Dynamic risk analysis of dust explosions

Zhi, Yuan (2015) Dynamic risk analysis of dust explosions. Doctoral (PhD) thesis, Memorial University of Newfoundland.

[English] PDF - Accepted Version Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Download (4MB)

Abstract

Dust explosion is a continuous threat to equipment safety and human health in process industries. Although many works have been performed in the context of dust explosion mechanism and its prevention measures, a comprehensive risk analysis model which can be applied in various industries is absent. One of the barriers to such a risk model has been the wide variety of industries threatened by dust explosions, as well as complex and interlinked contributors to dust explosions. Selecting safety measures satisfying the requirements of safety regulations and the limitation of budget at the same time has been another barrier. Moreover, there has not been any work devoted to the propagation of dust-domino-effects, although it has frequently been reported in process industries. In this research, dust explosion root causes as well as other features such as ignition sources have been collected and listed in a comprehensive database. Applying Bow-tie (BT) diagram, a conventional quantitative risk analysis (QRA) method, a generic model of risk assessment for dust explosions has been established using the developed database. In this model, the basic causes contributing to dust explosions are organized according to their cause-effect relationships. Furthermore, potential consequences of dust explosions have been analyzed depending on the function/malfunction of relevant safety barriers. The applicability and efficacy of proposed safety measures to reduce the risk of dust explosions have also been discussed. To overcome the limitations of BT such as its inability to model conditional dependencies and common-cause failures, Bayesian network (BN) has been used in this research to capture dependencies and to perform diagnostic analysis and sequential learning. According to the results, dust particle properties, oxygen concentration and lack of safety training are identified as the most critical root causes leading to dust explosions. Further, a risk-based methodology has been proposed for cost-effective allocation of safety measures. Moreover, in this research, the occurrence probabilities of dust explosions in dust-domino-effects have been estimated based on BN.

Actions (login required)

View Item