Design of Scenario-Based Early Warning System for Process

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Industrial & Engineering Chemistry Research

Design of scenario-based early warning system for process operations Hangzhou Wang, Faisal Khan*, Salim Ahmed Safety and Risk Engineering Group, Faculty of Engineering and Applied Science, Memorial University, St. John’s, NL A1B 3X5, Canada (Correspondence author, phone: 709 864 8939 e-mail:[email protected]) Abstract: Alarm flooding is a significant problem in the process industries. To solve this problem, a scenario-based early warning system design methodology is proposed. It comprises three steps: i) scenario identification: events are identified by HAZOP analysis, variables are allocated to scenario-based group, the variables states correlated to the scenarios are identified; ii) model development: Bayesian network of all variables is learned from the process data, and the events nodes are appended according to expert knowledge to construct the Bayesian network model of scenariobased early warning system; and iii) model implementation: the model is applied online to monitor process, the monitored variables continuously produce evidence, update the events probabilities and also find the root causes, give events warning message together with the root cause to operators. The methodology implementation and silent points are explained with the help of an easy to follow simple case study. Keywords: Bayesian network, scenario-based warning, early warning system, structure learning, parameter estimation

1. Introduction Since process industries deal with hazardous materials in daily operations, it is important to monitor the state of a process in real time to identify any vulnerable condition before it leads to a more severe event. Process plants are usually dynamic and characterized by distributed processes, uncertainty, time constraints, complex subsystems, and a high degree of automation 1. These plants are equipped with distributed control systems (DCSs) to ensure continuous operation and high product quality. Typically, an alarm system is installed and maintained within the DCS. Furthermore, early warning systems are used to detect and monitor variable deviations from their predefined threshold limits. The plant’s control room operator monitors the DCS, continuously observing the automated execution of various control mechanisms to ensure proper operations while taking into account factors such as operability, product quality, and equipment reliability 2. Typically there may be multiple steady states 3-5 in chemical processes. Due to strong nonlinear characteristics with disturbances there would be unstable conditions, bifurcations 6-9, or even oscillatory phenomena 10, 11 near the singularity operating point 12-14 in processes. Deviations in processing facility are unavoidable. Conventional single variable-based warning systems are prone to cause flooding. Moreover, as process plants have become increasingly complex and highly integrated, this adds to the alarm monitoring and response requirements for the control room operator during highly stressful alarm flooding periods 15. Considering all the variables configured with alarm limits, it is inevitable that alarm floods happen 16.

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Alarm flooding is a problem because operators cannot keep up with the pace of alarms 17. The Engineering Equipment and Materials Users Association 18 guidelines have established that alarms should not exceed more than 10 alarms in the first 10 minutes following an upset in standard EEMUA 191. Another recommended standard is ISA 19-18.2 e “Management of Alarm Systems for the Process Industries”, in which the peak alarm rate is limited to 10 alarms per 10 minutes for less than approximately 2.5 hours per month. Ineffective operator alarm response during alarm flooding can have a significant impact on safe operations. In fact, alarm flooding has been identified as the root cause of significant process industry incidents such as Three Mile Island Nuclear plant 20, Texaco Pembroke Refinery 21, 22, etc. In the Texaco Pembroke Refinery accident, although the alarm system was intact, the operators faced an alarm flood of 275 within eleven minutes prior to the explosion. It is a number impossible for an operator to manage. To minimize alarm flooding and improve the alarm system performance, not only should redundant alarms be reduced, but also clear warning messages (possibly with the root cause with evidence) should be conveyed to operators. The root cause analysis is used to reduce redundant alarms. In early efforts early warnings 23-26 are proposed that group process variables according to abnormal process events and use the risk as the final indicator of an alarm. Gabbar27 developed causation models and link with accident scenarios using fault semantic networks (FSN). Hussain28 tuned fault semantic network using Bayesian theory for probabilistic fault diagnosis in process industry. Khakzad et al. 29 proposed a Bayesian Network (BN) based methodology for safety analysis in the process industry which was further extended to perform a dynamic safety analysis 30. Many natural early warning systems that rely on uncertain data have used BN to model the situation to warn of the disaster early 31, 32. The literature cited demonstrates the potential use of BN as a tool to develop real time scenario-based early warning systems. Here the scenario denotes the combination of system variables states, usually for an event that occurred in a certain scenario. Bayesian networks can be used for root cause analysis to reduce the redundant alarms, and also to construct the scenario-based early warning system, however, there are two insufficiencies: i) the Bayesian network built by expert experience is time consuming; ii) identification of the abnormal states of the process is not well developed. In the present work, these limitations are overcome; the Bayesian network model is constructed by both expert knowledge and data learning. The article is organized as follows: section 2 presents the methodology of scenario-based warning system development; section 3 uses a simple case study to demonstrate the proposed methodology; section 4 gives the conclusions.

2. The proposed scenario-based methodology The proposed scenario-based early warning system design methodology includes three main steps. First, scenario identification: the events associated with each scenario are identified; second, model development: a Bayesian network is constructed combining the expert knowledge with the event and structure learned from process data; third, model implementation: the model is applied to calculate the real time probability of an event occurrence and diagnose the root-causes of the state change. Finally, a proper warning is conveyed to operators. The flowchart of the proposed methodology is shown in Figure 1. The details of the methodology are explained in the following subsections.

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Identify process events

Chemical process

Chemical process

Collect process historical data

Monitor variables states in process

N Allocate variables to event based groups

Learn structure of monitored variables Bayesian network

Identify scenarios associate with events

Learn parameters of monitored variables Bayesian network

Is there a state change of monitored variable? N

Y Inference in Bayesian network to update event probability

Step 1 Scenario identification

Combine expert experience and data learning result Probability is high enough to warn

Y

Construct Bayesian network of variables and events Step 2 Model development

Diagnosis in Bayesian network to find root cause of current scenario

Give proper event based warning and root causes

Stop Step 3 Model implementation

Figure 1. Scenario-based warning system design methodology: offline part (step 1 and step 2); online part (step 3)

2.1.

Step 1: Scenario identification

2.1.1.

Identification of events

In this methodology, warnings are assigned to undesirable events. Events are defined as undesirable abnormal conditions or operational problems. Usually, the Hazard and Operability Study (HAZOP) is used to identify the potential events. After studying all possible hazards due to deviations of variables, the significant abnormal conditions that need a warning in the operation are identified as events.

2.1.2.

Variable allocation to the event

After identifying the significant events, monitored process variables are selected according to their abilities to distinguish between an abnormal event and a normal condition. The deviations of these process variables are defined as the symptoms of the event. Dalapatu et. al 26 described the detailed methodology for allocation of variables to event related groups using mutual information and cross correlation analysis.

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2.1.3.

Identification of corresponding scenario and links with scenario

Scenarios are defined as the process operating conditions that influence an event. Deviations of process variables and their correlations during an event are considered when determining scenarios. Deviation of one variable or a combination of different variables causes a scenario.

2.2.

Step 2: Development of Bayesian Network (BN) model

After identifying the event and the corresponding scenarios, the Bayesian network, constructed from a combination of both data learning and expert experience, is used to develop the scenario-based early warning system.

2.2.1.

Bayesian Network

A Bayesian network (BN) B = (G, P) graphically shows the relationship among variables of a probability distribution P. The Bayesian network contains two parts, a graphical structure that defines the qualitative representation and the conditional probabilities that define the quantitative representation (Pearl, 1988; Korb & Nicholson, 2003). The representation of the network structure is a directed acyclic graph (DAG) G = (V , E ) , where V denotes the set of nodes and E denotes the set of edges of the graph structure. Each node vi ∈ V corresponds to an uncertain variable X vi in X V for all i = 1,L, n . Each edge is a directed link between two nodes, which represents the direct causal relationship or the influence between linked nodes. The joint distribution P over the entire network can be factored as: n

P( X V ) = ∏ P( X vi | X Pa ( vi ) ) i =1

where X Pa ( vi ) denotes a set of parent variables of the variable X vi . This is the chain rule for Bayesian networks.

2.2.2.

Learn structure of Bayesian Network

The Bayesian network structure can be learned from process variables data. The structure learning problem is a search process to find the most useful network structure which can represent the given dataset. The possible structure, which is a directed acyclic graph, increases greatly with the increase of nodes. The structure learning problem has been proved to be a Non-deterministic Polynomial-time hard (NP-hard) problem 33. The structure learning task is a combinatorial optimization problem, where a search method operates in a search space associated with Bayesian networks. Bayesian network structure learning algorithms can be grouped in two categories including constraint based algorithms and score based algorithms. Constraint based algorithms learn the network structure by analyzing the probabilistic relations entailed by the Markov property of Bayesian networks with conditional independence tests and then constructing a graph which satisfies the corresponding d-separation statements. The resulting models are often interpreted as causal models even when learned from observational data 34. Constraint-based algorithms are all based on the inductive causation (IC) algorithm 35, which provides a theoretical framework for learning the structure causal models.

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Score based algorithms assign a score to each candidate Bayesian network and try to maximize it with some heuristic search algorithm. Greedy search algorithms are a common choice, but almost any kind of search procedure can be used. Score based algorithms, on the other hand, are simply applications of various general purpose heuristic search algorithms, such as hill climbing, tabu search, simulated annealing and various genetic algorithms. One of the most highly performing algorithms is known as the Greedy Thick Thinning (GTT) algorithm 36, 37. The structure produced from this algorithm is the structure where the dataset is most likely to be observed. The Bayesian network structure of variables in the process can be learned from the process values, including the correlations of all variables.

2.2.3.

Parameters learning for Bayesian Network

Given the structure of the Bayesian network, the commonly used method for parameters learning is the maximum likelihood estimation38. Given the Bayesian structure S, to learn the parameters θ , with the complete dataset is D in which d is a complete record, usually log-likelihood is used. LL( S | D) = ∑ log 2 P(d | S ) d∈D

The maximum likelihood estimation of θ is θˆ

θˆ = arg max LL( Sθ | D) θ

Solving this optimization problem, the optimal estimated parameters of the Bayesian network will be learned. At this step, the qualitative and quantitative correlation among all variables can be learned from data.

2.2.4.

Combine expert experience and data learning for Bayesian network

With the result from Step 1, this expert knowledge can help to construct the relations between the event and variables. Since events are related to the combination of monitored variables states, these events nodes can be appended to the monitored variables Bayesian network to formalize the scenario-based early warning system. There are two steps guided by expert knowledge: i) add relation edges from variables nodes to events nodes, ii) estimate the parameters (e.g. conditional probability tables) in event nodes. After appending the event nodes, which are guided by expert knowledge, to the monitored variables Bayesian network, which is learned from process variable data, the scenario-based warning system forming the Bayesian network can be constructed. The schematic is shown in Figure 2.

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All variables in process

V1

V2

V3 Scenario: combination of monitored variables states Root cause: backward diagnosis variable

V4

V5

Symptom: variable with evidence

V6

V7

V8

V9

Learned from process data All identified significant events

E1

E2

E3

Event: undesirable abnormal conditions or operational problems

Appended by expert knowledge

Figure 2. Schematic of the established scenario-based early warning system Bayesian network model.

2.3.

Step 3: Model implementation

2.3.1.

Evidence updating from real time variable state

Evidences can be updated in this warning system Bayesian network model continuously. This evidence is the real time variable state from the DCS alarm (e.g. HH, HI, NR, LO, LL respond to high-high, high, normal, low and low-low, respectively), or analysis results from process real time values by self-defined methods.

2.3.2.

Real time prediction of event occurrence

With this real time evidence, the probabilities of the symptom nodes are updated; also, the probability of the root cause (backward diagnosis), and events (forward inference) will change accordingly. Once the probability of the event occurrence exceeds the threshold, the event warning is annunciated. At the same time, the best explanations for the warning event, the root causes, are annunciated simultaneously. The schematic Figure 3 shows this dynamical procedure.

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E2

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init

Vi

unactivated

Vi

evidence

Vi

root cause

Vi

scenario elements

V2

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event

Vi

evidence and root cause

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(b)

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V1

V8

E1

(e)

E2

V9

E3

(f)

Figure 3． The dynamic procedure of scenario-based warning system. The dynamic procedure of the online monitor process is illustrated in Figure 3, and details are explained below: (a) Initialize the scenario-based early warning system (b) Observed evidence of V4, that is, the state of V4 has changed. (c) Observed evidence of V5 (d) Observed evidence of V6 (e) Evidence update, V5 returns to normal (f) Evidence update, V4 returns to normal In every situation, the probabilities of activated variables (including events and root causes) are evaluated to determine whether to give the event warnings or not. The proposed methodology is demonstrated by a small case study. The tank case study is used to explain how to construct the Bayesian network model and implement it in process monitoring.

3. Case study The implementation of the methodology is explained using a simple tank case study. The methodology is tested to be equally applicable to complex real life scenarios.

3.1.

Tank system 7



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3.1.1.

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The flowchart

The tank schematic is shown in Figure 4. The objective is to maintain a given level in the tank. There is a Distributed Control System (DCS) monitoring the state of the inlet flowrate, outlet flowrate and the level. There are three states of the inlet/outlet flowrate: LO, NR and HI, corresponding to low, normal and high, respectively. There are also three states of the level, the LO, NR and HI. When these flowrates, or the level, exceed the upper threshold, a HI alarm is activated by the DCS. Similarly, the LO alarm is activated when they are below the lower threshold. When these states from HI or LO return to normal, an NR notification is given.

Fi

H

Fo

Figure 4. Schematic of tank system

3.1.2.

Identify events (Step 1)

HAZOP analysis is carried out to identify these potential events that can occur during the operation with the tank. According to the HAZOP analysis results presented in Table 1, the significant abnormal events that can occur in the process are the tank flooding or running dry. Table 1: HAZOP study for the level control tank process. Variable

1

2

3

3.1.3.

Deviation

Consequences

Scenarios

High

Overflow of tank

Overflow

Low

Run dry of tank

Run dry

High

Run dry of tank

Run dry

Low

Overflow of tank

Overflow

High

Overflow of tank

Overflow

Low

Run dry of tank

Run dry

Level

Outlet flow

Inlet flow

Construct the Bayesian network of scenario-based warning system (Step 2)

This step includes two parts: i) learn the correlations of monitored variables from data and ii) append expert knowledge of events to construct the Bayesian network model of the scenario-based early warning system. The first part includes: prepare DCS alarm data, learn the Bayesian network structure, and learn the

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parameters. The second part is guided by expert’s knowledge, appending event nodes to the Bayesian network, and adding arcs between nodes and the conditional probability table values in these event nodes.

Prepare the DCS alarm data To demonstrate the proposed method, the synthetic DCS alarm data are listed in Table 2, where the state HI, LO, NR denote high, low and normal, respectively. Every alarm has a number to order these alarms, a tag to identify the warning source, and the type to describe the alarm which is activated by the state of the variable. Table 2. Synthetic tank DCS alarm data No.

Tags

Types

No.

Tags

Types

No.

Tags

Types

No.

Inlet

Outlet

1

Outlet

HI

21

Level

NR

41

Level

HI

61

Level

NR

2

Level

LO

22

Inlet

HI

42

Inlet

LO

62

Inlet

HI

3

Inlet

HI

23

Level

HI

43

Level

NR

63

Level

HI

4

Level

NR*

24

Inlet

LO

44

Inlet

NR

64

Inlet

LO

5

Inlet

NR

25

Outlet

HI

45

Outlet

NR

65

Outlet

HI

6

Outlet

NR

26

Level

LO

46

Outlet

HI

66

Level

LO

7

Inlet

HI

27

Inlet

NR

47

Inlet

HI

67

Inlet

NR

8

Level

HI

28

Outlet

LO

48

Inlet

NR

68

Inlet

HI

9

Inlet

NR

29

Level

NR

49

Level

LO

69

Level

NR

10

Inlet

LO

30

Level

HI

50

Outlet

NR

70

Inlet

NR

11

Outlet

HI

31

Outlet

HI

51

Outlet

HI

71

Level

LO

12

Level

NR

32

Level

NR

52

Outlet

NR

72

Outlet

NR

13

Level

LO

33

Level

LO

53

Level

NR

73

Level

NR

14

Inlet

NR

34

Outlet

NR

54

Outlet

LO

74

Outlet

LO

15

Outlet

LO

35

Outlet

LO

55

Level

HI

75

Level

HI

16

Level

NR

36

Level

NR

56

Level

NR

76

Outlet

HI

17

Outlet

NR

37

Level

HI

57

Outlet

NR

77

Level

NR

18

Outlet

HI

38

Inlet

LO

58

Inlet

LO

78

Level

LO

19

Level

LO

39

Level

NR

59

Level

LO

79

Outlet

NR

20

Outlet

NR

40

Inlet

NR

60

Inlet

NR

80

Level

NR

*NR: means return to the normal state.

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The analysis is based on the alarm dataset, and the larger dataset gives a better understanding of the system. In this case, the synthetic alarm dataset is small, just for the sake of demonstration. With these data, the correlations between monitored variables can be learned in forming of the Bayesian network.

Learning correlations of monitored variables Learning the correlations of monitored variables from data is a problem to learn the structure of the Bayesian network. The Bayesian network structure can be learned from DCS alarm record data. The structure learning problem is a search process to find the most useful network structure which can represent the given dataset. Here the K2 39 score function is introduced, and GTT 36, 37 learning method is used to learn the structure. The best structure, learned from the dataset, is found as shown in Figure 5. This directed acyclic graph (DAG) is the most likely structure in which the observed dataset is generated with the biggest probability.

Level

Inlet

Outlet

Figure 5. The Best DAG for dataset

Learning parameters In this demonstration, the learned parameters (marginal distributions, conditional probability table) of the monitored variables Bayesian network are shown in Table 3 and Figure 6. This illustrates the effectiveness of learning variables correlations and parameters from DCS alarm data. Table 3. Conditional probability table in node Level Inlet=LO Outlet

Level

Inlet=NR

Inlet=HI

LO

NR

HI

LO

NR

HI

LO

NR

HI

LO

0.14

0.25

0.40

0.21

0.40

0.48

0.33

0.12

0.37

NR

0.43

0.25

0.20

0.47

0.50

0.39

0.34

0.44

0.50

HI

0.43

0.50

0.40

0.32

0.10

0.13

0.33

0.44

0.13

10


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Figure 6. Bayesian network of monitored variables in the tank As the Bayesian network is a belief network, when more data are collected, the belief of the structure and the parameters are strengthened. That is, the structure and the parameters are continuously updated with the increasing dataset to get a better result. It is an incremental learning process.

Combine expert knowledge with monitored variables Bayesian network Expert experience will be introduced as guide knowledge to append these events to the monitored variables Bayesian network. In this case study, these events (tank overflow or running dry) are related to the variables Inlet, Outlet and Level in the tank system. After appending the event nodes to the Bayesian network, the structure of the scenario-based warning system is shown in Figure 7, and the new parameters within the conditional probability table are estimated by experts. These values are Bayesian probabilities and listed in Table 4. These values indicate the likelihood of event occurrence by expert experience. With these values as references, the final event warning give the probability values to describe the event occurrence likelihood.

Figure 7. Bayesian network model of scenario-based warning system for the tank Table 4. Expert experience of overflow probability under different variables states

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Inlet

Outlet

Level

Overflow probability

Run dry probability

HI

HI

HI

0.6

0.1

HI

HI

NR

0.5

0.2

HI

HI

LO

0.4

0.3

HI

NR

HI

0.8

0.2

HI

NR

NR

0.7

0.3

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NR

LO

0.6

0.4

HI

LO

HI

0.9

0.3

HI

LO

NR

0.8

0.4

HI

LO

LO

0.7

0.5

NR

HI

HI

0.4

0.5

NR

HI

NR

0.3

0.6

NR

HI

LO

0.2

0.7

NR

NR

HI

0.5

0.3

NR

NR

NR

0.4

0.4

NR

NR

LO

0.3

0.5

NR

LO

HI

0.7

0.2

NR

LO

NR

0.6

0.3

NR

LO

LO

0.5

0.4

LO

HI

HI

0.3

0.7

LO

HI

NR

0.2

0.8

LO

HI

LO

0.1

0.9

LO

NR

HI

0.4

0.6

LO

NR

NR

0.3

0.7

LO

NR

LO

0.2

0.8

LO

LO

HI

0.5

0.4

LO

LO

NR

0.4

0.5

LO

LO

LO

0.3

0.6

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With the combination of the monitored variables network learned from data, and the experience of experts about these events, the Bayesian network model of scenario-based warning system is constructed (as shown in Figure 7). With this model, the process can be monitored online with real time data.

3.1.4.

On line monitoring with scenario-based warning system (Step 3)

In this step, the scenario-based warning system is illustrated in three cases: The event probability updating, the scenario of tank overflow event and the scenario of the tank running dry event.

Event probability updating In this situation, the state of Level changes to HI, and then the state of Outlet changes to LO. Then the Inlet state returns to NR. The conventional DCS system describes these variables changes in Table 5. These changes are recorded in lines. The developed scenario-based warning system can monitor these variables’ states change process, and continuously update the probability of potential events. The results are presented in Figure 8, where a scenario-based warning is given when the probability of a certain event is greater than or equal to the threshold value (Here it is 70%). Table 5. Conventional DCS system Timestamp

Tag

Message Type

Alarm Identifier

……

……

……

……

YYYY/MM/DD HH:MM:SS1

Tank:Level

Alarm

HI


Tank:Outlet

Alarm

LO


Tank:Inlet

NR

NR

Time: YYYY/MM/DD HH:MM:SS Variable: NA State change to: NA Warning: NA Root cause: NA

(a)

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Time: YYYY/MM/DD HH:MM:SS1 Variable: Level State change to: HI Warning: NA Root cause: Outlet LO (Probability 36%) Inlet NR (Probability 45%)

(b) Time: YYYY/MM/DD HH:MM:SS2 Variable: Outlet State change to: LO Warning: NA Root cause: NA

(c)

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Time: YYYY/MM/DD HH:MM:SS3 Variable: Inlet State change to: NR Warning: Overflow (Probability 70%) Root cause: NA

(d) Figure 8. Scenario-based warning system: scenario of probability updating and event warning, (a) Initialize (b) Level state changes to HI (c) Outlet state changes to LO (d) Inlet state changes to NR These initial states of nodes are shown in Figure 8(a), and the marginal distributions of these nodes are also presented in the figure. These states of nodes (Level, Inlet and Outlet) are changed one by one in sequence, as recorded in the DCS system. First of all, the state of Level node changes to HI; with this change, these probabilities of events are updated accordingly in the scenario-based warning system. The probability of an overflow event is increased from 41% to 54%, while the probability of a run dry event is decreased from 49% to 40%. For this state change, the possible root causes are the Outlet node in state LO (with a probability of 36%) and Inlet node in state NR (with a probability of 45%). After the state change of Level node, it is followed by another new evidence, the Outlet node state changes to LO. These probabilities of events are updated with this evidence. The probability of an overflow event increases from 54% to 68%, while the run dry event is decreased from 40% to 27%, and there are no available root cause results for this state change. After that, the Inlet node state returns to NR. These probabilities of events are updated because of this change. The probability of an overflow event under this scenario is updated to 70%, and this is equal to the threshold value. It is a high probability, indicating that the tank is likely to overflow. As a result, the overflow event warning is generated and conveyed to operators. The desired action can be applied by operators in time to eliminate the chance of this unfavorable event occurring. Because the probability of a run dry event is decreased to 20%, this is a low probability and this event is not likely to happen under this scenario. In this scenario-based early warning system, these events probabilities are updated by the Bayesian network inference method. In some cases, there are variables nodes with uncertain states, because no state change of this variable is observed. The inference method can also update the probability of these variables nodes. If the probability of this node is updated by the backward diagnosis method in the Bayesian network, this node with an updated probability will be regarded as a possible root cause for the changed state. When the

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probability of the node is updated by the forward inference method in the Bayesian network, this node will be regarded as a part of the scenario for a certain event. When all states of these variables node are observed and determined gradually, these events probabilities will be updated accordingly. This probabilities updating procedure will be helpful to predict occurrences of these events, and will give a proper event warning message to operators in time. Compared to the conventional DCS system, the proposed scenario-based early warning system can find more information from these variables nodes states change. These variables nodes states changes are determined and recorded in a conventional DCS system. Based on this information, the proposed scenariobased early warning system can take every newly generated DCS record as new evidence, update these uncertain variables states and events probabilities continuously to monitor the process, and give a proper event warning message along with root causes to operators.

Scenario of tank overflow event In this case, a sequence of state changes that will cause the tank to overflow is studied. In this state change sequence, the variable node Inlet state changes to HI, then the Outlet node state changes to LO, and after that the variable Level state changes to HI. This state change sequence is listed in Table 6 in the conventional DCS system alarm record. The same state change sequence can be studied in the proposed scenario-based early warning system, and more information can be obtained in the proposed method, as summarized in Figure 9. Table 6. Conventional DCS systems Timestamp

Tag

Message Type

Alarm Identifier

……

……

……

……


Tank:Inlet

Alarm

HI


Tank:Outlet

Alarm

LO


Tank:Level

Alarm

HI

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(a) Time: YYYY/MM/DD HH:MM:SS1 Variable: Inlet State change to: HI Warning: NA Root cause: NA

(b)

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Time: YYYY/MM/DD HH:MM:SS2 Variable: Outlet State change to: LO Warning: Overflow (Probability 80%) Root cause: NA

(c) Time: YYYY/MM/DD HH:MM:SS3 Variable: Level State change to: HI Warning: Overflow (Probability 90%) Root cause: NA

(d) Figure 9. Scenario-based warning system: scenario of tank overflows, (a) Initialize (b) Inlet state changes to HI (c) Outlet state changes to LO (d) Level state changes to HI

When the state of variable node Inlet changes to HI, the probability of the overflow event is increased from 41% to 65%, while the run dry event probability decreases from 49% to 28%, as in Figure 9(b). Then the state of variable node Outlet changes to LO, and the probability of the overflow event is increased from 65% to 80%, an overflow event message with a probability of 80% is activated, while the run dry event probability increases from 28% to 40%, as in Figure 9(c).

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After that the state of the variable node Level changes to HI, and the probability of the overflow event is increased from 80% to 90%. This implies the overflow event is likely to happen, and then an overflow event message with a probability of 90% is generated and conveyed to operators. On the other hand, the run dry event probability decreases from 40% to 30%, as in Figure 9(d).

Scenario of tank runs dry event In this case, a sequence of state changes that will cause the tank to run dry is studied. The variable node Level state changes to LO, and then the Inlet node state changes to LO. After that the variable Outlet state changes to HI. This state change sequence is listed in Table 7 in a conventional DCS system alarm record. This state change sequence can be studied in the proposed scenario-based early warning system to get more information, as presented in Figure 10. Table 7. Conventional DCS systems Timestamp

Tag

Message Type

Alarm Identifier

……

……

……

……


Tank:Level

Alarm

LO


Tank:Inlet

Alarm

LO


Tank:Outlet

Alarm

HI


(a)

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Time: YYYY/MM/DD HH:MM:SS1 Variable: Level State change to: LO Warning: NA Root cause: Outlet HI (Probability 51%) Inlet NR (Probability 72%)

(b) Time: YYYY/MM/DD HH:MM:SS2 Variable: Inlet State change to: LO Warning: Run dry (Probability 83%) Root cause: NA

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Time: YYYY/MM/DD HH:MM:SS3 Variable: Outlet State change to: HI Warning: Run dry (Probability 90%) Root cause: NA

(d) Figure 10. Scenario-based warning system: scenario of tank runs dry, (a) Initialize (b) Level state changes to LO (c) Inlet state changes to LO (d) Outlet state changes to HI When the state of variable node Level changes to LO, the probability of the run dry event is increased from 49% to 60%, while the probability of the overflow event decreases from 41% to 29%, as in Figure 10(b). Then the state of variable node Inlet changes to LO; the probability of the run dry event is increased from 60% to 83%; a run dry event message with a probability of 83% is activated, while the overflow event decreases from 29% to 16%, as in Figure 10(c). After that the state of variable node Outlet changes to HI, and the probability of the run dry event is increased from 83% to 90%. This implies the run dry event is likely to happen, and then a run dry event message with a probability of 90% is generated and conveyed to operators. On the other hand, the overflow event probability decreases from 16% to 10%, as in Figure 10(d). Usually, these states of monitored variables in a scenario-based warning system vary all the time, with these variables states changes. Every time new evidence of a state change is observed, the probability of all other nodes including events nodes without evidence will be updated accordingly. From these three case studies, it can be seen that the proposed scenario-based early warning system can deal with the sequence of variable state changes. Compared to a conventional DCS alarm record, the proposed method can exploit more information from the state change sequence. During the state change process, the scenario-based early warning system could continuously update the probabilities of events when new evidence is observed. Once an event probability exceeds the acceptable upper threshold, a proper event warning message will be generated and conveyed to the operator. The proposed method exploits more information from the monitored variables than a DCS alarm system.

4. Discussion 4.1.

The process data 21



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In this paper the process data are mainly refer to historical data which are gathered from DCS warning record. Because in industrial process, there are plenty of DCS warning data. These data can be used to analyze the correlations between these monitored variables. In this proposed method, the Bayesian network is introduced to learn the structure and the parameters of these variables. This is an automatic method, with increase gathered data, the result will be improved gradually, and finally to discover all these correlations between variables in the process. The learning process is fast, as a result, this method can be applied online to monitor the process. The learning result is updated when new data are gathered, when there are some variation in the process, which can be determined by alarm system, also can be learned in this method. Consequently, the proposed method can always discover the correlations between monitored variables. Besides the DCS warning record, the automatically test data of product or feedstock also can be used in this proposed method. The Bayesian network can deal with the state of these tested substances. From the learning method, the correlations between monitored variables, material and product can be discovered in the process. This will be helpful to further understand the system.

4.2.

The expert experience

The expert experience is consist of two parts: i) the warning events and their attached scenarios, ii) the occurrence probability of these events given these scenarios. The warning event with the scenarios is predefined by expert experience in this proposed method, this is a method to find the event correlations to these monitored variables in system. In this paper, the HAZOP is introduced to determine these events and relationships with scenarios, it can be seen that other methods also can be used to find these events and determine these relationship in the system. Because the expert experience usually is qualitative, the occurrence usually is a relative result to indicate which event is more likely to happen given certain scenario. Here the occurrence probability value is a Bayesian probability which is a quantity that expert assign for the purpose of representing the state of event occurrence. With increased process data and better understanding of the system from the expert, the proposed early warning system can be improved greatly to better monitor the process.

4.3.

The advantage of learning method

In this paper, a simple case study is demonstrated to show the effectiveness of the proposed method. The result also can be obtain by other methods, such as HAZOP-chains etc. But with the increase number of variables and the online real time requirement, furthermore, the variations in system, the proposed method will performance better with advantages: i) the correlations of monitored variables are learned in machine learning method, it is a automatically applied method; ii) the learning method can deal with large data set of historical data within acceptable time period; iii) the result can be updated with new gathered data, as a result, the learned result can always reveal the current system information. These features makes it is practical to monitor the process online. Besides the learning method, the expert experience is provided as a supplement, these events are estimated by expert and assigned different occurrence likelihood values, with this values as reference, these events occurrence likelihood can be updated all the time when the process is running. By this way, the warning can be generated in format of event with estimated occurrence probability. Consequently, all variables monitored in DCS system can be studied, and the warning result can be generated as event-based warning massage with probability in real-time. In this method, the historical data and expert experience are combined to make the result more reliable (from historical data learning) and more clearly (expert experience to represent more meaningful result) to operators. As a result, this method

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is more practical to gives more rapid response to the system variation and more clear warning message to operators. In this proposed method, all monitored variables and their states are covered in the established Bayesian network model. Furthermore, these significant events in the process are attained by HAZOP analysis, and these concerned scenarios related to these significant events are further studied to better analysis root cause and predict the probabilities of these events occurrences.

5. Conclusions Despite the efforts to improve alarm systems, alarm flooding remains a significant problem in the process industries. To solve this problem, a scenario-based warning system design methodology is proposed. In this method, first, the events are identified by HAZOP analysis, variables are allocated to an event related group, and the scenarios correlated to the event are identified. Second, the Bayesian network of all monitored variables is learned from the process DCS alarm record historical data. With expert knowledge and according to the correlations between events and scenarios, these events nodes are appended to construct the Bayesian network model of the scenario-base early warning system. Third, the scenario-based early warning system is applied to online monitoring of the process; with these continuously observed state changes of variables as evidence, the Bayesian network forward inference method is used to update these events’ probabilities. The Bayesian network backward diagnosis method is used to find the root causes of the state change. Once the probability of a certain event exceeds the acceptable upper threshold, an event warning message will be generated and conveyed to operators, together with the root causes if available. Compared to a conventional DCS alarm record, more information can be exploited from the same sequence of variables states’ change with this proposed method; also more meaningful event warning messages are conveyed to operators, and desired action can be applied by operators in time to reduce the occurrence probability of this unfavorable event.

6. Acknowledgments Authors gratefully acknowledge the financial support provided by a Vale Research Chair Grant, the Research & Development Corporation (RDC), and the Natural Sciences and Engineering Research Council (NSERC) of Canada.

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