- Email: [email protected]
Novel hybrid representation of knowledge in the expert system for real-time faults diagnosis
996
Process SystemsEngineering2003 B. Chenand A.W.Westerberg(editors) 9 2003 Publishedby ElsevierScienceB.V.
Novel Hybrid Representation of Knowledge in the Expert System for Real-time Faults Diagnosis Yu Qian, Yanrong Jiang, Yanqin Wen, Xiuxi Li, Yanbin Jiang School of Chemical Engineering, South China University of Technology Guangzhou, 510640, P. R. China Email: [email protected]
Abstract This paper presents the development and implementation of an expert system for real-time fault diagnosis of chemical processes. The expert system is applied as a real-time computer aided decision support system, providing operation suggestions to help field operators when abnormal situations occur. Representation of knowledge, structure of the knowledge base, and access to expertise are technically considered. Industrial application indicates that the expert system works efficiently and promptly.
Keywords fault diagnosis, expert system, representation of knowledge 1. INTRODUCTION With the highly automatization of modem chemical and petrochemical plants, control systems are adopted to control the production process. Once abnormal situations occur, the control system alerts signals and executes a set of pre-set emergency responding actions to protect safety of personnel and production installations. In real production environment, it is difficult for operators to analyze the cause of abnormal situations and response promptly tl-2l. It is important to monitor production processes automatically, to analyze and diagnose faults as early as possible, to help operators to deal with abnormal situations. Nowadays it is possible due to the easy access data directly from real-time data base indicating process situation. Two sorts of methods are currently used in fault diagnosis: model based, or heuristic rule based. Since reaction mechanism is usually complicated for many processes, it is difficult to establish precise mathematical model. Faults in chemical process occur quickly, allow short time to detect and deal with. Due to above characteristics, rule based expert system technique is used in chemical process for real-time fault diagnosis in our work. 2. STRUCTURE OF REAL-TIME FAULT DIAGNOSIS EXPERT SYSTEM When abnormal situations arise in chemical processes, an expert system reasons out the causes and propose proper correcting solution in due time. The reasoning is based on data from the control system and the expertise contained in the knowledge base. t3-41 Inferring time
997 in expert system relates to its inference mode, knowledge base structure, computer performance and programming language tS]. The proposed structure of the expert system is shown in Figure 1. 1) User interface: Graphic User Interface (GUI) and menu are adopted to implement the communication between the user and the system. 2) Monitor: This module is used to monitor the operation of the process. Some data indicating process situations, such as temperature, pressure, and flow, are directly provided by the control system. If the situation is judged to be abnormal, the monitor module will execute computation according to the pre-set program. The results are stored to the integrated database as faults premonition facts, notifying the inference machine concurrently. 3) Inference machine and explanation module: Inference machine controls and executes problem solving. In this work, a mixed reasoning mechanism is adopted in the inference machine. A heuristic bounded deep first search strategy based on AND/OR graph is used as the reasoning strategy. The explanation module traces the inference route and provides the explanation of reasoning to the user upon request. 4) Knowledge acquisition and management: This module transfers the input expert knowledge into the interior knowledge of the expert system. The management module is used to operate on the knowledge base, such as add, delete and so on. 5) Knowledge base: The knowledge base stores common knowledge and domain knowledge of diagnosis. The diagnosis knowledge is represented in different forms and structures. A multi-level structure is applied to the knowledge base construction. 6) Integrated database: The integrated database stores the facts generated during process monitoring and the middle information produced in the reasoning process. ...............................................................................................................................................................................................
I I
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In 'i ven 'noi of knowledge
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i i GUI
II
~
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!
I| II || ,.... o
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I I [D~
i Inference Machine and Explanation
Result of reseaning I
Explanation
model
Fig. 1. Fault Diagnosis Real-time Expert System Structure
"1 II II II II II II
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Operator
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998 3. IMPLEMENTATION OF THE EXPERT SYSTEM 3.1. Representation of domain knowledge A hybrid representation of knowledge including facts, functions and production rules is adopted in this work. Fact is sorted as dynamic fact and static fact according to whether the fact is varied with time. Function improves the ability of numerical computation, which is usually weak in systems developed by common expert system development tools. Production rule is paramount component of knowledge representation. We adopt two types of rules: (1) general production rule, which is of no time constraint; and (2) "time constraint extended production''[6], which is with time varying. Structure of a time constraint extended production rule is as follows: IF < condition list> A N D < time restriction> T H E N
Only when the conditions and time restriction of the rule are all satisfied, the rule is activated and applied. Both of general production rule and time constraint extended production rule are adopted in this expert system. 3.2. Building of the knowledge base Domain knowledge is stored in the knowledge base. The domain knowledge is grouped into four classes in this expert system: (1) premonitory; (2) expertise knowledge; (3) meta-knowledge; and (4) decision knowledge. Premonitor is the description of the appearance of fault characters, which is the lowest level knowledge of fault reasoning. Expertise knowledge is summarized from the practical experience of domain experts. It is a judgment of the causal relation between fault and premonitor. Meta-knowledge is used to describe the structure and content of domain knowledge, helping to select experience knowledge, optimizing the system structure. It is knowledge of the highest level. Decision knowledge is the step to deal with the fault when it Occurs.
Based on part of author's previous work, a multi-level structure is applied in the knowledge base constructiont781. The Meta-knowledge base is the highest level of the knowledge base. Meta-knowledge helps to use the domain knowledge. It provides control strategies for the searching in knowledge base. It plans and designs the solution steps of signed tasks. It also demonstrates knowledge of rules, such as the authority of rules provider. We take advantage of separating the meta-knowledge from experience knowledge. 3.3. Implementation of the memory database The memory database stores status information of process needed by fault diagnosis, middle results generated by reasoning process and the sub-knowledge base module read in advance from the knowledge base. Overcast technique and buffer are applied in the construction of the memory database. When the system needs to access rules, the buffer and the memory sub-knowledge base is considered at first. The search is much faster than to the knowledge base on disk, which is
999 considered late if the rules are not found in the buffer of the memory sub-knowledge base. The rule is returned when it is matched. According to the analysis of the pertinence on instrument variables in related knowledge base and meta-knowledge, related rules that would be used in the next reasoning step will be selected to the memory sub-knowledge base and cover the previous content. The algorithm is proven to be effective, so the probability that finds needed rule in the buffer and the memory sub-knowledge is great when the reasoning process search for new rules. Thus the speed of the search increases. 3.4. Development and implementation of the inference machine Inference machine is the key component of expert system. As a real-time expert system for fault diagnosis, a rapid response speed is critical. In our work, the mixed reasoning strategy is adopted, which is regarding of most efficient in reasoning speed. Measurements from the industrial process, such as pressure, temperature are gathered. The premonitory facts are computed by the monitor module, and then sent to the integrated database. The forward reasoning is necessarily activated. During the forward reasoning process, a number of supposed targets are proposed. The supposed target must be determined to be true or false by backward reasoning. To ensure the requirement of safety of the industrial process, the expert system must monitor and make decision under the situation of process whether faults will arise or not. So the expert system has to test all the final conclusion factors. This is a target driven backward reasoning. A heuristic bounded deep first search strategy based on AND/OR graph is adopted as the reasoning strategy, which ensures the efficiency of reasoning and reduces expenses of search in space of problem. 4. PERFORMANCE OF THE SYSTEM AND DISCUSSION 4.1. Case introduction The real-time fault diagnosis expert system is applied to FCCU in the petroleum refining process. FCC is one of the most important production processes of oil refining industry. Heavy oil is cracked into lower molecular weight hydrocarbons, which are either blended to finished products or are routed to downstream units for processing. Gasoline is the major end product. The reaction mechanism is too complicated to establish precise mathematic model. Operators of FCCU process have accumulated much expertise knowledge during long production processes. FCCU generally consists of three parts: reactor regenerator, fractionation, and absorption-stabilization system. Reactor-regenerator is the key part of FCCU. It is an active installation that faults easily occur. Reaction time in the reactor is short as about one to four seconds. 4.2. Process of diagnosis The data are continuously acquired from control system and stored in the real-time database. When abnormal situation is doubt to occur, the monitor module gets the premonitions of
1000 I temperature of hoisting pipe is error I
'
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temperature of hoisting pipe is abno reactor inflow temperature is too
I .....m_T110>650
m_T109>600
hoisting pipe middle temperature is abnormal
I I
I ,
re_T120>670
..... I
Fig. 2. The process of reasoning faults according to the fixed arithmetic. The fault premonition facts are stored in integrated database and the inference machine is informed to deal with them. Then the inference machine matches the fault diagnosis facts with the conditions and results of rules in the knowledge base. Mixed reasoning is carried out if facts match rules well. The reasoning result decides whether the system runs normally or not. If fault is confirmed, the warning message will be sent to the graphic user interface and search the operation suggestion in the decision database to guide the operators to solve the problem. A diagnosis example is given in the following. Temperature of hoisting pipe is very important variable, because outflow oil of hoisting pipe enters reactor. The temperature of hoisting cannot be measured directly. It must be judged to be abnormal or error by expertise. There are four measurable variables of temperature related to the hoisting pipe: reactor inflow temperature T110, hoisting pipe middle temperature T120, hoisting pipe up temperature T109, and reactor outflow temperature T 101. There are four rules about error of hoisting pipe.
Rule 1" IF m_T110(O)>650 THEN reactor inflow temperature is too high Rule 8: IF m_T120(O)>670 THEN hoisting pipe middle temperature is abnormal. Rule 9: IF reactor inflow temperature is too high, AND hoisting pipe middle temperature is abnormal THEN temperature of hoisting pipe is abnormal. Rule 10." IF temperature of hoisting pipe is abnormal AND m_T109(0) >600 THEN temperature of hoisting pipe is in error. When T l l 0 is 739.86, and T120 is 639.52, preconditions of rule 1 and rule 8 are satisfied. The diagnosis system carries out reasoning and forms a reasoning tree. The reasoning process is shown in Figure 3. The operators are reported that the temperature of hoisting pipe is in error. The operators can only know the measurable variables by the control system. Without the expert system, the operators cannot know whether an unmeasured variable is in abnormal condition, which may cause serious accident.
1001 4.3. Analyses and discussion The monitor module calculates the important instrument variables to obtain facts needed by reasoning, and store the fact in the memory data base, which avoid the large amount of repeating calculation to get the same fact during the referring process. It saves much time and improves the speed of reasoning. The memory database improves the speed of access to knowledge. It was shown in experiment that the time for access to rules decreased much under the condition of high hit rate of access to rules. A multi-level structure is applied on the knowledge base. It makes the access to knowledge faster, the maintenance of the knowledge base easier. A heuristic bounded deep first search strategy based on AND/OR graph is adopted in reasoning strategy, which ensures the efficiency of reasoning and decrease the quantity of search in space of problem. The bounded search ensures that the referring process finished within given time.
5. CONCLUSION A real-time expert system is developed for monitoring and diagnosis of chemical processes. The representation of knowledge, knowledge base, inference machine and the relations among them are investigated. The application on FCCU process indicates that this system achieves satisfying effect of diagnosis and has a fast response speed for practical industrial cases. The system helps operators a lot to eliminating potential faults. The system also decreases the loss brought by unstable process situations. When new fault occurs, the stored data helps the domain expert to analyze the reason of the fault, and give earlier prediction of the trend. 6. ACKNOWLEDGEMENTS Financial supports from the National Natural Science Foundation of China (No.29976015), China Major Basic Research Development Program (No.G20000263), the Outstanding Young Professor Funds of the Education Ministry of China are gratefully acknowledged. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8]
W. L. Lin, Y. Qian, Control and Instnmaents in Chem. Ind., 27(2000) 1. X.X. Li, Y.B. Jiang and Y. Qian, Control and Instrtmaents in Chem. Ind., 28(2001) 1. D. Ruiz, J. Canton and J.M. Nougues, Computers Chem. Engng., 25(2001) 829. D. Ruiz, J.M. Nougues and L. Puigjaner, Computers Chem. Engng., 23(1999) 219. S.P. Guang, Computer Engineering & Science, 18(1996) 121. F.Z. Bai and X.H. Huang, Journal of China Univ. of Sci. and Techn., 24(1994) 443. Q.M. Huang, X.X. Li, Y.R. Jiang and Y. Qian, Computers & Appl. Chem., 18(2001) 129. Y. Qian, Q.M. Huang, X.X. Li and Y.B. Jiang, The proceedings of PSE Asia, (2000) 27.
Process SystemsEngineering2003 B. Chenand A.W.Westerberg(editors) 9 2003 Publishedby ElsevierScienceB.V.
Novel Hybrid Representation of Knowledge in the Expert System for Real-time Faults Diagnosis Yu Qian, Yanrong Jiang, Yanqin Wen, Xiuxi Li, Yanbin Jiang School of Chemical Engineering, South China University of Technology Guangzhou, 510640, P. R. China Email: [email protected]
Abstract This paper presents the development and implementation of an expert system for real-time fault diagnosis of chemical processes. The expert system is applied as a real-time computer aided decision support system, providing operation suggestions to help field operators when abnormal situations occur. Representation of knowledge, structure of the knowledge base, and access to expertise are technically considered. Industrial application indicates that the expert system works efficiently and promptly.
Keywords fault diagnosis, expert system, representation of knowledge 1. INTRODUCTION With the highly automatization of modem chemical and petrochemical plants, control systems are adopted to control the production process. Once abnormal situations occur, the control system alerts signals and executes a set of pre-set emergency responding actions to protect safety of personnel and production installations. In real production environment, it is difficult for operators to analyze the cause of abnormal situations and response promptly tl-2l. It is important to monitor production processes automatically, to analyze and diagnose faults as early as possible, to help operators to deal with abnormal situations. Nowadays it is possible due to the easy access data directly from real-time data base indicating process situation. Two sorts of methods are currently used in fault diagnosis: model based, or heuristic rule based. Since reaction mechanism is usually complicated for many processes, it is difficult to establish precise mathematical model. Faults in chemical process occur quickly, allow short time to detect and deal with. Due to above characteristics, rule based expert system technique is used in chemical process for real-time fault diagnosis in our work. 2. STRUCTURE OF REAL-TIME FAULT DIAGNOSIS EXPERT SYSTEM When abnormal situations arise in chemical processes, an expert system reasons out the causes and propose proper correcting solution in due time. The reasoning is based on data from the control system and the expertise contained in the knowledge base. t3-41 Inferring time
997 in expert system relates to its inference mode, knowledge base structure, computer performance and programming language tS]. The proposed structure of the expert system is shown in Figure 1. 1) User interface: Graphic User Interface (GUI) and menu are adopted to implement the communication between the user and the system. 2) Monitor: This module is used to monitor the operation of the process. Some data indicating process situations, such as temperature, pressure, and flow, are directly provided by the control system. If the situation is judged to be abnormal, the monitor module will execute computation according to the pre-set program. The results are stored to the integrated database as faults premonition facts, notifying the inference machine concurrently. 3) Inference machine and explanation module: Inference machine controls and executes problem solving. In this work, a mixed reasoning mechanism is adopted in the inference machine. A heuristic bounded deep first search strategy based on AND/OR graph is used as the reasoning strategy. The explanation module traces the inference route and provides the explanation of reasoning to the user upon request. 4) Knowledge acquisition and management: This module transfers the input expert knowledge into the interior knowledge of the expert system. The management module is used to operate on the knowledge base, such as add, delete and so on. 5) Knowledge base: The knowledge base stores common knowledge and domain knowledge of diagnosis. The diagnosis knowledge is represented in different forms and structures. A multi-level structure is applied to the knowledge base construction. 6) Integrated database: The integrated database stores the facts generated during process monitoring and the middle information produced in the reasoning process. ...............................................................................................................................................................................................
I I
II II
In 'i ven 'noi of knowledge
~
i i GUI
II
~
,4'
!
I| II || ,.... o
I! I|
I I [D~
i Inference Machine and Explanation
Result of reseaning I
Explanation
model
Fig. 1. Fault Diagnosis Real-time Expert System Structure
"1 II II II II II II
I
Operator
[
998 3. IMPLEMENTATION OF THE EXPERT SYSTEM 3.1. Representation of domain knowledge A hybrid representation of knowledge including facts, functions and production rules is adopted in this work. Fact is sorted as dynamic fact and static fact according to whether the fact is varied with time. Function improves the ability of numerical computation, which is usually weak in systems developed by common expert system development tools. Production rule is paramount component of knowledge representation. We adopt two types of rules: (1) general production rule, which is of no time constraint; and (2) "time constraint extended production''[6], which is with time varying. Structure of a time constraint extended production rule is as follows: IF < condition list> A N D < time restriction> T H E N
Only when the conditions and time restriction of the rule are all satisfied, the rule is activated and applied. Both of general production rule and time constraint extended production rule are adopted in this expert system. 3.2. Building of the knowledge base Domain knowledge is stored in the knowledge base. The domain knowledge is grouped into four classes in this expert system: (1) premonitory; (2) expertise knowledge; (3) meta-knowledge; and (4) decision knowledge. Premonitor is the description of the appearance of fault characters, which is the lowest level knowledge of fault reasoning. Expertise knowledge is summarized from the practical experience of domain experts. It is a judgment of the causal relation between fault and premonitor. Meta-knowledge is used to describe the structure and content of domain knowledge, helping to select experience knowledge, optimizing the system structure. It is knowledge of the highest level. Decision knowledge is the step to deal with the fault when it Occurs.
Based on part of author's previous work, a multi-level structure is applied in the knowledge base constructiont781. The Meta-knowledge base is the highest level of the knowledge base. Meta-knowledge helps to use the domain knowledge. It provides control strategies for the searching in knowledge base. It plans and designs the solution steps of signed tasks. It also demonstrates knowledge of rules, such as the authority of rules provider. We take advantage of separating the meta-knowledge from experience knowledge. 3.3. Implementation of the memory database The memory database stores status information of process needed by fault diagnosis, middle results generated by reasoning process and the sub-knowledge base module read in advance from the knowledge base. Overcast technique and buffer are applied in the construction of the memory database. When the system needs to access rules, the buffer and the memory sub-knowledge base is considered at first. The search is much faster than to the knowledge base on disk, which is
999 considered late if the rules are not found in the buffer of the memory sub-knowledge base. The rule is returned when it is matched. According to the analysis of the pertinence on instrument variables in related knowledge base and meta-knowledge, related rules that would be used in the next reasoning step will be selected to the memory sub-knowledge base and cover the previous content. The algorithm is proven to be effective, so the probability that finds needed rule in the buffer and the memory sub-knowledge is great when the reasoning process search for new rules. Thus the speed of the search increases. 3.4. Development and implementation of the inference machine Inference machine is the key component of expert system. As a real-time expert system for fault diagnosis, a rapid response speed is critical. In our work, the mixed reasoning strategy is adopted, which is regarding of most efficient in reasoning speed. Measurements from the industrial process, such as pressure, temperature are gathered. The premonitory facts are computed by the monitor module, and then sent to the integrated database. The forward reasoning is necessarily activated. During the forward reasoning process, a number of supposed targets are proposed. The supposed target must be determined to be true or false by backward reasoning. To ensure the requirement of safety of the industrial process, the expert system must monitor and make decision under the situation of process whether faults will arise or not. So the expert system has to test all the final conclusion factors. This is a target driven backward reasoning. A heuristic bounded deep first search strategy based on AND/OR graph is adopted as the reasoning strategy, which ensures the efficiency of reasoning and reduces expenses of search in space of problem. 4. PERFORMANCE OF THE SYSTEM AND DISCUSSION 4.1. Case introduction The real-time fault diagnosis expert system is applied to FCCU in the petroleum refining process. FCC is one of the most important production processes of oil refining industry. Heavy oil is cracked into lower molecular weight hydrocarbons, which are either blended to finished products or are routed to downstream units for processing. Gasoline is the major end product. The reaction mechanism is too complicated to establish precise mathematic model. Operators of FCCU process have accumulated much expertise knowledge during long production processes. FCCU generally consists of three parts: reactor regenerator, fractionation, and absorption-stabilization system. Reactor-regenerator is the key part of FCCU. It is an active installation that faults easily occur. Reaction time in the reactor is short as about one to four seconds. 4.2. Process of diagnosis The data are continuously acquired from control system and stored in the real-time database. When abnormal situation is doubt to occur, the monitor module gets the premonitions of
1000 I temperature of hoisting pipe is error I
'
rrnal [~~
temperature of hoisting pipe is abno reactor inflow temperature is too
I .....m_T110>650
m_T109>600
hoisting pipe middle temperature is abnormal
I I
I ,
re_T120>670
..... I
Fig. 2. The process of reasoning faults according to the fixed arithmetic. The fault premonition facts are stored in integrated database and the inference machine is informed to deal with them. Then the inference machine matches the fault diagnosis facts with the conditions and results of rules in the knowledge base. Mixed reasoning is carried out if facts match rules well. The reasoning result decides whether the system runs normally or not. If fault is confirmed, the warning message will be sent to the graphic user interface and search the operation suggestion in the decision database to guide the operators to solve the problem. A diagnosis example is given in the following. Temperature of hoisting pipe is very important variable, because outflow oil of hoisting pipe enters reactor. The temperature of hoisting cannot be measured directly. It must be judged to be abnormal or error by expertise. There are four measurable variables of temperature related to the hoisting pipe: reactor inflow temperature T110, hoisting pipe middle temperature T120, hoisting pipe up temperature T109, and reactor outflow temperature T 101. There are four rules about error of hoisting pipe.
Rule 1" IF m_T110(O)>650 THEN reactor inflow temperature is too high Rule 8: IF m_T120(O)>670 THEN hoisting pipe middle temperature is abnormal. Rule 9: IF reactor inflow temperature is too high, AND hoisting pipe middle temperature is abnormal THEN temperature of hoisting pipe is abnormal. Rule 10." IF temperature of hoisting pipe is abnormal AND m_T109(0) >600 THEN temperature of hoisting pipe is in error. When T l l 0 is 739.86, and T120 is 639.52, preconditions of rule 1 and rule 8 are satisfied. The diagnosis system carries out reasoning and forms a reasoning tree. The reasoning process is shown in Figure 3. The operators are reported that the temperature of hoisting pipe is in error. The operators can only know the measurable variables by the control system. Without the expert system, the operators cannot know whether an unmeasured variable is in abnormal condition, which may cause serious accident.
1001 4.3. Analyses and discussion The monitor module calculates the important instrument variables to obtain facts needed by reasoning, and store the fact in the memory data base, which avoid the large amount of repeating calculation to get the same fact during the referring process. It saves much time and improves the speed of reasoning. The memory database improves the speed of access to knowledge. It was shown in experiment that the time for access to rules decreased much under the condition of high hit rate of access to rules. A multi-level structure is applied on the knowledge base. It makes the access to knowledge faster, the maintenance of the knowledge base easier. A heuristic bounded deep first search strategy based on AND/OR graph is adopted in reasoning strategy, which ensures the efficiency of reasoning and decrease the quantity of search in space of problem. The bounded search ensures that the referring process finished within given time.
5. CONCLUSION A real-time expert system is developed for monitoring and diagnosis of chemical processes. The representation of knowledge, knowledge base, inference machine and the relations among them are investigated. The application on FCCU process indicates that this system achieves satisfying effect of diagnosis and has a fast response speed for practical industrial cases. The system helps operators a lot to eliminating potential faults. The system also decreases the loss brought by unstable process situations. When new fault occurs, the stored data helps the domain expert to analyze the reason of the fault, and give earlier prediction of the trend. 6. ACKNOWLEDGEMENTS Financial supports from the National Natural Science Foundation of China (No.29976015), China Major Basic Research Development Program (No.G20000263), the Outstanding Young Professor Funds of the Education Ministry of China are gratefully acknowledged. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8]
W. L. Lin, Y. Qian, Control and Instnmaents in Chem. Ind., 27(2000) 1. X.X. Li, Y.B. Jiang and Y. Qian, Control and Instrtmaents in Chem. Ind., 28(2001) 1. D. Ruiz, J. Canton and J.M. Nougues, Computers Chem. Engng., 25(2001) 829. D. Ruiz, J.M. Nougues and L. Puigjaner, Computers Chem. Engng., 23(1999) 219. S.P. Guang, Computer Engineering & Science, 18(1996) 121. F.Z. Bai and X.H. Huang, Journal of China Univ. of Sci. and Techn., 24(1994) 443. Q.M. Huang, X.X. Li, Y.R. Jiang and Y. Qian, Computers & Appl. Chem., 18(2001) 129. Y. Qian, Q.M. Huang, X.X. Li and Y.B. Jiang, The proceedings of PSE Asia, (2000) 27.