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A Bayesian network is applied in this contribution in order to assess the risks of a selected production unit in a fossil power station. A general framework for the risk assessment of production units of a power station is presented first by implementing statistical methods and Bayesian networks. Special emphasis is given to the input data consisting of failure rates which are obtained on the basis of recorded data and expert judgements. The consequences of failure are divided into economical and human (societal): economic consequences include outages of key technological devices, societal consequences cover potential injuries and fatalities. Probabilistic risk assessment methods are applied to the selected production unit of a power station. The influence of the uncertainties in the considered technical parameters on the availability of the unit is assessed and the acceptance of the calculated availability represented through the mean value and the standard deviation is discussed. Societal risks given in terms of weighted injuries and fatalities are obtained and respective risk acceptance criteria are presented. Uncertainties affecting the risks are discussed. It appears that the proposed framework provides a valuable assessment of the influence individual devices and their components on availability and societal risk. For that purpose the used methodology, intentionally simplified for operational applications, includes important factors affecting risks of production units. It is concluded that Bayesian networks are a transparent method for the probabilistic risk assessment of complex technological systems. The results of the performed analyses can be easily updated when additional information becomes available as illustrated in characteristic examples.
Prescriptive and risk-based methods including qualitative, semi-quantitative and quantitative approaches exist for the risk assessment of road tunnels. This paper provides an overview and a brief discussion of these methods and uses representative traffic and accident data in Greece in a typical tunnel conforming with the minimum infrastructure requirements of the European Directive 2004/54/EC to compare and discuss the resulting risk for each of these methods. Conclusions regarding the implementation of risk analysis methods and the use of the associated existing national guideline are drawn and recommendations for further developments in standards are provided. An integrated framework for the optimal selection of safety measures based on risk reduction and socio-economic considerations is proposed and its applicability in risk analysis of road tunnels is discussed. (C) 2016 Elsevier Ltd. All rights reserved.
Natural and technological hazards have a major impact on environmental and human safety. Risk assessment plays an essential role in the risk management of the effects of such hazards. This contribution summarizes first the general formulation of risk depending on two parameters: hazard probability and associated consequences. Problems faced in industrial applications based on the experience of the author are discussed. Risk acceptance criteria for the contributors to risk i.e. human, economic and environment are critically reviewed. Resilience based criteria are also included and conclusions for future developments are provided.
The assessment of existing infrastructures in the energy sector is of great economic significance worldwide. Fossil power stations are reaching their design service life and rational decisions concerning extensions of service life, maintenance and replacements of devices should be based on updated information of the actual conditions of the energy devices and their components, and on cost-benefit analysis using risk analysis and probabilistic optimisation procedures.
The contribution provides an integrated framework for probabilistic reliability and risk assessment of existing energy production units considering availability and human safety criteria. An extensive case study focused on risks of an energy production unit in a fossil power station is provided to support practical applications. A Bayesian network is thereby implemented to assess the risks of the selected production unit. Special emphasis is given to the input data consisting of failure rates obtained from recorded data and expert judgements. The influence of uncertainties in the considered performance indicators on the availability of the unit is analysed. It is shown that a reasonably simplified framework can provide a valuable assessment of the influence of individual devices and their components on availability and societal risk, identifying thus the major risk contributors. (C) 2017 Elsevier Ltd. All rights reserved.