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Implementation of knowledge maintenance modules in an expert system for fault diagnosis of chemical process operation
Expert Systems with Applications 28 (2005) 249–257 www.elsevier.com/locate/eswa
Implementation of knowledge maintenance modules in an expert system for fault diagnosis of chemical process operation Yu Qian*, Minjian Zheng, Xiuxi Li, Li Lin School of Chemical and Energy Engineering, South China University of Technology, Guangzhou 510640, People’s Republic of China
Abstract Based on the authors previous work of developing an expert system for fault diagnosis of chemical processes, this paper presents principles and approaches of the knowledge base maintenance in the expert system. Development and implementation of maintenance modules for the knowledge base in the expert system for fault diagnosis of fluid catalytic cracking unit (FCCU) are reported in detail. Algorithms are proposed for integrality verification of five inconsistencies among heuristic rules: contradiction, redundancy, subsumption, circulation, and reclusion. The modules ensure the knowledge base and the expert system to work stably and efficiently. DELPHI and SQL SERVER are adopted in developing the modules. Application in FCCU fault diagnosis expert system shows that the modules work rationally and efficiently with user-friendly interface, besides its strong interactive feature and better performance of the expert system. The modules have been in continuous use in the expert system of fault diagnosis of FCCU installation in a refinery for the last two years. q 2004 Published by Elsevier Ltd. Keywords: Expert system; Knowledge base (KB); Knowledge maintenance; Conflict verification; Fluid catalytic cracking unit (FCCU)
1. Introduction Based on the advances in artificial intelligence and computer technique, expert systems make use of human expertise in many fields to reason and judge (Cai & Xu, 2000). By imitating the reasoning processes of human experts, such systems are able to solve complex problems, which can only be solved by human experts in their domain. The authors developed earlier an expert system for fault diagnosis of process operations of the fluid catalytic cracking unit (Qian, Li, Jiang, & Wen, 2003). The fluid catalytic cracking unit (FCCU) is one of the most important production processes of the crude oil refining industry. In FCCU, heavy oil is cracked into lower molecular weight hydrocarbons, which are either blended to finished products or are routed to downstream units for further processing. Gasoline is the major end products. FCCU generally consists of three parts: reactor–regenerator, fractionation, and absorption–stabilization system. A simplified flow sheet of main parts of FCCU is shown in Fig. 1. The left part of * Corresponding author. Tel.: C86 20 87112046; fax: C86 20 87113735. E-mail address: [email protected] (Y. Qian). 0957-4174/$ - see front matter q 2004 Published by Elsevier Ltd. doi:10.1016/j.eswa.2004.10.005
the figure is the reactor and regenerator, while the right part is the fractioning distillation column series. At the bottom right corner of the figure, the feedstock heavy oil is introduced and heated in the furnace up to a temperature of 230–250 8C. Recycle stock pumped from the cycle stock tank, mixed with fresh feedstock at the entrance of the blender, go through four groups of atomizing nozzle prior to entering the riser of the reactor. In the riser, the material flow meets the regenerated catalyst at a high temperature of 730–750 8C from the second dense bed in the regenerator; thus, its temperature rises rapidly while flowing upwards with catalyst. The materials are catalytically cracked into a series of products of gasoline, light diesels fuel, liquefied gas, stripped gas, slurry oil, and other gas products, while coke aggregates on the surface of catalyst. The reacted oil gas, at a temperature of 510 8C, is rapidly separated from the coke aggraded catalyst via the sharp separator at the exist of riser, in order to avoid further secondary reaction. The oil gas in the settler passes through the plenum chamber and then enters the fractionation chamber. Among the different facilities in FCCU, the reactor–regenerator is the most critical equipment where faults and abnormal situations occur frequently. The faults may lead to emergent shut down of the installation. Burning and burst of the equipment
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Fig. 1. A simplified flowsheet of main parts of FCCU.
and pipes may happen, which could injure personnel in the worst cases. The expert system was developed and applied to analysing and estimating root-causes of the faults in abnormal situations, and propose reasonable correction measures to ensure a safe and steady FCCU operation (Bai & Huag, 1994). During the applications of the expert system to FCCU, it is found that quality of the knowledge base is critical to the performance of the expert system. The knowledge base is continuously in a process of improvement (Huang, Li & Qian, 2001). On one hand, human experts would add new knowledge to KB or modify the existing knowledge heuristics when new situations occur; on the other hand, as the amount of knowledge in the KB increases, and contents of the knowledge become more complicated, there may be contradiction, redundancy, subsumption, circulation, and reclusion among heuristic rules (Lu, 1998). The maintenance of KB becomes an important issue to a successful and efficient expert system (Qian, Huang & Li, 2000). How to guarantee the consistency of the knowledge? How does the knowledge in KB work efficiently? These are critical technical problems in the maintenance of KB, which is investigated and implemented in detailed in this work.
2. Structure of the expert system and maintenance of the knowledge base The expert system developed in this work consists of monitor, inference machine and explanation module, knowledge base (KB), knowledge acquisition and management, integrated databases, and the user interface. The structure of the expert system is shown in Fig. 2. The knowledge base stores common knowledge and domain knowledge of process diagnosis for FCCU operation.
The diagnosis knowledge is represented in different forms and structures. There are numerous expert rules in the knowledge base. Knowledge acquisition and management module transfers the input expert knowledge into the interior knowledge of the expert system (Jiang, 2002). An Integrality verification module assures that the newly added knowledge is not in conflict with the ever-existing knowledge (Li, Jiang & Qian, 2001). Maintenance of the knowledge base including following operation: add, delete, modify, and adjust storage organization of rules. (1) Add rules. The newly added rules must be verified, including accuracy verification, redundancy verification and consistency verification. There are four principles in the operation of adding rules: † If new rules can be represented in existing rules, the new one need not be added and the KB need not be changed. † If new rules with parts of existing rules together can replace some old ones, the new rules may be added to the KB while the replaceable rules should be deleted. † If the newly added knowledge is in conflict with existing knowledge, domain experts shall be consulted to verify which one is more appropriate to be accepted, while the conflict one should be deleted or modified accordingly. † If the newly added knowledge is not in conflict with the ever-existing knowledge, the new rules may be added to the KB. (2) Delete rules. The useless rules should be adjusted or deleted in time. It is very important to verify the KB again after deleting rules to ensure the deletion is correct. (3) Modify rules. When modifying the rules, the safety of the KB and the consistency of knowledge must be ensured. There are three cases in the modification of rules:
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Fig. 2. Structure of the expert system for fault diagnosis of FCCU.
† Add antecedents or consequents of rules; † Reduce antecedents or consequents of rules; † Replace antecedents or consequents of rules. Generally speaking, in a rule, too many antecedents would lead to situation that no consequents could be inferred where it should be, while too few antecedents could lead to the other end that wrong consequents may be inferred where should not be inferred. So, a successful modification of rules is discriminating out errors in the rules, while maintaining consistency of the knowledge base. (4) Adjust storage organization of rules. Maintenance of KB aims at increasing operating efficiency. It is very necessary to adjust storage organization after modifying, adding and deleting knowledge many times. One of the principals of adjusting is that the most frequent used ‘busy rules’ are put on the top of the queue be first searched. The interface of the maintenance module of KB developed in this work is shown in Fig. 3. The module contains a number of operations: adding, deleting, modifying. In consideration of complex structure of FCCU expert system, huge quantity of instrumentations, and the request from knowledge engineers, a number of operations and functions of rules browse, rules search and help are implemented in the module. In the GUI, there are five buttons including adding, appending, deleting, modifying, help and exist at the top of the interface. In the left part of the interface, three windows show list of rules, rule name, and reliability, respectively. A figure of tree-type structure is used to show the inference process. The parameters of sub-composite
object, variable time axis, function of variable treatment, linked operating sign, function of object treatment, object time axis, comparison operator, numerical value and termination type. The result of analysis is shown in the right of the figure including consequents and measures. The window of result browse is at the bottom of the interface.
3. Verification of knowledge integrality Expert system is in fact an organized knowledge system. Thus, the performance and the efficacy depend on both of quantity and quality of the knowledge in the system (Wu & Yu, 2001). Detection and screening out of erroneous knowledge, updating out-of-date knowledge, improving the accuracy of existing knowledge are the most important link in the construction of expert system. 3.1. Validation and revision of knowledge In order to guarantee the consistency of rules in the KBS, effective measures have to be adopted to validate the knowledge. In generally, consistency is considered as situation where consequents are similar when antecedents in two rules are similar. It is difficult, however, to give a strict definition of similarity. In our work, five kinds of inconsistency in heuristic rules are discriminated and screened. They are contradiction, redundancy, subsumption, circulation, and reclusion. Contradiction. The situation where the conditions and consequences in a rule are contrary or, conditions in two rules are identical while their consequents are different.
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Fig. 3. Interface of the maintenance module of the knowledge base.
Conflict rules are not allowed to exist in KB at any situation. There are mainly situations as follows: (a) Self-contradiction. Contrary consequents may be directly inferred from same condition of a same rule; (b) Chain self-contradiction. Contrary consequents may be inferred from one of the rule antecedents, via inference chains; (c) Mutual contradiction. The conditions of two rules are identical, but their consequences are contrary. (d) Reclusive mutual contradiction. The correct rule is not in KB, but a contrary consequence exists in KB instead; (e) Transitive contradiction. The conditions of two rules are identical, but the contrary consequences are inferred through one of the inference chains; (f) Reclusive transitive contradiction. The correct rule chain in not in KB, but a contrary rule chain exits instead. Redundancy. Redundant rules depress efficiency of the system. Not only they raise size of the rule base, but also make the maintenance of KB harder. Thus, redundant rules have to be as screening out. In some circumstances, however, a number of redundant rules of special knowledge are necessary to improve performance of the KB system. There are two kinds of redundancies: (a) equivalent redundant rule: the antecedents and consequents of one rule are essentially equivalent to the antecedents and consequents of another rule. In this situation, one of the two rules has to be deleted. (b) Subsumed redundancy rule: the antecedent
(consequent) of one rule includes the antecedent (consequent) of another rule. This can be classified into five subclasses of redundancy rules: AND antecedents subsumed redundancy, OR antecedents subsumed redundancy, AND consequents subsumed redundancy, OR consequents subsumed redundancy, and the hybrid subsumed redundancy. Subsumption. If parts of antecedents in two rules are identical while one antecedent includes more additional proposition,and the consequents are also identical, it is considered as subsumption. Circulation. Circulation occurs when the antecedents and consequents of one rule are identical, or the intermediate/ final consequent of a rule is identical with the antecedent of this rule. Circular rules affect the system badly. On one hand, a circular inference may lead to nowhere; on the other hand, it may cause infinite loop, makes system paralyzed. There are the situations as follows: (a) self-circularity: the antecedent and the consequent of a rule have the same predicate; (b) chain of self-circularity: the antecedent and intermediate/final consequent of a rule have the same predicate; (c) reclusive circularity: right rules are not in KB, while the related rule of circular dependency exists in KB. Reclusion. If the antecedent and consequent of a rule is contrary, or the intermediate/final consequent of a rule is contrary to that of another rule, it is considered as reclusion. 3.2. Implementation of integrality verification The integrality verification algorithms presented in this paper includes three main modules: initialization,
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precondition, and inconsistency verification. The last module set includes modules for five classes of inconsistency: contradiction, redundancy, subsumption, circularity, and reclusion. The initialization module copies the contents of KB into the memory database and constructs the rule arrays in the memory. At first the contents of all rules are called into the variable sets of memory database. This includes contents of all antecedents, composite antecedents, sub-composite antecedents and consequents. Then, scattered contents are organized into the rule prototypes according to the structure of rules, and stored in the rule variables. At last, the global variables in the subsystem are initialized. The initialization module constructs the rule inference chain. It is also used to verify the complex inference chain in the process of integrality verification. The algorithm is implemented with a function module. The function stores the rule inference chain in the form of linked list, whose node is a storage unit and records the rule number. The number is the only ID of the rule. The link between nodes is implemented with the pointer. Since the length of the linked list varies dynamically, the doubly linked list, whose nodes have both prior pointer and hinder pointer, is applied in the function to make the operations of adding and deleting easier. A recursive algorithm is adopted in construction of the rule chain. The recursive function searches all rules in KB to find whether there is a condition of forming inference chain. When there is, a new node is created and the current rule number is recorded in order to link the node and inference chain. When there is not a inference chain, the recursive function would delete the node in the end of the chain and backtrack to search new inference chain recursively from the node in the end of the sub-chain. Therefore, the recursion is a process of searching network and involves a big amount of numerical computation. Due to unpredictable cycle number, it is very possible an extreme computational expenses for the system, in the worst cases, causes infinite loop and makes system paralyzed if those factors are not considered carefully in the operation of the system. Among five modules of inconsistency verification, here a working flow of contradiction verification is shown as an example: Contradiction verification
(2) Compare consequents Compare consequents of two rules randomly and variables of the couple of contrary consequents; If consequents are contrary, add the couple of rules to the recording arrays of rule inference chain. (3) Compare inference chain Search all the elements of inference chain arrays; Judge whether consequents of the couple of inference chains are contrary; If the antecedents are identical while the consequents are contrary, output the rule chains. End.
4. Integration into the expert system for FCCU fault diagnosis The case introduced in this paper is from an industrial installation of FCCU (Maoming Refinery, 1997). Table 1 The example rules used in case study Rule 2
Rule 3
Rule 7
Rule 8
Rule 9
Rule 10
Rule 14
(1) Compare antecedents Compare all antecedents of two rules randomly; Compare all composite antecedents of two rules randomly; If antecedent of observation type, compare contents of all sub-composite objects; If antecedent of consequent type, compare contents of all antecedents; Record the couple of rules whose antecedents are identical in the corresponding antecedents recording variable.
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Rule 20
Rule 21
Antecedent 1 Consequent 1 Measures 1 Measures 2 Antecedent 1 Consequent 1 Measures 1 Measures 2 Antecedent 1 Consequent 1 Measures 1 Measures 2 Antecedent 1 Consequent 1 Measures 1 Measures 2 Antecedent 1 Consequent 1 Measures 1 Antecedent 1 Consequent 1 Measures 1 Measures 2 Antecedent 1 Antecedent 2 Consequent 1 Measures 1 Antecedent 1 Consequent 1 Measures 1 Measures 2 Antecedent 1 Consequent 1 Measures 1 Measures 2
The liquid level of the oil refining tank rises Quantity of the rich gas rises Add fresh catalyst Raise temperature of the reaction The liquid level of the oil refining tank drops Quantity of the rich gas drops Increase volume of the main air inlet Manually operate the slide valve of regenerator The liquid level of the oil refining tank rises Quantity of the rich gas drops Raise temperature of the reaction Increase volume of the main air inlet Quantity of the rich gas drops Quantity of crude gasoline drops Open the vale of heat exchanger fully Increase the pressure of reactor and regenerator Quantity of crude gasoline drops The liquid level of the oil refining tank rises Reduce the duty of heat exchanger outside Quantity of rich gas rises The liquid level of the oil refining tank rises Add fresh catalyse Raise the temperature of the reaction Quantity of rich gas drops Temperature of the reactor and the regenerator rises Quantity of crude gasoline drops Raise the temperature of the reaction m_T109(1)Cm_T118(1) Extent of reaction drops Add fresh catalyse Raise the temperature reaction m_T109(1)Cm_T105(1) Extent of reaction rises Adjust the pressure Raise the temperature of the reaction
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As the guidelines for safety and steady operation, rules in the operation instruction manual are all concertedly recognized by the domain experts and have also been verified in long period of industrial practice. A set of rules is fabricated purposely to cover a range of five kinds of inconsistencies mentioned earlier. Twenty-one of rules are added into the original rule base, to be tested for knowledge integrality. A part of these rules are listed in Table 1. These nine rules and their combination represent most of the erroneous types mentioned above. Based on the proposed algorithms, the functions of contradiction verification, redundancy verification, subsumption verification, circularity verification and reclusion verification are all implemented. Functions of browsing rules and demonstrating the structure chart of the rules are also supported to make the verification easier. An interface of the knowledge maintenance system is shown in Fig. 4. In this snapshot, verification of rules is shown. In the top of the interface, there are a number of operation buttons including rule demo, verification on redundancy, circularity, contradiction, subsumption, and reclusion, respectively. The window of result preview shows the analysis process of contrary rule at the right part of the interface. 4.1. Result of the verification The results of verification are displayed in the process of the system running. Following listed are four examples of them. Example 1. Contradiction of rules. There is a contradiction between Rule 2 and Rule 7, as shown in the verification window of Fig. 4. The antecedents of Rule 2 and Rule 7 both
are ‘the liquid level of the oil refining tank rises’. The consequent of Rule 2 (quantity of rich gas rises), however, is contrary to the consequent of Rule 7 (quantity of rich gas drops). It is forbidden that Rule 2 and Rule 7 both exist in the rule base. As warned and suggested by the system, the expert deletes erroneous Rule 2, according to the real industrial practice. Example 2. Redundancy of rules. Rule 3 and Rule 7 are redundant. The antecedent 1 of Rule 3 (the liquid level of the oil refining tank drops) is contrary to the antecedent 1 of Rule 7 (the liquid level of the oil refining tank rises), while their consequents are identical (quantity of rich gas drops). Thus, Rule 3 and Rule 7 could not exist in the rule base at the same time. As warned and suggested by the system, the expert deletes erroneous Rule 3 according to the experience. Example 3. Subsumption of rules. Rule 14 subsumes Rule 8. There is a composite sub-antecedent in Rule 14. It is an OR relation between the sub-antecedent 1 (quantity of the rich gas drops) and the sub-antecedent 2 (temperature of the reactor and the regenerator rises). The antecedent of Rule 8 (quantity of the rich gas drops) is identical with the sub-antecedent 1 of Rule 14 and their consequents are also identical. Rule 8 shall be deleted. Example 4. Circularity of rules. Circularity is found in an inference chain composed of Rule 7, Rule 8, and Rule 9, as shown in the window of Fig. 4. From an antecedent that the liquid level of the oil refining tank rises, the consequent inferred from Rule 7 is that quantity of the rich gas drops, which is identical with the antecedent of Rule 8. Further inferred from Rule 8 is the consequent that quantity of the crude gasoline drops, which is identical with the antecedent of Rule 9. The consequent of Rule 9 turns out to be that
Fig. 4. The interface of rule integrality verification.
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the liquid level of the oil refining tank rises, which is actually identical with the antecedent of Rule 7. To break the circularity cycle, the system suggests delete Rule 8, which is also confirmed by the expert. 4.2. Analysis and discussion During the use of and the test of the knowledge base, it is found that circular inference chain, such as Rule 7/Rule 8/Rule 9/Rule 7//, may lead to huge searching expenses or even infinite circulation. This problem is also critical to maintenance of the knowledge base. Therefore, among the five verification procedures for inconsistency, the module of circularity verification has the highest priority. That means the circular verification is carried out first to remove the conditions of self-growing of inference chain. As a result, the amount of reporting cases can be greatly reduced, and the verification expense of system may also be reduced. On the other hand, in the process of verification, the domain experts are required to participate at some points, particularly in circumstance that inconsistent rules exist in KB. Inconsistency verification is also important to renewal of the knowledge base. When new knowledge is added, there is an automatic check inconsistency between the new rules and existing rules. While finding the inconsistency, the new rules could not be added to KB to guarantee the consistency and accuracy of KB. Development and implementation of the knowledge maintenance module in the expert system proves itself a successful application in a real-time fault diagnosis of FCCU in industrial practice. It verifies effectively inconsistent rules and meets the demands of engineers.
5. The module in application to the industry Since September 2002, the knowledge maintenance module has been implemented in the expert system for real-time fault diagnosis of FCCU system developed earlier
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(Qian et al., 2003). During the applications of the expert system to FCCU, new knowledge and rules need to be added into the existing expert knowledge base from time to time, according to the changes of operating conditions and other circumstances. When adding new rules to the knowledge base by operator, the new rules represent changes of the new situations and improve the function of the system. In case of not being verified properly, some new rules added could conflict with the original knowledge. Due to changes of operating condition, the operator wants to add a new rule into the knowledge base. The content of the rule is as follows: Too high temperature of the riser outlet leads to too small flowrate of the gas stripping steam. This rule is quantized as a rule as represented in the knowledge base: Temperature of the riser outlet T101O520 8C/Flowrate of the gas stripping steam FC101!2.0 t/h. Before added into the knowledge base, the new rule has to be verified and screened by the integrality verification module. The module shows that there would be some problem if this new rule was added into the knowledge base: It is in conflict with Rule 5 and Rule 6 in the knowledge base. Rule 5: Too small flowrate of the gas stripping steam leads to too high temperature of the scorch tank outlet. Rule 6: Too high temperature of the scorch tank outlet leads to too high temperature of the riser outlet. This is a circularity found in the inference chain composed of Rule 5, Rule 6, and the new rule, as shown following: Flowrate of the gas stripping steam FC101!2.0 t/h (the antecedent of Rule 5)/Temperature of the scorch tank
Fig. 5. The running interface of knowledge verification module for Rules 5, 6 and the new rule.
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Fig. 6. The main interface of the expert system for fault diagnosis of FCCU (reactor–regenerator section).
outlet T102O700 8C (the consequent of Rule 5, also the antecedent of Rule 6)/Temperature of the rise outlet T101O520 8C (the consequent of Rule 6, also the antecedent of the new rule)/Flowrate of the gas stripping steam FC101!2.0 t/h (back to the beginning of this reasoning chain). The running interface of the knowledge verification module is shown in Fig. 5. The new rule could not pass the screening procedure of the verification maintenance module of the expert system. It is finally confirmed that addition of the new rule was unnecessary. The operation of the adding was finally canceled according to decision of the superintendent. After implementation of the maintenance and verification module in the knowledge base, the module is capable of preventing the mill operators from adding erroneous and conflict knowledge, guaranteeing correctness and high efficiency of the diagnosis system. By far, this expert system for fault diagnosis has been installed and implemented successfully in the FCCU of Maoming oil refinery in southern China area. The main interface of this system is shown in Fig. 6. This expert system has contributed the FCCU unit in safe and steady running since 2002.
6. Conclusions A knowledge maintenance module is developed and implemented in an expert system for real-time fault diagnosis of a complex petro-chemical process FCCU system. The maintenance modules of knowledge base consist of the management module of knowledge base and the knowledge integrality verification modules. Inconsistency of knowledge examined in the proposed module includes contradiction, redundancy, subsumption, circulation, and reclusion. Case study indicates that this system meets requirements of the expert system of fault diagnosis of FCCU satisfactorily, reduces computational expense of system, and improves the efficiency of the knowledge base effectively. It is hard for knowledge engineers to maintain and update a KB in terms of complexity of rule structure and fuzzy operation on linguistic heuristic rules. In this knowledge base maintenance module, the classified verification operations simplify the maintenance work for knowledge base, while the visualized and lucid interface shows clearly the structure of rules. While maintaining the KBS, the integrality verification module verifies the inconsistent rules, prevents the erroneous rules from being added to the KB, guarantees consistency of knowledge, and ensures that the inference machine work properly and effectively.
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Acknowledgements Financial supports from the National Natural Science Foundation of China (Nos 20225620 and 20376025), China Major Basic Research Development Program (No. G200000263), the Outstanding Young Professor Funds from the Education Ministry of China and Guangdong Province are gratefully acknowledged.
References Bai, F. Z., & Huang, X. H. (1994). An integrated development environment for real-time expert system applied on a kind of industrial control. Journal of the China University of Science and Technology, 24(4), 443– 448. Cai, Z. X., & Xu, G. Y. (2000). Artificial intelligence: principles and applications, (p. 131). Beijing: Tsinghua University Press.
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Huang, Q. M., Li, X. X., & Qian, Y. (2001). A fault diagnosis expert system based on fault tree analysis for lubricating de-waxing process. Computers and Applied Chemistry, 18, 129–133. Jiang, Y. R. (2002). Implementation of an expert system for real-time fault diagnosis of chemical processes, Maste Thesis of South China University of Technology. Guangzhou, China Li, X. X., Jiang, Y. B., & Qian, Y. (2001). The development of the platform of integrated operation system for chemical process. Control and Instruments in Chemical Industry, 28(2), 1–3. Lu, Z. F. (1998). Research of knowledge in the knowledge base of expert system. Computer Study, 3, 1–3. Qian, Y., Huang, Q. M., & Li, X. X. (2000). An integrated process operation system platform and fault diagnosis for the lubricating oil dewaxing process. The proceedings of PSE Asia, Kyoto. Qian, Y., Li, X., Jiang, Y., & Wen, Y. (2003). An expert system for realtime fault diagnosis of complex chemical processes. Expert Systems with Applications, 24(4), 425–432. The Refinery of Maoming Petrochemical Corporation (1997). Operating manual of FCCU (pp. 5–145), Maoming, China. Wu, B., & Yu, X. (2001). Expert system, (pp. 9–69). Beijing: Beijing Institute of Technology Press.
Implementation of knowledge maintenance modules in an expert system for fault diagnosis of chemical process operation Yu Qian*, Minjian Zheng, Xiuxi Li, Li Lin School of Chemical and Energy Engineering, South China University of Technology, Guangzhou 510640, People’s Republic of China
Abstract Based on the authors previous work of developing an expert system for fault diagnosis of chemical processes, this paper presents principles and approaches of the knowledge base maintenance in the expert system. Development and implementation of maintenance modules for the knowledge base in the expert system for fault diagnosis of fluid catalytic cracking unit (FCCU) are reported in detail. Algorithms are proposed for integrality verification of five inconsistencies among heuristic rules: contradiction, redundancy, subsumption, circulation, and reclusion. The modules ensure the knowledge base and the expert system to work stably and efficiently. DELPHI and SQL SERVER are adopted in developing the modules. Application in FCCU fault diagnosis expert system shows that the modules work rationally and efficiently with user-friendly interface, besides its strong interactive feature and better performance of the expert system. The modules have been in continuous use in the expert system of fault diagnosis of FCCU installation in a refinery for the last two years. q 2004 Published by Elsevier Ltd. Keywords: Expert system; Knowledge base (KB); Knowledge maintenance; Conflict verification; Fluid catalytic cracking unit (FCCU)
1. Introduction Based on the advances in artificial intelligence and computer technique, expert systems make use of human expertise in many fields to reason and judge (Cai & Xu, 2000). By imitating the reasoning processes of human experts, such systems are able to solve complex problems, which can only be solved by human experts in their domain. The authors developed earlier an expert system for fault diagnosis of process operations of the fluid catalytic cracking unit (Qian, Li, Jiang, & Wen, 2003). The fluid catalytic cracking unit (FCCU) is one of the most important production processes of the crude oil refining industry. In FCCU, heavy oil is cracked into lower molecular weight hydrocarbons, which are either blended to finished products or are routed to downstream units for further processing. Gasoline is the major end products. FCCU generally consists of three parts: reactor–regenerator, fractionation, and absorption–stabilization system. A simplified flow sheet of main parts of FCCU is shown in Fig. 1. The left part of * Corresponding author. Tel.: C86 20 87112046; fax: C86 20 87113735. E-mail address: [email protected] (Y. Qian). 0957-4174/$ - see front matter q 2004 Published by Elsevier Ltd. doi:10.1016/j.eswa.2004.10.005
the figure is the reactor and regenerator, while the right part is the fractioning distillation column series. At the bottom right corner of the figure, the feedstock heavy oil is introduced and heated in the furnace up to a temperature of 230–250 8C. Recycle stock pumped from the cycle stock tank, mixed with fresh feedstock at the entrance of the blender, go through four groups of atomizing nozzle prior to entering the riser of the reactor. In the riser, the material flow meets the regenerated catalyst at a high temperature of 730–750 8C from the second dense bed in the regenerator; thus, its temperature rises rapidly while flowing upwards with catalyst. The materials are catalytically cracked into a series of products of gasoline, light diesels fuel, liquefied gas, stripped gas, slurry oil, and other gas products, while coke aggregates on the surface of catalyst. The reacted oil gas, at a temperature of 510 8C, is rapidly separated from the coke aggraded catalyst via the sharp separator at the exist of riser, in order to avoid further secondary reaction. The oil gas in the settler passes through the plenum chamber and then enters the fractionation chamber. Among the different facilities in FCCU, the reactor–regenerator is the most critical equipment where faults and abnormal situations occur frequently. The faults may lead to emergent shut down of the installation. Burning and burst of the equipment
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Fig. 1. A simplified flowsheet of main parts of FCCU.
and pipes may happen, which could injure personnel in the worst cases. The expert system was developed and applied to analysing and estimating root-causes of the faults in abnormal situations, and propose reasonable correction measures to ensure a safe and steady FCCU operation (Bai & Huag, 1994). During the applications of the expert system to FCCU, it is found that quality of the knowledge base is critical to the performance of the expert system. The knowledge base is continuously in a process of improvement (Huang, Li & Qian, 2001). On one hand, human experts would add new knowledge to KB or modify the existing knowledge heuristics when new situations occur; on the other hand, as the amount of knowledge in the KB increases, and contents of the knowledge become more complicated, there may be contradiction, redundancy, subsumption, circulation, and reclusion among heuristic rules (Lu, 1998). The maintenance of KB becomes an important issue to a successful and efficient expert system (Qian, Huang & Li, 2000). How to guarantee the consistency of the knowledge? How does the knowledge in KB work efficiently? These are critical technical problems in the maintenance of KB, which is investigated and implemented in detailed in this work.
2. Structure of the expert system and maintenance of the knowledge base The expert system developed in this work consists of monitor, inference machine and explanation module, knowledge base (KB), knowledge acquisition and management, integrated databases, and the user interface. The structure of the expert system is shown in Fig. 2. The knowledge base stores common knowledge and domain knowledge of process diagnosis for FCCU operation.
The diagnosis knowledge is represented in different forms and structures. There are numerous expert rules in the knowledge base. Knowledge acquisition and management module transfers the input expert knowledge into the interior knowledge of the expert system (Jiang, 2002). An Integrality verification module assures that the newly added knowledge is not in conflict with the ever-existing knowledge (Li, Jiang & Qian, 2001). Maintenance of the knowledge base including following operation: add, delete, modify, and adjust storage organization of rules. (1) Add rules. The newly added rules must be verified, including accuracy verification, redundancy verification and consistency verification. There are four principles in the operation of adding rules: † If new rules can be represented in existing rules, the new one need not be added and the KB need not be changed. † If new rules with parts of existing rules together can replace some old ones, the new rules may be added to the KB while the replaceable rules should be deleted. † If the newly added knowledge is in conflict with existing knowledge, domain experts shall be consulted to verify which one is more appropriate to be accepted, while the conflict one should be deleted or modified accordingly. † If the newly added knowledge is not in conflict with the ever-existing knowledge, the new rules may be added to the KB. (2) Delete rules. The useless rules should be adjusted or deleted in time. It is very important to verify the KB again after deleting rules to ensure the deletion is correct. (3) Modify rules. When modifying the rules, the safety of the KB and the consistency of knowledge must be ensured. There are three cases in the modification of rules:
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Fig. 2. Structure of the expert system for fault diagnosis of FCCU.
† Add antecedents or consequents of rules; † Reduce antecedents or consequents of rules; † Replace antecedents or consequents of rules. Generally speaking, in a rule, too many antecedents would lead to situation that no consequents could be inferred where it should be, while too few antecedents could lead to the other end that wrong consequents may be inferred where should not be inferred. So, a successful modification of rules is discriminating out errors in the rules, while maintaining consistency of the knowledge base. (4) Adjust storage organization of rules. Maintenance of KB aims at increasing operating efficiency. It is very necessary to adjust storage organization after modifying, adding and deleting knowledge many times. One of the principals of adjusting is that the most frequent used ‘busy rules’ are put on the top of the queue be first searched. The interface of the maintenance module of KB developed in this work is shown in Fig. 3. The module contains a number of operations: adding, deleting, modifying. In consideration of complex structure of FCCU expert system, huge quantity of instrumentations, and the request from knowledge engineers, a number of operations and functions of rules browse, rules search and help are implemented in the module. In the GUI, there are five buttons including adding, appending, deleting, modifying, help and exist at the top of the interface. In the left part of the interface, three windows show list of rules, rule name, and reliability, respectively. A figure of tree-type structure is used to show the inference process. The parameters of sub-composite
object, variable time axis, function of variable treatment, linked operating sign, function of object treatment, object time axis, comparison operator, numerical value and termination type. The result of analysis is shown in the right of the figure including consequents and measures. The window of result browse is at the bottom of the interface.
3. Verification of knowledge integrality Expert system is in fact an organized knowledge system. Thus, the performance and the efficacy depend on both of quantity and quality of the knowledge in the system (Wu & Yu, 2001). Detection and screening out of erroneous knowledge, updating out-of-date knowledge, improving the accuracy of existing knowledge are the most important link in the construction of expert system. 3.1. Validation and revision of knowledge In order to guarantee the consistency of rules in the KBS, effective measures have to be adopted to validate the knowledge. In generally, consistency is considered as situation where consequents are similar when antecedents in two rules are similar. It is difficult, however, to give a strict definition of similarity. In our work, five kinds of inconsistency in heuristic rules are discriminated and screened. They are contradiction, redundancy, subsumption, circulation, and reclusion. Contradiction. The situation where the conditions and consequences in a rule are contrary or, conditions in two rules are identical while their consequents are different.
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Fig. 3. Interface of the maintenance module of the knowledge base.
Conflict rules are not allowed to exist in KB at any situation. There are mainly situations as follows: (a) Self-contradiction. Contrary consequents may be directly inferred from same condition of a same rule; (b) Chain self-contradiction. Contrary consequents may be inferred from one of the rule antecedents, via inference chains; (c) Mutual contradiction. The conditions of two rules are identical, but their consequences are contrary. (d) Reclusive mutual contradiction. The correct rule is not in KB, but a contrary consequence exists in KB instead; (e) Transitive contradiction. The conditions of two rules are identical, but the contrary consequences are inferred through one of the inference chains; (f) Reclusive transitive contradiction. The correct rule chain in not in KB, but a contrary rule chain exits instead. Redundancy. Redundant rules depress efficiency of the system. Not only they raise size of the rule base, but also make the maintenance of KB harder. Thus, redundant rules have to be as screening out. In some circumstances, however, a number of redundant rules of special knowledge are necessary to improve performance of the KB system. There are two kinds of redundancies: (a) equivalent redundant rule: the antecedents and consequents of one rule are essentially equivalent to the antecedents and consequents of another rule. In this situation, one of the two rules has to be deleted. (b) Subsumed redundancy rule: the antecedent
(consequent) of one rule includes the antecedent (consequent) of another rule. This can be classified into five subclasses of redundancy rules: AND antecedents subsumed redundancy, OR antecedents subsumed redundancy, AND consequents subsumed redundancy, OR consequents subsumed redundancy, and the hybrid subsumed redundancy. Subsumption. If parts of antecedents in two rules are identical while one antecedent includes more additional proposition,and the consequents are also identical, it is considered as subsumption. Circulation. Circulation occurs when the antecedents and consequents of one rule are identical, or the intermediate/ final consequent of a rule is identical with the antecedent of this rule. Circular rules affect the system badly. On one hand, a circular inference may lead to nowhere; on the other hand, it may cause infinite loop, makes system paralyzed. There are the situations as follows: (a) self-circularity: the antecedent and the consequent of a rule have the same predicate; (b) chain of self-circularity: the antecedent and intermediate/final consequent of a rule have the same predicate; (c) reclusive circularity: right rules are not in KB, while the related rule of circular dependency exists in KB. Reclusion. If the antecedent and consequent of a rule is contrary, or the intermediate/final consequent of a rule is contrary to that of another rule, it is considered as reclusion. 3.2. Implementation of integrality verification The integrality verification algorithms presented in this paper includes three main modules: initialization,
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precondition, and inconsistency verification. The last module set includes modules for five classes of inconsistency: contradiction, redundancy, subsumption, circularity, and reclusion. The initialization module copies the contents of KB into the memory database and constructs the rule arrays in the memory. At first the contents of all rules are called into the variable sets of memory database. This includes contents of all antecedents, composite antecedents, sub-composite antecedents and consequents. Then, scattered contents are organized into the rule prototypes according to the structure of rules, and stored in the rule variables. At last, the global variables in the subsystem are initialized. The initialization module constructs the rule inference chain. It is also used to verify the complex inference chain in the process of integrality verification. The algorithm is implemented with a function module. The function stores the rule inference chain in the form of linked list, whose node is a storage unit and records the rule number. The number is the only ID of the rule. The link between nodes is implemented with the pointer. Since the length of the linked list varies dynamically, the doubly linked list, whose nodes have both prior pointer and hinder pointer, is applied in the function to make the operations of adding and deleting easier. A recursive algorithm is adopted in construction of the rule chain. The recursive function searches all rules in KB to find whether there is a condition of forming inference chain. When there is, a new node is created and the current rule number is recorded in order to link the node and inference chain. When there is not a inference chain, the recursive function would delete the node in the end of the chain and backtrack to search new inference chain recursively from the node in the end of the sub-chain. Therefore, the recursion is a process of searching network and involves a big amount of numerical computation. Due to unpredictable cycle number, it is very possible an extreme computational expenses for the system, in the worst cases, causes infinite loop and makes system paralyzed if those factors are not considered carefully in the operation of the system. Among five modules of inconsistency verification, here a working flow of contradiction verification is shown as an example: Contradiction verification
(2) Compare consequents Compare consequents of two rules randomly and variables of the couple of contrary consequents; If consequents are contrary, add the couple of rules to the recording arrays of rule inference chain. (3) Compare inference chain Search all the elements of inference chain arrays; Judge whether consequents of the couple of inference chains are contrary; If the antecedents are identical while the consequents are contrary, output the rule chains. End.
4. Integration into the expert system for FCCU fault diagnosis The case introduced in this paper is from an industrial installation of FCCU (Maoming Refinery, 1997). Table 1 The example rules used in case study Rule 2
Rule 3
Rule 7
Rule 8
Rule 9
Rule 10
Rule 14
(1) Compare antecedents Compare all antecedents of two rules randomly; Compare all composite antecedents of two rules randomly; If antecedent of observation type, compare contents of all sub-composite objects; If antecedent of consequent type, compare contents of all antecedents; Record the couple of rules whose antecedents are identical in the corresponding antecedents recording variable.
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Rule 20
Rule 21
Antecedent 1 Consequent 1 Measures 1 Measures 2 Antecedent 1 Consequent 1 Measures 1 Measures 2 Antecedent 1 Consequent 1 Measures 1 Measures 2 Antecedent 1 Consequent 1 Measures 1 Measures 2 Antecedent 1 Consequent 1 Measures 1 Antecedent 1 Consequent 1 Measures 1 Measures 2 Antecedent 1 Antecedent 2 Consequent 1 Measures 1 Antecedent 1 Consequent 1 Measures 1 Measures 2 Antecedent 1 Consequent 1 Measures 1 Measures 2
The liquid level of the oil refining tank rises Quantity of the rich gas rises Add fresh catalyst Raise temperature of the reaction The liquid level of the oil refining tank drops Quantity of the rich gas drops Increase volume of the main air inlet Manually operate the slide valve of regenerator The liquid level of the oil refining tank rises Quantity of the rich gas drops Raise temperature of the reaction Increase volume of the main air inlet Quantity of the rich gas drops Quantity of crude gasoline drops Open the vale of heat exchanger fully Increase the pressure of reactor and regenerator Quantity of crude gasoline drops The liquid level of the oil refining tank rises Reduce the duty of heat exchanger outside Quantity of rich gas rises The liquid level of the oil refining tank rises Add fresh catalyse Raise the temperature of the reaction Quantity of rich gas drops Temperature of the reactor and the regenerator rises Quantity of crude gasoline drops Raise the temperature of the reaction m_T109(1)Cm_T118(1) Extent of reaction drops Add fresh catalyse Raise the temperature reaction m_T109(1)Cm_T105(1) Extent of reaction rises Adjust the pressure Raise the temperature of the reaction
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As the guidelines for safety and steady operation, rules in the operation instruction manual are all concertedly recognized by the domain experts and have also been verified in long period of industrial practice. A set of rules is fabricated purposely to cover a range of five kinds of inconsistencies mentioned earlier. Twenty-one of rules are added into the original rule base, to be tested for knowledge integrality. A part of these rules are listed in Table 1. These nine rules and their combination represent most of the erroneous types mentioned above. Based on the proposed algorithms, the functions of contradiction verification, redundancy verification, subsumption verification, circularity verification and reclusion verification are all implemented. Functions of browsing rules and demonstrating the structure chart of the rules are also supported to make the verification easier. An interface of the knowledge maintenance system is shown in Fig. 4. In this snapshot, verification of rules is shown. In the top of the interface, there are a number of operation buttons including rule demo, verification on redundancy, circularity, contradiction, subsumption, and reclusion, respectively. The window of result preview shows the analysis process of contrary rule at the right part of the interface. 4.1. Result of the verification The results of verification are displayed in the process of the system running. Following listed are four examples of them. Example 1. Contradiction of rules. There is a contradiction between Rule 2 and Rule 7, as shown in the verification window of Fig. 4. The antecedents of Rule 2 and Rule 7 both
are ‘the liquid level of the oil refining tank rises’. The consequent of Rule 2 (quantity of rich gas rises), however, is contrary to the consequent of Rule 7 (quantity of rich gas drops). It is forbidden that Rule 2 and Rule 7 both exist in the rule base. As warned and suggested by the system, the expert deletes erroneous Rule 2, according to the real industrial practice. Example 2. Redundancy of rules. Rule 3 and Rule 7 are redundant. The antecedent 1 of Rule 3 (the liquid level of the oil refining tank drops) is contrary to the antecedent 1 of Rule 7 (the liquid level of the oil refining tank rises), while their consequents are identical (quantity of rich gas drops). Thus, Rule 3 and Rule 7 could not exist in the rule base at the same time. As warned and suggested by the system, the expert deletes erroneous Rule 3 according to the experience. Example 3. Subsumption of rules. Rule 14 subsumes Rule 8. There is a composite sub-antecedent in Rule 14. It is an OR relation between the sub-antecedent 1 (quantity of the rich gas drops) and the sub-antecedent 2 (temperature of the reactor and the regenerator rises). The antecedent of Rule 8 (quantity of the rich gas drops) is identical with the sub-antecedent 1 of Rule 14 and their consequents are also identical. Rule 8 shall be deleted. Example 4. Circularity of rules. Circularity is found in an inference chain composed of Rule 7, Rule 8, and Rule 9, as shown in the window of Fig. 4. From an antecedent that the liquid level of the oil refining tank rises, the consequent inferred from Rule 7 is that quantity of the rich gas drops, which is identical with the antecedent of Rule 8. Further inferred from Rule 8 is the consequent that quantity of the crude gasoline drops, which is identical with the antecedent of Rule 9. The consequent of Rule 9 turns out to be that
Fig. 4. The interface of rule integrality verification.
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the liquid level of the oil refining tank rises, which is actually identical with the antecedent of Rule 7. To break the circularity cycle, the system suggests delete Rule 8, which is also confirmed by the expert. 4.2. Analysis and discussion During the use of and the test of the knowledge base, it is found that circular inference chain, such as Rule 7/Rule 8/Rule 9/Rule 7//, may lead to huge searching expenses or even infinite circulation. This problem is also critical to maintenance of the knowledge base. Therefore, among the five verification procedures for inconsistency, the module of circularity verification has the highest priority. That means the circular verification is carried out first to remove the conditions of self-growing of inference chain. As a result, the amount of reporting cases can be greatly reduced, and the verification expense of system may also be reduced. On the other hand, in the process of verification, the domain experts are required to participate at some points, particularly in circumstance that inconsistent rules exist in KB. Inconsistency verification is also important to renewal of the knowledge base. When new knowledge is added, there is an automatic check inconsistency between the new rules and existing rules. While finding the inconsistency, the new rules could not be added to KB to guarantee the consistency and accuracy of KB. Development and implementation of the knowledge maintenance module in the expert system proves itself a successful application in a real-time fault diagnosis of FCCU in industrial practice. It verifies effectively inconsistent rules and meets the demands of engineers.
5. The module in application to the industry Since September 2002, the knowledge maintenance module has been implemented in the expert system for real-time fault diagnosis of FCCU system developed earlier
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(Qian et al., 2003). During the applications of the expert system to FCCU, new knowledge and rules need to be added into the existing expert knowledge base from time to time, according to the changes of operating conditions and other circumstances. When adding new rules to the knowledge base by operator, the new rules represent changes of the new situations and improve the function of the system. In case of not being verified properly, some new rules added could conflict with the original knowledge. Due to changes of operating condition, the operator wants to add a new rule into the knowledge base. The content of the rule is as follows: Too high temperature of the riser outlet leads to too small flowrate of the gas stripping steam. This rule is quantized as a rule as represented in the knowledge base: Temperature of the riser outlet T101O520 8C/Flowrate of the gas stripping steam FC101!2.0 t/h. Before added into the knowledge base, the new rule has to be verified and screened by the integrality verification module. The module shows that there would be some problem if this new rule was added into the knowledge base: It is in conflict with Rule 5 and Rule 6 in the knowledge base. Rule 5: Too small flowrate of the gas stripping steam leads to too high temperature of the scorch tank outlet. Rule 6: Too high temperature of the scorch tank outlet leads to too high temperature of the riser outlet. This is a circularity found in the inference chain composed of Rule 5, Rule 6, and the new rule, as shown following: Flowrate of the gas stripping steam FC101!2.0 t/h (the antecedent of Rule 5)/Temperature of the scorch tank
Fig. 5. The running interface of knowledge verification module for Rules 5, 6 and the new rule.
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Fig. 6. The main interface of the expert system for fault diagnosis of FCCU (reactor–regenerator section).
outlet T102O700 8C (the consequent of Rule 5, also the antecedent of Rule 6)/Temperature of the rise outlet T101O520 8C (the consequent of Rule 6, also the antecedent of the new rule)/Flowrate of the gas stripping steam FC101!2.0 t/h (back to the beginning of this reasoning chain). The running interface of the knowledge verification module is shown in Fig. 5. The new rule could not pass the screening procedure of the verification maintenance module of the expert system. It is finally confirmed that addition of the new rule was unnecessary. The operation of the adding was finally canceled according to decision of the superintendent. After implementation of the maintenance and verification module in the knowledge base, the module is capable of preventing the mill operators from adding erroneous and conflict knowledge, guaranteeing correctness and high efficiency of the diagnosis system. By far, this expert system for fault diagnosis has been installed and implemented successfully in the FCCU of Maoming oil refinery in southern China area. The main interface of this system is shown in Fig. 6. This expert system has contributed the FCCU unit in safe and steady running since 2002.
6. Conclusions A knowledge maintenance module is developed and implemented in an expert system for real-time fault diagnosis of a complex petro-chemical process FCCU system. The maintenance modules of knowledge base consist of the management module of knowledge base and the knowledge integrality verification modules. Inconsistency of knowledge examined in the proposed module includes contradiction, redundancy, subsumption, circulation, and reclusion. Case study indicates that this system meets requirements of the expert system of fault diagnosis of FCCU satisfactorily, reduces computational expense of system, and improves the efficiency of the knowledge base effectively. It is hard for knowledge engineers to maintain and update a KB in terms of complexity of rule structure and fuzzy operation on linguistic heuristic rules. In this knowledge base maintenance module, the classified verification operations simplify the maintenance work for knowledge base, while the visualized and lucid interface shows clearly the structure of rules. While maintaining the KBS, the integrality verification module verifies the inconsistent rules, prevents the erroneous rules from being added to the KB, guarantees consistency of knowledge, and ensures that the inference machine work properly and effectively.
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Acknowledgements Financial supports from the National Natural Science Foundation of China (Nos 20225620 and 20376025), China Major Basic Research Development Program (No. G200000263), the Outstanding Young Professor Funds from the Education Ministry of China and Guangdong Province are gratefully acknowledged.
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