Application of fuzzy logic to explosion risk assessment

2011 ◽  
Vol 24 (6) ◽  
pp. 780-790 ◽  
Author(s):  
Adam S. Markowski ◽  
M. Sam Mannan ◽  
Agata Kotynia ◽  
Henryk Pawlak
2004 ◽  
Vol 60 (2-4) ◽  
pp. 233-239 ◽  
Author(s):  
Luis E Gallego ◽  
Oscar Duarte ◽  
Horacio Torres ◽  
Mauricio Vargas ◽  
Johny Montaña ◽  
...  
Keyword(s):  

2021 ◽  
Author(s):  
Abdelrahman Ibrahim Mostafa ◽  
Abdelrahman Mostafa Rashed ◽  
Yasmin Ashraf Alsherif ◽  
Yomna Nagah Enien ◽  
Menatalla Kaoud ◽  
...  

2017 ◽  
Vol 121 ◽  
pp. 11016
Author(s):  
Mihaela Părăian ◽  
Sorin Burian ◽  
Mihai Magyari ◽  
Lucian Moldovan

2016 ◽  
Vol 35 (1) ◽  
pp. 21-35 ◽  
Author(s):  
Jianwei Cheng ◽  
Xixi Zhang ◽  
Apurna Ghosh

In the coal mining industry, explosions or mine fires present the most hazardous safety threats for coal miners or mine rescue members. Hence, the determination of the mine atmosphere explosibility and its evolution are critical for the success of mine rescues or controlling the severity of a mine accident. However, although there are numbers of methods which can be used to identify the explosibility, none of them could well indicate the change to the explosion risk time evolution. The reason is that the underground sealed atmospheric compositions are so complicated and their dynamical changes are also affected by various influence factors. There is no one method that could well handle all such considerations. Therefore, accurately knowing the mine atmospheric status is still a complicated problem for mining engineers. Method of analyzing the explosion safety margin for an underground sealed atmosphere is urgently desired. This article is going to propose a series of theoretical explosion risk assessment models to fully analyze the evolution of explosion risk in an underground mine atmosphere. Models are based on characteristics of the Coward explosibility diagram with combining mathematical analyzing approaches to address following problems: (1) for an “not-explosive” atmosphere, judging the evolution of explosion risk and estimating the change-of-state time span from “not-explosive” to “explosive” and (2) for an “explosive” atmosphere, estimating the “critical” time span of moving out of explosive zone and stating the best risk mitigation strategy. Such research efforts could not only help mine operators understand the explosibility risk of a sealed mine atmosphere but also provide a useful tool to wisely control explosive atmosphere away from any dangers. In order to demonstrate research findings, case studies for derived models are shown and are also used to instruct readers how to apply them. The results provide useful information for effectively controlling an explosive underground sealed atmosphere.


Author(s):  
G.B.S. Alekhya ◽  
K. Shashikanth ◽  
M. Anjaneya Prasad

2021 ◽  
Vol 19 (3) ◽  
pp. 101-124
Author(s):  
Ako Rita Erhovwo ◽  
Okpako Abugor Ejaita ◽  
Duke Oghorodi

Risk assessment methodology in general has been around for quite a while, its prominence in the E-banking field is a fairly recent phenomenon. We are at the point where risk assessments are critical to the overall function of banks. Banks are required to assess the processes underlying their operations against potential threats, vulnerabilities, and their potential impact, which helps in revealing the risk exposure level, and the residual risks. Identifying clearly a risk assessment methodology is often the first step of assessing and evaluating risk associated with an organization operation. This paper presents a risk assessment methodology for Ebanking Operational Risk. The proposed risk assessment methodology consists of four major steps: a risk model, assessment approach, analysis approach and a risk assessment process. The main tool of the proposed risk assessment methodology is the risk assessment process. The assessment process gives detailed explanation with respect to which models or techniques may be applied and how they are expressed. In this paper the risk assessment technique is built upon fuzzy logic (FL) concept and Bayesian network (BN). In fuzzy logic, an element is included with a degree of membership. Bayesian network is an inference classifier that is capable of representing conditional independencies. The Bayesian and fuzzy logic–based risk assessment process gives good predictions for risk learning and inference in the E-banking systems. Keywords: Fuzzy logic, Bayesian network, risk assessment methodology, operational risk, Ebanking


2019 ◽  
Vol 27 ◽  
pp. 59-66 ◽  
Author(s):  
Hayriye Korkmaz ◽  
Emre Canayaz ◽  
Sibel Birtane Akar ◽  
Zehra Aysun Altikardes

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