A Model for the Domino Effect Analysis in Quantitative Risk Assessment

2013 ◽  
Vol 328 ◽  
pp. 314-317
Author(s):  
Ming Liang Chen ◽  
Zhi Qiang Geng ◽  
Qun Xiong Zhu
Keyword(s):  
Risk Assessment ◽  
Quantitative Methods ◽  
Process Industry ◽  
Quantitative Risk Assessment ◽  
Domino Effect ◽  
Effect Analysis ◽  
Chemical Process Industry ◽  
Industrial Sites ◽  
Event Trees

The domino effect is responsibility for many most destructive accidents in the chemical process industry. The catastrophic consequences are not only affecting the industrial sites, but also people and environment. However, quantitative methods which take in to account the domino effect are still missing. A model for quantitative assessment of the domino effect is presented. The probabilities of occurrence are obtained by the event trees. The frequencies of different accidents can be obtained by applying the proposed method. The results of the case study show that the domino effect should be taken into account in quantitative risk assessment (QRA).

scholarly journals Domino Effect Analysis, Assessment and Prevention in Process Industries

2015 ◽  
Vol 3 (6) ◽  
pp. 481-498
Author(s):  
Jun Wu ◽  
Hui Yang ◽  
Yuan Cheng
Keyword(s):  
Risk Assessment ◽  
Game Theory ◽  
Chemical Process ◽  
Process Industry ◽  
Quantitative Risk Assessment ◽  
Common Phenomenon ◽  
Domino Effect ◽  
Process Industries ◽  
Effect Analysis ◽  
Chemical Process Industry

AbstractDomino effect is a fairly common phenomenon in process industry accidents, which makes many process industry accidents serious and the consequent losses enhanced. Domino effect of the major accidents in chemical cluster is emphasized. Many researchers have studied domino effect in chemical clusters from different perspectives. In the review, we summarize the research from three aspects: The statistical analysis of domino accidents in chemical process industry, the evaluation of domino accidents and the prevention of domino accidents in chemical clusters by game theory. From the analysis, we can find the characteristic of domino accidents such as the time and the location, the origin and causes of domino accidents. The methods of assessing domino effects such as quantitative risk assessment (QRA), Bayesian networks (BN) and Monte Carlo simulation (MCS) are analyzed. The prevention of domino accidents in chemical clusters using game theory is seldom, and there is still much space for improvement in enterprises’ efforts to prevent risk of domino accidents.

Method for Hazard Analysis of Chemical Process Equipment Taking Domino Effect into Account

2013 ◽  
Vol 321-324 ◽  
pp. 2456-2459
Author(s):  
Ming Liang Chen ◽  
Zhi Qiang Geng ◽  
Qun Xiong Zhu
Keyword(s):  
Chemical Process ◽  
Hazard Analysis ◽  
Process Industry ◽  
Process Equipment ◽  
Process Conditions ◽  
Domino Effect ◽  
Effect Analysis ◽  
Chemical Process Industry ◽  
Chemical Process Equipment

The hazard of chemical process equipment consists of two parts: the inherent hazard of process equipment and the hazard from domino effect among equipments. The inherent hazard of equipment depends on the properties of the substance present in the equipment and the specific process conditions. The domino effect is responsibility for many most destructive accidents in the chemical process industry. However, domino effect is either not considered at all or is done with much less rigour than is warranted. A method was proposed to evaluate the hazard of chemical process equipment. The inherent hazard and the hazard from domino effect were considered in the method. The procedure for the domino effect analysis among equipments was presented to evaluate the hazard from the domino effect. The method was implemented in a case study. The results show that it can be used to select the process equipment which should be intensive monitored.

Method for Estimating the Domino Effect in Quantitative Assessment

2013 ◽  
Vol 319 ◽  
pp. 536-540
Author(s):  
Ming Liang Chen ◽  
Zhi Qiang Geng ◽  
Qun Xiong Zhu
Keyword(s):  
Chemical Process ◽  
Quantitative Assessment ◽  
Process Industry ◽  
Risk Assessments ◽  
Domino Effect ◽  
Chemical Process Industry ◽  
Industrial Sites ◽  
The Impact

Accidents caused by the domino effect are the most destructive accidents in the chemical process industry. These chains of accidents may lead to catastrophic consequences and may affect not only the industrial sites, but also people, environment and economy. However, quantitative risk assessments do not usually take the domino effect into account in a detailed, systematic way, mostly because of its complexity and the difficulties involved in its incorporation. A method for quantitative assessment of domino effects is presented. The consequence and probability of a certain accident can be estimated. The domino sequences from the initial accident to the last accident can be obtained. The method has been implemented in a case study. The results show that it can indeed be used to estimate the impact of the domino effect in quantitative assessment.

scholarly journals Case Study of Expected Loss Failure Mode and Effect Analysis Model Based on Maintenance Data

2021 ◽  
Vol 11 (16) ◽  
pp. 7349
Author(s):  
Seungsik Min ◽  
Hyeonae Jang
Keyword(s):  
Risk Assessment ◽  
Failure Mode ◽  
Failure Detection ◽  
Quantitative Risk Assessment ◽  
Expected Loss ◽  
Effect Analysis ◽  
Assessment Indicator ◽  
Application Procedure ◽  
Record Data

Failure mode and effect analysis (FMEA) is one of the most widely employed pre-evaluation techniques to avoid risks during the product design and manufacturing phases. Risk priority number (RPN), a risk assessment indicator used in FMEA, is widely used in the field due to its simple calculation process, but its limitations as an absolute risk assessment indicator have been pointed out. There has also been criticism of the unstructured nature and lack of systematicity in the FMEA procedures. This work proposes an expected loss-FMEA (EL-FMEA) model that organizes FMEA procedures and structures quantitative risk assessment metrics. In the EL-FMEA model, collectible maintenance record data is defined and based on this, the failure rate of components and systems and downtime and uptime of the system are calculated. Moreover, based on these calculated values, the expected economic loss is computed considering the failure detection time. It also provides an alternative coefficient to evaluate whether or not a detection system is installed to improve the expected loss of failure. Finally, a case study was conducted based on the maintenance record data, and the application procedure of the EL-FMEA model was presented in detail, and the practicality of this model was verified through the results.

Application of failure mode and effects analysis to reduce microplastic emissions

2021 ◽  
pp. 0734242X2110031
Author(s):  
Ana Pires ◽  
Paula Sobral
Keyword(s):  
Failure Mode ◽  
Quantitative Methods ◽  
Policy Instruments ◽  
Effect Analysis ◽  
Advantages And Disadvantages ◽  
Heuristic Technique ◽  
Single Use ◽  
Regulatory Measures ◽  
Plastic Products

A complete understanding of the occurrence of microplastics and the methods to eliminate their sources is an urgent necessity to minimize the pollution caused by microplastics. The use of plastics in any form releases microplastics to the environment. Existing policy instruments are insufficient to address microplastics pollution and regulatory measures have focussed only on the microbeads and single-use plastics. Fees on the use of plastic products may possibly reduce their usage, but effective management of plastic products at their end-of-life is lacking. Therefore, in this study, the microplastic–failure mode and effect analysis (MP–FMEA) methodology, which is a semi-qualitative approach capable of identifying the causes and proposing solutions for the issue of microplastics pollution, has been proposed. The innovative feature of MP–FMEA is that it has a pre-defined failure mode, that is, the release of microplastics to air, water and soil (depending on the process) or the occurrence of microplastics in the final product. Moreover, a theoretical recycling plant case study was used to demonstrate the advantages and disadvantages of this method. The results revealed that MP–FMEA is an easy and heuristic technique to understand the failure-effect-causes and solutions for reduction of microplastics and can be applied by researchers working in different domains apart from those relating to microplastics. Future studies can include the evaluation of the use of MP–FMEA methodology along with quantitative methods for effective reduction in the release of microplastics.

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