안전성 분석의 자동화를 위한 다중모델 접근방법

서정철*,김구회,이병우,강병관,강경욱,오영석,윤인섭

공군사관학교 화학과*, 서울대학교 화학공학과

Multimodel Approach to Automated Hazard Analysis

Jung Chul Suh*,K.Kim, B.Lee,K.Kang,Y.Oh, En Sup Yoon

Korea Air Force Academy*, Seoul National University

1. Introdution

Hazard analysis is one of the basic tasks to ensure the safety of chemical plants. However, it is an arduous, tedious, time-consuming work and requires multidisciplinary knowledge, and demands considerable cognitive load from the analysts. To overcome these problems, there have been attempts to automate this work by utilizing computer technology, particularly in the area of knowledge-based technique. However, Many of the past approaches to automated hazard analysis lack some(or all) of the following desired properties;

□SAFEGUARD CONSIDERATION

The existing systems do not consider safeguards. Many hazards, particularly in relatively uncomplicated situations, can be immediately apparent. So much part of potential hazard is already known to the designer beforehand. Safeguards are prepared for those kinds of hazards. But it is difficult for the designer to prepare for all the cases which evolve through complex routes. For the good hazard analysis, it is important to consider the existing safeguards, to verify their validity and to list hazards which are not covered by the safeguards. That is fit to the very purpose of performing the hazard analysis.

□ACCIDENT DIVERSITY

The type of accidents in chemical plants is various, but, compared to that, the existing systems are limited in the variety of accidents they represent. Generally hazards can be classified as physical hazards and chemical hazards. The existing system can only deal with limited parts of them - usually those of physical hazards.

□CAUSE AND CONSEQUENCE DIVERSITY

A malfunction of an unit can influence the whole process via stream integration. So the cause and consequence of accidents must be analyzed from the whole process structure. Furthermore, if one malfunction is analyzed more thoroughly, it can be found that the malfunction is caused by other malfunctions. For example, valve stuck closed is a immediate malfunction which can cause variable deviation, but this is a result from overtight packing, lubrication dried out, or overtight closure, etc. In order to clarify various cause of faults, the relationship between ultimate malfunction and immediate malfunction must be identified and represented.

□PATHWAY LEADING TO ACCIDENTS

An accident may be said to be the discrete sequence of events. To manage potential accidents in chemical process effectively, the pathway from ultimate malfunction(or initiating event) to the actual accident should be identified. The existing systems present simply causes and consequences of variable deviation, so it is hard to know what happens during the development of accidents.

□VARIOUS HAZARD ANALYSIS REASONING

The existing systems either infer hazard from variable deviation, or identify hazard by positing fault. However it, can't be said which method is superior because both methods are mutually complementary. So if they are applied to the same process, the quality of hazard analysis will be enhanced.

The previous approaches have drawbacks mentioned above due to their limitations in capturing and utilizing all forms of available information. Therefore, it is really important to make an appropriate knowledge representation which is fit to the objective of developing expert system.

2. Knowledge Modeling

In this study, the informations required for hazard analysis is identified through the accident mechanism and since they have their own distinctive feature, it is irrational to deal with such informations in one knowledge domain. Therefore, these informations is represented in different 4 sub-domains. The central idea in this approach is to represent a chemical process adequately for hazard analysis in view of function, behavior, structure, and material property.

Unit knowledge base is devised to model a process unit. This model represents physical hazards. It consists of unit behavior model and unit function model. In unit knowledge base, a process unit is modeled in different terms of variable and function. Variables describing unit are divided into inlet, internal, and outlet variables explicitly, by interconnecting behavior model and function model through these variables, the complicated details can be captured in this approach, which has been difficult to capture in the other models. In unit function model, malfunction hierarchy from ultimate malfunction to immediate malfunction is constructed and it can represent malfunction in various way.

A chemical process is composed of basic units and through the connection of these basic units, the variable deviation of one unit influences on the variable of the adjacent unit. Because of these interaction, in order to reason about cause and effect of malfunction or deviation throughout the whole system, the spatial arrangement of units, which is connective relation, must be known. It is organizational knowledge base that has knowledge about this. In order to represent the connective relation among the units efficiently, the process streams in a plant are decomposed according to their function and then process structure is restructured. The purpose of this stream decomposition is to organize the process structure into a efficient form for hazard analysis. Hazard analysis reasoning operates on this modified process structure. In addition, safeguards are classified according to their function and included in organizational knowledge base. Safeguards are those equipped to ensure the safety of process, which include indicator, alarm, interlock system and rupture disk, etc. It works only under special circumstances. They are ignored in other fields(e.g. fault diagnosis, dynamic simulation, control, etc.). But in view of safety, safeguards can function in accident mechanism, so these safeguards must be taken into account for the reliable safety analysis. But the most previous approaches do not deal with safeguards. The structure of their model could not accommodate safeguard, and their main objective was to identify hazard by analyzing only process units. In this model, safeguards are included in modeling, which is treated in organizational knowledge base.

One of the main reasons why the safety of chemical processes should be paid much attention to is that chemical plants deal with hazardous materials. Furthermore, since some properties of materials may vary with physical state, the hazard analysis of chemical processes become complicated. Thus, in order to take these features into consideration in the course of analysis, the knowledge base has to contain underlying information about those properties. Such properties have been already defined by several standards or codes, each of which covers various kinds of chemical substances. The proper use of these standards or codes could save a lot of effort which is made to find out chemical hazards. In this study, NFPA(National Fire Protection Association) code is adopted. Material knowledge base consists of Hazard indices and Reaction matrix. Hazard indices represent flammability hazard rating(Nf), health hazard rating(Nh), reactivity hazard rating(Nr) according to NFPA code. Each index has five ratings from 0 to 4. The higher degree represents more dangerous situation. While these indices represent hazardous properties of each material properly, they do not have enough information to account for unwanted chemical reaction. There is a possibility that unexpected reaction could occur in chemical plants, and therefore considerable knowledge of chemistry and of reaction kinetics are required to recognize these reactions. Reaction matrix is introduced to simplify this work. Through material knowledge base, our approach can treat various chemical hazards including unwanted reaction which the other system can not deal with.

3. Reasoning Algorithm

The developed system has the following three hazard analysis algorithms;

□Deviation analysis algorithm

□Malfunction analysis algorithm

□Accident analysis algorithm

It can perform hazard analysis in view of malfunction and deviation as well. These results can be mutually complemented, so it can detect more exhaustively the potential accident than other hazard analysis systems with the only one viewpoint. As a result, the quality of hazard analysis can also be enhanced. Accident analysis algorithm incorporates the physical state of process with material knowledge base, by which all conceivable types of accidents can be represented. The developed system is successfully demonstrated by being applied to DAP process and Olefin dimerization plant.

4. Conclusion

The previous attempts have represented chemical plant through qualitative causal model, qualitative constraint model, and/or production rule. But they have limits in representing the knowledge needed for hazard analysis. The devised model has more information for hazard analysis than any other model, and it is enough to perform hazard analysis.

Another significance of this work is that the developed system can provide whole pathway leading to accident. It shows the whole procedure starting from ultimate malfunction to the actual accidents. Besides, it can find out the potential accident which are not covered by the existing safeguard.

In order to manage the risk efficiently, potential accident is to be known by hazard identification method, and then accident probability and consequence are to be known. Fault tree analysis is the most frequently used method to evaluate probability. There have been many attempts at automatic construction of fault tree since 1970's. But there have been no attempts to correlate it with the automation of hazard identification at all. However, in order to perform fault tree analysis, top event (usually important accident) is to be known, which is obtained by applying the hazard identification methods (e.g. HAZOP, FMEA) to the process of interest. Hazard identification method is intended to identify the accident in the plant exhaustively by bottom-up approach, and fault tree analysis is intended to find out the process of accident (event combination leading to accident) by top-down approach. These two methods differ only in that the direction of analysis and the term used. The previous systems simply present the cause and consequence of accident, so their result is barely helpful to the hazard assessment. But the developed system can present the intermediate steps and the result can be linked to the hazard assessment efficiently. In this respect, we are developing a goal-directed hazard analysis algorithm like FTA which uses the proposed model.

References

[1] Jung Chul Suh, Sangbum Lee, En Sup Yoon, "New Strategy for Automated Hazard Analysis of Chemical Plants : Part 1. Knowledge Modeling", Journal of Loss Prevention in the process industries, vol. 10 no. 2, 1997.

[2] Jung Chul Suh, Sangbum Lee, En Sup Yoon, "New Strategy for Automated Hazard Analysis of Chemical Plants : Part 2. Reasoning Algorithm and Case Study", Journal of Loss Prevention in the process industries, vol. 10 no. 2,  1997.