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Early detection and identification of dangerous states in chemical plants using neural networks
Journal of Loss Prevention in the Process Industries 12 (1999) 451–453 www.elsevier.com/locate/jlp
Early detection and identification of dangerous states in chemical plants using neural networks Joachim Neumann *, Go¨rge Deerberg, Stefan Schlu¨ter Fraunhofer-Institute for Environmental, Safety and Energy Technology UMSICHT, Osterfelder Strasse 3, D-46047 Oberhausen, Germany
Abstract The suitability of pattern recognition for safety diagnosis of chemical plants will be discussed. Therefore, experiments in a miniplant and with a process simulator are carried out. The process characteristics are treated with different recognition methods and classified with the aid of expert know how. Afterwards, the trained system can be used for process diagnosis. The capability of neural networks for this problem could be shown. 1999 Elsevier Science Ltd. All rights reserved. Keywords: Safety technology; Fault diagnosis; Early detection; Exothermic reaction
1. Introduction Human error is one main cause of accidents in complex chemical plants. According to the “Major Accident Reporting System” (Benuzzi, & Zaldivar, 1991) of the European Research Centre at Ispra (Italy), the vast majority of accidents in chemical plants could have been prevented, if the experience of experts regarding the behaviour of the chemical plant on the one hand, and the sequence of incidents during similar situations on the other hand had been properly implemented. Therefore, the objective is to apply neural networks as an additional supervision system. These self-learning algorithms are expected to detect and assess critical situations unaffected by operator errors. For example, the reasons for human errors and uncertainties in making decisions under critical situations could be: 쐌 쐌 쐌 쐌
lack of attention due to the overtiredness misinterpretation of the state of the plant under stress inappropriate response to unknown situations, and overstraining of personnel due to an excessive presentation of information. A special system of pattern recognition procedures
* Corresponding author. Tel: ⫹ 49-208-85980; fax: ⫹ 49-2088598290; e-mail: [email protected]
was developed to assist users of chemical plants to prevent accidents. Therefore, a neural network had to be trained, before it was able to recognize, to detect and to assess the different process data and process states. When applied to the qualitative classification of process states, the neural network first has to be trained by process data (real world) from normal operations as well as from different faulty states. The results of both, from the experiments and from the simulation, were classified with the help of expert knowledge and used to develop a supervising method of pattern recognition. The efficiency of neural networks to identify normal process conditions and different faulty states could be proved with the experimental results as well as with the simulated data.
2. Neural network approach The working principle of supervision method based on neural network is shown in Fig. 1. When applying to the qualitative classification of process states, the neural network first has to be trained by process data from normal conditions and different faulty states as well. In this learning phase, the network is simultaneously confronted with pairs of feature vectors and target vectors at the input and output layer, respectively. To achieve a good classification ability, the input vectors have to consist of symptoms characterizing the respective fault state, while
0950–4230/99/$ - see front matter 1999 Elsevier Science Ltd. All rights reserved. PII: S 0 9 5 0 - 4 2 3 0 ( 9 8 ) 0 0 0 5 9 - X
452
J. Neumann et al. / Journal of Loss Prevention in the Process Industries 12 (1999) 451–453
Fig. 1.
Identification of dangerous operating states in a chemical plant using neural networks.
the output vectors contain the expertise knowledge on the specific fault. During this supervised learning procedure, the connection strengths between the neurons of the different layers are altered by means of a special learning algorithm, and an iterative procedure is used for reducing the error between the actual and desired (target) output vector when the trained network is checked with testing data. Finally, the basic knowledge is stored in the connection strength as the so-called weight factors. Then, the neural network can also classify unknown pattern vectors correctly when they belong to the trained classes. Besides extracting suitable features of process states, it is important to choose the appropriate type and the optimum topology of the neural network.
3. Experimental equipment and process simulation To teach the neural network, a laboratory reactor has been used. It consists of stirred tank reactor (2 l steel vessel) with a jacket heat-exchanger, feed tanks, valves, pumps, measuring and control devices (Fig. 1). As a reference process, the esterification between acetic anhydride and methanol was chosen. This exothermic chemical reaction can be controlled within a certain temperature range. However, it has also enough selfreinforcing potential for heat production to investigate runaway phenomena as a function of temperature and concentration. Dangerous process conditions which cannot be carried out in experimental set-ups were simulated by a special developed process simulator CaSi (Calorimeter Simulator). This simulator is based on physical models for every component of the chemical process. They were
linked to one model, which is able to simulate dynamically the technical process and the measuring and control equipment. As a reference process, the esterfication between acetic anhydride and methanol was chosen. Different experiments (semibatch and continuous process) were run in a laboratory reactor, and simulated with different networks (Schlu¨ter, Neumann, Steiff, Schmitt, Hessel & van der Vorst, 1998). Thus, the process simulator and the real lab-scale plant were used to train and test the neural network for different normal and faulty conditions of the continuous process including the start-up and transition region. For a semibatch process the duration and starting time of a disturbance are important. Some typical faults are: 쐌 쐌 쐌 쐌 쐌 쐌
deviation in catalyst mass deviation in dosing time of the second component stirrer failure during dosage addition of wrong components interruption of cooling during reaction sudden change in input temperature
4. Results First we discuss the results of neural networks which were trained with several single semibatch-experiments. The output of the net could be improved by using several experiments of the same type. (Fig. 2.) For the next part of the investigation, semibatch and continuous experiments and simulations provide the datasets for training and test several types of neural networks. The dataset for training contains about 200,000 patterns including several scenarios of disturbances. The
J. Neumann et al. / Journal of Loss Prevention in the Process Industries 12 (1999) 451–453
453
short source code. A new system was developed in order to get an on-line prediction during the operation of a chemical plant. 5. Conclusion
Fig. 2. All operation states classes in a semibatch-experiment (target and result of the neural network). The neural net was trained with 19 datasets of complete semibatch-experiments.
Fig. 3. Some operating states of the continuous experiment within a cooling breakdown. The reactor temperature and the jacket temperature give additional information.
best results give a network with 50 up to 100 hidden units. (Fig. 3.) The trained neural network can be transposed in a
The application of neural networks to identify dangerous process states in chemical reactors has been investigated. The efficiency of the neural network approach could be demonstrated by data sets of the reference process which were delivered both from simulation programmes and measurements in a laboratory reactor. The example of a cooling breakdown shows that a network which is trained with simulation data of this failure and other data of the real plant will properly classify the real operating states. The prediction by the neural net can be improved by using all experimental information as soon as possible. If new dangerous situations occur the net should be trained with the new information based on real measurements and state classifications. It is not necessary to train on-line but the neural net must be updated shortly. Our results show that the three-layer perceptron network provides promising assignments to normal and faulty states of the investigated reference process. References Benuzzi, A., & Zaldivar, J.M. (Eds.). (1991). Safety of Chemical Batch Reactors and Storage Tanks. Dordrecht: Kluwer Academic Publishers. Schlu¨ter, S., Neumann, J., Steiff, A., Schmitt, W., Hessel, G., & van der Vorst, K. (1998). Fru¨herkennung gefa¨hrlicher Betriebszusta¨nde in Chemieanlagen mit neuronalen Netzen (Vol. 46 2) (pp. 104– 110). Oldenbourg: Automatisierungstechnik.
Early detection and identification of dangerous states in chemical plants using neural networks Joachim Neumann *, Go¨rge Deerberg, Stefan Schlu¨ter Fraunhofer-Institute for Environmental, Safety and Energy Technology UMSICHT, Osterfelder Strasse 3, D-46047 Oberhausen, Germany
Abstract The suitability of pattern recognition for safety diagnosis of chemical plants will be discussed. Therefore, experiments in a miniplant and with a process simulator are carried out. The process characteristics are treated with different recognition methods and classified with the aid of expert know how. Afterwards, the trained system can be used for process diagnosis. The capability of neural networks for this problem could be shown. 1999 Elsevier Science Ltd. All rights reserved. Keywords: Safety technology; Fault diagnosis; Early detection; Exothermic reaction
1. Introduction Human error is one main cause of accidents in complex chemical plants. According to the “Major Accident Reporting System” (Benuzzi, & Zaldivar, 1991) of the European Research Centre at Ispra (Italy), the vast majority of accidents in chemical plants could have been prevented, if the experience of experts regarding the behaviour of the chemical plant on the one hand, and the sequence of incidents during similar situations on the other hand had been properly implemented. Therefore, the objective is to apply neural networks as an additional supervision system. These self-learning algorithms are expected to detect and assess critical situations unaffected by operator errors. For example, the reasons for human errors and uncertainties in making decisions under critical situations could be: 쐌 쐌 쐌 쐌
lack of attention due to the overtiredness misinterpretation of the state of the plant under stress inappropriate response to unknown situations, and overstraining of personnel due to an excessive presentation of information. A special system of pattern recognition procedures
* Corresponding author. Tel: ⫹ 49-208-85980; fax: ⫹ 49-2088598290; e-mail: [email protected]
was developed to assist users of chemical plants to prevent accidents. Therefore, a neural network had to be trained, before it was able to recognize, to detect and to assess the different process data and process states. When applied to the qualitative classification of process states, the neural network first has to be trained by process data (real world) from normal operations as well as from different faulty states. The results of both, from the experiments and from the simulation, were classified with the help of expert knowledge and used to develop a supervising method of pattern recognition. The efficiency of neural networks to identify normal process conditions and different faulty states could be proved with the experimental results as well as with the simulated data.
2. Neural network approach The working principle of supervision method based on neural network is shown in Fig. 1. When applying to the qualitative classification of process states, the neural network first has to be trained by process data from normal conditions and different faulty states as well. In this learning phase, the network is simultaneously confronted with pairs of feature vectors and target vectors at the input and output layer, respectively. To achieve a good classification ability, the input vectors have to consist of symptoms characterizing the respective fault state, while
0950–4230/99/$ - see front matter 1999 Elsevier Science Ltd. All rights reserved. PII: S 0 9 5 0 - 4 2 3 0 ( 9 8 ) 0 0 0 5 9 - X
452
J. Neumann et al. / Journal of Loss Prevention in the Process Industries 12 (1999) 451–453
Fig. 1.
Identification of dangerous operating states in a chemical plant using neural networks.
the output vectors contain the expertise knowledge on the specific fault. During this supervised learning procedure, the connection strengths between the neurons of the different layers are altered by means of a special learning algorithm, and an iterative procedure is used for reducing the error between the actual and desired (target) output vector when the trained network is checked with testing data. Finally, the basic knowledge is stored in the connection strength as the so-called weight factors. Then, the neural network can also classify unknown pattern vectors correctly when they belong to the trained classes. Besides extracting suitable features of process states, it is important to choose the appropriate type and the optimum topology of the neural network.
3. Experimental equipment and process simulation To teach the neural network, a laboratory reactor has been used. It consists of stirred tank reactor (2 l steel vessel) with a jacket heat-exchanger, feed tanks, valves, pumps, measuring and control devices (Fig. 1). As a reference process, the esterification between acetic anhydride and methanol was chosen. This exothermic chemical reaction can be controlled within a certain temperature range. However, it has also enough selfreinforcing potential for heat production to investigate runaway phenomena as a function of temperature and concentration. Dangerous process conditions which cannot be carried out in experimental set-ups were simulated by a special developed process simulator CaSi (Calorimeter Simulator). This simulator is based on physical models for every component of the chemical process. They were
linked to one model, which is able to simulate dynamically the technical process and the measuring and control equipment. As a reference process, the esterfication between acetic anhydride and methanol was chosen. Different experiments (semibatch and continuous process) were run in a laboratory reactor, and simulated with different networks (Schlu¨ter, Neumann, Steiff, Schmitt, Hessel & van der Vorst, 1998). Thus, the process simulator and the real lab-scale plant were used to train and test the neural network for different normal and faulty conditions of the continuous process including the start-up and transition region. For a semibatch process the duration and starting time of a disturbance are important. Some typical faults are: 쐌 쐌 쐌 쐌 쐌 쐌
deviation in catalyst mass deviation in dosing time of the second component stirrer failure during dosage addition of wrong components interruption of cooling during reaction sudden change in input temperature
4. Results First we discuss the results of neural networks which were trained with several single semibatch-experiments. The output of the net could be improved by using several experiments of the same type. (Fig. 2.) For the next part of the investigation, semibatch and continuous experiments and simulations provide the datasets for training and test several types of neural networks. The dataset for training contains about 200,000 patterns including several scenarios of disturbances. The
J. Neumann et al. / Journal of Loss Prevention in the Process Industries 12 (1999) 451–453
453
short source code. A new system was developed in order to get an on-line prediction during the operation of a chemical plant. 5. Conclusion
Fig. 2. All operation states classes in a semibatch-experiment (target and result of the neural network). The neural net was trained with 19 datasets of complete semibatch-experiments.
Fig. 3. Some operating states of the continuous experiment within a cooling breakdown. The reactor temperature and the jacket temperature give additional information.
best results give a network with 50 up to 100 hidden units. (Fig. 3.) The trained neural network can be transposed in a
The application of neural networks to identify dangerous process states in chemical reactors has been investigated. The efficiency of the neural network approach could be demonstrated by data sets of the reference process which were delivered both from simulation programmes and measurements in a laboratory reactor. The example of a cooling breakdown shows that a network which is trained with simulation data of this failure and other data of the real plant will properly classify the real operating states. The prediction by the neural net can be improved by using all experimental information as soon as possible. If new dangerous situations occur the net should be trained with the new information based on real measurements and state classifications. It is not necessary to train on-line but the neural net must be updated shortly. Our results show that the three-layer perceptron network provides promising assignments to normal and faulty states of the investigated reference process. References Benuzzi, A., & Zaldivar, J.M. (Eds.). (1991). Safety of Chemical Batch Reactors and Storage Tanks. Dordrecht: Kluwer Academic Publishers. Schlu¨ter, S., Neumann, J., Steiff, A., Schmitt, W., Hessel, G., & van der Vorst, K. (1998). Fru¨herkennung gefa¨hrlicher Betriebszusta¨nde in Chemieanlagen mit neuronalen Netzen (Vol. 46 2) (pp. 104– 110). Oldenbourg: Automatisierungstechnik.