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Fault diagnosis system support for reactive scheduling in multipurpose batch chemical plants
European Symposiumon ComputerAidedProcessEngineering- 10 S. Pierucci(Editor) 9 2000ElsevierScienceB.V. All rightsreserved.
745
Fault diagnosis system support for reactive scheduling in multipurpose batch chemical plants Diego Ruiz, Jordi Cant6n, Jos6 Maria Nougu6s, Antonio Espufia and Luis Puigjaner* Chemical Engineering Department, Universitat Polit6cnica de Catalunya, Av. Diagonal 647, E-08028 Barcelona, Spain In this work, a simple strategy for the development and implementation of a Fault Diagnosis System (FDS) that interacts with a schedule optimiser in batch chemical plants is presented. The proposed FDS consists in an Artificial Neural Network (ANN) structure supplemented with a Knowledge Based Expert System (KBES) in a block-oriented configuration. The information needed to implement the FDS includes a historical database of past batches, a Hazard and Operability (HAZOP) analysis and a model of the plant. A motivating case study is presented to show the results of the proposed methodology. 1. INTRODUCTION The complexity of process control in present batch chemical plants affects the performance of the supervision tasks making it very difficult. Therefore, operators need a support for decision-making when a deviation from the normal operating conditions occurs. This support is also necessary at the upper levels in the decision-making system as is the planning and scheduling level. Due to its inherent flexibility, batch plants can operate efficiently under different scenarios if the consequences of abnormal situations can be anticipated. A robust Fault Diagnosis System (FDS), that timely provides the fault information to the scheduling level, allows to improve the efficiency of the reactive scheduling, to update the schedules in the most effective way. In this work, a simple strategy for the development and implementation of a FDS that interacts with the schedule optimiser is presented. 2. FAULT DIAGNOSIS IN BATCH PROCESSES Currently, the use of pattern recognition methods based on Artificial Neural Networks (ANNs) and the use of statistical techniques are matter of research. The problem of the traditional ANNs related to totally capture the space and time characteristics of process signals is overcome with the use of wavelet functions. With respect to the use of statistical techniques, Multiway Principal Component Analysis (MPCA) has shown good results in batch process monitoring [ 1]. However, it has some drawbacks like the difficult isolation and localisation of the fault. Finally, in order to combine the strengths of both pattern recognition and inference methods, adaptive neuro-fuzzy systems are being developed. The idea is to obtain an adaptive learning diagnosis system with transparent knowledge representation. Some combinations are subjects of current research. *To whom correspondence must be sent
746 In all the above cases, the main problem is the complex strategy of implementation that delays their application in real industrial plants. It is important to take into account that the information given by the FDS of a batch plant has to be used at different levels in the decision-making hierarchy structure, including the advanced control system and the scheduling system. While developing and implementing a FDS, this important aspect must be considered. This work is focused on the implementation of a robust FDS support for the reactive scheduling system in multipurpose batch chemical plants. 3. FAULT DIAGNOSIS SYSTEM STRUCTURE The proposed FDS consists in an Artificial Neural Network (ANN) structure supplemented with a Knowledge Based Expert System (KBES) in a block-oriented configuration. The system combines the adaptive learning diagnostic procedure of the ANN and the transparent deep knowledge representation of the KBES. It has been successfully applied to complex steady state chemical plants [2]. Figure 1 shows the Fault diagnosis system structure. M1 is the subset of the direct and indirect measurements and/or observations from the plant, and is selected as input of the ANN approach. N1 is the set of q "pre-faults" diagnosed by the ANN approach. The values Nl(i), i from 1 to q, are usually between 0 and 1. They are the input of the Fuzzy Logic System (FLS). M2 is a set of r direct and indirect measurements and/or observations from the plant, which is selected as input of the FLS. The inference engine of the FLS has the knowledge base, expressed in a set of if-then rules. These rules are of two types: those containing process deep knowledge and those that are built from experience of the ANN's performance. In Figure 2 a scheme of the set of rules is presented. The outputs F (/'),j from 1 t o f are theffaults considered. The information needed to implement the FDS includes a historical database, a Hazard and Operability (HAZOP) analysis and a model of the chemical plant. The historical database that includes information related with normal and abnormal operating conditions can be used to train the ANN structure. The on-line measurements from the plant are the ANN's inputs. The ANN's outputs are the signals of different suspected faults. These outputs are a subset of the set of KBES's inputs. The ANN, not only has the advantage of a classifier but also it can be retrained during use. By this way, the changing operating conditions of chemical plants should not affect the FDS's performance.
Plant Mll
FLS t
LI~ ANN[-~ Fuzzificatioengi n//IJnnfeere/\ nce
~F
Based on experience with ANN performance
IF N1 (I) is ... A N D
(M2)... THEN/70") (M2)...THEN
F(/) is...
(M2)... T H E N
F(j) is...
. . .
IF N1 (q) is ... A N D
IF M2(1) is ... A N D . . . T H E N F(/) is...
Based on process deep knowledge
. . .
IF M2(i) is ... A N D ...THEN F(/) is... . . .
IF M2(r) is ... AND... T H E N F(/) is...
Defuzzification
Fig. 2. Scheme of the set of rules. Fig. 1. Neuro-fuzzy FDS
is...
. . .
IF N1 (i) is... A N D
747 The HAZOP analysis is useful to build the process deep knowledge base (KBES) of the plant. This base relies on the knowledge of the operators and engineers about the process and allows formulating artificial intelligence algorithms. A model of the plant can be used to obtain plant operation experience through simulation. The simulation can provide data on infrequent faults because in the cases of faults that rarely occur it is not possible to test the FDS using only plant data. In addition, by testing the ANN with the model, the development of the rules based on experience with ANN performance is straightforward. The model is also useful for testing and validating the FDS. The methodology can be summarised in the following steps: 1)Model the process;
2)Define the faults; 3)Determine measurements; 4)Simulate the faults; 5)Train an ANN, 6)Design the KBES using fuzzy logic; 6)Test the new system by simulation," 7)Design the adaptive method; 8)Test with the model; 9)Implementation in the real plant. 4. H A Z O P A N A L Y S I S IN B A T C H P L A N T S
HAZOP is one of the most powerful hazard identification methods available and has been well described in the literature. In the case of batch processes the HAZOP analysis examines every stage of the batch process sequence. Table 1 shows an example of a line in the HAZOP analysis of a batch process (Stage: Reactor charge; Unit: Tank n~ Node: Pipe from tank 1 to pump 1; Objective: To provide reactant to the reactors; Variable: Flow) Table 1. An example of a HAZOP analysis line Guideword Causes Consequences LiSV~..... Pumpl Time needed ibr reactor malfunction charge increased
Corrective actions Switch to pump 2
Safegu~ds Maintenance tests
Extending the HAZOP method to fault diagnosis characterisations provides a more "down to earth" approach for implementing an operator support system. The rules are kept simple to avoid the general problems of large rule-based knowledge systems, such as contradictory rules, large amounts of irrelevant information and complex tree structures [3]. The HAZOP analysis allows to: a) Generate the if-then rules for the KBES; b) Determine the information to be sent to other levels in the information system. 4.1. Generation of if then rules from H A Z O P analysis Not all the variables considered in the HAZOP analysis are measurements from the plant. Some variables can be observed (estimated). Regarding the generation of IF-THEN rules, only the measured and the observable variables should be considered. In general, the term "Causes" in the HAZOP analysis corresponds to the root cause of a deviation and it can be designated by the term fault. Only the causes that have been defined in the set of faults are considered at this step. In the Example, the fault is "pump 1 malfunction". Consequently, the conversion to an if-then rule is: IF Flow IS Low THEN "Pump malfunction" IS HIGH. The adjustment of the membership functions is the final step. 4.2. Determination of the information to be sent to other levels The FDS receives sensor data from the plant and the control signals. They can be continuous signals (temperatures, flowrates, pressures) or discrete signals (valves open or close, pumps on or off). The outputs are a set of suspected faults. The signal corresponding to
748 each suspected fault is considered binary (0 or 1). This output can be used by the advanced control module in order to take control actions, or by the operators who have to make decisions or by other levels in the computer system as the scheduling system. The output of the FDS has different forms according to the level of information. Table 2 shows the information at different levels from the FDS output when the fault "Pump 1 malfunction" is diagnosed. This evaluation corresponds to the explained Example. Note that the construction of that table is straightforward from the HAZOP analysis. Table 2. Information to different levels based on HAZOP analysis Module Translation from the FDS .....Control system Switch to pump 2 Scheduling system Time needed for tank charge increased Operators' console Check pump 1 5. REACTIVE SCHEDULING When a deviation from the predicted schedule in a multipurpose batch plant is diagnosed, the FDS activates the reactive scheduling module to minimise the effect of this deviation on the remaining schedule. The planning and scheduling system uses event operation networks (EON) modelling system [4]. The EON model has proved to be an efficient way to describe time constraints between operations in a complex production structure, as it is the batch process. The EON model is built using a general recipe description and other guidelines from ISA $88 specification. On the first step, according to the present situation of the plant, and the client orders, a first batch sequence is generated using the information provided by the recipe and the stage levels. Then, an EON graph is generated using the information located at the operation level description of the recipes, and the information generated in the previous step about the unit assignments and task sequence. Finally different methods can be used to adjust the proposed solution under the constraints imposed by the different resources required. Once the schedule is running on the real (or simulated) plant, the control system communicates to the planning system the deviations detected from the proposed plan. With this information, the scheduling system generates new information that is sent back to the control system. This feedback closes the loop between the planning and scheduling system and the control system. 6. CASE STUDY: MULTIPURPOSE BATCH CHEMICAL PLANT Figure 3 shows the flowsheet of the considered multipurpose batch chemical plant used as case study. It is constituted by three tank reactors, three heat exchangers and the necessary pumps and valves to allow changes of configuration. Equipment of this plant is fully interconnected and the instrumentation allows configuration changes by software. Two recipes with two stages each one have been considered. Figure 4 shows the representation of recipes in a Gantt chart performing two batches. Table 3 shows the operation description and the operation times corresponding to the two recipes considered. In both recipes the time of the operation with code 5 are different depending on the reactor chosen to perform the second stage.
749
....
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R2
i il
iiiiii!iiiiNl
.....
Time
Fig. 4. Gantt chart performing two batches Fig. 3. Flowsheet of the multipurpose batch chemical plant Table 3. Operation description and operation times (hours) Operafibn ........ Stage Descriptidi] Unit code code 1 1 Load tank 1 T1 2 1 Stirring / Homogenising T1 3 1 Discharge to R1 / R2 T1 4 2 Load reactor R1/R2 5 2 Reaction R1 5 2 Reaction R2 6 2 Discharge of final product R1/R2 7 2 Reactor cleaning R1/R2 .
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Recipe 1
Recipe 2
0.066 0.084 0.066 0.066 0.25 0.33 0.066 0.167
0.066 0.167 0.066 0.066 0.33 0.416 0.066 0.066
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Two different faults have been considered to test the implementation strategy. The first one corresponds to a delayed operation caused by a pump malfunction. Figure 5 shows the tank level profiles, comparing a normal batch, an abnormal batch without FDS and an abnormal batch with the FDS implemented. It can be observed that the rapid diagnosis and communication to the supervisory control level allows reducing the delay. The whole schedule considered in this study consists of 5 batches of product A (recipe 1) and 6 batches of product B (recipe 2). The makespan of the initial schedule is 4.37 hours. The second abnormal situation considered is the unavailability of a piece of equipment, in this case the Reactor 2. It has been simulated that the unavailability lasts for fifty minutes since the beginning of the schedule. Figure 6 shows the Gantt charts. Table 5 summarises a comparison of the results taking into account the plant functioning without a FDS and with the FDS and reactive scheduling for the two considered abnormal situations 7. CONCLUSIONS A simple strategy for the development and implementation of a FDS that interacts with the schedule optimiser in multipurpose batch chemical plants has been presented. Besides, two examples of abnormal situations have been shown in a multipurpose batch chemical plant. The proposed integration of the FDS in the information system shows promising results by significant improvement in the production efficiency. Industrial applications of the proposed system are straightforward because of the simplicity of implementation. Therefore, future work includes the implementation of the system in real industrial scenarios.
750
Fig. 5. Tank levels profiles, Normal batch; Abnormal batch without FDS; Abnormal batch with FDS support
Table 4. Abnormal situation management performance comparison (makespan). Abnormal situation
Delayed operation Unavailability (R2)
Without the FDS support 4.43 h (+1.4%) 4.83 h (+10.5%)
With FDS & reactive scheduling 4.39 h (+0.5%) 4.70 h (+7.6%)
Fig. 6. Abnormal situation management comparison: a) Initial schedule, b) Realised schedule without the FDS support and c) With FDS and reactive scheduling.
ACKNOWLEDGEMENTS Financial support from the European Community is gratefully acknowledged (projects IC18CT98-0271 and IMS 26691). Nouguds was sponsored by Generalitat de Catalunya, II Pla de Recerca, TDOC Grant. REFERENCES
1. P. Nomikos and J.F. MacGregor, AIChE Journal, 40 (1994) 1361-1375. 2. Ruiz, D., Nougu6s, J. M. and Puigjaner, L, Computers & Chemical Engineering, 23S (1999) $219-222. 3. Wennersten, R., Narfeldt, R., Gr~infors, A. and Sj6kvist, Computers & Chemical Engineering 20S (1996) $665-670. 4. Graells, M., Cant6n, J., Peschaud, B. and Puigjaner L., Computers & Chemical Engineering, 22S (1998), $395-402.
745
Fault diagnosis system support for reactive scheduling in multipurpose batch chemical plants Diego Ruiz, Jordi Cant6n, Jos6 Maria Nougu6s, Antonio Espufia and Luis Puigjaner* Chemical Engineering Department, Universitat Polit6cnica de Catalunya, Av. Diagonal 647, E-08028 Barcelona, Spain In this work, a simple strategy for the development and implementation of a Fault Diagnosis System (FDS) that interacts with a schedule optimiser in batch chemical plants is presented. The proposed FDS consists in an Artificial Neural Network (ANN) structure supplemented with a Knowledge Based Expert System (KBES) in a block-oriented configuration. The information needed to implement the FDS includes a historical database of past batches, a Hazard and Operability (HAZOP) analysis and a model of the plant. A motivating case study is presented to show the results of the proposed methodology. 1. INTRODUCTION The complexity of process control in present batch chemical plants affects the performance of the supervision tasks making it very difficult. Therefore, operators need a support for decision-making when a deviation from the normal operating conditions occurs. This support is also necessary at the upper levels in the decision-making system as is the planning and scheduling level. Due to its inherent flexibility, batch plants can operate efficiently under different scenarios if the consequences of abnormal situations can be anticipated. A robust Fault Diagnosis System (FDS), that timely provides the fault information to the scheduling level, allows to improve the efficiency of the reactive scheduling, to update the schedules in the most effective way. In this work, a simple strategy for the development and implementation of a FDS that interacts with the schedule optimiser is presented. 2. FAULT DIAGNOSIS IN BATCH PROCESSES Currently, the use of pattern recognition methods based on Artificial Neural Networks (ANNs) and the use of statistical techniques are matter of research. The problem of the traditional ANNs related to totally capture the space and time characteristics of process signals is overcome with the use of wavelet functions. With respect to the use of statistical techniques, Multiway Principal Component Analysis (MPCA) has shown good results in batch process monitoring [ 1]. However, it has some drawbacks like the difficult isolation and localisation of the fault. Finally, in order to combine the strengths of both pattern recognition and inference methods, adaptive neuro-fuzzy systems are being developed. The idea is to obtain an adaptive learning diagnosis system with transparent knowledge representation. Some combinations are subjects of current research. *To whom correspondence must be sent
746 In all the above cases, the main problem is the complex strategy of implementation that delays their application in real industrial plants. It is important to take into account that the information given by the FDS of a batch plant has to be used at different levels in the decision-making hierarchy structure, including the advanced control system and the scheduling system. While developing and implementing a FDS, this important aspect must be considered. This work is focused on the implementation of a robust FDS support for the reactive scheduling system in multipurpose batch chemical plants. 3. FAULT DIAGNOSIS SYSTEM STRUCTURE The proposed FDS consists in an Artificial Neural Network (ANN) structure supplemented with a Knowledge Based Expert System (KBES) in a block-oriented configuration. The system combines the adaptive learning diagnostic procedure of the ANN and the transparent deep knowledge representation of the KBES. It has been successfully applied to complex steady state chemical plants [2]. Figure 1 shows the Fault diagnosis system structure. M1 is the subset of the direct and indirect measurements and/or observations from the plant, and is selected as input of the ANN approach. N1 is the set of q "pre-faults" diagnosed by the ANN approach. The values Nl(i), i from 1 to q, are usually between 0 and 1. They are the input of the Fuzzy Logic System (FLS). M2 is a set of r direct and indirect measurements and/or observations from the plant, which is selected as input of the FLS. The inference engine of the FLS has the knowledge base, expressed in a set of if-then rules. These rules are of two types: those containing process deep knowledge and those that are built from experience of the ANN's performance. In Figure 2 a scheme of the set of rules is presented. The outputs F (/'),j from 1 t o f are theffaults considered. The information needed to implement the FDS includes a historical database, a Hazard and Operability (HAZOP) analysis and a model of the chemical plant. The historical database that includes information related with normal and abnormal operating conditions can be used to train the ANN structure. The on-line measurements from the plant are the ANN's inputs. The ANN's outputs are the signals of different suspected faults. These outputs are a subset of the set of KBES's inputs. The ANN, not only has the advantage of a classifier but also it can be retrained during use. By this way, the changing operating conditions of chemical plants should not affect the FDS's performance.
Plant Mll
FLS t
LI~ ANN[-~ Fuzzificatioengi n//IJnnfeere/\ nce
~F
Based on experience with ANN performance
IF N1 (I) is ... A N D
(M2)... THEN/70") (M2)...THEN
F(/) is...
(M2)... T H E N
F(j) is...
. . .
IF N1 (q) is ... A N D
IF M2(1) is ... A N D . . . T H E N F(/) is...
Based on process deep knowledge
. . .
IF M2(i) is ... A N D ...THEN F(/) is... . . .
IF M2(r) is ... AND... T H E N F(/) is...
Defuzzification
Fig. 2. Scheme of the set of rules. Fig. 1. Neuro-fuzzy FDS
is...
. . .
IF N1 (i) is... A N D
747 The HAZOP analysis is useful to build the process deep knowledge base (KBES) of the plant. This base relies on the knowledge of the operators and engineers about the process and allows formulating artificial intelligence algorithms. A model of the plant can be used to obtain plant operation experience through simulation. The simulation can provide data on infrequent faults because in the cases of faults that rarely occur it is not possible to test the FDS using only plant data. In addition, by testing the ANN with the model, the development of the rules based on experience with ANN performance is straightforward. The model is also useful for testing and validating the FDS. The methodology can be summarised in the following steps: 1)Model the process;
2)Define the faults; 3)Determine measurements; 4)Simulate the faults; 5)Train an ANN, 6)Design the KBES using fuzzy logic; 6)Test the new system by simulation," 7)Design the adaptive method; 8)Test with the model; 9)Implementation in the real plant. 4. H A Z O P A N A L Y S I S IN B A T C H P L A N T S
HAZOP is one of the most powerful hazard identification methods available and has been well described in the literature. In the case of batch processes the HAZOP analysis examines every stage of the batch process sequence. Table 1 shows an example of a line in the HAZOP analysis of a batch process (Stage: Reactor charge; Unit: Tank n~ Node: Pipe from tank 1 to pump 1; Objective: To provide reactant to the reactors; Variable: Flow) Table 1. An example of a HAZOP analysis line Guideword Causes Consequences LiSV~..... Pumpl Time needed ibr reactor malfunction charge increased
Corrective actions Switch to pump 2
Safegu~ds Maintenance tests
Extending the HAZOP method to fault diagnosis characterisations provides a more "down to earth" approach for implementing an operator support system. The rules are kept simple to avoid the general problems of large rule-based knowledge systems, such as contradictory rules, large amounts of irrelevant information and complex tree structures [3]. The HAZOP analysis allows to: a) Generate the if-then rules for the KBES; b) Determine the information to be sent to other levels in the information system. 4.1. Generation of if then rules from H A Z O P analysis Not all the variables considered in the HAZOP analysis are measurements from the plant. Some variables can be observed (estimated). Regarding the generation of IF-THEN rules, only the measured and the observable variables should be considered. In general, the term "Causes" in the HAZOP analysis corresponds to the root cause of a deviation and it can be designated by the term fault. Only the causes that have been defined in the set of faults are considered at this step. In the Example, the fault is "pump 1 malfunction". Consequently, the conversion to an if-then rule is: IF Flow IS Low THEN "Pump malfunction" IS HIGH. The adjustment of the membership functions is the final step. 4.2. Determination of the information to be sent to other levels The FDS receives sensor data from the plant and the control signals. They can be continuous signals (temperatures, flowrates, pressures) or discrete signals (valves open or close, pumps on or off). The outputs are a set of suspected faults. The signal corresponding to
748 each suspected fault is considered binary (0 or 1). This output can be used by the advanced control module in order to take control actions, or by the operators who have to make decisions or by other levels in the computer system as the scheduling system. The output of the FDS has different forms according to the level of information. Table 2 shows the information at different levels from the FDS output when the fault "Pump 1 malfunction" is diagnosed. This evaluation corresponds to the explained Example. Note that the construction of that table is straightforward from the HAZOP analysis. Table 2. Information to different levels based on HAZOP analysis Module Translation from the FDS .....Control system Switch to pump 2 Scheduling system Time needed for tank charge increased Operators' console Check pump 1 5. REACTIVE SCHEDULING When a deviation from the predicted schedule in a multipurpose batch plant is diagnosed, the FDS activates the reactive scheduling module to minimise the effect of this deviation on the remaining schedule. The planning and scheduling system uses event operation networks (EON) modelling system [4]. The EON model has proved to be an efficient way to describe time constraints between operations in a complex production structure, as it is the batch process. The EON model is built using a general recipe description and other guidelines from ISA $88 specification. On the first step, according to the present situation of the plant, and the client orders, a first batch sequence is generated using the information provided by the recipe and the stage levels. Then, an EON graph is generated using the information located at the operation level description of the recipes, and the information generated in the previous step about the unit assignments and task sequence. Finally different methods can be used to adjust the proposed solution under the constraints imposed by the different resources required. Once the schedule is running on the real (or simulated) plant, the control system communicates to the planning system the deviations detected from the proposed plan. With this information, the scheduling system generates new information that is sent back to the control system. This feedback closes the loop between the planning and scheduling system and the control system. 6. CASE STUDY: MULTIPURPOSE BATCH CHEMICAL PLANT Figure 3 shows the flowsheet of the considered multipurpose batch chemical plant used as case study. It is constituted by three tank reactors, three heat exchangers and the necessary pumps and valves to allow changes of configuration. Equipment of this plant is fully interconnected and the instrumentation allows configuration changes by software. Two recipes with two stages each one have been considered. Figure 4 shows the representation of recipes in a Gantt chart performing two batches. Table 3 shows the operation description and the operation times corresponding to the two recipes considered. In both recipes the time of the operation with code 5 are different depending on the reactor chosen to perform the second stage.
749
....
[iiiiiii~l~iiiiiiii~iiiiiiliii}iiiiiiiiiiiiiii~iiii]iiiiii!il}t iiiii!iiiil]
R2
i il
iiiiii!iiiiNl
.....
Time
Fig. 4. Gantt chart performing two batches Fig. 3. Flowsheet of the multipurpose batch chemical plant Table 3. Operation description and operation times (hours) Operafibn ........ Stage Descriptidi] Unit code code 1 1 Load tank 1 T1 2 1 Stirring / Homogenising T1 3 1 Discharge to R1 / R2 T1 4 2 Load reactor R1/R2 5 2 Reaction R1 5 2 Reaction R2 6 2 Discharge of final product R1/R2 7 2 Reactor cleaning R1/R2 .
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Recipe 1
Recipe 2
0.066 0.084 0.066 0.066 0.25 0.33 0.066 0.167
0.066 0.167 0.066 0.066 0.33 0.416 0.066 0.066
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Two different faults have been considered to test the implementation strategy. The first one corresponds to a delayed operation caused by a pump malfunction. Figure 5 shows the tank level profiles, comparing a normal batch, an abnormal batch without FDS and an abnormal batch with the FDS implemented. It can be observed that the rapid diagnosis and communication to the supervisory control level allows reducing the delay. The whole schedule considered in this study consists of 5 batches of product A (recipe 1) and 6 batches of product B (recipe 2). The makespan of the initial schedule is 4.37 hours. The second abnormal situation considered is the unavailability of a piece of equipment, in this case the Reactor 2. It has been simulated that the unavailability lasts for fifty minutes since the beginning of the schedule. Figure 6 shows the Gantt charts. Table 5 summarises a comparison of the results taking into account the plant functioning without a FDS and with the FDS and reactive scheduling for the two considered abnormal situations 7. CONCLUSIONS A simple strategy for the development and implementation of a FDS that interacts with the schedule optimiser in multipurpose batch chemical plants has been presented. Besides, two examples of abnormal situations have been shown in a multipurpose batch chemical plant. The proposed integration of the FDS in the information system shows promising results by significant improvement in the production efficiency. Industrial applications of the proposed system are straightforward because of the simplicity of implementation. Therefore, future work includes the implementation of the system in real industrial scenarios.
750
Fig. 5. Tank levels profiles, Normal batch; Abnormal batch without FDS; Abnormal batch with FDS support
Table 4. Abnormal situation management performance comparison (makespan). Abnormal situation
Delayed operation Unavailability (R2)
Without the FDS support 4.43 h (+1.4%) 4.83 h (+10.5%)
With FDS & reactive scheduling 4.39 h (+0.5%) 4.70 h (+7.6%)
Fig. 6. Abnormal situation management comparison: a) Initial schedule, b) Realised schedule without the FDS support and c) With FDS and reactive scheduling.
ACKNOWLEDGEMENTS Financial support from the European Community is gratefully acknowledged (projects IC18CT98-0271 and IMS 26691). Nouguds was sponsored by Generalitat de Catalunya, II Pla de Recerca, TDOC Grant. REFERENCES
1. P. Nomikos and J.F. MacGregor, AIChE Journal, 40 (1994) 1361-1375. 2. Ruiz, D., Nougu6s, J. M. and Puigjaner, L, Computers & Chemical Engineering, 23S (1999) $219-222. 3. Wennersten, R., Narfeldt, R., Gr~infors, A. and Sj6kvist, Computers & Chemical Engineering 20S (1996) $665-670. 4. Graells, M., Cant6n, J., Peschaud, B. and Puigjaner L., Computers & Chemical Engineering, 22S (1998), $395-402.