Risk Assessment and Reduction (Best Practices for Driving Down Construction and Operational Costs)

Support Task B, the Fire Probabilistic Risk Assessment (FPRA) Database, is an important organizational task that directly supports nearly all of the NUREG/CR-6850 FPRA development tasks (Reference 1). As a result, the database structure can become quite complex. Westinghouse has created a FPRA Database to support the Wolf Creek Generating Station (WCGS) FPRA development project and has acquired a number of lessons learned and best practices that can be applied to the development of a FPRA for any nuclear power plant. The purpose of this paper is to provide an overview of the WCGS FPRA Database structure and to share the lessons learned and best practices acquired during its development.

Reducing uncertainty bounds of two-dimensional hydrodynamic model output by constraining model roughness

10.5194/nhess-2018-369 ◽

2019 ◽

Author(s):

Punit Kumar Bhola ◽

Jorge Leandro ◽

Markus Disse

Keyword(s):

Risk Assessment ◽

Best Practices ◽

Confidence Interval ◽

Hydrodynamic Model ◽

Water Levels ◽

Model Output ◽

Two Dimensional ◽

Flood Risk Assessment ◽

Uncertainty Bound ◽

Measured Water

Abstract. The consideration of uncertainties in flood risk assessment has received increasing attention over the last two decades. However, the assessment is not reported in practice due to the lack of best practices and too wide uncertainty bounds. We present a method to constrain the model roughness based on measured water levels and reduce the uncertainty bounds of a two-dimensional hydrodynamic model. Results show that the maximum uncertainty in roughness generated an uncertainty bound in the water level of 1.26 m (90 % confidence interval) and by constraining roughness, the bounds can be reduced as much as 0.92 m.

How to assess future security threats to critical infrastructure systems? Lessons learnt and best practices from a security risk assessment of the ERTMS in Norway

Safety and Reliability – Theory and Applications ◽

10.1201/9781315210469-144 ◽

2017 ◽

Author(s):

M Maal ◽

K Brattekås ◽

K Nystuen ◽

R Windvik

Keyword(s):

Risk Assessment ◽

Best Practices ◽

Critical Infrastructure ◽

Security Threats ◽

Security Risk ◽

Infrastructure Systems ◽

Lessons Learnt ◽

Security Risk Assessment ◽

Critical Infrastructure Systems

Electricity Infrastructure Technical Security

Safety and Security Issues in Technical Infrastructures - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-7998-3059-7.ch001 ◽

2020 ◽

pp. 1-30

Author(s):

Martin Hromada ◽

David Rehak ◽

Neil Walker

Keyword(s):

Complex System ◽

Risk Assessment ◽

Risk Management ◽

Risk Analysis ◽

Best Practices ◽

Risk Identification ◽

Security Measures ◽

Identification Methods ◽

General Energy ◽

Electricity Infrastructure

In general, energy infrastructure is a basic but very complex system of elements, interconnections, functional inputs and outputs, which creates the need to break down subsystems, systems, and infrastructure areas. The aim of this chapter is therefore to discuss the possible implementation of approaches to risk assessment and risk management in relation to the application of technical security measures. This chapter of the book will therefore discuss risk analysis methods where the transition from general approaches to risk analysis, through risk identification methods and procedures and the assessment of major industrial and technological risks, to specific risk analysis methodologies for electricity infrastructures, will be presented. An important part of the chapter is also the introduction of practical approaches and methodologies that are accepted as “best practices” in connection with ensuring the technical security of electricity infrastructures.

A critical analysis of vulnerable populations in the Durham-York energy-from-waste incinerator human health risk assessment

10.32920/ryerson.14668179.v1 ◽

2021 ◽

Author(s):

Nazira Panchbhaya

Keyword(s):

Risk Assessment ◽

Best Practices ◽

Health Risk ◽

Pregnant Women ◽

Human Health ◽

Health Risk Assessment ◽

Vulnerable Populations ◽

Human Health Risk ◽

Human Health Risk Assessment ◽

Waste Incinerator

A health risk assessment is an essential tool to assess effects of polluting facilities on vulnerable populations. The Durham-York Energy Centre is the first incinerator built in Ontario in over 20 years and it has caused public controversy due to the health effects of such facilities. The proponents conducted a human health risk assessment (HHRA) according to best practices to effectively protect the public. The objective of this thesis is to establish a HHRA best practice framework for vulnerable populations, particularly pregnant women and fetuses, to confirm if best practices were achieved and determine whether this group was adequately considered in the HHRA. Analysis ultimately showed that the Durham-York HHRA complied with most best practices but the failure to identify pregnant women and fetuses as a vulnerable population lead to some important deficiencies and omissions in the assessment.

Reducing uncertainties in flood inundation outputs of a two-dimensional hydrodynamic model by constraining roughness

Natural Hazards and Earth System Science ◽

10.5194/nhess-19-1445-2019 ◽

2019 ◽

Vol 19 (7) ◽

pp. 1445-1457 ◽

Cited By ~ 2

Author(s):

Punit Kumar Bhola ◽

Jorge Leandro ◽

Markus Disse

Keyword(s):

Risk Assessment ◽

Best Practices ◽

Confidence Interval ◽

Hydrodynamic Model ◽

Water Levels ◽

Flood Inundation ◽

Two Dimensional ◽

Flood Risk Assessment ◽

Uncertainty Bound ◽

Measured Water

Abstract. The consideration of uncertainties in flood risk assessment has received increasing attention over the last 2 decades. However, the assessment is not reported in practice due to the lack of best practices and too wide uncertainty bounds. We present a method to constrain the model roughness based on measured water levels and reduce the uncertainty bounds of a two-dimensional hydrodynamic model. Results show that the maximum uncertainty in roughness generated an uncertainty bound in the water level of 1.26 m (90 % confidence interval) and by constraining roughness, the bounds can be reduced as much as 0.92 m.