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Structuring diagnostic knowledge for large-scale process systems
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Computers Chem. Engng Vol. 22, No. 12, pp. 1897—1905, 1998 1998 Elsevier Science Ltd All rights reserved. Printed in Great Britain S0098-1354(98)00227-0 0098—1354/98 $ — see front matter
Structuring diagnostic knowledge for large-scale process systems P.R. Prasad , J.F. Davis *, Y. Jirapinyo , John R. Josephson and M. Bhalodia Department of Chemical Engineering and Laboratory for AI Research, The Ohio State University, 140 W. 19th Avenue, Columbus, OH 43210, USA Laboratory for AI Research, Computer and Information Sciences Department, The Ohio State University, Columbus, OH 43210, USA Exxon Research and Engineering Co., Florham Park, NJ 07932, USA (Received 28 May 1996; revised 3 March 1998) Abstract A set of guidelines is described for generating an initial organization of knowledge for distributed diagnosis of a process plant. The diagnostic knowledge is organized hierarchically by primary processing systems (commonly feed, reaction, and separation in chemical plants), subsystems, components, behaviors and malfunction modes. The resulting classification hierarchy decomposes the diagnostic problem solving into coordinated, distributed modules, where different modules may use different methods to address specific local subproblems. Classification hierarchies, organized in this way, provide effective modularity for organizing large-scale, knowledge-based diagnostic systems, which are difficult to construct without pertinent organizing principles. Such hierarchies provide a framework for systematic knowledge acquisition and maintenance. Application of the guidelines emphasizes readily available sources of knowledge, considers common design and operating objectives of process plants, draws upon operating expertise and builds on generic process characteristics. Application is illustrated for a fluidized catalytic cracking unit and a paraxylene production unit. 1998 Elsevier Science Ltd. All rights reserved. Keywords: knowledge-based systems; process diagnosis; knowledge representation Knowledge-based systems for process-plant diagnosis Brief review of approaches Many diagnostic knowledge-based systems (KBSs) have used model-based, symptom pattern matching, digraph, or fault tree methods, either singly or in combination, as knowledge organization and manipulation approaches. It is well known that by aggregating input—output causal models for the individual components in a process operation, model-based methods enable consideration of many kinds of possible composite behaviors, but do very little to constrain the possibilities. The advantage is that it is possible to discover symptom-malfunction relationships that have not been pre-enumerated. The disadvantage is that descriptions of large numbers of feasible and infeasible behaviors can be generated, many of which will not be realistic or diagnostically relevant under the circumstances, and so hypothesis generation must be carefully managed. In contrast,
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symptom pattern matching methods do not allow for causal-path considerations at run time but instead precompile causal paths into direct symptom-malfunction relations. An advantage is that symptom pattern matching is very direct and performance levels can be very high, while a disadvantage is that satisfactory performance is completely dependent on the ability to pre-enumerate all of the relations. Digraphs and fault trees take intermediate positions in the tradeoff between flexibility and efficiency. Each of these approaches has its place, depending on the demands of an application, the characteristics of the process to be diagnosed, and the forms of knowledge available. It is furthermore well recognized that scale-up is a problem for all these approaches. With increases in the number of input variables, potential output conclusions, complexity of subprocess interactions, and the spatial and temporal distribution of effects, the responsiveness, accuracy, and resolution of diagnostic KBSs can deteriorate dramatically. Difficulties in construction, verification, and maintenance increase rapidly with increasing system size and
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complexity, and they can prohibit successful implementation. Both digraph and fault tree methods have many problems when applied to physically complex systems (Wilcox and Himmelblau, 1994; Chen and Modarres, 1992). Kramer (1987) has discussed scaleup problems with symptom pattern matching methods using quantitative models with non-Boolean reasoning. Catino and Ungar (1995) have described the increase in time and memory required for qualitative simulations as the device complexity increases.
whole-part hierarchy as a primary basis for modularizing a diagnostic knowledge base. This takes full advantage of the modularity of the process that has been engineered into the design. Although whole-part considerations are routine in plant operations, we note that model-based reasoning approaches, digraphs, fault trees, and symptom-pattern approaches, which have often been used as the primary knowledge representation structures for large-scale systems, typically do not take advantage of this obvious process decomposition.
Malfunction hierarchies
Hierarchical classification
Practically, the complexity of large-scale process operations requires some form of decomposition to manage and distribute diagnostic decision making into a set of focused but coordinated problem solving modules. Some kind of discipline, therefore, must be applied so that such a decomposition is accomplished systematically. Moreover, the diversity of types of subsystems and components may call for applying different diagnostic methods to different problemsolving modules. A well-designed modular KBS should be able to avoid generating a combinatorially explosive number of behaviors while remaining responsive to the many combinations of events that are possible at any given point of time. A global perspective must also be maintained so that the results of localized modules can be coordinated. Thus, the deployment of an effective and practical large-scale KBS calls for the explicit adoption of a systematic methodology for decomposing knowledge and decision making into manageable units, for deciding which methods best fit which subproblems, and for coordinating the conclusions of localized modules. To the extent possible, the first step toward an effective decomposition is to design a system of knowledge modules that mirror the structure of ‘‘modules’’ of the process system to be diagnosed (these modules will be described later). Faults can then be isolated on the basis of locally relevant behaviors and symptoms without generating long chains of inferences or hypothesizing unnecessarily complex combinations of behaviors. An important benefit of this organizing principle is that it facilitates knowledge acquisition according to how the process plant was conceptualized and designed. Knowledge is partitioned into manageable chunks, associated with named and identifiable process-system elements. Knowledge acquisition becomes modular, with each knowledge module directing a small, manageable knowledge acquisition effort focused on gathering diagnostic knowledge for its corresponding system element. This same modularity facilitates debugging and maintenance of a knowledge base, especially important practical considerations for large diagnostic systems. This approach therefore leverages the fact that engineered processes typically display hierarchical whole-part organization (system—subsystem, device— component). It is therefore convenient to use the
What is typically wanted in diagnosis is a fault description that is as specific as possible. Thus, a reasonable way to organize modules for diagnosis is as a hierarchy of malfunction categories, ordered by diagnostic specificity, where as many as possible of the hierarchical modules and links mirror the whole-part organization of the process system, and tip nodes represent malfunction modes of specific device or device components. Note that such a hierarchy may not be strictly a tree because a component can belong to more than one subsystem. With this hierarchical model, each node in the hierarchy defines a localized KBS module specialized for the task of establishing or rejecting the hypothesis that a malfunction exists of the type represented by the node. The problem-solving task associated with each node is, therefore, readily defined, and a method can be customized for the available forms of domain knowledge and for the information expected to be available at run time. Individual modules in the malfunction hierarchy might use any of the diagnostic approaches (model-based, fault tree, etc.) to make decisions about whether to accept or reject the associated hypothesis. In practice, high-level malfunction categories are typically monitored and evaluated continuously. Lower-level categories are evaluated only if highlevel categories are established. The evaluation of a malfunction situation proceeds through the hierarchy from general to specific until one or more lowest-level, most-specific malfunction categories are established, or until further progress cannot be made using the current information. To support human decision making, notification is typically based on establishing a high-level malfunction category, but advice is given based on the lowest level, which relates most directly to corrective actions that can be taken. Hierarchical classification (HC) is the task of classifying an object, event, or situation with respect to a taxonomic hierarchy. It is a task that has been extensively studied over many years both from a generic reasoning task perspective and from an application perspective (e.g., Gomez and Chandrasekaran, 1984; Ramesh et al., 1992; Sravana, 1994). In using HC for process diagnosis, a process plant is judged to belong to one or more malfunction categories, the categories being hierarchically organized according to their specificity. Thus, we speak here
Diagnostic knowledge for large-scale systems of hierarchical classification as the process of using a classification hierarchy on a new case, not as the building of the hierarchy itself. As we have described in these previous papers, one of several overall control strategies for HC is Establish-Refine: top—down, prune-or-pursue, where a classification hypothesis is either established, and processing goes on to refine it by considering more specific hypotheses, or the hypothesis is rejected, and the search is pruned at that point. HC is therefore not only a structure by which a complex operation is decomposed into localized considerations but also a strategy for diagnosis. HC has been found to be a useful core processing strategy for diagnosis in medical, nuclear, and chemical-process domains. The use of multitiered hierarchies for diagnosis of large-scale systems is not novel. Finch and Kramer (1988) and Chen and Modarres (1992) have also addressed the need for a modeling formalism to describe large and complex processes at an appropriate level of detail for narrowing diagnostic focus. In the approach of Finch and Kramer, and as proposed by Shafaghi et al. (1984), each hypothesis in the hierarchy corresponds to a control-loop system. Chen and Modarres (1992) proposed a diagnostic and correction-planning system, FAX, which uses a hierarchical decision process for fault administration. Their hierarchy is a goal-tree — success-tree model. Hypotheses are generated by defining the top plant goal or objective, and then decomposing the goal vertically downward to progressively more detailed subgoals. Symptom pattern matching and Bayes theorem are used in the inference mechanism. To ensure knowledge completeness and to resolve the interdependency among the nodes of the same level, Chen and Modarres require that, when looking downward from any goal toward the bottom of the goal tree, it must be possible to define explicitly how the specific goal or subgoal is satisfied; and when looking upward from any subgoal towards the top of the tree, it must be possible to define explicitly why the specific goal or subgoal must be satisfied. They also express concern that these rules are somewhat broad and the resulting structure is somewhat arbitrary. Organizing the diagnostic hierarchy Without a good way to organize knowledge and inference, diagnostic systems are inefficient, difficult to debug, difficult to validate, and difficult to maintain for large systems. Our experience has focused on hierarchical classification because it has been shown to be one effective way to achieve the necessary orderly structure. Large-scale KBSs based on hierarchical classification are constructed by developing first the decomposition structure and then localized hypothesis-evaluation methods. We describe here a set of guidelines — together a strategy — for organizing a diagnostic classification hierarchy that allows for
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a combination of behavioral, functional, and structural decompositions but recognizes the predominance of function and structure in process plant decomposition, a characteristic described earlier. This strategy can be applied systematically to initialize a reasonably close-to-final decomposition structure that can be modified as needed to meet the operational demands of a particular application as the knowledge for hypothesis evaluation is acquired and coded. Characterizing the hierarchical knowledge structure The first major stage of constructing a classification-based diagnostic KBS focuses on identifying the malfunction hypotheses that will be represented in the hierarchy. Establishing a first reasonable hierarchy is what we call initializing the hierarchy. Once the first hierarchy is in place, then knowledge acquisition for building the local hypothesis evaluation models proceeds systematically by filling in the local symptomatic relationships node by node — a much more efficient and focused approach than considering the system in its entirety throughout the development. We have observed that the process of building the local hypothesis evaluation models can be substantially enhanced if the first hierarchy can be constructed systematically with minimal involvement of the experts, and prior to time intensive knowledge acquisition sessions with them. Types of knowledge Four types of knowledge are commonly available for diagnosis: E
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Structural knowledge — knowledge about the specific components in the plant and how they are connected. Functional knowledge — knowledge about the intended functions and operating conditions of the various systems, subsystems, and hardware components of a plant. Malfunction knowledge — knowledge of what can go wrong, derived from experience and the failure of intended functions and operating conditions. Behavioral knowledge — knowledge about the causal consequences of various normal and malfunction conditions.
Structural knowledge is information about the process topology; i.e. knowledge about what equipment items are available and how they are connected. Process-and-instrument (P & I) diagrams describe the structure and process topology in complete detail. Because each equipment item is designed to perform a specific function in the plant, functional knowledge is typically available in design manuals for the plant. Such information should also be readily available from a person who has a thorough understanding of the process (without necessarily being a diagnostic expert). Moreover, since the processcontrol equipment depicted in a P & I diagram is
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designed to ensure that a specific function of the plant is achieved, the understanding of these diagrams provides an important source of functional knowledge. Malfunction knowledge is knowledge about what can go wrong. Such knowledge includes the set of possible malfunctions, which are constituents of the possible answers to a diagnostic problem. An initial set of malfunctions can be inferred from functional knowledge, failures of intended functions, and from structural knowledge, as failures of components and connections. Behavioral knowledge may include theoretically derived knowledge of the causal chain of events that would likely occur when a given malfunction propagates through the plant, and empirical knowledge of associations between specific malfunctions and their typical symptoms. Relevant behavioral knowledge is typically available from an experienced diagnostic expert who has experience with abnormal situations in a plant and can reason about those that may occur as ‘‘what would happen if ’’ scenarios. While there is supporting documentation for identifying behaviors in the form of operations and maintenance logs, behavioral information depends heavily on expertise. As stated previously, functional and structural knowledge are especially useful for structuring a classification hierarchy for large-scale plant operations. Reflecting the engineered modularization of the plant, functional and structural knowledge tend to define the majority of malfunction categories. Nevertheless, a certain significant percentage of malfunction categories are not readily associated with subsystems or physically distinct components. These categories are generally observed as situations that arise as a result of material or information interactions and/or feedback that cut(s) across systems and subsystems. Moreover, some processing plants, or portions of them, are not well instrumented, so distinguishing malfunction categories to the level of specific subsystems or components may not be possible in practice. In weakly instrumented plants, symptoms may only appear causally, well downstream from an initial fault, necessitating the use of causal knowledge to isolate and identify the fault. These situations are not appropriately dealt with using malfunction categories that are defined functionally or structurally; instead, they require what we refer to as behavioral categories that cross functional and structural boundaries (Prasad, 1993). Guidelines for developing an initial diagnostic hierarchy We set up a diagnostic hierarchy, first based on general operating objectives, then based on primary processing systems (usually feed, reaction, and separation in chemical plants); next, proceeding down the system hierarchy, based on subsystems that are recognized by the appearance of control loops and recycle streams as represented in flowsheets or P & I diagrams; then based on recognized equipment
components; and, finally, based on malfunction modes. We thus give a kind of ‘‘content theory’’ of how to usefully distribute knowledge over classification hierarchies or, at least, how to begin distributing the knowledge at the outset of the knowledge-acquisition process. Entities, all of these types — operating objectives, primary process systems, subsystems, components, behaviors, and malfunction modes — are represented as nodes in a hierarchy, linked with a relationship of ‘‘above’’ and ‘‘below’’. Malfunction situations that do not find a logical association in this resulting hierarchy identify points in the knowledge acquisition to look for a behavioral decomposition. An underlying premise that we put forward is that the diagnostic hierarchy should have mixed knowledge types rather than being constructed as multiple hierarchies with single knowledge types. Top node. At the top of the hierarchy is a single node, which is there as a representational convenience, tying subhierarchies into a single hierarchy. It can be thought of as representing the whole entity to be diagnosed, and the most general diagnostic hypothesis, namely, that ‘‘something is wrong’’. General operating objectives. We recognize three distinct plant operating objectives that may be threatened, giving rise to a need for diagnosis: E E E
Production — produce a target amount of product. Product quality — maintain the quality of the product Safety — maintain the safety of personnel, protect the environment, maintain the integrity of the plant.
For an ambitious full-plant diagnostic system, these three objectives can be represented explicitly as nodes, forming the first layer below the top node. Conceptually, each would represent the hypotheses that something is wrong with regard to the corresponding operating objective. PX (Prasad, 1993) was constructed for diagnosis of both production and product- quality problems in a paraxylene production unit. These two objectives were represented explicitly as upper nodes in its diagnostic hierarchy. Structuring knowledge for a production objective The following heuristic strategy can be applied to any flowsheet (P & I diagram) to help initialize a classification hierarchy below production, product quality, and safety nodes. The strategy applies most directly for the nodes in the hierarchy below the production operating objective, but it is useful below the product quality and safety objectives whenever, as commonly occurs, threats to these objectives can be readily associated with failures in subsystems or physically distinct components. Primary process systems. Boundaries are drawn around equipment groups on the flowsheet that belong to each primary process system. In chemical processing plants, the primary process systems are typically one or a combination of feed, reaction, and separation. Malfunction categories associated with
Diagnostic knowledge for large-scale systems these major processing stages form explicit high-level nodes in the diagnostic hierarchy, either just below the root node or below a node representing the production or other major objective. Where recycle streams connect major process systems, such streams are represented by explicit nodes in the hierarchy, either at the same level as the process systems or below the process systems that they connect. If a primary process system has readily distinguishable parallel subsystems, such as multiple feeds, multiple reaction stages, or multiple separation systems, they are represented explicitly as nodes in the hierarchy below the primary system of which they are subsystems. Subsystems. Subsystems are modules that have been engineered into the process design so they represent meaningful groupings from a process operation and, often, a human-interaction perspective; as such they commonly are meaningful units for guiding knowledge acquisition from experts. The class of malfunctions associated with the same subsystem is often a meaningful grouping from a diagnostic perspective as well because of the common availability of subsystem-level symptoms such as controlled parameters being out of range or unstable. Within an equipment group corresponding to a major process system, subsystem, or parallel subsystem, a control system spanning one or more equipment items indicates a distinguishable subsystem comprising those equipment items, which is then represented as a single node in the malfunction hierarchy. The function of the subsystem can often be identified by identifying the parameter controlled by the control system. Besides recognizing subsystems by their controls, diagnostic subsystems can also be recognized by the existence of internal or external recycle streams or be based on an understanding of closely integrated process systems; i.e. reactor and cooling systems diagnostically need to be thought of as one system at the subsystem level. Furthermore, when a subsystem is spanned by a recycle stream, this indicates tight coupling of components, and experience has shown that we can anticipate the existence of malfunctions that are not readily associated with any sub-subsystem; i.e. functional or structural. These malfunctions require nodes that are based on behaviors that are causally linked. Examples include contamination problems and other substance degradations that persist around the loop. Such malfunctions, spanning as they do the whole subsystem, are represented as nodes directly under the node representing the subsystem. Just what these malfunctions are may not be apparent when initializing the hierarchy based on an interpretation of the flowsheet, but they should be investigated during knowledge acquisition with the experts. Decomposition based on subsystems proceeds recursively in the same way until the level of individual equipment items has been reached.
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Equipment items. Equipment items are represented as nodes linked below the subsystems of which they are components (there may be more than one). Recycle streams connecting major processing systems can be thought of as either pseudo-system or equipment items or as behavioral concepts. In either case they are represented explicitly as nodes in the hierarchy. If the effects of the recycle are balanced between the two parts of the process that are connected, a single node can be used as a behavioral concept, thereby treating the stream as affecting both parts. However, subsequent knowledge acquisition might reveal that all of the important malfunctions associated with a recycle stream can be associated with one end or the other. In this case, it is sufficient to locate the node for recycle-stream malfunctions below only that node representing the part of the process which is affected. Modes of failure. Below the component level, further malfunction nodes may be based on mode of failure. For example a valve malfunction may be further broken down to subcategories: stuck-open, stuck-closed, unresponsive to control, and leaking. Whether to explicitly represent failure modes as a level of detail below the level of components is determined by whether failure modes can be distinguished by their observational consequences and whether it would make any difference for corrective action. Illustrating the strategy Illustrating and evaluating the general utility of the knowledge-structuring guidelines requires application to multiple industrial implementations of large-scale process diagnostic systems. A given diagnostic implementation must use the quidelines to initialize the hierarchy and, to evaluate the completeness and effectiveness of the initialized hierarchy, the implementation must also proceed through the refinement of the diagnostic hierarchy and fill in of the local diagnostic problem solvers. Given the difficulty and cost of achieving a single evaluation point, we draw evaluative conclusions by comparing application to a paraxylene plant implementation to a fluidized catalytic cracking unit implementation used as a reference application CATCRACKER is a large-scale KBS using hierarchical classification for diagnosis of the feed system and reactor—regenerator portions of a fluidized catalytic cracking unit. Similarly, PX is a large-scale diagnostic KBS of the refrigeration, crystallization, and separation units of a paraxylene production plant. The details of CATCRACKER and PX are reported in Prasad (1993), Prasad and Davis (1993) and Ramesh et al. (1992). CATCRACKER was constructed prior to a clear formulation of the knowledgeorganization strategy described in this paper. It is reanalyzed here to illustrate how the guidelines work in practice and to show that their use produces
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a hierarchy that is close to the one actually used in the diagnostic system. The PX system used the knowledge-organization strategy described here from the outset to initialize the diagnostic hierarchy and to guide the knowledge-acquisition process. A working prototype of the PX knowledge-base system for the entire plant, representing more than 500 malfunctions, was constructed with only four days of interaction with the experts. Application of the guidelines in constructing both CATCRACKER and PX provides insight into the general utility of the approach. Application in constructing PX provides insight into effectiveness. In this paper, we focus on illustrative examples of applying the knowledge structure guidelines rather than describing the classification hierarchies in full detail. Details can be found in the references listed previously. Catcracker A description follows showing the application of the knowledge-organization guidelines to the construction of CATCRACKER. The diagnosis is oriented toward threats to production; the operational objectives of maintaining product quality and safety are not considered. Step 1: From the flowsheets (not shown here for economy of space) boundaries are drawn around the equipment groups that belong to each of the major process systems: feed system, reactor system, and separation system. The feed system consists of equipment items such as the raw oil charge drum, raw oil charge pumps, heat exchanger train, furnace, and injectors. The reactor system consists of such items as the reactor, blower, regenerator, and spent catalyst valve. The separation system consists of the fractionator. In accordance with the major process systems, the first level of the hierarchy is initialized as shown in Fig. 1. Step 2: Further grouping of the equipment is carried out by examining the span of control loops. This step results in the following groupings. E
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Group I — raw oil charge drum, raw oil charge pumps, heat exchanger trains, and a furnace system group. Group II — injectors. Group III — reactor—regenerator system.
system, and the feed-atomization system, respectively. Group III is a single group because there are numerous interactions between the reactor and the regenerator, via the catalyst circulation loop and the associated control system, so these items must be considered together. As a result of grouping the reactor and the regenerator together, we can expect there to be malfunctions associated with the group that are not readily associated with any sub-subsystem but require a behavioral category. We indicate this possibility with a ‘‘?’’. At this point the hierarchy is as shown in Fig. 2. Step 3: The regulatory systems in the detailed flowsheet that control various operating parameters are located, identified with functional subsystems, and each one becomes a single node in the hierarchy. The feed temperature and feed-flow systems thus become nodes under the feed-preheat system node; also the reaction temperature control, air feed system, and stripper system become nodes under reactor—regenerator (see Fig. 3). Note that an expectation of a behavioral node is carried along as a ‘‘?’’. Step 4: Structural decomposition of the identified subsystems are as follows (see Fig. 4). 1 Feed-temperature system: feed-temperature controller, feed-temperature control valve, heat exchangers, thermocouple. 2 Feed-flow system: feed-flow controller, feed-flow control valve, heat exchangers, flow meter. 3 Feed atomization: injectors. 4 Reaction temperature control: reaction temperature controller, regenerator slide valve. 5 Air feed system: air feed-flow controller, air heater, blower, air flow meter, vent. 6 Stripper system: stripper flow meter, stripper flow controller, stripper valve, steam feed. This initial hierarchy is almost identical with the actual hierarchy used in CATCRACKER. The difference between the two is a result of additional information that was uncovered during interviews with the experts. For example, the experts revealed that catalyst loss is one of the major diagnostic concerns about this process. As anticipated, this is an example of a diagnostic category that is readily associated with the reactor—regenerator system but not with any of its subsystems, and thus this category helps to fill in the ‘‘?’’ in the figure.
Groups I and II correspond to functionally distinguishable subsystems consisting of the feed-preheat
PX
Fig. 1. Catcracker decomposition from Step 1.
Fig. 2. Catcracker decomposition from Step 2.
The classification hierarchy for the PX system was initialized using the same strategy as for
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Fig. 3. Catcracker decomposition from Step 3.
Fig. 4. Catcracker decomposition from Step 4.
CATCRACKER to investigate how well the initialization procedure generalizes. As indicated earlier, the PX hierarchy is very large with over 500 nodes. For the purpose of illustration, we expand only on a small portion of the production hierarchy to demonstrate the application of the guidelines and to compare to the initialization procedure for CATCRACKER. The top-level decomposition for PX is based on the objectives of the plant. For the purpose of developing this system the emphasis was on two distinct objectives, namely, production of a target amount of paraxylene and maintaining its quality above a certain minimum. These two objectives are represented explicitly as nodes immediately below the top node (see the final hierarchy below). The production branch can be functionally decomposed into the primary systems: product separation system (encompassing crystallizers and centrifuges), the refrigeration system, and the feed system (we are diagnostically only concerned with feed quality as it affects production). This leads to the decomposition as shown in Fig. 5. Decomposition continues by drawing boundaries around equipment items on the process flowsheet within the separation and refrigeration systems, respectively. However, it is not immediately apparent from a systems and subsystems analysis of the flowsheet that separation is carried out in two stages that involve multiple plant systems. The first stage is
Fig. 5. PX decomposition reflecting primary systems.
responsible for recovering as much paraxylene as possible, while the second stage is responsible for purification. Decomposing the separation system into its constituent stages captures key interactions that are embedded in the plant operation and therefore need to be accounted for in the diagnostic decomposition. These first- and second-stage nodes are comparable to the formation of a reactor—regenerator node in CATCRACKER and are represented explicitly in the decomposition as shown in Fig. 6. Again, to constrain the size of the example and focus only on the application of the guidelines, we expand only the first-stage node. Arguments relevant to the second stage are similar. With respect to the first stage, the temperature of the contents of each crystallizer is controlled indirectly by controlling the pressure of refrigerant in the head drums. The existence of this control loop gives strong evidence for interactions between the head drums and the crystallizers. The continuous flow of refrigerant between
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Fig. 6. PX decomposition showing stage grouping.
these equipment items provides additional confirmation of the existence of strong interactions. As stated above, these interactions are analogous to the tight coupling of reactor and regenerator systems of CATCRACKER and similarly lead to decomposition considerations which are analogous. Specifically, for each stage of the separation system, the crystallizers and refrigerant drums of that stage are grouped together for diagnosis rather than considering the refrigerant drums to be part of the refrigeration system, a subsystem view. As such there is high likelihood for needing behavioral nodes to capture cross-system behaviors. As with CATCRACKER this is indicated by a ‘‘?’’. Further decomposition of each stage in the separation system is done on the basis of subsystems and regulatory systems represented on the flowsheet within the first-stage boundaries already delineated. The detailed flowsheet immediately reveals the existence of crystallizer-level control, refrigerant-drum level control, and crystallizer-temperature control as the regulatory systems. Lastly, the centrifuges form a separate subsystem. Combining these
Fig. 7. PX decomposition showing first stage expansion.
Fig. 8. PX decomposition showing component tip nodes.
considerations leads to the next level decomposition (Fig. 7). Finally, the initialized hierarchy is enhanced structurally with equipment components considered to be failure points in the plants (Fig. 8). This initialized hierarchy was generated with only an introduction to the PX process by the process experts. It was then used as the basis for constructing the PX diagnostic system through detailed interactions with the process experts. The initialized hierarchy can be compared with the corresponding segment of the system that was actually implemented (Fig. 9). A comparison shows that the initialized and final hierarchies are close. The main differences lie in the identification of the behavioral nodes, i.e. resolution of the ‘‘?’’, and fill-in at the tip node level with modes of failures. With respect to the behavioral nodes, the final hierarchy shows that two nodes were identified. These were crystrallizer heat transfer (HT X Fer) and crystallizer overloaded. Neither of these nodes is a physical system or subsystem. Rather they represent process behavioral considerations that cut across portions of relevant systems and subsystems. This is immediately clear by looking at the expansion of crystallizer overloaded. Here we see, aspects of both the first-stage and second-stage systems brought into consideration. These are process considerations not accounted for in the system expansion of the first stage or the second stage (not shown).
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Fig. 9. PX decomposition used in diagnostic systems.
Conclusions Malfunction hierarchies provide modularity for organizing large-scale knowledge-based diagnostic systems. An engineering understanding of P & I diagrams and flowsheets, along with information about operating objectives, provide the means to initially structure a malfunction hierarchy. Flowsheets are useful for extracting functional and structural knowledge; regulatory control loops and recycle streams provide information for identifying functionally interacting equipment items and identifying the locations of behavioral decompositions. Necessary interactions with the domain experts, whose time is often expensive, can be reduced by using knowledge gathered from these sources to structure an initial hierarchy, which then provides organization and focus to knowledge-acquisition sessions. The guidelines presented are expected to be generic for many process plants, although none of them will be applicable to all. Acknowledgements We gratefully acknowledge the support of the Exxon Research Foundation in the development of these knowledge structuring guidelines References Catino, C.A. and Ungar, L.H. (1995) A model-based approach to automated hazard identification of chemical plants. A.I.Ch.E. J. 41(1), 97—109.
Chen, L.W. and Modarres, M. (1992) Hierarchical decision process for fault administration. Comput. Chem. Engng 16(5), 425—448. Finch, F.E. and Kramer, M.A. (1988) Narrowing diagnostic focus using functional decomposition, A.I.Ch.E. J. 34(1), 25—36. Gomez, F. and Chandrasekaran, B. (1984) Knowledge organization and distribution for medical diagnosis. In W.J. Clancey and E.H. Shortliffe (Eds.), Readings in Medical Artificial Intelligence (pp. 320—338). Reading, MA: Addison-Wesley. Kramer, M.A. (1987) Malfunction diagnosis using quantitative models with non-Boolean reasoning in expert systems, A.I.Ch.E. J. 33(Jan), 1130—140. Prasad, P.R. (1993) Diagnostic knowledge-based systems for continuous chemical processes: Enhanced knowledge acquisition and generic problem-solving framework. Ph.D. Dissertation, The Ohio State University. Prasad, P.R. and Davis, J.F. (1993) Generic nature of the task-based framework for plant-wide diagnosis. In Artificial Intelligence in Process Engineering, A.I.Ch.E. Annual Meeting, St. Louis. Ramesh, T.S., Davis, J.F. and Schwenzer, G.M. (1992) Knowledge-based diagnostic systems for continuous process operations based upon the task framework. Comput. Chem. Engng 16, 109—127. Shafaghi, A., Andow, P.K. and Lees, F.P. (1984) Fault tree synthesis based on control loop structure. Chem. Engng Res Des. 62, 101—110. Sravana, K.K. (1994) Diagnostic knowledge-based systems for batch chemical processes: Hypothesis queuing and evaluation. Ph.D. Dissertation, The Ohio State University. Wilcox, N.A. and Himmelblau, D.M. (1994) The possible cause and effect graphs (PCEG) model for fault diagnosis — I methodology. Comput. Chem. Engng 18(2), 103—116.
Computers Chem. Engng Vol. 22, No. 12, pp. 1897—1905, 1998 1998 Elsevier Science Ltd All rights reserved. Printed in Great Britain S0098-1354(98)00227-0 0098—1354/98 $ — see front matter
Structuring diagnostic knowledge for large-scale process systems P.R. Prasad , J.F. Davis *, Y. Jirapinyo , John R. Josephson and M. Bhalodia Department of Chemical Engineering and Laboratory for AI Research, The Ohio State University, 140 W. 19th Avenue, Columbus, OH 43210, USA Laboratory for AI Research, Computer and Information Sciences Department, The Ohio State University, Columbus, OH 43210, USA Exxon Research and Engineering Co., Florham Park, NJ 07932, USA (Received 28 May 1996; revised 3 March 1998) Abstract A set of guidelines is described for generating an initial organization of knowledge for distributed diagnosis of a process plant. The diagnostic knowledge is organized hierarchically by primary processing systems (commonly feed, reaction, and separation in chemical plants), subsystems, components, behaviors and malfunction modes. The resulting classification hierarchy decomposes the diagnostic problem solving into coordinated, distributed modules, where different modules may use different methods to address specific local subproblems. Classification hierarchies, organized in this way, provide effective modularity for organizing large-scale, knowledge-based diagnostic systems, which are difficult to construct without pertinent organizing principles. Such hierarchies provide a framework for systematic knowledge acquisition and maintenance. Application of the guidelines emphasizes readily available sources of knowledge, considers common design and operating objectives of process plants, draws upon operating expertise and builds on generic process characteristics. Application is illustrated for a fluidized catalytic cracking unit and a paraxylene production unit. 1998 Elsevier Science Ltd. All rights reserved. Keywords: knowledge-based systems; process diagnosis; knowledge representation Knowledge-based systems for process-plant diagnosis Brief review of approaches Many diagnostic knowledge-based systems (KBSs) have used model-based, symptom pattern matching, digraph, or fault tree methods, either singly or in combination, as knowledge organization and manipulation approaches. It is well known that by aggregating input—output causal models for the individual components in a process operation, model-based methods enable consideration of many kinds of possible composite behaviors, but do very little to constrain the possibilities. The advantage is that it is possible to discover symptom-malfunction relationships that have not been pre-enumerated. The disadvantage is that descriptions of large numbers of feasible and infeasible behaviors can be generated, many of which will not be realistic or diagnostically relevant under the circumstances, and so hypothesis generation must be carefully managed. In contrast,
* Author to whom correspondence should be addressed.
symptom pattern matching methods do not allow for causal-path considerations at run time but instead precompile causal paths into direct symptom-malfunction relations. An advantage is that symptom pattern matching is very direct and performance levels can be very high, while a disadvantage is that satisfactory performance is completely dependent on the ability to pre-enumerate all of the relations. Digraphs and fault trees take intermediate positions in the tradeoff between flexibility and efficiency. Each of these approaches has its place, depending on the demands of an application, the characteristics of the process to be diagnosed, and the forms of knowledge available. It is furthermore well recognized that scale-up is a problem for all these approaches. With increases in the number of input variables, potential output conclusions, complexity of subprocess interactions, and the spatial and temporal distribution of effects, the responsiveness, accuracy, and resolution of diagnostic KBSs can deteriorate dramatically. Difficulties in construction, verification, and maintenance increase rapidly with increasing system size and
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complexity, and they can prohibit successful implementation. Both digraph and fault tree methods have many problems when applied to physically complex systems (Wilcox and Himmelblau, 1994; Chen and Modarres, 1992). Kramer (1987) has discussed scaleup problems with symptom pattern matching methods using quantitative models with non-Boolean reasoning. Catino and Ungar (1995) have described the increase in time and memory required for qualitative simulations as the device complexity increases.
whole-part hierarchy as a primary basis for modularizing a diagnostic knowledge base. This takes full advantage of the modularity of the process that has been engineered into the design. Although whole-part considerations are routine in plant operations, we note that model-based reasoning approaches, digraphs, fault trees, and symptom-pattern approaches, which have often been used as the primary knowledge representation structures for large-scale systems, typically do not take advantage of this obvious process decomposition.
Malfunction hierarchies
Hierarchical classification
Practically, the complexity of large-scale process operations requires some form of decomposition to manage and distribute diagnostic decision making into a set of focused but coordinated problem solving modules. Some kind of discipline, therefore, must be applied so that such a decomposition is accomplished systematically. Moreover, the diversity of types of subsystems and components may call for applying different diagnostic methods to different problemsolving modules. A well-designed modular KBS should be able to avoid generating a combinatorially explosive number of behaviors while remaining responsive to the many combinations of events that are possible at any given point of time. A global perspective must also be maintained so that the results of localized modules can be coordinated. Thus, the deployment of an effective and practical large-scale KBS calls for the explicit adoption of a systematic methodology for decomposing knowledge and decision making into manageable units, for deciding which methods best fit which subproblems, and for coordinating the conclusions of localized modules. To the extent possible, the first step toward an effective decomposition is to design a system of knowledge modules that mirror the structure of ‘‘modules’’ of the process system to be diagnosed (these modules will be described later). Faults can then be isolated on the basis of locally relevant behaviors and symptoms without generating long chains of inferences or hypothesizing unnecessarily complex combinations of behaviors. An important benefit of this organizing principle is that it facilitates knowledge acquisition according to how the process plant was conceptualized and designed. Knowledge is partitioned into manageable chunks, associated with named and identifiable process-system elements. Knowledge acquisition becomes modular, with each knowledge module directing a small, manageable knowledge acquisition effort focused on gathering diagnostic knowledge for its corresponding system element. This same modularity facilitates debugging and maintenance of a knowledge base, especially important practical considerations for large diagnostic systems. This approach therefore leverages the fact that engineered processes typically display hierarchical whole-part organization (system—subsystem, device— component). It is therefore convenient to use the
What is typically wanted in diagnosis is a fault description that is as specific as possible. Thus, a reasonable way to organize modules for diagnosis is as a hierarchy of malfunction categories, ordered by diagnostic specificity, where as many as possible of the hierarchical modules and links mirror the whole-part organization of the process system, and tip nodes represent malfunction modes of specific device or device components. Note that such a hierarchy may not be strictly a tree because a component can belong to more than one subsystem. With this hierarchical model, each node in the hierarchy defines a localized KBS module specialized for the task of establishing or rejecting the hypothesis that a malfunction exists of the type represented by the node. The problem-solving task associated with each node is, therefore, readily defined, and a method can be customized for the available forms of domain knowledge and for the information expected to be available at run time. Individual modules in the malfunction hierarchy might use any of the diagnostic approaches (model-based, fault tree, etc.) to make decisions about whether to accept or reject the associated hypothesis. In practice, high-level malfunction categories are typically monitored and evaluated continuously. Lower-level categories are evaluated only if highlevel categories are established. The evaluation of a malfunction situation proceeds through the hierarchy from general to specific until one or more lowest-level, most-specific malfunction categories are established, or until further progress cannot be made using the current information. To support human decision making, notification is typically based on establishing a high-level malfunction category, but advice is given based on the lowest level, which relates most directly to corrective actions that can be taken. Hierarchical classification (HC) is the task of classifying an object, event, or situation with respect to a taxonomic hierarchy. It is a task that has been extensively studied over many years both from a generic reasoning task perspective and from an application perspective (e.g., Gomez and Chandrasekaran, 1984; Ramesh et al., 1992; Sravana, 1994). In using HC for process diagnosis, a process plant is judged to belong to one or more malfunction categories, the categories being hierarchically organized according to their specificity. Thus, we speak here
Diagnostic knowledge for large-scale systems of hierarchical classification as the process of using a classification hierarchy on a new case, not as the building of the hierarchy itself. As we have described in these previous papers, one of several overall control strategies for HC is Establish-Refine: top—down, prune-or-pursue, where a classification hypothesis is either established, and processing goes on to refine it by considering more specific hypotheses, or the hypothesis is rejected, and the search is pruned at that point. HC is therefore not only a structure by which a complex operation is decomposed into localized considerations but also a strategy for diagnosis. HC has been found to be a useful core processing strategy for diagnosis in medical, nuclear, and chemical-process domains. The use of multitiered hierarchies for diagnosis of large-scale systems is not novel. Finch and Kramer (1988) and Chen and Modarres (1992) have also addressed the need for a modeling formalism to describe large and complex processes at an appropriate level of detail for narrowing diagnostic focus. In the approach of Finch and Kramer, and as proposed by Shafaghi et al. (1984), each hypothesis in the hierarchy corresponds to a control-loop system. Chen and Modarres (1992) proposed a diagnostic and correction-planning system, FAX, which uses a hierarchical decision process for fault administration. Their hierarchy is a goal-tree — success-tree model. Hypotheses are generated by defining the top plant goal or objective, and then decomposing the goal vertically downward to progressively more detailed subgoals. Symptom pattern matching and Bayes theorem are used in the inference mechanism. To ensure knowledge completeness and to resolve the interdependency among the nodes of the same level, Chen and Modarres require that, when looking downward from any goal toward the bottom of the goal tree, it must be possible to define explicitly how the specific goal or subgoal is satisfied; and when looking upward from any subgoal towards the top of the tree, it must be possible to define explicitly why the specific goal or subgoal must be satisfied. They also express concern that these rules are somewhat broad and the resulting structure is somewhat arbitrary. Organizing the diagnostic hierarchy Without a good way to organize knowledge and inference, diagnostic systems are inefficient, difficult to debug, difficult to validate, and difficult to maintain for large systems. Our experience has focused on hierarchical classification because it has been shown to be one effective way to achieve the necessary orderly structure. Large-scale KBSs based on hierarchical classification are constructed by developing first the decomposition structure and then localized hypothesis-evaluation methods. We describe here a set of guidelines — together a strategy — for organizing a diagnostic classification hierarchy that allows for
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a combination of behavioral, functional, and structural decompositions but recognizes the predominance of function and structure in process plant decomposition, a characteristic described earlier. This strategy can be applied systematically to initialize a reasonably close-to-final decomposition structure that can be modified as needed to meet the operational demands of a particular application as the knowledge for hypothesis evaluation is acquired and coded. Characterizing the hierarchical knowledge structure The first major stage of constructing a classification-based diagnostic KBS focuses on identifying the malfunction hypotheses that will be represented in the hierarchy. Establishing a first reasonable hierarchy is what we call initializing the hierarchy. Once the first hierarchy is in place, then knowledge acquisition for building the local hypothesis evaluation models proceeds systematically by filling in the local symptomatic relationships node by node — a much more efficient and focused approach than considering the system in its entirety throughout the development. We have observed that the process of building the local hypothesis evaluation models can be substantially enhanced if the first hierarchy can be constructed systematically with minimal involvement of the experts, and prior to time intensive knowledge acquisition sessions with them. Types of knowledge Four types of knowledge are commonly available for diagnosis: E
E
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Structural knowledge — knowledge about the specific components in the plant and how they are connected. Functional knowledge — knowledge about the intended functions and operating conditions of the various systems, subsystems, and hardware components of a plant. Malfunction knowledge — knowledge of what can go wrong, derived from experience and the failure of intended functions and operating conditions. Behavioral knowledge — knowledge about the causal consequences of various normal and malfunction conditions.
Structural knowledge is information about the process topology; i.e. knowledge about what equipment items are available and how they are connected. Process-and-instrument (P & I) diagrams describe the structure and process topology in complete detail. Because each equipment item is designed to perform a specific function in the plant, functional knowledge is typically available in design manuals for the plant. Such information should also be readily available from a person who has a thorough understanding of the process (without necessarily being a diagnostic expert). Moreover, since the processcontrol equipment depicted in a P & I diagram is
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designed to ensure that a specific function of the plant is achieved, the understanding of these diagrams provides an important source of functional knowledge. Malfunction knowledge is knowledge about what can go wrong. Such knowledge includes the set of possible malfunctions, which are constituents of the possible answers to a diagnostic problem. An initial set of malfunctions can be inferred from functional knowledge, failures of intended functions, and from structural knowledge, as failures of components and connections. Behavioral knowledge may include theoretically derived knowledge of the causal chain of events that would likely occur when a given malfunction propagates through the plant, and empirical knowledge of associations between specific malfunctions and their typical symptoms. Relevant behavioral knowledge is typically available from an experienced diagnostic expert who has experience with abnormal situations in a plant and can reason about those that may occur as ‘‘what would happen if ’’ scenarios. While there is supporting documentation for identifying behaviors in the form of operations and maintenance logs, behavioral information depends heavily on expertise. As stated previously, functional and structural knowledge are especially useful for structuring a classification hierarchy for large-scale plant operations. Reflecting the engineered modularization of the plant, functional and structural knowledge tend to define the majority of malfunction categories. Nevertheless, a certain significant percentage of malfunction categories are not readily associated with subsystems or physically distinct components. These categories are generally observed as situations that arise as a result of material or information interactions and/or feedback that cut(s) across systems and subsystems. Moreover, some processing plants, or portions of them, are not well instrumented, so distinguishing malfunction categories to the level of specific subsystems or components may not be possible in practice. In weakly instrumented plants, symptoms may only appear causally, well downstream from an initial fault, necessitating the use of causal knowledge to isolate and identify the fault. These situations are not appropriately dealt with using malfunction categories that are defined functionally or structurally; instead, they require what we refer to as behavioral categories that cross functional and structural boundaries (Prasad, 1993). Guidelines for developing an initial diagnostic hierarchy We set up a diagnostic hierarchy, first based on general operating objectives, then based on primary processing systems (usually feed, reaction, and separation in chemical plants); next, proceeding down the system hierarchy, based on subsystems that are recognized by the appearance of control loops and recycle streams as represented in flowsheets or P & I diagrams; then based on recognized equipment
components; and, finally, based on malfunction modes. We thus give a kind of ‘‘content theory’’ of how to usefully distribute knowledge over classification hierarchies or, at least, how to begin distributing the knowledge at the outset of the knowledge-acquisition process. Entities, all of these types — operating objectives, primary process systems, subsystems, components, behaviors, and malfunction modes — are represented as nodes in a hierarchy, linked with a relationship of ‘‘above’’ and ‘‘below’’. Malfunction situations that do not find a logical association in this resulting hierarchy identify points in the knowledge acquisition to look for a behavioral decomposition. An underlying premise that we put forward is that the diagnostic hierarchy should have mixed knowledge types rather than being constructed as multiple hierarchies with single knowledge types. Top node. At the top of the hierarchy is a single node, which is there as a representational convenience, tying subhierarchies into a single hierarchy. It can be thought of as representing the whole entity to be diagnosed, and the most general diagnostic hypothesis, namely, that ‘‘something is wrong’’. General operating objectives. We recognize three distinct plant operating objectives that may be threatened, giving rise to a need for diagnosis: E E E
Production — produce a target amount of product. Product quality — maintain the quality of the product Safety — maintain the safety of personnel, protect the environment, maintain the integrity of the plant.
For an ambitious full-plant diagnostic system, these three objectives can be represented explicitly as nodes, forming the first layer below the top node. Conceptually, each would represent the hypotheses that something is wrong with regard to the corresponding operating objective. PX (Prasad, 1993) was constructed for diagnosis of both production and product- quality problems in a paraxylene production unit. These two objectives were represented explicitly as upper nodes in its diagnostic hierarchy. Structuring knowledge for a production objective The following heuristic strategy can be applied to any flowsheet (P & I diagram) to help initialize a classification hierarchy below production, product quality, and safety nodes. The strategy applies most directly for the nodes in the hierarchy below the production operating objective, but it is useful below the product quality and safety objectives whenever, as commonly occurs, threats to these objectives can be readily associated with failures in subsystems or physically distinct components. Primary process systems. Boundaries are drawn around equipment groups on the flowsheet that belong to each primary process system. In chemical processing plants, the primary process systems are typically one or a combination of feed, reaction, and separation. Malfunction categories associated with
Diagnostic knowledge for large-scale systems these major processing stages form explicit high-level nodes in the diagnostic hierarchy, either just below the root node or below a node representing the production or other major objective. Where recycle streams connect major process systems, such streams are represented by explicit nodes in the hierarchy, either at the same level as the process systems or below the process systems that they connect. If a primary process system has readily distinguishable parallel subsystems, such as multiple feeds, multiple reaction stages, or multiple separation systems, they are represented explicitly as nodes in the hierarchy below the primary system of which they are subsystems. Subsystems. Subsystems are modules that have been engineered into the process design so they represent meaningful groupings from a process operation and, often, a human-interaction perspective; as such they commonly are meaningful units for guiding knowledge acquisition from experts. The class of malfunctions associated with the same subsystem is often a meaningful grouping from a diagnostic perspective as well because of the common availability of subsystem-level symptoms such as controlled parameters being out of range or unstable. Within an equipment group corresponding to a major process system, subsystem, or parallel subsystem, a control system spanning one or more equipment items indicates a distinguishable subsystem comprising those equipment items, which is then represented as a single node in the malfunction hierarchy. The function of the subsystem can often be identified by identifying the parameter controlled by the control system. Besides recognizing subsystems by their controls, diagnostic subsystems can also be recognized by the existence of internal or external recycle streams or be based on an understanding of closely integrated process systems; i.e. reactor and cooling systems diagnostically need to be thought of as one system at the subsystem level. Furthermore, when a subsystem is spanned by a recycle stream, this indicates tight coupling of components, and experience has shown that we can anticipate the existence of malfunctions that are not readily associated with any sub-subsystem; i.e. functional or structural. These malfunctions require nodes that are based on behaviors that are causally linked. Examples include contamination problems and other substance degradations that persist around the loop. Such malfunctions, spanning as they do the whole subsystem, are represented as nodes directly under the node representing the subsystem. Just what these malfunctions are may not be apparent when initializing the hierarchy based on an interpretation of the flowsheet, but they should be investigated during knowledge acquisition with the experts. Decomposition based on subsystems proceeds recursively in the same way until the level of individual equipment items has been reached.
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Equipment items. Equipment items are represented as nodes linked below the subsystems of which they are components (there may be more than one). Recycle streams connecting major processing systems can be thought of as either pseudo-system or equipment items or as behavioral concepts. In either case they are represented explicitly as nodes in the hierarchy. If the effects of the recycle are balanced between the two parts of the process that are connected, a single node can be used as a behavioral concept, thereby treating the stream as affecting both parts. However, subsequent knowledge acquisition might reveal that all of the important malfunctions associated with a recycle stream can be associated with one end or the other. In this case, it is sufficient to locate the node for recycle-stream malfunctions below only that node representing the part of the process which is affected. Modes of failure. Below the component level, further malfunction nodes may be based on mode of failure. For example a valve malfunction may be further broken down to subcategories: stuck-open, stuck-closed, unresponsive to control, and leaking. Whether to explicitly represent failure modes as a level of detail below the level of components is determined by whether failure modes can be distinguished by their observational consequences and whether it would make any difference for corrective action. Illustrating the strategy Illustrating and evaluating the general utility of the knowledge-structuring guidelines requires application to multiple industrial implementations of large-scale process diagnostic systems. A given diagnostic implementation must use the quidelines to initialize the hierarchy and, to evaluate the completeness and effectiveness of the initialized hierarchy, the implementation must also proceed through the refinement of the diagnostic hierarchy and fill in of the local diagnostic problem solvers. Given the difficulty and cost of achieving a single evaluation point, we draw evaluative conclusions by comparing application to a paraxylene plant implementation to a fluidized catalytic cracking unit implementation used as a reference application CATCRACKER is a large-scale KBS using hierarchical classification for diagnosis of the feed system and reactor—regenerator portions of a fluidized catalytic cracking unit. Similarly, PX is a large-scale diagnostic KBS of the refrigeration, crystallization, and separation units of a paraxylene production plant. The details of CATCRACKER and PX are reported in Prasad (1993), Prasad and Davis (1993) and Ramesh et al. (1992). CATCRACKER was constructed prior to a clear formulation of the knowledgeorganization strategy described in this paper. It is reanalyzed here to illustrate how the guidelines work in practice and to show that their use produces
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a hierarchy that is close to the one actually used in the diagnostic system. The PX system used the knowledge-organization strategy described here from the outset to initialize the diagnostic hierarchy and to guide the knowledge-acquisition process. A working prototype of the PX knowledge-base system for the entire plant, representing more than 500 malfunctions, was constructed with only four days of interaction with the experts. Application of the guidelines in constructing both CATCRACKER and PX provides insight into the general utility of the approach. Application in constructing PX provides insight into effectiveness. In this paper, we focus on illustrative examples of applying the knowledge structure guidelines rather than describing the classification hierarchies in full detail. Details can be found in the references listed previously. Catcracker A description follows showing the application of the knowledge-organization guidelines to the construction of CATCRACKER. The diagnosis is oriented toward threats to production; the operational objectives of maintaining product quality and safety are not considered. Step 1: From the flowsheets (not shown here for economy of space) boundaries are drawn around the equipment groups that belong to each of the major process systems: feed system, reactor system, and separation system. The feed system consists of equipment items such as the raw oil charge drum, raw oil charge pumps, heat exchanger train, furnace, and injectors. The reactor system consists of such items as the reactor, blower, regenerator, and spent catalyst valve. The separation system consists of the fractionator. In accordance with the major process systems, the first level of the hierarchy is initialized as shown in Fig. 1. Step 2: Further grouping of the equipment is carried out by examining the span of control loops. This step results in the following groupings. E
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Group I — raw oil charge drum, raw oil charge pumps, heat exchanger trains, and a furnace system group. Group II — injectors. Group III — reactor—regenerator system.
system, and the feed-atomization system, respectively. Group III is a single group because there are numerous interactions between the reactor and the regenerator, via the catalyst circulation loop and the associated control system, so these items must be considered together. As a result of grouping the reactor and the regenerator together, we can expect there to be malfunctions associated with the group that are not readily associated with any sub-subsystem but require a behavioral category. We indicate this possibility with a ‘‘?’’. At this point the hierarchy is as shown in Fig. 2. Step 3: The regulatory systems in the detailed flowsheet that control various operating parameters are located, identified with functional subsystems, and each one becomes a single node in the hierarchy. The feed temperature and feed-flow systems thus become nodes under the feed-preheat system node; also the reaction temperature control, air feed system, and stripper system become nodes under reactor—regenerator (see Fig. 3). Note that an expectation of a behavioral node is carried along as a ‘‘?’’. Step 4: Structural decomposition of the identified subsystems are as follows (see Fig. 4). 1 Feed-temperature system: feed-temperature controller, feed-temperature control valve, heat exchangers, thermocouple. 2 Feed-flow system: feed-flow controller, feed-flow control valve, heat exchangers, flow meter. 3 Feed atomization: injectors. 4 Reaction temperature control: reaction temperature controller, regenerator slide valve. 5 Air feed system: air feed-flow controller, air heater, blower, air flow meter, vent. 6 Stripper system: stripper flow meter, stripper flow controller, stripper valve, steam feed. This initial hierarchy is almost identical with the actual hierarchy used in CATCRACKER. The difference between the two is a result of additional information that was uncovered during interviews with the experts. For example, the experts revealed that catalyst loss is one of the major diagnostic concerns about this process. As anticipated, this is an example of a diagnostic category that is readily associated with the reactor—regenerator system but not with any of its subsystems, and thus this category helps to fill in the ‘‘?’’ in the figure.
Groups I and II correspond to functionally distinguishable subsystems consisting of the feed-preheat
PX
Fig. 1. Catcracker decomposition from Step 1.
Fig. 2. Catcracker decomposition from Step 2.
The classification hierarchy for the PX system was initialized using the same strategy as for
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Fig. 3. Catcracker decomposition from Step 3.
Fig. 4. Catcracker decomposition from Step 4.
CATCRACKER to investigate how well the initialization procedure generalizes. As indicated earlier, the PX hierarchy is very large with over 500 nodes. For the purpose of illustration, we expand only on a small portion of the production hierarchy to demonstrate the application of the guidelines and to compare to the initialization procedure for CATCRACKER. The top-level decomposition for PX is based on the objectives of the plant. For the purpose of developing this system the emphasis was on two distinct objectives, namely, production of a target amount of paraxylene and maintaining its quality above a certain minimum. These two objectives are represented explicitly as nodes immediately below the top node (see the final hierarchy below). The production branch can be functionally decomposed into the primary systems: product separation system (encompassing crystallizers and centrifuges), the refrigeration system, and the feed system (we are diagnostically only concerned with feed quality as it affects production). This leads to the decomposition as shown in Fig. 5. Decomposition continues by drawing boundaries around equipment items on the process flowsheet within the separation and refrigeration systems, respectively. However, it is not immediately apparent from a systems and subsystems analysis of the flowsheet that separation is carried out in two stages that involve multiple plant systems. The first stage is
Fig. 5. PX decomposition reflecting primary systems.
responsible for recovering as much paraxylene as possible, while the second stage is responsible for purification. Decomposing the separation system into its constituent stages captures key interactions that are embedded in the plant operation and therefore need to be accounted for in the diagnostic decomposition. These first- and second-stage nodes are comparable to the formation of a reactor—regenerator node in CATCRACKER and are represented explicitly in the decomposition as shown in Fig. 6. Again, to constrain the size of the example and focus only on the application of the guidelines, we expand only the first-stage node. Arguments relevant to the second stage are similar. With respect to the first stage, the temperature of the contents of each crystallizer is controlled indirectly by controlling the pressure of refrigerant in the head drums. The existence of this control loop gives strong evidence for interactions between the head drums and the crystallizers. The continuous flow of refrigerant between
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Fig. 6. PX decomposition showing stage grouping.
these equipment items provides additional confirmation of the existence of strong interactions. As stated above, these interactions are analogous to the tight coupling of reactor and regenerator systems of CATCRACKER and similarly lead to decomposition considerations which are analogous. Specifically, for each stage of the separation system, the crystallizers and refrigerant drums of that stage are grouped together for diagnosis rather than considering the refrigerant drums to be part of the refrigeration system, a subsystem view. As such there is high likelihood for needing behavioral nodes to capture cross-system behaviors. As with CATCRACKER this is indicated by a ‘‘?’’. Further decomposition of each stage in the separation system is done on the basis of subsystems and regulatory systems represented on the flowsheet within the first-stage boundaries already delineated. The detailed flowsheet immediately reveals the existence of crystallizer-level control, refrigerant-drum level control, and crystallizer-temperature control as the regulatory systems. Lastly, the centrifuges form a separate subsystem. Combining these
Fig. 7. PX decomposition showing first stage expansion.
Fig. 8. PX decomposition showing component tip nodes.
considerations leads to the next level decomposition (Fig. 7). Finally, the initialized hierarchy is enhanced structurally with equipment components considered to be failure points in the plants (Fig. 8). This initialized hierarchy was generated with only an introduction to the PX process by the process experts. It was then used as the basis for constructing the PX diagnostic system through detailed interactions with the process experts. The initialized hierarchy can be compared with the corresponding segment of the system that was actually implemented (Fig. 9). A comparison shows that the initialized and final hierarchies are close. The main differences lie in the identification of the behavioral nodes, i.e. resolution of the ‘‘?’’, and fill-in at the tip node level with modes of failures. With respect to the behavioral nodes, the final hierarchy shows that two nodes were identified. These were crystrallizer heat transfer (HT X Fer) and crystallizer overloaded. Neither of these nodes is a physical system or subsystem. Rather they represent process behavioral considerations that cut across portions of relevant systems and subsystems. This is immediately clear by looking at the expansion of crystallizer overloaded. Here we see, aspects of both the first-stage and second-stage systems brought into consideration. These are process considerations not accounted for in the system expansion of the first stage or the second stage (not shown).
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Fig. 9. PX decomposition used in diagnostic systems.
Conclusions Malfunction hierarchies provide modularity for organizing large-scale knowledge-based diagnostic systems. An engineering understanding of P & I diagrams and flowsheets, along with information about operating objectives, provide the means to initially structure a malfunction hierarchy. Flowsheets are useful for extracting functional and structural knowledge; regulatory control loops and recycle streams provide information for identifying functionally interacting equipment items and identifying the locations of behavioral decompositions. Necessary interactions with the domain experts, whose time is often expensive, can be reduced by using knowledge gathered from these sources to structure an initial hierarchy, which then provides organization and focus to knowledge-acquisition sessions. The guidelines presented are expected to be generic for many process plants, although none of them will be applicable to all. Acknowledgements We gratefully acknowledge the support of the Exxon Research Foundation in the development of these knowledge structuring guidelines References Catino, C.A. and Ungar, L.H. (1995) A model-based approach to automated hazard identification of chemical plants. A.I.Ch.E. J. 41(1), 97—109.
Chen, L.W. and Modarres, M. (1992) Hierarchical decision process for fault administration. Comput. Chem. Engng 16(5), 425—448. Finch, F.E. and Kramer, M.A. (1988) Narrowing diagnostic focus using functional decomposition, A.I.Ch.E. J. 34(1), 25—36. Gomez, F. and Chandrasekaran, B. (1984) Knowledge organization and distribution for medical diagnosis. In W.J. Clancey and E.H. Shortliffe (Eds.), Readings in Medical Artificial Intelligence (pp. 320—338). Reading, MA: Addison-Wesley. Kramer, M.A. (1987) Malfunction diagnosis using quantitative models with non-Boolean reasoning in expert systems, A.I.Ch.E. J. 33(Jan), 1130—140. Prasad, P.R. (1993) Diagnostic knowledge-based systems for continuous chemical processes: Enhanced knowledge acquisition and generic problem-solving framework. Ph.D. Dissertation, The Ohio State University. Prasad, P.R. and Davis, J.F. (1993) Generic nature of the task-based framework for plant-wide diagnosis. In Artificial Intelligence in Process Engineering, A.I.Ch.E. Annual Meeting, St. Louis. Ramesh, T.S., Davis, J.F. and Schwenzer, G.M. (1992) Knowledge-based diagnostic systems for continuous process operations based upon the task framework. Comput. Chem. Engng 16, 109—127. Shafaghi, A., Andow, P.K. and Lees, F.P. (1984) Fault tree synthesis based on control loop structure. Chem. Engng Res Des. 62, 101—110. Sravana, K.K. (1994) Diagnostic knowledge-based systems for batch chemical processes: Hypothesis queuing and evaluation. Ph.D. Dissertation, The Ohio State University. Wilcox, N.A. and Himmelblau, D.M. (1994) The possible cause and effect graphs (PCEG) model for fault diagnosis — I methodology. Comput. Chem. Engng 18(2), 103—116.