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On-line fault diagnosis system support for reactive scheduling in multipurpose batch chemical plants
Computers and Chemical Engineering 25 (2001) 829– 837 www.elsevier.com/locate/compchemeng
On-line fault diagnosis system support for reactive scheduling in multipurpose batch chemical plants Diego Ruiz, Jordi Canto´n, Jose´ Marı´a Nougue´s, Antonio Espun˜a, Luis Puigjaner * Uni6ersitat Polite`cnica de Catalunya, Chemical Engineering Department, A6. Diagonal 647, E-08028, Barcelona, Spain Received 10 May 2000; accepted 5 January 2001
Abstract 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 of 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. 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. Two motivating case studies are presented to show the results of the proposed methodology. The first corresponds to a fed-batch reactor. In this example, the FDS performance is demonstrated through the simulation of different process faults. The second case study corresponds to a multipurpose batch plant. In this case, the results of reactive scheduling are shown by simulating different abnormal situations. A performance comparison is made against the traditional scheduling approach without the support of the proposed FDS. © 2001 Elsevier Science Ltd. All rights reserved. Keywords: Batch plants; Fault diagnosis; Scheduling
Nomenclature M1 N1 M2 F
Subset of measurements from the plant selected as input to the ANN Vector of fault signals diagnosed by the ANN Set of measurements from the plant that is selected as input to the FLS Vector of fault signals diagnosed by the FDS
1. Introduction The complexity of process control in present batch chemical plants affects the execution 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 Abbre6iations: ANN, artificial neural network; EON, event operation network; FDS, fault diagnosis system; FLS, fuzzy logic system; HAZOP, hazard and operability study; KBES, knowledge-based expert system * Corresponding author. Tel.: +34-93-401-6733; fax: + 34-934017150. E-mail address: [email protected] (L. Puigjaner).
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 (Sanmartı´ et al., 1997). A robust fault diagnosis system (FDS), which timely provides the fault information to the scheduling level, makes it possible to improve the efficiency of reactive scheduling and update the schedules in the most effective way. It is necessary to examine the implications of linking the process monitoring and diagnosis functionalities into a comprehensive manufacturing control system (Reklaitis, 1996). In this work, a simple strategy for the development and implementation of a FDS that interacts with the
0098-1354/01/$ - see front matter © 2001 Elsevier Science Ltd. All rights reserved. PII: S0098-1354(01)00657-3
D. Ruiz et al. / Computers and Chemical Engineering 25 (2001) 829–837
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Table 3 Information to different levels based on HAZOP analysis Module
Translation from the FDS
Control system Scheduling system Operators’ console
Switch to pump 2 Time needed for tank charge increased Check pump 1
made against the traditional scheduling approach without the support of the proposed FDS. Fig. 1. Neuro-fuzzy fault diagnosis system.
2. Fault diagnosis in batch processes
Table 1 Scheme of the set of rules Based on experience with ANN performance
Based on process deep knowledge
IF N1(1) is…AND (M2 )…THEN F( j ) is… … IF N1(i) is…AND (M2 )…THEN F( j ) is… … IF N1(q) is…AND (M2 )…THEN F( j ) is… IF M2(1) is…AND…THEN F( j ) is… … IF M2(i) is…AND…THEN F( j ) is… … IF M2(r) is…AND…THEN F( j ) is…
schedule optimiser is presented. First, a brief overview of fault diagnosis in batch processes is presented. Next, the proposed FDS structure is described. Then, a summary of the methodology for the design and implementation of the FDS is explained. After that, the use of the hazard and operability analysis of the plant for the FDS implementation is treated in more detail. This analysis has a key importance in order to determine the information to be sent to the other levels. Then, the interaction with the reactive scheduling module is briefly analysed. Finally, two motivating case studies are presented to show the results of the proposed methodology. The first corresponds to a fed-batch reactor. In this example, the FDS performance is demonstrated through the simulation of different process faults. The second case study corresponds to a multipurpose batch plant. In this case, the results of the reactive scheduling are shown by simulating different abnormal situations. A performance comparison is
Most fault diagnosis approaches presented so far have been shown to be applicable to steady-state processes. These approaches can be divided into three groups: historical-based methods, model-based techniques and combinations of both. However, the application of these diagnosis approaches to batch chemical processes is usually difficult. In the past decades, research was focused on the use of either fundamental models or detailed knowledgebased models. The first monitoring procedure is based on estimation methods. The second relies on the knowledge of the operators and engineers about the process. More recently, the use of pattern recognition methods based on artificial neural networks (ANNs) and the use of statistical techniques are a 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. Studies on wavelet functions, of extensive use in signal processing, have advanced rapidly in the last years. Their application to fault diagnosis is being performed in two ways: 1. for feature extraction; the wavelet function outputs are then processed either by an ANN (Chen et al., 1999), by qualitative trend analysis (Vedam & Venkatasubramanian, 1997), or by a principal-component analysis approach (Bakshi, 1998); 2. as an activation function of the ANN (Zhao et al., 1998). With respect to the use of statistical techniques, multiway principal-component analysis (MPCA) has shown good results in batch process monitoring (Nomikos & MacGregor, 1994). This technique is currently used as reference in present research. The only information needed is a historical database of past batches. How-
Table 2 Example of a HAZOP analysis line Guideword
Causes
Consequences
Corrective actions
Safeguards
Low
Pump 1 malfunction
Time needed for reactor charge increased
Switch to pump 2
Maintenance tests
D. Ruiz et al. / Computers and Chemical Engineering 25 (2001) 829–837
ever, it has several drawbacks such as 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
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transparent knowledge representation. Some combinations are subject of current research (Leonhardt & Ayoubi, 1997): ANNs influenced by fuzzy logic (e.g. fuzzy models within ANNs), fuzzy systems influenced by ANNs (e.g. serial configuration), and hybrid neurofuzzy systems. In the last years, the application of
Fig. 2. Fed-batch reactor as it appears in the user interface.
Fig. 3. ANN response for a batch run with the initial problem of low reactant concentration in the feed (F1); , F1;
, F2;
F3;
.
D. Ruiz et al. / Computers and Chemical Engineering 25 (2001) 829–837
832 Table 4 FDS performance
3. Fault diagnosis system structure
Fault
Time needed by the FDS to isolate the fault (s)
Low feed reactant concentration (F1) Fouling of reactor temperature sensor (F2) Cooling system failure (F3)
14 8 19
combined methods for fault diagnosis has steadily been growing. In all the above cases, the main problem is the complex strategy of implementation that delays their application in real industrial plants. It is also important to consider 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.
The proposed FDS consists of 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 (Ruiz et al., 1999). Furthermore, the approach has been extended to batch processes by pre-processing the signal using multi-scale wavelets (Ruiz et al., 2000). Fig. 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 ‘‘prefaults’’ diagnosed by the ANN approach. The values N1(i ), with 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. When using fuzzy logic in a diagnosis environment, the following successive steps are involved: fuzzification of ‘‘crisp’’ values; inference using a rule base in which the logical operations are performed on the membership functions; and defuzzification to obtain ‘‘crisp’’ outputs. The inference engine has the knowledge base, expressed in a set of if–then rules. These rules are of
Fig. 4. Monitoring chart with its 95 and 99% control limits for a batch run (dotted line) with the initial problem of low reactant concentration in the feed.
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Fig. 5. Flowsheet of the multipurpose chemical plant. R1: Tank Reactor 1; R2: Tank Reactor 2; T1: Tank 1.
two types: those containing process deep knowledge coming from mathematical models and HAZOP analysis and those that are built from experience of the ANN’s performance. In Table 1, a scheme of the set of rules is presented. The outputs F ( j ), with j from 1 to f, are the f faults considered.
4. Summary of the proposed methodology 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, which 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, the so-called ‘‘residuals’’ in fault diagnosis, 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 can be retrained during use. In this way, the changing operating conditions of chemical plants should not affect the FDS’s performance. The HAZOP analysis can be useful to build the process deep knowledge base (KBES) of the plant. The KBES relies on the knowledge of the operators and engineers about the process and allows formulating artificial intelligence algorithms. Process engineers usually handle different kinds of plant models, at the different levels of plant design and operation. A model of the plant can be used to obtain plant-operation experience through simulation. Simula-
tion 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 proposed 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 the ANN; (6) design the KBES using fuzzy logic; (7) test the new system by simulation; (8) design the adaptive method; (9) test with the model; (10) implementation in the real plant. 5. HAZOP analysis in batch plants In most industrialised countries, standards require that hazard analyses be performed on a regular basis. HAZOP is the most widely used and recognised as the preferred approach in the chemical process industry. It is typically performed by a team of experts having specialised knowledge and expertise in the design, operation and maintenance of the plant.
Fig. 6. Gantt chart showing two batches.
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Table 5 Operation description and operation times (h) Operation code
Stage code
Description
Unit
Recipe 1
Recipe 2
1 2 3 4 5 5 6 7
1 1 1 2 2 2 2 2
Load tank 1 Stirring/homogenising Discharge to R1/R2 Load reactor Reaction Reaction Discharge of final product Reactor cleaning
T1 T1 T1 R1/R2 R1 R2 R1/R2 R1/R2
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
HAZOP is also one of the most powerful hazard identification methods available and has been well described in the literature. The imagination of a selected team is used to perturb a model of the system being studied by using a methodical procedure to identify potential accidents. The system is studied one element at a time, in a ‘‘Top Down’’ fashion. The design intention of each element is defined and then questioned using ‘‘Guide-words’’ to produce deviations from the intention. The causes, consequences and safeguards for each deviation are then discussed and recorded. Some approaches to automate HAZOP analysis have been reported (Vaidhyanathan & Venkatasubramanian, 1996). In the case of batch processes, the HAZOP analysis examines every stage of the batch process sequence. As an example: a line in the HAZOP analysis of a batch process is shown in Table 2. Extending the HAZOP method to fault diagnosis characterisation 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 (Wennersten et al., 1996).The HAZOP analysis makes it possible to: 1. generate the if–then rules for the KBES; 2. determine the information to be sent to other levels in the information system.
Consequently, the conversion to an if–then rule is as follows: IF Flow IS Low THEN ‘‘Pump malfunction’’ IS HIGH. The adjustment of the membership functions is the final step. 5.2. Determination of the information to be sent to other le6els 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 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 take 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 3 shows the information at different levels from the FDS output when the fault ‘‘Pump 1 malfunction’’ is diagnosed. This evaluation corresponds to the Case study II explained below. Note that the construction of that table is straightforward from the HAZOP analysis.
5.1. Generation of if– then rules from HAZOP 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 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 Table 2, the fault is ‘‘pump 1 malfunction’’.
Fig. 7. Tank levels profiles. : normal batch; : abnormal batch without FDS; : abnormal batch with FDS support.
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generated using the information provided by the recipe and the stage level. Then, an EON graph is generated using the information located at the operation level description of the recipe, and the information generated in the previous step of unit assignment and task sequencing. 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 and supervisory 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.
7. Case study I: Fed-batch reactor This first case study corresponds to a fed-batch reactor. The FDS performance is demonstrated through the simulation of different process faults.
7.1. Description Fig. 8. Comparison of abnormal situation management: (a) initial schedule, (b) schedule performed without the FDS support and (c) schedule with FDS and reactive scheduling. Table 6 Abnormal situation (makespan)
management
performance
comparison
Abnormal situation
Without the FDS support
With FDS and reactive scheduling
Delayed operation Unavailability (R2)
4.43 h (+1.4%)
4.39 h (+0.5%)
4.83 h (+10.5%) 4.70 h (+7.6%)
6. 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 the Event Operation Networks (EON) modelling system (Graells et al., 1998). 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 S88 specification. In the first step, according to the present situation of the plant and the client orders, a first batch sequence is
Fig. 2 shows the flowsheet of the analysed fed-batch reactor as it appears to the user via the programmed interface using Lab Windows CVI®. The reaction used was the oxidation of Na2SO3 with H2O2. This is a well-known exothermic reaction. The pilot scale reactor consists of a 5 l tank reactor, with a data acquisition system based on GPIB bus and PC software. As mentioned above, the reactor was operated in fed-batch mode, being the H2O2 fed into Na2S2O3. All the software for on-line control was developed in C, and the system analysis and model parameters adjustment was made in Matlab® developed modules (Nougue´s et al., 1999). The step analysed is as follows: from the mixer tank, a constant flow of reactants is discharged to the continuous stirred tank reactor (CSTR) by opening the valve V1. The reaction takes place in the CSTR and is refrigerated constantly by opening valve Vref. Temperatures, flow rates and reactant concentrations are displayed in the human –machine interface shown in Fig. 2.
7.2. Fault-diagnosis system performance Neural network training was performed with data from simulated abnormal situations in the plant. The inputs to the ANN block are as follows: Mixer level, Reactor level, reactants concentration (observable by inference), reactor temperature and the time from the start of the operation. The outputs are the suspected faults:
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low feed reactant concentration (F1) fouling of reactor temperature sensor (F2) cooling system failure (F3). Fig. 3 shows the response of the ANN block when a low reactant concentration in the feed is simulated. The ANN needs 14 s to recognise the fault F1 pattern. In the first seconds, a low signal of Fault F2 appears but decays inmediately. Furthermore, a very low signal of Fault F3 appears with Fault F1 signal. In this case, both low signals cannot be considered as false diagnosis cases, as it is correctly interpreted by the FLS next (false diagnosis is duly filtered). Moreover, it suggests the importance of the use of the supplement (KBES) in order to eliminate any possibility of a false multiple diagnosis. Table 4 summarises the FDS performance taking into account the three suspected faults for this case. The main feature is the rapid and correct diagnosis. On-line MPCA (Nomikos & MacGregor, 1994) was performed in order to obtain a reference for the ANN performance. Fig. 4 shows one of the monitoring charts that allow detecting a fault in a batch process using the on-line MPCA. The points correspond to the score (T) of the first-principal component, while the continuous and dashed (upper and lower) lines correspond to the 95 and 99% control limits, respectively. The lower limit of 95% is overcome 3 min after the fault occurs, while the 99% control limit is overcome almost 4 min after. The Square Prediction error chart allows the simulated fault to be detected quickly, but the isolation of the fault is difficult because the on-line MPCA technique requires an additional complex expert system to do that. Otherwise, the ANN isolates the fault in less than a quarter of a minute (Fig. 3). After passing through the FLS, the final signal has a value of 1 for the output corresponding to this suspected fault.
8. Case study II: Multipurpose batch chemical plant The flowsheet of the multipurpose batch chemical plant considered for this case study is shown in Fig. 5. The plant consists of three tank reactors, three heat exchangers and the necessary pumps and valves to allow changes of process configuration. Equipment of this plant is fully interconnected, and the instrumentation allows configuration changes by software. Two recipes with two stages each have been considered. Fig. 6 shows the representation of the recipes in a Gantt chart performing two batches. Table 5 shows the operation description and the operation times corresponding to the two recipes considered. Tank T1 is used to mix the reactants, and then the mixture is discharged to the reactors R1 or R2 according to the schedule. Loading and discharge of tank T1 require the same time for both recipes 1 and 2. Otherwise, the time for homoge-
nizing and stirring is longer for recipe 2. Furthermore, the time needed for reactor cleaning is different for each recipe. In both recipes, the times of the operation with code 5 (Reaction) are different depending on the reactor chosen to perform the second stage.
8.1. Implementation results Two different faults have been considered to test the implementation strategy. The first corresponds to a delayed operation caused by a pump malfunction. Fig. 7 shows the tank level profiles, providing a comparison of a regular batch, an abnormal batch in the absence of 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 discharge of tank T1 (and loading of Reactor R1) needs almost 0.15 h instead of the presumed 0.06 h. With the support of the FDS, a switch to other pumps and a checking of pump 1 by the operator can be done, and the consequences of the abnormal situations can be reduced (approximately 0.08 h). Furthermore, the scheduling system can perform reactive scheduling (schedule optimiser), taking into account the delay. The second abnormal situation considered is the unavailability of a piece of equipment, in this case, the Reactor 2. The whole schedule considered in this study consists of five batches of product A (recipe 1) and eight batches of product B (recipe 2). Fig. 8a shows the initial schedule. The makespan of this initial schedule is 4.37 h. From a simulation, it has been shown that the unavailability of Reactor 2 lasts for 50 min since the beginning of the schedule. Fig. 8 shows three Gantt charts. The first corresponds to the initial schedule, the second corresponds to the abnormal situation without the FDS support, and the third considers the implementation of the FDS and the reactive scheduling. In the initial schedule, Recipe 1 is performed in Reactor 1 and recipe 2 in reactor R2. In the presence of the diagnosed abnormal situation, the schedule optimiser selects the reactor 2 to perform the rest of batches with recipe 1 when this piece of equipment is available again. Table 6 summarises a comparison of the results taking into account the plant functioning without and with the FDS, and reactive scheduling for the two considered abnormal situations. In the case of the simulated delayed operation (the first simulated abnormal situation), the makespan is increased only by 0.5% with the FDS support (0.9% less than the case without the FDS support). Otherwise, the FDS support to reactive scheduling reduces the impact of the second abnormal situation simulated. The makespan is increased by 7.6%, that is 2.9% less than the scenario without the FDS support. Savings of this order (2.9 and 0.9%) may represent a big improvement in terms of
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productivity in modern batch chemical plants, like pharmaceutical and fine chemicals manufacturing.
9. 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. The advantages of using the proposed FDS have also been shown by its implementation in a fed-batch reactor. The FDS has been shown to be superior to existing approaches in relation with fault isolation and the time needed for fault detection. 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 a 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.
Acknowledgements Financial support from the European Community is gratefully acknowledged (projects IC18-CT98-0271 and IMS 26691). Diego Ruiz is sponsored by Generalitat de Catalunya, TDOC Grant.
References Bakshi, B. R. (1998). Multiscale PCA with Application to Multivariate Statistical Process Monitoring. American Institute of Chemi-
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cal Engineering Journal, 44 (7), 1596– 1610. Chen, B. H., Wang, X. Z., Yang, S. H., & McGreavy, C. (1999). Application of wavelets and neural networks to diagnostic system development, 1, feature extraction. Computers and Chemical Engineering, 23, 899– 906. Graells, M., Canto´n, J., Peschaud, B., & Puigjaner, L. (1998). General approach and tool for the scheduling of complex production systems. Computers and Chemical Engineering, 22S, S395–402. Leonhardt, S., & Ayoubi, M. (1997). Methods of fault diagnosis. Control Engineering Practice, 5, 683– 692. Nomikos, P., & MacGregor, J. F. (1994). Monitoring batch processes using multiway principal component analysis. American Institute of Chemical Engineering Journal, 40 (8), 1361– 1375. Nougue´s, J. M., Grau, D., & Puigjaner, L. (1999). Parameter Estimation with Genetic Algorithm in Control of Fed-Batch Reactors. AIChE Annual Meeting, Dallas, No6ember 1999. Reklaitis, G. V. (1996). Future directions for research and development in batch processes systems enginerring. NATO ASI Series, Series F: Computer and System Sciences, 143, 20 – 42. Ruiz, D., Nougue´s, J. M., & Puigjaner, L (1999). On line process fault detection and diagnosis in plants with recycle. Computers and Chemical Engineering, 23S, S219– 222. Ruiz, D., Nougue´s, J. M., Caldero´n, Z., Espun˜a, A., & Puigjaner, L. (2000). Neural network based framework for fault diagnosis in batch chemical plants. Computers and Chemical Engineering, 24, 777– 784. Sanmartı´, E., Espun˜a, A., & Puigjaner, L. (1997). Batch production and preventive maintenance scheduling under equipment failure uncertainty. Computers and Chemical Engineering, 21, 1157– 1168. Vaidhyanathan, R., & Venkatasubramanian, V. (1996). A semiquantitative reasoning methodology for filtering and ranking HAZOP results in HAZOPExpert. Computers and Chemical Engineering, 53, 185– 203. Vedam, H., & Venkatasubramanian, V. (1997). A Wa6elet TheoryBased Adapti6e Trend Analysis System for Process Monitoring and Diagnosis, Proceedings of the American Control Conference, New Mexico (pp. 309– 313). Wennersten, R., Narfeldt, R., Gra¨nfors, A., & Sjo¨kvist, S. (1996). Process modelling in fault diagnosis. Computers and Chemical Engineering, 20S, S665– S670. Zhao, J., Chen, B., & Shen, J. (1998). Multidimensional non-orthogonal wavelet-sigmoid basis function neural network for dynamic process fault diagnosis. Computers and Chemical Engineering, 23, 83 – 92.
On-line fault diagnosis system support for reactive scheduling in multipurpose batch chemical plants Diego Ruiz, Jordi Canto´n, Jose´ Marı´a Nougue´s, Antonio Espun˜a, Luis Puigjaner * Uni6ersitat Polite`cnica de Catalunya, Chemical Engineering Department, A6. Diagonal 647, E-08028, Barcelona, Spain Received 10 May 2000; accepted 5 January 2001
Abstract 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 of 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. 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. Two motivating case studies are presented to show the results of the proposed methodology. The first corresponds to a fed-batch reactor. In this example, the FDS performance is demonstrated through the simulation of different process faults. The second case study corresponds to a multipurpose batch plant. In this case, the results of reactive scheduling are shown by simulating different abnormal situations. A performance comparison is made against the traditional scheduling approach without the support of the proposed FDS. © 2001 Elsevier Science Ltd. All rights reserved. Keywords: Batch plants; Fault diagnosis; Scheduling
Nomenclature M1 N1 M2 F
Subset of measurements from the plant selected as input to the ANN Vector of fault signals diagnosed by the ANN Set of measurements from the plant that is selected as input to the FLS Vector of fault signals diagnosed by the FDS
1. Introduction The complexity of process control in present batch chemical plants affects the execution 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 Abbre6iations: ANN, artificial neural network; EON, event operation network; FDS, fault diagnosis system; FLS, fuzzy logic system; HAZOP, hazard and operability study; KBES, knowledge-based expert system * Corresponding author. Tel.: +34-93-401-6733; fax: + 34-934017150. E-mail address: [email protected] (L. Puigjaner).
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 (Sanmartı´ et al., 1997). A robust fault diagnosis system (FDS), which timely provides the fault information to the scheduling level, makes it possible to improve the efficiency of reactive scheduling and update the schedules in the most effective way. It is necessary to examine the implications of linking the process monitoring and diagnosis functionalities into a comprehensive manufacturing control system (Reklaitis, 1996). In this work, a simple strategy for the development and implementation of a FDS that interacts with the
0098-1354/01/$ - see front matter © 2001 Elsevier Science Ltd. All rights reserved. PII: S0098-1354(01)00657-3
D. Ruiz et al. / Computers and Chemical Engineering 25 (2001) 829–837
830
Table 3 Information to different levels based on HAZOP analysis Module
Translation from the FDS
Control system Scheduling system Operators’ console
Switch to pump 2 Time needed for tank charge increased Check pump 1
made against the traditional scheduling approach without the support of the proposed FDS. Fig. 1. Neuro-fuzzy fault diagnosis system.
2. Fault diagnosis in batch processes
Table 1 Scheme of the set of rules Based on experience with ANN performance
Based on process deep knowledge
IF N1(1) is…AND (M2 )…THEN F( j ) is… … IF N1(i) is…AND (M2 )…THEN F( j ) is… … IF N1(q) is…AND (M2 )…THEN F( j ) is… IF M2(1) is…AND…THEN F( j ) is… … IF M2(i) is…AND…THEN F( j ) is… … IF M2(r) is…AND…THEN F( j ) is…
schedule optimiser is presented. First, a brief overview of fault diagnosis in batch processes is presented. Next, the proposed FDS structure is described. Then, a summary of the methodology for the design and implementation of the FDS is explained. After that, the use of the hazard and operability analysis of the plant for the FDS implementation is treated in more detail. This analysis has a key importance in order to determine the information to be sent to the other levels. Then, the interaction with the reactive scheduling module is briefly analysed. Finally, two motivating case studies are presented to show the results of the proposed methodology. The first corresponds to a fed-batch reactor. In this example, the FDS performance is demonstrated through the simulation of different process faults. The second case study corresponds to a multipurpose batch plant. In this case, the results of the reactive scheduling are shown by simulating different abnormal situations. A performance comparison is
Most fault diagnosis approaches presented so far have been shown to be applicable to steady-state processes. These approaches can be divided into three groups: historical-based methods, model-based techniques and combinations of both. However, the application of these diagnosis approaches to batch chemical processes is usually difficult. In the past decades, research was focused on the use of either fundamental models or detailed knowledgebased models. The first monitoring procedure is based on estimation methods. The second relies on the knowledge of the operators and engineers about the process. More recently, the use of pattern recognition methods based on artificial neural networks (ANNs) and the use of statistical techniques are a 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. Studies on wavelet functions, of extensive use in signal processing, have advanced rapidly in the last years. Their application to fault diagnosis is being performed in two ways: 1. for feature extraction; the wavelet function outputs are then processed either by an ANN (Chen et al., 1999), by qualitative trend analysis (Vedam & Venkatasubramanian, 1997), or by a principal-component analysis approach (Bakshi, 1998); 2. as an activation function of the ANN (Zhao et al., 1998). With respect to the use of statistical techniques, multiway principal-component analysis (MPCA) has shown good results in batch process monitoring (Nomikos & MacGregor, 1994). This technique is currently used as reference in present research. The only information needed is a historical database of past batches. How-
Table 2 Example of a HAZOP analysis line Guideword
Causes
Consequences
Corrective actions
Safeguards
Low
Pump 1 malfunction
Time needed for reactor charge increased
Switch to pump 2
Maintenance tests
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ever, it has several drawbacks such as 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
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transparent knowledge representation. Some combinations are subject of current research (Leonhardt & Ayoubi, 1997): ANNs influenced by fuzzy logic (e.g. fuzzy models within ANNs), fuzzy systems influenced by ANNs (e.g. serial configuration), and hybrid neurofuzzy systems. In the last years, the application of
Fig. 2. Fed-batch reactor as it appears in the user interface.
Fig. 3. ANN response for a batch run with the initial problem of low reactant concentration in the feed (F1); , F1;
, F2;
F3;
.
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832 Table 4 FDS performance
3. Fault diagnosis system structure
Fault
Time needed by the FDS to isolate the fault (s)
Low feed reactant concentration (F1) Fouling of reactor temperature sensor (F2) Cooling system failure (F3)
14 8 19
combined methods for fault diagnosis has steadily been growing. In all the above cases, the main problem is the complex strategy of implementation that delays their application in real industrial plants. It is also important to consider 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.
The proposed FDS consists of 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 (Ruiz et al., 1999). Furthermore, the approach has been extended to batch processes by pre-processing the signal using multi-scale wavelets (Ruiz et al., 2000). Fig. 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 ‘‘prefaults’’ diagnosed by the ANN approach. The values N1(i ), with 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. When using fuzzy logic in a diagnosis environment, the following successive steps are involved: fuzzification of ‘‘crisp’’ values; inference using a rule base in which the logical operations are performed on the membership functions; and defuzzification to obtain ‘‘crisp’’ outputs. The inference engine has the knowledge base, expressed in a set of if–then rules. These rules are of
Fig. 4. Monitoring chart with its 95 and 99% control limits for a batch run (dotted line) with the initial problem of low reactant concentration in the feed.
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Fig. 5. Flowsheet of the multipurpose chemical plant. R1: Tank Reactor 1; R2: Tank Reactor 2; T1: Tank 1.
two types: those containing process deep knowledge coming from mathematical models and HAZOP analysis and those that are built from experience of the ANN’s performance. In Table 1, a scheme of the set of rules is presented. The outputs F ( j ), with j from 1 to f, are the f faults considered.
4. Summary of the proposed methodology 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, which 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, the so-called ‘‘residuals’’ in fault diagnosis, 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 can be retrained during use. In this way, the changing operating conditions of chemical plants should not affect the FDS’s performance. The HAZOP analysis can be useful to build the process deep knowledge base (KBES) of the plant. The KBES relies on the knowledge of the operators and engineers about the process and allows formulating artificial intelligence algorithms. Process engineers usually handle different kinds of plant models, at the different levels of plant design and operation. A model of the plant can be used to obtain plant-operation experience through simulation. Simula-
tion 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 proposed 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 the ANN; (6) design the KBES using fuzzy logic; (7) test the new system by simulation; (8) design the adaptive method; (9) test with the model; (10) implementation in the real plant. 5. HAZOP analysis in batch plants In most industrialised countries, standards require that hazard analyses be performed on a regular basis. HAZOP is the most widely used and recognised as the preferred approach in the chemical process industry. It is typically performed by a team of experts having specialised knowledge and expertise in the design, operation and maintenance of the plant.
Fig. 6. Gantt chart showing two batches.
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Table 5 Operation description and operation times (h) Operation code
Stage code
Description
Unit
Recipe 1
Recipe 2
1 2 3 4 5 5 6 7
1 1 1 2 2 2 2 2
Load tank 1 Stirring/homogenising Discharge to R1/R2 Load reactor Reaction Reaction Discharge of final product Reactor cleaning
T1 T1 T1 R1/R2 R1 R2 R1/R2 R1/R2
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
HAZOP is also one of the most powerful hazard identification methods available and has been well described in the literature. The imagination of a selected team is used to perturb a model of the system being studied by using a methodical procedure to identify potential accidents. The system is studied one element at a time, in a ‘‘Top Down’’ fashion. The design intention of each element is defined and then questioned using ‘‘Guide-words’’ to produce deviations from the intention. The causes, consequences and safeguards for each deviation are then discussed and recorded. Some approaches to automate HAZOP analysis have been reported (Vaidhyanathan & Venkatasubramanian, 1996). In the case of batch processes, the HAZOP analysis examines every stage of the batch process sequence. As an example: a line in the HAZOP analysis of a batch process is shown in Table 2. Extending the HAZOP method to fault diagnosis characterisation 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 (Wennersten et al., 1996).The HAZOP analysis makes it possible to: 1. generate the if–then rules for the KBES; 2. determine the information to be sent to other levels in the information system.
Consequently, the conversion to an if–then rule is as follows: IF Flow IS Low THEN ‘‘Pump malfunction’’ IS HIGH. The adjustment of the membership functions is the final step. 5.2. Determination of the information to be sent to other le6els 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 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 take 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 3 shows the information at different levels from the FDS output when the fault ‘‘Pump 1 malfunction’’ is diagnosed. This evaluation corresponds to the Case study II explained below. Note that the construction of that table is straightforward from the HAZOP analysis.
5.1. Generation of if– then rules from HAZOP 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 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 Table 2, the fault is ‘‘pump 1 malfunction’’.
Fig. 7. Tank levels profiles. : normal batch; : abnormal batch without FDS; : abnormal batch with FDS support.
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generated using the information provided by the recipe and the stage level. Then, an EON graph is generated using the information located at the operation level description of the recipe, and the information generated in the previous step of unit assignment and task sequencing. 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 and supervisory 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.
7. Case study I: Fed-batch reactor This first case study corresponds to a fed-batch reactor. The FDS performance is demonstrated through the simulation of different process faults.
7.1. Description Fig. 8. Comparison of abnormal situation management: (a) initial schedule, (b) schedule performed without the FDS support and (c) schedule with FDS and reactive scheduling. Table 6 Abnormal situation (makespan)
management
performance
comparison
Abnormal situation
Without the FDS support
With FDS and reactive scheduling
Delayed operation Unavailability (R2)
4.43 h (+1.4%)
4.39 h (+0.5%)
4.83 h (+10.5%) 4.70 h (+7.6%)
6. 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 the Event Operation Networks (EON) modelling system (Graells et al., 1998). 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 S88 specification. In the first step, according to the present situation of the plant and the client orders, a first batch sequence is
Fig. 2 shows the flowsheet of the analysed fed-batch reactor as it appears to the user via the programmed interface using Lab Windows CVI®. The reaction used was the oxidation of Na2SO3 with H2O2. This is a well-known exothermic reaction. The pilot scale reactor consists of a 5 l tank reactor, with a data acquisition system based on GPIB bus and PC software. As mentioned above, the reactor was operated in fed-batch mode, being the H2O2 fed into Na2S2O3. All the software for on-line control was developed in C, and the system analysis and model parameters adjustment was made in Matlab® developed modules (Nougue´s et al., 1999). The step analysed is as follows: from the mixer tank, a constant flow of reactants is discharged to the continuous stirred tank reactor (CSTR) by opening the valve V1. The reaction takes place in the CSTR and is refrigerated constantly by opening valve Vref. Temperatures, flow rates and reactant concentrations are displayed in the human –machine interface shown in Fig. 2.
7.2. Fault-diagnosis system performance Neural network training was performed with data from simulated abnormal situations in the plant. The inputs to the ANN block are as follows: Mixer level, Reactor level, reactants concentration (observable by inference), reactor temperature and the time from the start of the operation. The outputs are the suspected faults:
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low feed reactant concentration (F1) fouling of reactor temperature sensor (F2) cooling system failure (F3). Fig. 3 shows the response of the ANN block when a low reactant concentration in the feed is simulated. The ANN needs 14 s to recognise the fault F1 pattern. In the first seconds, a low signal of Fault F2 appears but decays inmediately. Furthermore, a very low signal of Fault F3 appears with Fault F1 signal. In this case, both low signals cannot be considered as false diagnosis cases, as it is correctly interpreted by the FLS next (false diagnosis is duly filtered). Moreover, it suggests the importance of the use of the supplement (KBES) in order to eliminate any possibility of a false multiple diagnosis. Table 4 summarises the FDS performance taking into account the three suspected faults for this case. The main feature is the rapid and correct diagnosis. On-line MPCA (Nomikos & MacGregor, 1994) was performed in order to obtain a reference for the ANN performance. Fig. 4 shows one of the monitoring charts that allow detecting a fault in a batch process using the on-line MPCA. The points correspond to the score (T) of the first-principal component, while the continuous and dashed (upper and lower) lines correspond to the 95 and 99% control limits, respectively. The lower limit of 95% is overcome 3 min after the fault occurs, while the 99% control limit is overcome almost 4 min after. The Square Prediction error chart allows the simulated fault to be detected quickly, but the isolation of the fault is difficult because the on-line MPCA technique requires an additional complex expert system to do that. Otherwise, the ANN isolates the fault in less than a quarter of a minute (Fig. 3). After passing through the FLS, the final signal has a value of 1 for the output corresponding to this suspected fault.
8. Case study II: Multipurpose batch chemical plant The flowsheet of the multipurpose batch chemical plant considered for this case study is shown in Fig. 5. The plant consists of three tank reactors, three heat exchangers and the necessary pumps and valves to allow changes of process configuration. Equipment of this plant is fully interconnected, and the instrumentation allows configuration changes by software. Two recipes with two stages each have been considered. Fig. 6 shows the representation of the recipes in a Gantt chart performing two batches. Table 5 shows the operation description and the operation times corresponding to the two recipes considered. Tank T1 is used to mix the reactants, and then the mixture is discharged to the reactors R1 or R2 according to the schedule. Loading and discharge of tank T1 require the same time for both recipes 1 and 2. Otherwise, the time for homoge-
nizing and stirring is longer for recipe 2. Furthermore, the time needed for reactor cleaning is different for each recipe. In both recipes, the times of the operation with code 5 (Reaction) are different depending on the reactor chosen to perform the second stage.
8.1. Implementation results Two different faults have been considered to test the implementation strategy. The first corresponds to a delayed operation caused by a pump malfunction. Fig. 7 shows the tank level profiles, providing a comparison of a regular batch, an abnormal batch in the absence of 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 discharge of tank T1 (and loading of Reactor R1) needs almost 0.15 h instead of the presumed 0.06 h. With the support of the FDS, a switch to other pumps and a checking of pump 1 by the operator can be done, and the consequences of the abnormal situations can be reduced (approximately 0.08 h). Furthermore, the scheduling system can perform reactive scheduling (schedule optimiser), taking into account the delay. The second abnormal situation considered is the unavailability of a piece of equipment, in this case, the Reactor 2. The whole schedule considered in this study consists of five batches of product A (recipe 1) and eight batches of product B (recipe 2). Fig. 8a shows the initial schedule. The makespan of this initial schedule is 4.37 h. From a simulation, it has been shown that the unavailability of Reactor 2 lasts for 50 min since the beginning of the schedule. Fig. 8 shows three Gantt charts. The first corresponds to the initial schedule, the second corresponds to the abnormal situation without the FDS support, and the third considers the implementation of the FDS and the reactive scheduling. In the initial schedule, Recipe 1 is performed in Reactor 1 and recipe 2 in reactor R2. In the presence of the diagnosed abnormal situation, the schedule optimiser selects the reactor 2 to perform the rest of batches with recipe 1 when this piece of equipment is available again. Table 6 summarises a comparison of the results taking into account the plant functioning without and with the FDS, and reactive scheduling for the two considered abnormal situations. In the case of the simulated delayed operation (the first simulated abnormal situation), the makespan is increased only by 0.5% with the FDS support (0.9% less than the case without the FDS support). Otherwise, the FDS support to reactive scheduling reduces the impact of the second abnormal situation simulated. The makespan is increased by 7.6%, that is 2.9% less than the scenario without the FDS support. Savings of this order (2.9 and 0.9%) may represent a big improvement in terms of
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productivity in modern batch chemical plants, like pharmaceutical and fine chemicals manufacturing.
9. 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. The advantages of using the proposed FDS have also been shown by its implementation in a fed-batch reactor. The FDS has been shown to be superior to existing approaches in relation with fault isolation and the time needed for fault detection. 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 a 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.
Acknowledgements Financial support from the European Community is gratefully acknowledged (projects IC18-CT98-0271 and IMS 26691). Diego Ruiz is sponsored by Generalitat de Catalunya, TDOC Grant.
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