Probalistic Energy Flow Calculation Method based on Event Tree Analysis

Abstract The navigation in restricted waters imposes several challenges when compared to open sea navigation. Smaller dimensions, higher traffic density and the dynamics of obstacles such as sandbanks are examples of contributors to the difficulty. Due to these aspects, local experienced maritime pilots go onboard in order to support the ship’s crew with their skills and specific regional knowledge. Despite these efforts, several accidents still occur around the world. In order to contribute to a better understanding of the events composing accidental sequences, this paper presents a hybrid modelling specific for restricted waters. The main techniques used are the fault tree analysis and event tree analysis. The former provides a framework to investigate the causes, while the latter allows modelling the sequence of actions necessary to avoid an accident. The models are quantified using statistical data available in the literature and a prospective human performance model developed by the Technique for Early Consideration of Human Reliability (TECHR). The results include combined estimates of human error probabilities and technical failure probabilities, which can be used to inform the causation factor for a waterway risk analysis model. In other words, given that the ship encounters a potential accidental scenario while navigating, the proposed models allow computing the failure probability that of the evasive actions sequence. The novelty of this work resides on the possibility of explicitly considering dynamicity and recovery actions when computing the causation factor, what is not a typical feature of similar works. The results obtained were compared with several results available in the literature and have been shown to be compatible.

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Statistical Analysis and Prediction of Fatal Accidents in the Metallurgical Industry in China

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17113790 ◽

2020 ◽

Vol 17 (11) ◽

pp. 3790 ◽

Cited By ~ 1

Author(s):

Qingwei Xu ◽

Kaili Xu

Keyword(s):

Metallurgical Industry ◽

Grey System Theory ◽

Severe Accident ◽

Event Tree ◽

Grey System ◽

Event Tree Analysis ◽

Safety Measures ◽

Fatal Accidents ◽

Tree Analysis ◽

Bow Tie

The metallurgical industry is a significant component of the national economy. The main purpose of this study was to establish a composite risk analysis method for fatal accidents in the metallurgical industry. We collected 152 fatal accidents in the Chinese metallurgical industry from 2001 to 2018, including 141 major accidents, 10 severe accidents, and 1 extraordinarily severe accident, together resulting in 731 deaths. Different from traffic or chemical industry accidents, most of the accidents in the metallurgical industry are poisoning and asphyxiation accidents, which account for 40% of the total number of fatal accidents. As the original statistical data of fatal accidents in the metallurgical industry have irregular fluctuations, the traditional prediction methods, such as linear or quadratic regression models, cannot be used to predict their future characteristics. To overcome this issue, the grey interval predicting method and the GM(1,1) model of grey system theory are introduced to predict the future characteristics of fatal accidents in the metallurgical industry. Different from a fault tree analysis or event tree analysis, the bow tie model integrates the basic causes, possible consequences, and corresponding safety measures of an accident in a transparent diagram. In this study, the bow tie model was used to identify the causes and consequences of fatal accidents in the metallurgical industry; then, corresponding safety measures were adopted to reduce the risk.

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