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An application of expert systems technology in process engineering
AN APPLICATION OF EXPERT SYSTEMS TECHNOL OG Y IN PROCESS ENGINEERING
/
-/ 7
/
J. M. Davidson
Expert systBms have found wide applicability in Wocossengineering, particularly in design and plant operations. Most prominent among these appiicatio¢~ are fault diagnostic and operations g u l d ~ expert systems. This paper describes an expert system designed to allow operators to diagnose and correct a polymerization pcocov~and correct for off-spec matedal batch to batch resulting from chemisW and operatk)n dovtatiocm.The system looks at interaction, multiple cause and effect, and the use of ratios and is designed by partitioning the process based on expert knowledge.
INTRODUCTION
Artificial intelligence (AI) is an advanced form of computer science. The goal of AI is to construct computer systems that can emulate the thinking and reasoning processes of man. For practical purposes, the field of AI may be divided into two areas: (1) Symbolic Programming - manipulating symbols that represent knowledge about some domain to provide a higher level of problem solving (2) Cognitive Science- developing systems to model learning and reasoning, speech, language, vision, common sense, and the generation of heuristics (good rules of judgment). Computer systems directly incorporating specific knowledge about a domain are called "knowledge-based" systems. The acquisition and manipulation of knowledge (set of data together with the inferences and decisions made in regards to that data) is the basis for current progress in AI. Cognitive science lacks a fundamental basis and represents a "moving target" in AI research. Practical applications at present are built using compiled, or "shallow," human expert knowledge in a narrow domain. Several case studies representing solutions to problems in ISSN 0019-0578/89J01/0031/05/$2.50 O ISA 1989
the domain are used to guide rule development and gradually "u'ain" the system to get an acceptable answer. New problems are posed to the system and the program "sorts" through the knowledge in some organized fashion to propose a "best" answer to the problem. Another approach is "model-based reasoning" [1] in which systems are able to deduce operation patterns from a description of the system's interconnections and individual component functionality. These programs are called "expert systems" because "expertise" in some form is directly integrated into the program's design and provides the means for expressing the pa~icular knowledge that comprises the system. Generally, an expert system is limited to the domain for which it was builL It cannot extrapolate and provide a " n e w " (not contained within its knowledge base) answer. The expert system can cover a larger feasible solution space than a human expert, especially if the human fails to take all domain factors into account during problem solution. In this way, the expert system acts as an advisor to the human expert, allowing him to consider a wider array of potential solutions to a problem. Some of the benefits of an AI approach to problem solving are the following: ISA Transactions. Vol. 28, No. 1
31
(1) Allows a novice to perform at an expert's level in a domain (2) Provides a method to easily transfer expert knowledge (3) Insures consistency and quality control of problem solution These factors taken together can spell productivity inc~ and reduced operating costs in the plant by insuring prompt and human-like decision-making responses to upsets and operation changes. The general guidelines to follow in choosing an application are as follows: (1) Task fairly well-defined (no common sense knowledge) (2) Uncertain and unknown data must be dealt with (3) Domain knowledge poorly organized and hard to update (4) Task mostlybased on judgment or qualitative information (5) Task is important and performed frequently (6) Productivity improvement warrants expense of development (7) Expert available on regular basis to help develop knowledge base (8) Case studies available to check system performances (9) User available to do thorough test of system for field use An expert system consists of an inference engine, knowledge base, input/output (user interface), and a global data area where the progress of the analysis is monitored. Such systems can be built using languages like LISP or C or by using "shells"--programs that provide all the above components except the knowledge base (which is designed by the
user). ENGINEERING APPLICATIONS The two primary areas of interest in process engineering are design and plant operations. The objective is to minimize cost while maximizing productivity. A wide array of expert systems have now been fielded. [2] Design requires knowledge of process subsystems, their interrelationships and interactions, and the global (overall) X3
behavior of the system (for optimization). An expert system could incorporate "good" design practices and principles directly as part of the design process. Optimizers can then adjust numerical parameters in accordance with the "best" (approved) design methodology. Moreover, details on hazards and safety and control strategies can be an integral part of the design environmenL Operation requires knowledge of how to manipulate "patterns" of process response to satisfy constraints and eliminate process perturbations due to feedstock changes, ambient and operating changes, market conditions for products, etc. An expert system can recognize these patterns of operation and combine them with the knowledge base and infer how to reconfignre plant operation in a more timely fashion. The final manipulation would consist of, in part, adjustments to control loop set points. [3, 4] This paper will present a diagnostic expert system designed to recommend corrective actions and offer explanations for causes relating to off-spec polymer production. The knowledge base is a compilation of an expert chemist's expertise in diagnosing deviations in the polymer extrusion and cure rate and the occurrence of severely degraded polymer product due to chemistry imbalances. EXPERT SYSTEM DESIGN A flow diagram of the polymer process is shown in Figure 1. The two feeds X1 and X2 are reacted in unit 1, then complexed with reactant X3 in unit 2. Unit 3 is the main polymerization reactor where reactant X4 and the complexed feed from unit 2 form the polymer. Unit 4 is a separation and purification process that recycles X3 and X4 back to mix with fresh reactant makeup streams of X3 and X4. A series of interviews with the chemist defined the basic chemical and operational processes at work in producing the polymer. Next, the problem was partitioned based on the location of the problems with respect to the units and to the fundamental causes for imbalances in stoichiometry that lead to deviations in extrusion and/or cure rate from desired
MAKEUP
X4
RECYCLE X3, X4
xl x2
_[ ul
L
U2
L
.I o4
U3
rl
TO FINISHING
r2
WASTES
Figure 1. Polymer Process 32
ISA Transactions. Vol. 28, No. 1
RAW POLYMER
FEED PREPARATION
(ul)
COMPLEX FORMATION (U2)
X
X
POLYMERIZATION
(U3)
PROBLEM Pl
X
X
P2
X
P3
X
P4
X
X
P5
X
P6
Figure 2. Problems/Units
SYMPTOM
ER
CR
Pl
PZ
P3
P4
P~
1OR2
HIGH
HIGH
X
---
X
---
1 OR2
HIGH
HIGH
---
X
X
---
1 OR2
HIGH
HIGH
1 OR2
HIGH
HIGH
1 OR2
HIGH
HIGH
1 OR2 1 OR2 3
NOMINAL HIGH
X X
X
......
P6
CAUSE
---
2
---
3
X
4
"'"
S
HIGH
X
---
3
LOW
X
---
1
.Dnl
X
---
1
X
---
X
---
@
•
ER = EXTRUSION RATE CR = CURE RATE
Figure 3. Symptoms/Problems/Causes ISA Transactions. Vol. 28, No. 1
33
values. Both chemical and physical processes cause the polymer to go off-spec. The opcxator's job is to maintain proper batch recipes and operating conditions to maintain proscribed target polymer properties. This partitioning is shown in Figures 2 and 3. In this way, the "decision" trees associated with the IF-THEN-ELSE rules can bc pruned beforehand using the process knowledge available. For example, symptoms 1 (extrusion rate) and 2 (cure rate) in Figure 3 for level values of "high" simultaneously involve all six problem areas (P1-P6) and all five causes (C1-C5). For the symptom set where symptom 1 is "nominal" and symptom 2 is "high," only P1 is a contributing factor. Symptom 3 is the occurrence of a severely degraded polymer product involving problem areas P1, P2, and P4. Control information in the form of "recta-rules" (rules that tell the expert system what combination of knowledge rules to allow the system to "jump" to certain conclusions when sufficient data have been collected during a diagnosis to ascertain the causes for the symptoms. The design also allows the selection of a range of hypotheses that are tested and ranked according to confidence factors, and, concurrenfly, information is obtained that is used to test "pattern rules" that, if fired, will indicate specific overall causes (mischarged X1, analysis error on X2, improper unit operation, etc.) and provide both advice on what to check and an explanation of how that particular conclusion was reached. The' 'pattern rules" effectivelycapture the modularity of the process in the production rule format but are not as effective as the use of frames and object-oriented programming. However, for purposes of prototyping, it was considered a reasonable compromise. Figure 4 illustrates the format of these rules. Unit 4 was not involved in the analysis. The antecedent components are used to focus on selection of a symptom (in some cases), a focus hypothesis (in some cases used to narrow down the search), and the individual patterns of terms that describe a particular event (like the fact that unit 2 has no contribution to make to the occurrence of a "low flow of X3"). Finally, the consequent pan of the rule specifies which "fault" the system would focus on for further analysis (for example, "check for an analysis error on
X2"). DISCUSSION Several difficulties were overcome during the development of this system. First, a great deal of interaction existed among the process modules as would be expected for a process unit. This is in contrast to simpler systems that can be diagnosed by a "repair manual" logic. This made rule formation delicate in that loops of logic had to be averted yet still allow the expert system to come to a valid conclusion. Associated with process units also is the problem of multiple causes and multiple effects. For example, an upset unit can cause a problem that exhibits itself as a set of effects (heat transfer swings and reactor pressure excursions). Conversely, several effects can be indicative of several causes, so a backward chaining procedure has to unravel the individual
34
ISA Transactions" Vol. 28, No. 1
UI: U2: U3:
Feed Preparation Complex Formation Polymerization Reactor
PATTERN RULE FORMAT GENERALSYMPTOM FOCUSHYPOTHESIS (PATTERN: U1) (PATTERN: U2) (PATTERN: U3) FAULT FOCUS
Figure 4. Process Modules
causes before making assignments of confidence factors to each cause. A second situation involved the handling of "induced causes" or problems that arose because one problem (a"low flow of X3") was inversely related to another problem (a corresponding "high flow of X4"). In these cases, the hypotheses were allowed to not only make assignment of their own confidence factors but also jointly make assignments of confidence factors on these "induced causes" as well. In this way, during a session, the system can sort out the primary causes and also provide a list of canscs that arc related by the interactions inherent in the process. Another problem was that two ratios, rl and r2, were used with the pattern rules to aid in ascertaining the final call that was to be made on the advice and explanation of the specific diagnosis. The use of ratios introduces the "fan-out" problem in which a combinatorial set of inputs (involved in the ratio) can lead to similar ratio values. That is, given the inputs you can determine the value of the ratio but given the ratio you cannot necessarily determine the unique inputs that gave that ratio value. A procedure was developed that allows the system to determine for itself whether or not analysis can proceed so as to disallow invalid conclusions.
In general, this expert system application has been very successful and has reproduced a wide variety of real cases for off-spec polymer. An obvious extension would be to redo the knowledge in a frame-and-rule format and incorporate models of process to provide a "model-based" reasoning basis for in-depth diagnosis. SUMMARY An expert system for diagnosis of off-spec polymer has been developed as a prototype. The program dealt with questions involving process interaction, multiple cause/effect, and the use of ratios in coming to valid conclusions. A wide variety of cases studies were tested and reproduced and the system provided to the polymer pilot plant (before plant demonstration) for testing. A user interface is being conslructed to facilitate use of the program. The initial system
was successfully developed on an IBM PC/AT TM using a commercially available expert system shell written in C. REFERENCES (1) Venkatasubramanian, V., and Rich, Steven I-I., "ModelBased Reasoning in Fault Diagnosis," AIChE Spring Meeting, March 29-April 2, 1987, Houston, Texas. (2) Baur, Paul S., 1987, "Development Tools Aid in Fielding Expert Systems," InTech, April pp. 7-15. (3) Kaemmerer, William F., 1985, "Using Process Models with Expert Systems to Aid Process Control Operators," Amer. Control Conf. (4th 1985, Boston, Mass., June 19-21, pp. 892-897. (4) Kerridge, A. E., 1987, "Operators Can Use Expert Systems," Hydrocarbon Processing, September, pp. 97-105.
ISA Transactions. Vol. 28, No. 1
35
36
ISA Transactions. Vol. 28, No. 1
/
-/ 7
/
J. M. Davidson
Expert systBms have found wide applicability in Wocossengineering, particularly in design and plant operations. Most prominent among these appiicatio¢~ are fault diagnostic and operations g u l d ~ expert systems. This paper describes an expert system designed to allow operators to diagnose and correct a polymerization pcocov~and correct for off-spec matedal batch to batch resulting from chemisW and operatk)n dovtatiocm.The system looks at interaction, multiple cause and effect, and the use of ratios and is designed by partitioning the process based on expert knowledge.
INTRODUCTION
Artificial intelligence (AI) is an advanced form of computer science. The goal of AI is to construct computer systems that can emulate the thinking and reasoning processes of man. For practical purposes, the field of AI may be divided into two areas: (1) Symbolic Programming - manipulating symbols that represent knowledge about some domain to provide a higher level of problem solving (2) Cognitive Science- developing systems to model learning and reasoning, speech, language, vision, common sense, and the generation of heuristics (good rules of judgment). Computer systems directly incorporating specific knowledge about a domain are called "knowledge-based" systems. The acquisition and manipulation of knowledge (set of data together with the inferences and decisions made in regards to that data) is the basis for current progress in AI. Cognitive science lacks a fundamental basis and represents a "moving target" in AI research. Practical applications at present are built using compiled, or "shallow," human expert knowledge in a narrow domain. Several case studies representing solutions to problems in ISSN 0019-0578/89J01/0031/05/$2.50 O ISA 1989
the domain are used to guide rule development and gradually "u'ain" the system to get an acceptable answer. New problems are posed to the system and the program "sorts" through the knowledge in some organized fashion to propose a "best" answer to the problem. Another approach is "model-based reasoning" [1] in which systems are able to deduce operation patterns from a description of the system's interconnections and individual component functionality. These programs are called "expert systems" because "expertise" in some form is directly integrated into the program's design and provides the means for expressing the pa~icular knowledge that comprises the system. Generally, an expert system is limited to the domain for which it was builL It cannot extrapolate and provide a " n e w " (not contained within its knowledge base) answer. The expert system can cover a larger feasible solution space than a human expert, especially if the human fails to take all domain factors into account during problem solution. In this way, the expert system acts as an advisor to the human expert, allowing him to consider a wider array of potential solutions to a problem. Some of the benefits of an AI approach to problem solving are the following: ISA Transactions. Vol. 28, No. 1
31
(1) Allows a novice to perform at an expert's level in a domain (2) Provides a method to easily transfer expert knowledge (3) Insures consistency and quality control of problem solution These factors taken together can spell productivity inc~ and reduced operating costs in the plant by insuring prompt and human-like decision-making responses to upsets and operation changes. The general guidelines to follow in choosing an application are as follows: (1) Task fairly well-defined (no common sense knowledge) (2) Uncertain and unknown data must be dealt with (3) Domain knowledge poorly organized and hard to update (4) Task mostlybased on judgment or qualitative information (5) Task is important and performed frequently (6) Productivity improvement warrants expense of development (7) Expert available on regular basis to help develop knowledge base (8) Case studies available to check system performances (9) User available to do thorough test of system for field use An expert system consists of an inference engine, knowledge base, input/output (user interface), and a global data area where the progress of the analysis is monitored. Such systems can be built using languages like LISP or C or by using "shells"--programs that provide all the above components except the knowledge base (which is designed by the
user). ENGINEERING APPLICATIONS The two primary areas of interest in process engineering are design and plant operations. The objective is to minimize cost while maximizing productivity. A wide array of expert systems have now been fielded. [2] Design requires knowledge of process subsystems, their interrelationships and interactions, and the global (overall) X3
behavior of the system (for optimization). An expert system could incorporate "good" design practices and principles directly as part of the design process. Optimizers can then adjust numerical parameters in accordance with the "best" (approved) design methodology. Moreover, details on hazards and safety and control strategies can be an integral part of the design environmenL Operation requires knowledge of how to manipulate "patterns" of process response to satisfy constraints and eliminate process perturbations due to feedstock changes, ambient and operating changes, market conditions for products, etc. An expert system can recognize these patterns of operation and combine them with the knowledge base and infer how to reconfignre plant operation in a more timely fashion. The final manipulation would consist of, in part, adjustments to control loop set points. [3, 4] This paper will present a diagnostic expert system designed to recommend corrective actions and offer explanations for causes relating to off-spec polymer production. The knowledge base is a compilation of an expert chemist's expertise in diagnosing deviations in the polymer extrusion and cure rate and the occurrence of severely degraded polymer product due to chemistry imbalances. EXPERT SYSTEM DESIGN A flow diagram of the polymer process is shown in Figure 1. The two feeds X1 and X2 are reacted in unit 1, then complexed with reactant X3 in unit 2. Unit 3 is the main polymerization reactor where reactant X4 and the complexed feed from unit 2 form the polymer. Unit 4 is a separation and purification process that recycles X3 and X4 back to mix with fresh reactant makeup streams of X3 and X4. A series of interviews with the chemist defined the basic chemical and operational processes at work in producing the polymer. Next, the problem was partitioned based on the location of the problems with respect to the units and to the fundamental causes for imbalances in stoichiometry that lead to deviations in extrusion and/or cure rate from desired
MAKEUP
X4
RECYCLE X3, X4
xl x2
_[ ul
L
U2
L
.I o4
U3
rl
TO FINISHING
r2
WASTES
Figure 1. Polymer Process 32
ISA Transactions. Vol. 28, No. 1
RAW POLYMER
FEED PREPARATION
(ul)
COMPLEX FORMATION (U2)
X
X
POLYMERIZATION
(U3)
PROBLEM Pl
X
X
P2
X
P3
X
P4
X
X
P5
X
P6
Figure 2. Problems/Units
SYMPTOM
ER
CR
Pl
PZ
P3
P4
P~
1OR2
HIGH
HIGH
X
---
X
---
1 OR2
HIGH
HIGH
---
X
X
---
1 OR2
HIGH
HIGH
1 OR2
HIGH
HIGH
1 OR2
HIGH
HIGH
1 OR2 1 OR2 3
NOMINAL HIGH
X X
X
......
P6
CAUSE
---
2
---
3
X
4
"'"
S
HIGH
X
---
3
LOW
X
---
1
.Dnl
X
---
1
X
---
X
---
@
•
ER = EXTRUSION RATE CR = CURE RATE
Figure 3. Symptoms/Problems/Causes ISA Transactions. Vol. 28, No. 1
33
values. Both chemical and physical processes cause the polymer to go off-spec. The opcxator's job is to maintain proper batch recipes and operating conditions to maintain proscribed target polymer properties. This partitioning is shown in Figures 2 and 3. In this way, the "decision" trees associated with the IF-THEN-ELSE rules can bc pruned beforehand using the process knowledge available. For example, symptoms 1 (extrusion rate) and 2 (cure rate) in Figure 3 for level values of "high" simultaneously involve all six problem areas (P1-P6) and all five causes (C1-C5). For the symptom set where symptom 1 is "nominal" and symptom 2 is "high," only P1 is a contributing factor. Symptom 3 is the occurrence of a severely degraded polymer product involving problem areas P1, P2, and P4. Control information in the form of "recta-rules" (rules that tell the expert system what combination of knowledge rules to allow the system to "jump" to certain conclusions when sufficient data have been collected during a diagnosis to ascertain the causes for the symptoms. The design also allows the selection of a range of hypotheses that are tested and ranked according to confidence factors, and, concurrenfly, information is obtained that is used to test "pattern rules" that, if fired, will indicate specific overall causes (mischarged X1, analysis error on X2, improper unit operation, etc.) and provide both advice on what to check and an explanation of how that particular conclusion was reached. The' 'pattern rules" effectivelycapture the modularity of the process in the production rule format but are not as effective as the use of frames and object-oriented programming. However, for purposes of prototyping, it was considered a reasonable compromise. Figure 4 illustrates the format of these rules. Unit 4 was not involved in the analysis. The antecedent components are used to focus on selection of a symptom (in some cases), a focus hypothesis (in some cases used to narrow down the search), and the individual patterns of terms that describe a particular event (like the fact that unit 2 has no contribution to make to the occurrence of a "low flow of X3"). Finally, the consequent pan of the rule specifies which "fault" the system would focus on for further analysis (for example, "check for an analysis error on
X2"). DISCUSSION Several difficulties were overcome during the development of this system. First, a great deal of interaction existed among the process modules as would be expected for a process unit. This is in contrast to simpler systems that can be diagnosed by a "repair manual" logic. This made rule formation delicate in that loops of logic had to be averted yet still allow the expert system to come to a valid conclusion. Associated with process units also is the problem of multiple causes and multiple effects. For example, an upset unit can cause a problem that exhibits itself as a set of effects (heat transfer swings and reactor pressure excursions). Conversely, several effects can be indicative of several causes, so a backward chaining procedure has to unravel the individual
34
ISA Transactions" Vol. 28, No. 1
UI: U2: U3:
Feed Preparation Complex Formation Polymerization Reactor
PATTERN RULE FORMAT GENERALSYMPTOM FOCUSHYPOTHESIS (PATTERN: U1) (PATTERN: U2) (PATTERN: U3) FAULT FOCUS
Figure 4. Process Modules
causes before making assignments of confidence factors to each cause. A second situation involved the handling of "induced causes" or problems that arose because one problem (a"low flow of X3") was inversely related to another problem (a corresponding "high flow of X4"). In these cases, the hypotheses were allowed to not only make assignment of their own confidence factors but also jointly make assignments of confidence factors on these "induced causes" as well. In this way, during a session, the system can sort out the primary causes and also provide a list of canscs that arc related by the interactions inherent in the process. Another problem was that two ratios, rl and r2, were used with the pattern rules to aid in ascertaining the final call that was to be made on the advice and explanation of the specific diagnosis. The use of ratios introduces the "fan-out" problem in which a combinatorial set of inputs (involved in the ratio) can lead to similar ratio values. That is, given the inputs you can determine the value of the ratio but given the ratio you cannot necessarily determine the unique inputs that gave that ratio value. A procedure was developed that allows the system to determine for itself whether or not analysis can proceed so as to disallow invalid conclusions.
In general, this expert system application has been very successful and has reproduced a wide variety of real cases for off-spec polymer. An obvious extension would be to redo the knowledge in a frame-and-rule format and incorporate models of process to provide a "model-based" reasoning basis for in-depth diagnosis. SUMMARY An expert system for diagnosis of off-spec polymer has been developed as a prototype. The program dealt with questions involving process interaction, multiple cause/effect, and the use of ratios in coming to valid conclusions. A wide variety of cases studies were tested and reproduced and the system provided to the polymer pilot plant (before plant demonstration) for testing. A user interface is being conslructed to facilitate use of the program. The initial system
was successfully developed on an IBM PC/AT TM using a commercially available expert system shell written in C. REFERENCES (1) Venkatasubramanian, V., and Rich, Steven I-I., "ModelBased Reasoning in Fault Diagnosis," AIChE Spring Meeting, March 29-April 2, 1987, Houston, Texas. (2) Baur, Paul S., 1987, "Development Tools Aid in Fielding Expert Systems," InTech, April pp. 7-15. (3) Kaemmerer, William F., 1985, "Using Process Models with Expert Systems to Aid Process Control Operators," Amer. Control Conf. (4th 1985, Boston, Mass., June 19-21, pp. 892-897. (4) Kerridge, A. E., 1987, "Operators Can Use Expert Systems," Hydrocarbon Processing, September, pp. 97-105.
ISA Transactions. Vol. 28, No. 1
35
36
ISA Transactions. Vol. 28, No. 1