Abstract
Chemical industries are complicated and dynamic to handle. Dynamic characteristics could include inspection time interval, ageing of components in plants and other dependent criteria can influence dynamic processes. The conventional risk assessment could measure dynamic changes in processes but only in limited capacity. Hence, it is significant to develop method for timedependent effects to predict the probabilities failure rates for components in plant with time. In this study, dynamic risk assessment has been developed a technique based on Bayesian network (BN). BN is a structure optimal which organize cause-effect relations and dynamic BN capture change variables over time. This study proposed to develop dynamic accident modelling to improve risk analysis in the context of Bhopal industries. A methyl isocyanate (MIC) gas leakage scenario in the plant was quantified through identifying hazards through fault tree analysis. It has been observed that the developed method was able to provide updated probability failure of different components with time. In this study, Bhopal cases would be illustrated the mapping procedure of FT into BN and to identify factors to have significant influence on an event occurrence. Rupture disk, gas leakage and water accumulation in pipe contribute into gas explosion in Bhopal where the components and safety barriers started to fail by year and no inspection had done. Therefore, the finding showed a method for dynamic risk assessment which enable of providing updated probability of event occurrences through failure rates, considering sequential dependencies with time. The failure rates were simulated in GeNIe software. Therefore, dynamic characteristics could reduce cost of inspection, downtime and other maintenances. The possibilities of failure rate values for components tend to increase with time. But with maintenance work were done on equipment in every one year, then possibilities of failure rates become decreases and low possibilities of breakdown. The Bhopal failures were demonstrated the effect of sequential; dependency of one component on another component contributes to the risk. It could be concluded that with the increases in year of inspection interval, the probability of top event, MIC gas released to the surrounding would be reduced.
Metadata
Item Type: | Conference or Workshop Item (Paper) |
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Creators: | Creators Email / ID Num. Adnan, Nurul Izzatul Nadia izzatul96nadia@gmail.com Mohd Ariff, Mohd Azahar azahar.ariff@uitm.edu.my Abdul Razak, Noor ‘Aina nooraina@uitm.edu.my |
Contributors: | Contribution Name Email / ID Num. Advisor Nasuha, Norhaslinda UNSPECIFIED Chief Editor Isa, Norain UNSPECIFIED |
Subjects: | T Technology > TD Environmental technology. Sanitary engineering > Air pollution and its control |
Divisions: | Universiti Teknologi MARA, Pulau Pinang > Permatang Pauh Campus > Faculty of Chemical Engineering |
Journal or Publication Title: | 9th Virtual Science Invention Innovation Conference (SIIC) 2020 |
Page Range: | pp. 122-124 |
Keywords: | Dynamic, Fault Tree, Bayesian Network, Bhopal Disaster, Genie Software |
Date: | 2020 |
URI: | https://ir.uitm.edu.my/id/eprint/81577 |