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

Rym Kanes, Maria Clementina Ramirez Marengo, Hazem Abdel-Moati, Jack Cranefield, Luc Vechot

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

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 (BT), Layer of protection 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 tool that monitors, in real time, the potential increases in risk levels as a result of pre-identified risk factors and process safety related data, using Bayesian Belief Networks (BN). The development of the tool involved two phases: 1) Development of a methodology that establishes the framework for the tool and 2) Development of the tool itself with the use of JAVA programming language. The overall tool is to be called PULSE, which stands for Process Unit Life Safety Evaluation. In this context, the paper presents a case study of the 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 BN, modification of the skeletal BN to incorporate reliability data, and insertion of equipment failure evidence into the BN for dynamic modeling. In addition, an overview of PULSE is presented. 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.

Original languageEnglish
Pages (from-to)142-153
Number of pages12
JournalJournal of Loss Prevention in the Process Industries
Volume50
DOIs
Publication statusPublished - 1 Jan 2017

Fingerprint

Bayesian networks
Risk assessment
risk assessment
Safety
Safety Management
Qatar
quantitative risk assessment
Life Cycle Stages
management systems
life cycle (organisms)
risk factors
risk management process
Gas industry
unit process
Equipment Safety
Equipment Failure
risk assessment process
Programming Languages
Life cycle
Chemical reactions

Keywords

  • Bayesian networks applications
  • Bowtie analysis
  • Process safety performance metrics
  • Reliability

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Food Science
  • Chemical Engineering(all)
  • Safety, Risk, Reliability and Quality
  • Energy Engineering and Power Technology
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Cite this

Developing a framework for dynamic risk assessment using Bayesian networks and reliability data. / Kanes, Rym; Ramirez Marengo, Maria Clementina; Abdel-Moati, Hazem; Cranefield, Jack; Vechot, Luc.

In: Journal of Loss Prevention in the Process Industries, Vol. 50, 01.01.2017, p. 142-153.

Research output: Contribution to journalArticle

Kanes, Rym ; Ramirez Marengo, Maria Clementina ; Abdel-Moati, Hazem ; Cranefield, Jack ; Vechot, Luc. / Developing a framework for dynamic risk assessment using Bayesian networks and reliability data. In: Journal of Loss Prevention in the Process Industries. 2017 ; Vol. 50. pp. 142-153.
@article{23ed4087f223483ebcf23f2a09604579,
title = "Developing a framework for dynamic risk assessment using Bayesian networks and reliability data",
abstract = "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 (BT), Layer of protection 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 tool that monitors, in real time, the potential increases in risk levels as a result of pre-identified risk factors and process safety related data, using Bayesian Belief Networks (BN). The development of the tool involved two phases: 1) Development of a methodology that establishes the framework for the tool and 2) Development of the tool itself with the use of JAVA programming language. The overall tool is to be called PULSE, which stands for Process Unit Life Safety Evaluation. In this context, the paper presents a case study of the 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 BN, modification of the skeletal BN to incorporate reliability data, and insertion of equipment failure evidence into the BN for dynamic modeling. In addition, an overview of PULSE is presented. 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.",
keywords = "Bayesian networks applications, Bowtie analysis, Process safety performance metrics, Reliability",
author = "Rym Kanes and {Ramirez Marengo}, {Maria Clementina} and Hazem Abdel-Moati and Jack Cranefield and Luc Vechot",
year = "2017",
month = "1",
day = "1",
doi = "10.1016/j.jlp.2017.09.011",
language = "English",
volume = "50",
pages = "142--153",
journal = "Journal of Loss Prevention in the Process Industries",
issn = "0950-4230",
publisher = "Elsevier BV",

}

TY - JOUR

T1 - Developing a framework for dynamic risk assessment using Bayesian networks and reliability data

AU - Kanes, Rym

AU - Ramirez Marengo, Maria Clementina

AU - Abdel-Moati, Hazem

AU - Cranefield, Jack

AU - Vechot, Luc

PY - 2017/1/1

Y1 - 2017/1/1

N2 - 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 (BT), Layer of protection 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 tool that monitors, in real time, the potential increases in risk levels as a result of pre-identified risk factors and process safety related data, using Bayesian Belief Networks (BN). The development of the tool involved two phases: 1) Development of a methodology that establishes the framework for the tool and 2) Development of the tool itself with the use of JAVA programming language. The overall tool is to be called PULSE, which stands for Process Unit Life Safety Evaluation. In this context, the paper presents a case study of the 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 BN, modification of the skeletal BN to incorporate reliability data, and insertion of equipment failure evidence into the BN for dynamic modeling. In addition, an overview of PULSE is presented. 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.

AB - 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 (BT), Layer of protection 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 tool that monitors, in real time, the potential increases in risk levels as a result of pre-identified risk factors and process safety related data, using Bayesian Belief Networks (BN). The development of the tool involved two phases: 1) Development of a methodology that establishes the framework for the tool and 2) Development of the tool itself with the use of JAVA programming language. The overall tool is to be called PULSE, which stands for Process Unit Life Safety Evaluation. In this context, the paper presents a case study of the 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 BN, modification of the skeletal BN to incorporate reliability data, and insertion of equipment failure evidence into the BN for dynamic modeling. In addition, an overview of PULSE is presented. 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.

KW - Bayesian networks applications

KW - Bowtie analysis

KW - Process safety performance metrics

KW - Reliability

UR - http://www.scopus.com/inward/record.url?scp=85030710042&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85030710042&partnerID=8YFLogxK

U2 - 10.1016/j.jlp.2017.09.011

DO - 10.1016/j.jlp.2017.09.011

M3 - Article

VL - 50

SP - 142

EP - 153

JO - Journal of Loss Prevention in the Process Industries

JF - Journal of Loss Prevention in the Process Industries

SN - 0950-4230

ER -