The complexity of corrosion mechanisms in harsh offshore environments poses safety and integrity challenges to oil and gas operations. Exploring the unstable interactions and complex mechanisms required an advanced probabilistic model. The current study presents the development of a probabilistic approach for a consequence-based assessment of subsea pipelines exposed to complex corrosion mechanisms. The Bayesian Probabilistic Network (BPN) is applied to structurally learn the propagation and interactions among under-deposit corrosion and microbial corrosion for the failure state prediction of the asset. A two-step consequences analysis is inferred from the failure state to establish the failure impact on the environment, lives, and economic losses. The essence is to understand how the interactions between the under-deposit and microbial corrosion mechanisms’ nodes influence the likely number of spills on the environment. The associated cost of failure consequences is predicted using the expected utility decision theory. The proposed approach is tested on a corroding subsea pipeline (API X60) to predict the degree of impact of the failed state on the asset’s likely consequences. At the worst degradation state, the failure consequence expected utility gives (Formula presented.) The influence-based model provides a prognostic tool for proactive integrity management planning for subsea systems exposed to stochastic degradation in harsh offshore environments.

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Multiple factors contribute to occupational accidents including individual, job, environmental, organizational, and family issues. Most of them are latent factors and hard to model how and what extent they influence incidents occurrence. Although past research has included an occupational incident context, this attention has rarely provided a holistic instrument for the dynamic causal modeling of different influencing factors. Hence, the present study is aimed at developing a concrete instrument for identifying and modeling various influencing factors on occupational accident occurrence. After a comprehensive literature review and employing occupational safety and industrial psychological experts to achieve a reasonable conceptual model, the primary structure of the instrument was developed. Several systematic attempts were made to identify items (questions), define contributing factors, assess content and face validity, analyze its reliability, construct validity, criterion validity, and assess the model's fitness using advanced statistics tests by SPSS.22 and LISREL9.3 programs. Finally, dynamic hybrid Bayesian Network (DHBN) based on the Confirmatory Factor Analysis (CFA) technique was developed to model accident occurrence and simulate the behavior of the main influencing factors over ten years under uncertainty. After standardization of the proposed instrument, a comprehensive study was conducted via the participation of 700 workers from thirty-eight manufacturing companies to illustrate its effectiveness and modeling capability. The findings revealed the effectiveness of the proposed instrument in the causation modeling of occupational incidents, dynamic modeling of contributing factors, and risk-based decision making for occupational incidents management. The proposed instrument can serve as a holistic tool for accurately identifying and dynamic modeling of different latent influencing factors, and making tailored safety decisions in different workplace context.

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Quantitative risk assessment (QRA) has played an effective role in improving safety of process systems during the last decades. However, QRA conventional techniques such as fault tree and bow-tie diagram suffer from drawbacks as being static and ineffective in handling uncertainty, which hamper their application to risk analysis of process systems. Bayesian network (BN) has well proven as a flexible and robust technique in accident modeling and risk assessment of engineering systems. Despite its merits, conventional applications of BN have been criticized for the utilization of crisp probabilities in assessing uncertainty. The present study is aimed at alleviating this drawback by developing a Fuzzy Bayesian Network (FBN) methodology to deal more effectively with uncertainty. Using expert elicitation and fuzzy theory to determine probabilities, FBN employs the same reasoning and inference algorithms of conventional BN for predictive analysis and probability updating. A comparison between the results of FBN and BN, especially in critically analysis of root events, shows the outperformance of FBN in providing more detailed, transparent and realistic results.

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Risk analysis in process systems is very important to design effective strategies for preventing and mitigating potential major accidents. Although conventional techniques as Bow-tie (BT) have widely been used in risk assessment of process systems, they fall short in effectively modelling epistemic uncertainty which is prevailing in risk assessment of process systems. The present study is aimed at alleviating this shortcoming by incorporating fuzzy set theory into BT, developing a so-called fuzzy extended Bow tie (FEBT) model. FEBT, compared with previous fuzzy BT methods, uses the intuitionistic fuzzy numbers and thus provides a more accurate cause – consequence model of accident scenarios. A natural gas transmission network is used to demonstrate the application of FEBT.

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Background and aims: Process systems due to processed under severe operational conditions and deal with large amounts of flammable and explosive materials have always led to many catastrophic accidents. Risk assessment is a useful tool for designing effective strategies for preventing and controlling these accidents. Conventional risk assessment methods have major deficiencies, including uncertainty in the obtained results and the completely static nature, therefore, the present study is aimed at applying a dynamic and quantitative approach to assess the safety risks of city natural gas pressure regulating stations. Methods: First, according to the standard of the Total Company (GS EP SAF 253), the reference (credible) event scenario was determined, then a qualitative, quantitative and dynamic modeling of the cause-consequence accident scenario model using Bayesian Network (BN) is provided and next, deductive and abductive probabilistic reasoning are conducted by means of constructed BN model. PHAST 7.11 program is employed to modeled and evaluated of different consequences of the scenario. Finally, the risk of accident scenario consequences was calculated, evaluated and updated. Results: 43 root events in occurrence of the credible event scenario of the gas stations were identified. Among the identified causes, the human failures (85%), process failures (10%) and mechanical failures (5%) had the highest contributing to occurrence in the accident scenario, respectively. Occurrence probability of the scenario is determined 7.11 ×10-2. Safety barriers, especially emergency shutdown valves (ESD), had a significant role in reducing the consequences severity. The risk of all three of consequences including jet fire, flash fire and vapor cloud explosion is located in unacceptable area. Conclusion: The use of BNs provides a comprehensive qualitative, quantitative and dynamic graphical modeling of the accident scenario. The abductive reasoning ability of these networks is capable to reducing the uncertainty and updating the probability of occurrence of root events and final the consequences. Using BNs along with consequences modeling, leads to a slightly more dynamic, precise and practical risk assessment in process plants.

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Pipelines are one of the most popular and effective ways of transporting hazardous materials, especially natural gas. However, the rapid development of gas pipelines and stations in urban areas has introduced a serious threat to public safety and assets. Although different methods have been developed for risk analysis of gas transportation systems, a comprehensive methodology for risk analysis is still lacking, especially in natural gas stations. The present work is aimed at developing a dynamic and comprehensive quantitative risk analysis (DCQRA) approach for accident scenario and risk modeling of natural gas stations. In this approach, a FMEA is used for hazard analysis while a Bow-tie diagram and Bayesian network are employed to model the worst-case accident scenario and to assess the risks. The results have indicated that the failure of the regulator system was the worst-case accident scenario with the human error as the most contributing factor. Thus, in risk management plan of natural gas stations, priority should be given to the most probable root events and main contribution factors, which have identified in the present study, in order to reduce the occurrence probability of the accident scenarios and thus alleviate the risks.@en