other properties is also applied later. Based on these strategy notions the following task structure has been designed. Some of the sub-tasks are still complex in which case the sub-task needs to be decomposed further. • Collect-fixed-input • Analysis-reaction-network • Collect-variable-input • Derive-hard-features • Apply-hard-features • Apply-specific-and-soft-properties The tasks are executed in the order in which they are listed, the first two tasks are outside the iterative cycle and the other tasks are placed inside the iterative cycle. Each task will be described below.
DESCRIPTION OF THE TASKS The task collect-fixed-input collects fixed input. This includes: Problem class, components, reactants, products, components in the feed streams to the overall process, etc. The task analysis-reaction-network performs an analysis of the reaction network. A generalised reaction network has been developed. Each reaction network is treated as a special case of the generalised network. The generalised network covers the examples provided by Levenspiel, (Levenspiel, 1972). The task collect-variable-inputcollects variable input. Variable input includes: Streams to the reactor, desired level of conversion, etc. Collect-variable-input is the first task inside the iterative cycle. Note that results from the task analysis-reaction-network are available when the user specifies the variable input. This will support the user and keeps the number of iterations limited. The task derive-hard-features derives the hard features of the reactor selection problem. A hard feature is a feature of the reactor selection problem that must always be satisfied and cannot be compromised. Hard features are derived by abstraction of the fixed and variable input and apply to each reactor within a problem class. The hard features are explained below: • Scale: a measure for the scale of the process. It applies to every problem class. • Heat: a measure for the temperature level in the reactor, the heat duty and the direction of transfer. This hard feature applies also to every problem class. • Catalyst deactivation: a measure for the deactivation time of the catalyst. It applies only to problem classes involving a heterogeneous catalyst. The task apply-hard-features applies the hard features. For each problem class a set of reactors is available. The hard features are used as a 'sieve' to make a first selection from the set of reactors. Reactors satisfying the hard features pass the 'sieve'. A hard feature is satisfied when there is a match for the hard feature and the corresponding reactor property. The reactor properties used for matching with hard features are called hard properties. The other reactors are inappropriate for the current selection problem. The set of reactors passing the 'sieve' is called the hard selection.
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The task apply-specific-and-soft-properties selects the best reactor(s) from the hard selection. Before explaining the selection process, to which the hard selection is subjected, a summary of behavioural goals is given: • The selection process must be flexible, be able to deal with any particular hard selection. • The selection process should not depend on rigorous mathematical models but on weaker models, like orders of magnitude, (Mavrovouniotis and Stephanopoulos, 1988). • The selection process should recognise situations where weaker models fail, necessitating the generation of more alternatives in order to break the 'impasse'. The selection process, to which the hard selection is subjected, uses specific and soft properties: • Specific properties: strictly required not compromised. A specific property does not apply to every reactor in a problem class. • Soft properties: compromised. A undesired value for one soft property can be compensated by a desired value for another soft property. Soft properties apply to every reactor in a problem class. The three behavioural goals given above are met by defining a State Space, in which sensible moves are performed as subsequent steps in the selection process. The initial state is a matrix of the reactors being members of the hard selection and the specific and soft properties, see Figure2. All reactors in hard selection
Specific properties
Soft properties P1 P2 P3
P4
Reactor 1
1 2
V
V
V
V
1
V
V
V
V
V V
V V
V V
V V
Reactor2 Reactor3 Reactor4
j i 12 3 i
Figure2. Example of a matrix and initial state. Two moves are possible: splitting or elimination: • A matrix can be split, resulting in a state containing smaller matrices. Each new matrix will be treated individually, forming a selection problem on its own. • Reactors can be eliminated from a matrix, resulting in a state containing fewer reactors than the original one. When there are no matrices that contain more than one reactor, a solution has been reached. A selection step on the basis of soft properties results in elimination of reactors from a matrix or splitting of a matrix. Only the soft properties that do not have the same value for every reactor can be used in selection. In other words, only certain columns in the matrix are of interest. The selection process will be focused on these columns. The reactors are sorted from desired to undesired, a sort being based on a single property. Atter the reactors have been sorted based on individual properties, it is determined whether the difference in property values represents an important effect. Only the properties that represent an important effect will be used to obtain a move. A reactor having lower values on all important properties than some other reactor is eliminated. This method of elimination of worse options from any perspective is called 'strict predominance', (Russell and Norvig, 1995). Hence, if strict predominance exists, the move is: elimination of the reactor. Figure3 shows the principle. When no strict predominance can be established in the properties representing important effects, this means a state in which the weaker models are failing. The 'impasse' will be broken by splitting, each new matrix representing an individual reactor selection problem. The best reactors that result from these individual problems can only be compared by interpretation of results obtained from rigorous mathematical models or experiments.
European Symposium on Computer Aided Process Engineering---6. Part A
This region dominates over reactorl and 3 ~x
=~
(-R-eactor 1
This region dominates over reactor3 (-~eactor3
SI69
This region dominates over all reactors 7Reactor2
This region I dominates over I reactor3 and 4 ~Reactor4 Ranking of important property B Figure3. Strict predominance.
A selection step on the basis of specific properties is always elimination, since specific properties must always be satisfied. When a specific property cannot be satisfied, all reactors requiring this specific property must be eliminated. Checking a specific property results in a specific question to the user. The user is free to answer questions about specific properties.
EXAMPLES A few examples of individual selection steps are given below, all examples being taken from the problem class gascatalyst: • In fluidised catalytic cracking the deactivation time of the catalyst is less than a second• The hard feature catalyst deactivation is very selective in this case. Only one reactor, a dilute phase riser, will be left after the task applyhard-features. The initial state is a matrix that encompasses only one reactor, the solution is reached instantaneously. • A state in a selection problem that has to find the best reactor for the partial oxidation of a hydrocarbon can be a matrix that contains fixed bed reactors with cooling elements as well as fluidised beds with cooling elements. For partial oxidation both mixing of heat and maintaining plug-flow are often desired. The fixed beds have a low ranking on mixing of heat but a high ranking on plug-flow, for the fluidised beds it is the opposite. This is an example of an 'impasse', that must be solved by splitting. • A selection problem that has reached a state in which a matrix contains two radial flow reactors one operating in mode in-out and one operating in mode out-in, will progress as follows. The only soft property that does not have the same value for all reactors in the matrix is direction of flow, so this will be the important property. In case of expansion the reactor operated in mode in-out will be ranked higher. This is an example of a soft property that gives rise to elimination late in the selection process• • Reactors that rely on forced flow through a packed bed cannot be applied when a feed stream contains dust. This is an example of elimination on the basis of a specific property. A step by step example for the partial oxidation of a hydrocarbon is given below: • The reactors in the problem class gas-catalyst form the set of possible solutions• • The hard features are used as a sieve, the hard feature heat is very selective, scale and catalyst deactivation are not selective. • The first move in the task apply specific and soft properties is described in the second example given above, so two matrices are created that form individual selection problems. • Elimination in the matrix containing different alternatives of fixed bed reactors is done on the basis of other soft properties, for example pressure drop.
DISCUSSION The consulting system READPERT (Droge et al., 1994) encompasses four modules: general reactor type, operating conditions, heat transfer equipment and technical reactor• In the last module, technical reactor, the link between basic engineering ideas and technical reactors is made. This part has a scope that is comparable with our work. Droge puts forward that technical reactors are often chosen on the basis of the designers preferences for certain c~
zo: t3(A),.;
5
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European Symposium on Computer Aided Process Engineering--6. Part A
criteria. The following example is given: Often problems concerning heat transfer are solved first, after that mass transfer problems and eventually selectivity problems, which implies that desirable features from reaction kinetics point of view are subordinate. READPERT, on the contrary, tries to satisfy features in exactly the reverse sequence. However to our view, the criteria sequence should not be predetermined at all (creative). In the work presented here the selection of a technical reactor is done by the task apply-hard-features and the task apply-specific-and-soft-properties. When it is easy to satisfy the hard features the 'sieve' is simply not very selective, the hard selection will still encompass many reactors. The task apply-hard-features always precedes the task apply-specific-and-soft-properties. The character of the hard features makes this possible, the hard features must always be satisfied, cannot be compromised and apply to each reactor within a problem class. The selection process to which the hard selection is subjected, is allowed to develop along its own path. This is guaranteed by the definition of a state space and a strategy to obtain moves. A selection problem can result in a complex path but it is also possible that the final state is reached instantly. The strategy that is used to obtain moves identifies the important features of the current selection problem by investigation of the matrix. For example, features that are desired from the reaction kinetics point of view can be applied early, late or not at all. The possibility for each selection problem to develop along its own path is the fundamental difference between this work and READPERT. Our system tries to act like an intelligent system, some relevant notions are: • Changes m perspective: used to increase the inventory of reactors. The principle of adding a phase in addition to the minimum number of phases is very suitable for reactor selection. It facilitates for example the use of a fluidised bed in the problem class gas for the purpose of mixing heat. • Reduction of trial and error: the selection process does not involve rigorous mathematical models, this is done deliberately. Rigorous mathematical modelling will result in trial and error, trying one reactor after an other. The objective of the KBS is however to reduce trial and error by the usage of knowledge. • Unforced specific questioning: the task apply specific and soft properties prevents to force the user to answer a question about a specific property. A difficult question concerning a specific property can become irrelevant. This happens when the reactor, requiring this specific property to be satisfied, is eliminated on the basis of soft properties. Due to the absence of forced answering of questions concerning specific properties, a selection problem can develop along different paths. • Teaching by backtracking: this flexible path-finding-feature can be utilised to introduce teaching elements in the KBS. It should be possible to backtrack on these paths up to the point where a specific property was applied. The user can specify another value and see how the selection problem develops in this case. In this way the KBS obtains the characteristics of a teaching system.
CONCLUSION The task structure presented will be the framework for building a KBS for reactor selection. Two distinctively different types of input are recognised, which was necessary to fit the KBS into the conceptual design process. An engineer selecting a reactor is faced with a creative task, there is no predetermined set of important concepts, but this does not mean that the task reactor selection is totally unstructured. The selection problem is structured by introducing a task structure, problem classes and three types of reactor properties. Yet creativity is conserved by allowing each reactor selection problem to develop along its own path. The knowledge acquisition process is also structured, the introduction of problem classes and the fact that three distinctively different types of reactor properties are recognised, provides guidance for this process.
LITERATURE Droge, T., Schembecker, G., Westhaus, U., and Simmrock, K.H., 1994, Heuristisch-numerisches Beratungssystem fur die Reaktorauswahl bei der Verfahrensplanung. Chemie-lngenieur-Technik 66, 1043-1050. Levenspiel, O., 1972 Chemical reaction engineering, Wiley International Edition Mavrovouniotis, M. L., and Stephanopoulos, G., 1988, Formal order-of-magnitude reasoning in process engineering. Comput. chem. Engng, 12, 867-880. Russell, S., and Norvig, P, 1995, Artificial Intelligence a Modern Approach, pp. 480-481. Prentice Hall Schembecker, G., Droge, T., Westhaus, U., and Simmrock, K.H., 1995a. A heuristic-numeric consulting system for the choice of chemical reactors. AIChE symposium Series, 91,336-339. Schembecker, G., Droge, T., Westhaus, U., and Simmrock, K.H., 1995b. READPERT - development, selection and design of chemical reactors. Chemical engineering and processing 34, 317-322.
6
Pergamon
S0098-1391(96)00038-5
Copyright © 1996 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0098-1354/96 $15.00 + 0.00
A KNOWLEDGE BASED SYSTEM FOR REACTOR SELECTION RALPH JACOBS, WOUTER N. H. JANSWEIJER*, PIET IEDEMA Department of Chemical Engineering, University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV AMSTERDAM, The NETHERLANDS *Department of Social Science Informatics, University of Amsterdam, Roeterstraat 15, 1018 WB AMSTERDAM, The NETHERLANDS Abstract - In this paper a knowledge based system (KBS) for reactor selection is discussed. The objective of the KBS is to select the best reactor for a chemical process of known kinetics. The most important step in developing a KBS is to decide which tasks are needed to solve the problem. The main-task reactor selection is decomposed into sub-tasks, according to a task structure. Reactor selection is an integral part of conceptual design of a chemical process which has following consequences for the task structure. A reactor engineer will first use criteria that apply to all selection problems. Then he tries to identify the important concepts of the current selection problem. For other selection problems the set of important concepts may be totally different. It is argued that reactor selection is a creative task since no predetermined set of important concepts exists that applies to each selection problem.
INTRODUCTION The reactor selection problem can be stated as follows: Given a description of a chemical process and many types of chemical reactors, select the best reactor for the process. So reactor selection is the task of choosing the best reactor from a set of existing reactors. The description of the chemical process includes information on reaction kinetics. A reactor selection problem can be solved when it fits into a problem class known to the KBS. The problem classes are shown in Figurel; a detailed discussion will be given in the section problem classes. This defines the scope of the KBS.
Gas
I ~ E
Liquid
I~
/Gas-liquid
1Liquid-liquid l
~
l Gas-catalyst
Gas-liquid
catalyst Phase in which !11 reaction occurs.
ILILiquid-catalyst Figurel. Problem classes.
Apart from the problem classes some additional assumptions are made to make the problem tractable: • Desired behaviour addresses conversion in terms of the mass balance: a desired component is produced or an undesired component has to be removed. This excludes for example furnaces, for which desired behaviour addresses conversion in terms of the energy balance. • Special reaction domains are excluded, since they require special focusing: e.g. biochemical reactions, photochemical reactions, polymerisation and reacting solids. A computer based consulting system for reactor selection, READPERT, is already available, (Droge, et al., 1994), (Schembecker, et al., 1995a) and (Schembecker, et al., 1995b). READPERT and this work will be compared in the discussion.
KBS AND CONCEPTUAL DESIGN A reactor is an integral part of a chemical process, so the KBS must have a place in conceptual design of chemical processes. Conceptual design of chemical processes can be viewed as a process existing of layers of different complexity. See Tablel. S165
S166
Layer Flow-sheet Unit Laboratory
European Symposium on Computer Aided Process Engineering--6. Part A Complexity High Moderate Low
Tablel. Layers in conceptual design. The KBS selects a reactor, so it operates at the unit layer. It has to communicate with the two adjacent layers: laboratory and flow-sheet. Since the reaction kinetics are known, no changes will occur at the laboratory layer. Communication with the laboratory layer is therefore simple, input from this layer can be obtained at the start of the program and will not be subject to change. The communication with the flow-sheet layer is more complex, since in the conceptual stage the flow-sheet is still subject to change. As long as the results from reactor selection are unknown, the flow-sheet cannot be fixed, and neither the input from the flow-sheet layer required by the KBS can be fixed. This mutual dependence gives rise to an iterative cycle. Consequently, two types of input to the KBS are distinguished: • Fixed input, which does not change during the selection process, it is obtained from the laboratory layer. Tasks that require only fixed input are dealt with outside the iterative cycle. • Variable input, which may change during the selection process, it is obtained from the flow-sheet layer. Tasks requiring variable input must be placed inside the iterative cycle.
PROBLEM CLASSES The classification of reactor problems is based on the minimum number of phases that must be present to achieve conversion. The phase in which reaction occurs should always be present and naturally the heterogeneous catalyst must be present when required. However, as an additional requirement, a reasonable level of conversion should be obtainable. 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 a necessity from solubility and stoichiometric point of view. In this way the rule: "classification of reactor problems is based on the minimum number of phases", can be maintained. In our system, the determination of the problem class is not performed by the KBS, but the problem class is provided by the user. This is possible, since the reaction phase and the necessity of a heterogeneous catalyst should be derivable from the kinetic paper. Stoichiometry and solubility are already unambiguous at the laboratory layer, at this stage of conceptual design it is easy to identify the relevant problem class. Note that the scope of the KBS has been defined more strictly by the discussion above. The phases depicted in Figurel are the minimum number of phases that must be present in the reactor. The determination of the problem class is more than just providing necessary input. The user is forced to investigate whether or not the reactor selection problem is within the problem solving capabilities of the KBS. When a selection problem does not fit into one of the problem classes, the KBS should not be used. The relevant problem class identified, does not completely fix the phases that will be present in the reactor that is finally selected. On the basis of technical judgement it can be advantageous, although not strictly necessary, to add a phase in addition to the minimum number. This results in a phenomenon named changes in perspective, a reactor that is applicable in two problem classes has two perspectives. An example is given below: • Perspectivel: Packed bed of catalyst particles; the particles catalyse a gas phase reaction. Problem class: Gascatalyst. • Perspective2: Packed bed of inert particles; the particles help to create a hydrodynamic regime. Problem class: Gas.
European Symposiumon Computer Aided Process Engineering---6.Part A
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TASK STRUCTURE The reactor selection task is decomposed into sub-tasks, based on two important strategy notions: • The notion of fixed and variable input, already discussed when positioning the KBS as part of the conceptual design process. • The notion that the program eventually has to reproduce the problem solving strategies of the engineer selecting a reactor. This is not necessary but the problem solving strategy of an engineer is a good starting point. The task structure is influenced by the fact that an engineer will not use all his knowledge about reactors simultaneously. Knowledge that is generally applicable and describing constraints that must be satisfied and cannot be compromised, is applied first. These criteria apply to all selection problems. Knowledge describing constraints that must be satisfied and cannot be compromised, but being specific for one reactor is applied later. Knowledge that does not describe constraints but properties that can be compromised against other properties is also applied later. Based on these strategy notions the following task structure has been designed. Some of the sub-tasks are still complex in which case the sub-task needs to be decomposed further. • Collect-fixed-input • Analysis-reaction-network • Collect-variable-input • Derive-hard-features • Apply-hard-features • Apply-specific-and-soft-properties The tasks are executed in the order in which they are listed, the first two tasks are outside the iterative cycle and the other tasks are placed inside the iterative cycle. Each task will be described below.
DESCRIPTION OF THE TASKS The task collect-fixed-input collects fixed input. This includes: Problem class, components, reactants, products, components in the feed streams to the overall process, etc. The task analysis-reaction-network performs an analysis of the reaction network. A generalised reaction network has been developed. Each reaction network is treated as a special case of the generalised network. The generalised network covers the examples provided by Levenspiel, (Levenspiel, 1972). The task collect-variable-inputcollects variable input. Variable input includes: Streams to the reactor, desired level of conversion, etc. Collect-variable-input is the first task inside the iterative cycle. Note that results from the task analysis-reaction-network are available when the user specifies the variable input. This will support the user and keeps the number of iterations limited. The task derive-hard-features derives the hard features of the reactor selection problem. A hard feature is a feature of the reactor selection problem that must always be satisfied and cannot be compromised. Hard features are derived by abstraction of the fixed and variable input and apply to each reactor within a problem class. The hard features are explained below: • Scale: a measure for the scale of the process. It applies to every problem class. • Heat: a measure for the temperature level in the reactor, the heat duty and the direction of transfer. This hard feature applies also to every problem class. • Catalyst deactivation: a measure for the deactivation time of the catalyst. It applies only to problem classes involving a heterogeneous catalyst. The task apply-hard-features applies the hard features. For each problem class a set of reactors is available. The hard features are used as a 'sieve' to make a first selection from the set of reactors. Reactors satisfying the hard features pass the 'sieve'. A hard feature is satisfied when there is a match for the hard feature and the corresponding reactor property. The reactor properties used for matching with hard features are called hard properties. The other reactors are inappropriate for the current selection problem. The set of reactors passing the 'sieve' is called the hard selection.
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European Symposium on Computer Aided Process Engineering--6. Part A
The task apply-specific-and-soft-properties selects the best reactor(s) from the hard selection. Before explaining the selection process, to which the hard selection is subjected, a summary of behavioural goals is given: • The selection process must be flexible, be able to deal with any particular hard selection. • The selection process should not depend on rigorous mathematical models but on weaker models, like orders of magnitude, (Mavrovouniotis and Stephanopoulos, 1988). • The selection process should recognise situations where weaker models fail, necessitating the generation of more alternatives in order to break the 'impasse'. The selection process, to which the hard selection is subjected, uses specific and soft properties: • Specific properties: strictly required not compromised. A specific property does not apply to every reactor in a problem class. • Soft properties: compromised. A undesired value for one soft property can be compensated by a desired value for another soft property. Soft properties apply to every reactor in a problem class. The three behavioural goals given above are met by defining a State Space, in which sensible moves are performed as subsequent steps in the selection process. The initial state is a matrix of the reactors being members of the hard selection and the specific and soft properties, see Figure2. All reactors in hard selection
Specific properties
Soft properties P1 P2 P3
P4
Reactor 1
1 2
V
V
V
V
1
V
V
V
V
V V
V V
V V
V V
Reactor2 Reactor3 Reactor4
j i 12 3 i
Figure2. Example of a matrix and initial state. Two moves are possible: splitting or elimination: • A matrix can be split, resulting in a state containing smaller matrices. Each new matrix will be treated individually, forming a selection problem on its own. • Reactors can be eliminated from a matrix, resulting in a state containing fewer reactors than the original one. When there are no matrices that contain more than one reactor, a solution has been reached. A selection step on the basis of soft properties results in elimination of reactors from a matrix or splitting of a matrix. Only the soft properties that do not have the same value for every reactor can be used in selection. In other words, only certain columns in the matrix are of interest. The selection process will be focused on these columns. The reactors are sorted from desired to undesired, a sort being based on a single property. Atter the reactors have been sorted based on individual properties, it is determined whether the difference in property values represents an important effect. Only the properties that represent an important effect will be used to obtain a move. A reactor having lower values on all important properties than some other reactor is eliminated. This method of elimination of worse options from any perspective is called 'strict predominance', (Russell and Norvig, 1995). Hence, if strict predominance exists, the move is: elimination of the reactor. Figure3 shows the principle. When no strict predominance can be established in the properties representing important effects, this means a state in which the weaker models are failing. The 'impasse' will be broken by splitting, each new matrix representing an individual reactor selection problem. The best reactors that result from these individual problems can only be compared by interpretation of results obtained from rigorous mathematical models or experiments.
European Symposium on Computer Aided Process Engineering---6. Part A
This region dominates over reactorl and 3 ~x
=~
(-R-eactor 1
This region dominates over reactor3 (-~eactor3
SI69
This region dominates over all reactors 7Reactor2
This region I dominates over I reactor3 and 4 ~Reactor4 Ranking of important property B Figure3. Strict predominance.
A selection step on the basis of specific properties is always elimination, since specific properties must always be satisfied. When a specific property cannot be satisfied, all reactors requiring this specific property must be eliminated. Checking a specific property results in a specific question to the user. The user is free to answer questions about specific properties.
EXAMPLES A few examples of individual selection steps are given below, all examples being taken from the problem class gascatalyst: • In fluidised catalytic cracking the deactivation time of the catalyst is less than a second• The hard feature catalyst deactivation is very selective in this case. Only one reactor, a dilute phase riser, will be left after the task applyhard-features. The initial state is a matrix that encompasses only one reactor, the solution is reached instantaneously. • A state in a selection problem that has to find the best reactor for the partial oxidation of a hydrocarbon can be a matrix that contains fixed bed reactors with cooling elements as well as fluidised beds with cooling elements. For partial oxidation both mixing of heat and maintaining plug-flow are often desired. The fixed beds have a low ranking on mixing of heat but a high ranking on plug-flow, for the fluidised beds it is the opposite. This is an example of an 'impasse', that must be solved by splitting. • A selection problem that has reached a state in which a matrix contains two radial flow reactors one operating in mode in-out and one operating in mode out-in, will progress as follows. The only soft property that does not have the same value for all reactors in the matrix is direction of flow, so this will be the important property. In case of expansion the reactor operated in mode in-out will be ranked higher. This is an example of a soft property that gives rise to elimination late in the selection process• • Reactors that rely on forced flow through a packed bed cannot be applied when a feed stream contains dust. This is an example of elimination on the basis of a specific property. A step by step example for the partial oxidation of a hydrocarbon is given below: • The reactors in the problem class gas-catalyst form the set of possible solutions• • The hard features are used as a sieve, the hard feature heat is very selective, scale and catalyst deactivation are not selective. • The first move in the task apply specific and soft properties is described in the second example given above, so two matrices are created that form individual selection problems. • Elimination in the matrix containing different alternatives of fixed bed reactors is done on the basis of other soft properties, for example pressure drop.
DISCUSSION The consulting system READPERT (Droge et al., 1994) encompasses four modules: general reactor type, operating conditions, heat transfer equipment and technical reactor• In the last module, technical reactor, the link between basic engineering ideas and technical reactors is made. This part has a scope that is comparable with our work. Droge puts forward that technical reactors are often chosen on the basis of the designers preferences for certain c~
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criteria. The following example is given: Often problems concerning heat transfer are solved first, after that mass transfer problems and eventually selectivity problems, which implies that desirable features from reaction kinetics point of view are subordinate. READPERT, on the contrary, tries to satisfy features in exactly the reverse sequence. However to our view, the criteria sequence should not be predetermined at all (creative). In the work presented here the selection of a technical reactor is done by the task apply-hard-features and the task apply-specific-and-soft-properties. When it is easy to satisfy the hard features the 'sieve' is simply not very selective, the hard selection will still encompass many reactors. The task apply-hard-features always precedes the task apply-specific-and-soft-properties. The character of the hard features makes this possible, the hard features must always be satisfied, cannot be compromised and apply to each reactor within a problem class. The selection process to which the hard selection is subjected, is allowed to develop along its own path. This is guaranteed by the definition of a state space and a strategy to obtain moves. A selection problem can result in a complex path but it is also possible that the final state is reached instantly. The strategy that is used to obtain moves identifies the important features of the current selection problem by investigation of the matrix. For example, features that are desired from the reaction kinetics point of view can be applied early, late or not at all. The possibility for each selection problem to develop along its own path is the fundamental difference between this work and READPERT. Our system tries to act like an intelligent system, some relevant notions are: • Changes m perspective: used to increase the inventory of reactors. The principle of adding a phase in addition to the minimum number of phases is very suitable for reactor selection. It facilitates for example the use of a fluidised bed in the problem class gas for the purpose of mixing heat. • Reduction of trial and error: the selection process does not involve rigorous mathematical models, this is done deliberately. Rigorous mathematical modelling will result in trial and error, trying one reactor after an other. The objective of the KBS is however to reduce trial and error by the usage of knowledge. • Unforced specific questioning: the task apply specific and soft properties prevents to force the user to answer a question about a specific property. A difficult question concerning a specific property can become irrelevant. This happens when the reactor, requiring this specific property to be satisfied, is eliminated on the basis of soft properties. Due to the absence of forced answering of questions concerning specific properties, a selection problem can develop along different paths. • Teaching by backtracking: this flexible path-finding-feature can be utilised to introduce teaching elements in the KBS. It should be possible to backtrack on these paths up to the point where a specific property was applied. The user can specify another value and see how the selection problem develops in this case. In this way the KBS obtains the characteristics of a teaching system.
CONCLUSION The task structure presented will be the framework for building a KBS for reactor selection. Two distinctively different types of input are recognised, which was necessary to fit the KBS into the conceptual design process. An engineer selecting a reactor is faced with a creative task, there is no predetermined set of important concepts, but this does not mean that the task reactor selection is totally unstructured. The selection problem is structured by introducing a task structure, problem classes and three types of reactor properties. Yet creativity is conserved by allowing each reactor selection problem to develop along its own path. The knowledge acquisition process is also structured, the introduction of problem classes and the fact that three distinctively different types of reactor properties are recognised, provides guidance for this process.
LITERATURE Droge, T., Schembecker, G., Westhaus, U., and Simmrock, K.H., 1994, Heuristisch-numerisches Beratungssystem fur die Reaktorauswahl bei der Verfahrensplanung. Chemie-lngenieur-Technik 66, 1043-1050. Levenspiel, O., 1972 Chemical reaction engineering, Wiley International Edition Mavrovouniotis, M. L., and Stephanopoulos, G., 1988, Formal order-of-magnitude reasoning in process engineering. Comput. chem. Engng, 12, 867-880. Russell, S., and Norvig, P, 1995, Artificial Intelligence a Modern Approach, pp. 480-481. Prentice Hall Schembecker, G., Droge, T., Westhaus, U., and Simmrock, K.H., 1995a. A heuristic-numeric consulting system for the choice of chemical reactors. AIChE symposium Series, 91,336-339. Schembecker, G., Droge, T., Westhaus, U., and Simmrock, K.H., 1995b. READPERT - development, selection and design of chemical reactors. Chemical engineering and processing 34, 317-322.
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