Bayesian network-based risk analysis methodology: A case of atmospheric and vacuum distillation unit

Zhang, Junyan; Cai, Baoping; Mulenga, Kabwe; Liu, Yiliu; Xie, Min

doi:10.1016/j.psep.2018.06.012

Cited by 18 publications

(6 citation statements)

References 19 publications

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“…Definition 12. The fuzzy probability of occurrence for leaf node T is defined using Equation (15) after determining the CM-IT1 FS-based a priori probabilities and conditional probabilities. The updated fuzzy a priori probability, called fuzzy posteriori probability, is defined using Equation (16).…”

Section: New Fuzzy Bn Inference Algorithmmentioning

confidence: 99%

“…BN inference has been explored as a potential valuable technique to conduct the risk assessment [9]. Recently, BN inference has been extensively employed for risk assessments in many complex systems that include nu-clear power plants (NPPs) [10][11][12], petrochemical plants [13][14][15][16], construction projects [17][18][19][20], hazardous material transportation [21][22][23][24], etc. However, BN inference uses crisp probabilities; thus, it is challenging to deal with the uncertain information from experts' knowledge [25,26].…”

Section: Introductionmentioning

confidence: 99%

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A New Fuzzy Bayesian Inference Approach for Risk Assessments

Xu,

Sui,

et al. 2024

Symmetry

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Bayesian network (BN) inference is an important statistical tool with additive symmetry. However, BN inference cannot deal with the uncertain, fuzzy, random, and conflicting information from experts’ knowledge in the process of conducting a risk assessment. To tackle this issue, a new fuzzy BN inference approach for risk assessments was proposed based on cloud model (CM), interval type-2 fuzzy set (IT2 FS), interval type-2 fuzzy logic system (IT2 FLS), modified Dempster–Shafer (D-S) evidence theory (ET), and Latin hypercube sampling (LHS) methods along the following lines. Firstly, CM was integrated into IT2 FS, and CM-based IT2 FS (CM-IT2 FS) was defined in the IT2 FLS. Secondly, modified D-S ET was utilized to determine the CM-IT2 FS-based a priori probabilities, and the CM-IT2 FS-based BN model was established. Thirdly, the CM-IT2 FS-based a priori probabilities were reduced to the CM-IT1 FS-based ones using a type reducer in the IT2 FLS, LHS was applied to propose a new fuzzy BN inference algorithm, and then, the new algorithm was used in a typical case to perform the fuzzy BN positive inference for risk prediction and the fuzzy BN reverse inference for risk sensitivity analysis. Finally, the BN inference results were analyzed using the proposed algorithm and the two common BN inference algorithms, and the effectiveness of the proposed approach was validated. It can be concluded that the proposed approach was both accurate and promising.

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Section: New Fuzzy Bn Inference Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Fuzzy Bayesian Inference Approach for Risk Assessments

Xu,

Sui,

et al. 2024

Symmetry

View full text Add to dashboard Cite

show abstract

“…In the BORA model, the influencing diagram is used to take the installation specific conditions of RIFs into account. More Bayesian networks related QRA articles in the domain of the offshore petroleum industry are reviewed in (Cai et al 2013;Cai et al 2018;Khakzad et al 2013;Zhang et al 2018).…”

Section: Modelling Techniquesmentioning

confidence: 99%

Quantitative risk modelling in the offshore petroleum industry: integration of human and organizational factors

Zhen

Vinnem

Yang

et al. 2019

Ships and Offshore Structures

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In-depth investigations of major offshore accidents show that technical, human, operational and organizational risk influencing factors (RIFs) all have crucial effects on the accident sequences. Nonetheless, the current generation of quantitative risk analysis (QRA) in the offshore petroleum industry has focused on technical safety systems while applications and findings in the non-technical fields are to a large extent missing. There have also been parallel efforts to develop methods for the formal inclusion of human and organizational factors (HOFs) into QRA. Examples from the offshore petroleum industry include ORIM, BORA, Risk_OMT, etc. This paper presents a review of QRA models that have been developed for the offshore petroleum industry, allowing HOFs integrated in a systematical way. The main intention of this study is to summarize and evaluate how these QRA models effectively seek answers to the key questions in this line of research: (i) What are the RIFs that affect the risk? (ii) How do these factors influence the risk? (iii) How much do these factors contribute to the risk? Further, the weakness and challenges of the reviewed models are pinpointed based on a substantial data set of actual leaks that have occurred in the Norwegian sector. Following the close scrutiny of these models, their progress, limitations, validity and suitability are addressed and discussed in detail. Based on these insights, future work is suggested to enhance and improve the QRA framework for including the installation specific conditions of technical and non-technical RIFs in a more comprehensive and defensible way.

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“…7 Hence, inherently safer designs or practices are key priorities to prevent or mitigate some incidents with devastating consequences that may occur after any fault. 8,9 Process system failure or unreliability features enormous costs impacting negatively the productivity standards of companies. 10 For this reason, reliable system configuration is an important design issue while considering reliability and process safety concepts.…”

Section: Introductionmentioning

confidence: 99%

“…The diverse consequences of accidents or near-miss in world oil refineries affect all areas of society (companies, government and community) with injuries, fatalities, significant property damage, and economic losses. , Common contributors to process safety incidents in refineries might be plant maturing and aging, lack of maintenance, failure of installed components, natural disasters, poor alarm management, or poor safety know-how. , In general, such incidents occur in processes with inadequate design or absence of safeguards, in which hazards are not recognized, safety systems are insufficient, and human factor issues are not addressed . Hence, inherently safer designs or practices are key priorities to prevent or mitigate some incidents with devastating consequences that may occur after any fault. , …”

Section: Introductionmentioning

confidence: 99%

Integrated Approach of Safety, Sustainability, Reliability, and Resilience Analysis via a Return on Investment Metric

Moreno-Sader

Jain

Tenorio

et al. 2019

ACS Sustainable Chem. Eng.

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The process design and technology selection are driven by multiple objectives including technical, economic, environmental, safety, and resilience drivers. Traditionally, technoeconomic analyses have been used to select and optimize the process design. Subsequently, environmental, safety, and resilience issues have been included in post-design analysis. With the recognition of the importance of including various objectives during the early stages of the process design, there has been a growing interest in the development of systematic methods for the multi-objective conceptual design. Recently, novel concepts have been proposed for the incorporation of sustainability and safety in designs through financial frameworks. In the current work, an extended approach is proposed for the incorporation of reliability, resilience, safety, and sustainability aspects during the conceptual design of the process system. In this proposed methodology, safety and sustainability weighted return on investment metric (SASWROIM) is extended to include impacts of resilience and reliability aspects with different design changes and configurations. For this purpose, a safety, sustainability, reliability, and resilience weighted return on investment metric (S2R2WROIM) is introduced. Multi-objective decision-making can thus be carried out for the optimal design based on this augmented method over the convention economic return on investment metric. The general methodology to screen process alternatives through S2R2WROIM begins with the synthesis and simulation of base case and alternative case flowsheet in Aspen HYSYS. This is followed by performance analysis, evaluation of various metrics, and applications of integrated metric to select the recommended design. A compressor process system case study in a hydrocracking process plant is demonstrated to illustrate the applications and benefits of the proposed methodology. The new approach provides decision makers with meaningful information and insights about safety, reliability, resilience, and sustainability along with the economic aspect.

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Bayesian network-based risk analysis methodology: A case of atmospheric and vacuum distillation unit

Cited by 18 publications

References 19 publications

A New Fuzzy Bayesian Inference Approach for Risk Assessments

A New Fuzzy Bayesian Inference Approach for Risk Assessments

Quantitative risk modelling in the offshore petroleum industry: integration of human and organizational factors

Integrated Approach of Safety, Sustainability, Reliability, and Resilience Analysis via a Return on Investment Metric

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