The bumpy road to better risk control: A Tour d'Horizon of new concepts and ideas

Pasman, Hans J.; Rogers, William J.

doi:10.1016/j.jlp.2014.12.003

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…This increasing trend is due to the advantages that Bayesian Networks provide in contrast to other static classical analysis methods such as fault trees, event trees and Bowties. BNs are also becoming one of the preferred tools for risk management applications [3] Currently, there has been significant work carried out on the use of BN to overcome the limitations of classical risk analysis. In this context, BNs have been applied for predicting the probability of occurrence of undesired consequences.…”

Section: Figure 2 Bayesian Networkdirected Acyclic Graphmentioning

confidence: 99%

Developing a framework for dynamic risk assessment using Bayesian networks and reliability data

Kanes

Marengo

Abdel-Moati

et al. 2017

Journal of Loss Prevention in the Process Industries

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Process Safety in the oil and gas industry is managed through a robust Process Safety Management (PSM) system that involves the assessment of the risks associated with a facility in all steps of its life cycle. Risk levels tend to fluctuate throughout the life cycle of many processes due to several time varying risk factors (performances of the safety barriers, equipment conditions, staff competence, incidents history, etc.). While current practices for quantitative risk assessments (e.g. Bow-tie analysis, LOPA, etc.) have brought significant improvements in the management of major hazards, they are static in nature and do not fully take into account the dynamic nature of risk and how it improves risk-based decision making In an attempt to continually enhance the risk management in process facilities, the oil and gas industry has put in very significant efforts over the last decade toward the development of process safety key performance indicators (KPI or parameters to be observed) to continuously measure or gauge the efficiency of safety management systems and reduce the risks of major incidents. This has increased the sources of information that are used to assess risks in real-time. The use of such KPIs has proved to be a major step forward in the improvement of process safety in major hazards facilities. Looking toward the future, there appears to be an opportunity to use the multiple KPIs measured at a process plant to assess the quantitative measure of risk levels at the facility on a time-variant basis.ExxonMobil Research Qatar (EMRQ) has partnered with the Mary Kay O'Connor Process Safety Center -Qatar (MKOPSC-Q) to develop a methodology that establishes a framework for a tool that monitors in real time the potential increases in risk levels as a result of pre-identified risk factors that would include the use of KPIs (leading or lagging) as observations or evidence using Bayesian Belief Networks (BN).In this context, the paper presents a case study of quantitative risk assessment of a process unit using BN. The different steps of the development of the BN are detailed, including: translation of a Bowtie into a skeletal BBN, modification of the skeletal BN to incorporate KPIs (loss of primary containment (LOPC), equipment, management and human related), and testing of the BBN with forward and backward inferences. The outcomes of the dynamic modeling of the BN with real time insertion of evidence are discussed and recommendation for the framework for a dynamic risk assessment tool are made.

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Section: Figure 2 Bayesian Networkdirected Acyclic Graphmentioning

confidence: 99%

Developing a framework for dynamic risk assessment using Bayesian networks and reliability data

Kanes

Marengo

Abdel-Moati

et al. 2017

Journal of Loss Prevention in the Process Industries

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“…More recently, resilience-driven strategies have been proposed for environmental systems (e.g., marine ecosystems) that are synergistically based on risk assessment to appropriately protect against uncertain and unexpected events such as, for example, oil spills [13]. In the past years, numerous overviews and summaries about conceptual and methodologial treatments of uncertainty within risk assessment have been published [14][15][16][17][18][19]. This is related to the increasing interest in uncertainty estimation, which is of great importance to consider possible random fluctuations, i.e., caused by lack of data.…”

Section: Introductionmentioning

confidence: 99%

Comparative Risk Assessment for Fossil Energy Chains Using Bayesian Model Averaging

Spada

Burgherr

2020

Energies

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The accident risk of severe (≥5 fatalities) accidents in fossil energy chains (Coal, Oil and Natural Gas) is analyzed. The full chain risk is assessed for Organization for Economic Co-operation and Development (OECD), 28 Member States of the European Union (EU28) and non-OECD countries. Furthermore, for Coal, Chinese data are analysed separately for three different periods, i.e., 1994–1999, 2000–2008 and 2009–2016, due to different data sources, and highly incomplete data prior to 1994. A Bayesian Model Averaging (BMA) is applied to investigate the risk and associated uncertainties of a comprehensive accident data set from the Paul Scherrer Institute’s ENergy-related Severe Accident Database (ENSAD). By means of BMA, frequency and severity distributions were established, and a final posterior distribution including model uncertainty is constructed by a weighted combination of the different models. The proposed approach, by dealing with lack of data and lack of knowledge, allows for a general reduction of the uncertainty in the calculated risk indicators, which is beneficial for informed decision-making strategies under uncertainty.

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Reliability Analysis of Process Systems Using Intuitionistic Fuzzy Set Theory

Yazdi

Kabir

Kumar

et al. 2023

Industrial and Applied Mathematics

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The bumpy road to better risk control: A Tour d'Horizon of new concepts and ideas

Cited by 6 publications

References 16 publications

Developing a framework for dynamic risk assessment using Bayesian networks and reliability data

Developing a framework for dynamic risk assessment using Bayesian networks and reliability data

Comparative Risk Assessment for Fossil Energy Chains Using Bayesian Model Averaging

Reliability Analysis of Process Systems Using Intuitionistic Fuzzy Set Theory

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