Probabilistic Risk Assessment analysis

Banks, W., Wells, J. E. (1992). A Probabilistic Risk Assessment Using Human Reliability Analysis Methods. In Proceedings of the International Conference on Hazard Identification and Risk Analysis, Human Factors, and Human Reliability in Process Safety. New York American Institute of Chemical Engineers, CCPS. 

Solomon, K.R., Giddings, J.M., and Maund, S.J. (2001). Probabilistic risk assessment of cotton pyrethroid.I. Distributional analysis of laboratory aquatic toxicity data. Environmental Toxicology and Chemistry 20, 652-659. 

The terminology used varies considerably. Hazard identification and risk assessment are sometimes combined into a general category called hazard evaluation. Risk assessment is sometimes called hazard analysis. A risk assessment procedure that determines probabilities is frequently called probabilistic risk assessment (PRA), whereas a procedure that determines probability and consequences is called quantitative risk analysis (QRA). 

Extensive general guidance for problem formulation exists already (e.g., USEPA 1998). This chapter reviews the main steps in problem formulation and discusses issues that require special consideration because of the use of uncertainty analysis in probabilistic risk assessment. 

Burmaster and Anderson (1994) have proposed 14 principles of good practice for using Monte Carlo techniques. They suggest that before an analyst undertakes a Monte Carlo risk assessment, the growing literature on probabilistic risk assessment should be thoroughly examined. Principles for a properly conducted Monte Carlo analysis have also been proposed by the USEPA (1997). 

HAZAN, on the other hand, is a process to assess the probability of occurrence of such accidents and to evaluate quantitatively the consequences of such happenings, together with value judgments, in order to decide the level of acceptable risk. HAZAN is also sometimes referred to as Probabilistic Risk Assessment (PRA) and its study uses the well-established techniques of Fault Tree Analysis and/or Event Tree Analysis ... 

David H. Johnson graduated from the Massachusetts Institute of Technology with an Sc.D. in nuclear engineering. Currently senior vice president and chief scientist of EQE International, Inc., Dr. Johnson has more than 20 years of experience in risk-based analysis for industry and government applications. His area of expertise is probabilistic risk assessments, including probabilistic modeling and investigation of the impacts of industrial projects. 

Hendley P, Holmes C, Kay S, Maund SJ, Travis KZ, Zhang M. 2001. Probabilistic risk assessment of cotton pyrethroids III. A spatial analysis of the Mississippi cotton landscape. Environ Toxicol Chem 20 669-678. 

In a probabilistic risk assessment, both variability and uncertainty in input variables can be taken into consideration. Variability represents the true heterogeneity in time, space, and of different members of a population. Examples of variability are interindividual variability in consumption and in sensitivity to, for instance, an allergen. Uncertainty is a lack of knowledge about the true value of the quantity. An example of uncertainty is associated with the limit of detection of an analytical method and the exploration of the threshold value outside the range of measurements. In contrast to the variability, uncertainty can be decreased, for example, by increasing the number of data points or using a more accurate method of analysis. 

Kruizinga, A.G., Briggs, D., Crevel, A., Knulst, A., Van den Bosch, L., and Houben, G.F. 2008. Probabilistic risk assessment model for allergens in food Sensitivity analysis of the minimum eliciting dose and food consumption. Food and Chemical Toxicology 46(5) 1437-1443 doi 10.1016/j.fct.09.109. 

Andres, T. H. and Hajas, W.C. (1993). Using iterated fractional factorial design to screen parameters in sensitivity analysis of a probabilistic risk assessment model. Proceedings of the Joint International Conference on Mathematical Models and Supercomputing in... 

Uncertainty analysis provides an evaluation of the key parameters that contribute to the uncertainty (e.g., variability and imprecision) involved in performing a risk assessment. Also known as probabilistic risk assessment, it provides information that enables decision makers to better understand the strengths, weaknesses, and assumptions inherent in the assessment and to evaluate the conclusions of the risk assessment accordingly. The result of the uncertainty analysis is a distribution of risks that a population may be potentially exposed to and thus, may be used by the risk manager to better understand the implication of the conclusions derived from the risk assessment and to support scientifically based and economically feasible hazardous waste management decisions. 

CPQRA A chemical process quantitative risk analysis is the process of hazard identification followed by numerical evaluation of incident consequences and frequencies, and their combination into an overall measure of risk when apphed to the chemical process industry. It is particularly applicable to episodic events. It differs from, but is related to, a probabilistic risk assessment (PRA), a quantitative tool used in the nuclear industry. 

Henley, E. J., and Kumamota, H. (1992), Probabilistic Risk Assessment Reliability Engineering, Design and Analysis, IEEE Press, New York. 

Toward the end of the Second World War, systems techniques such as fault tree analysis were introduced in order to predict the reliability and performance of military airplanes and missiles. The use of such techniques led to the formalization of the concept of probabilistic risk assessment (PRA). The publication of the Reactor Safety Study (NRC, 1975)—often referred to as the Rasmussen Report after the name of principal author, or by its subtitle WASH 1400—demonstrated the use of such techniques in the fledgling nuclear power business. Although WASH 1400 has since been supplanted by more advanced analysis techniques, the report was groundbreaking in its approach to system safety. 

The goal of human error quantification is to produce error probabilities, building on task analysis and error identification techniques to provide a probabilistic risk assessment (PRA). This provides numerical estimates of error likelihood and of the probability of overall likelihood of system breakdown. Quantification of error is the most difficult aspect of HRA, often heavily reliant on expert judgement, rather than the more rigorous approach of actual observation and recording of error frequencies. Such techniques are little used in healthcare but have been successfully applied to anaesthesia (Pate-Cornell and Bea, 1992). Nevertheless, some hospital tasks, such as blood transfusion, are highly structured and the quantification of errors probabilities would seem to be eminently feasible (Lyons et al, 2004). 

ASME RA-S-2002. 2002. Standard for probabilistic risk assessment for nuclear power plant applications, Section 4.5.5 Human reliability analysis . 

The sequences of events that may lead to vessel failure and their frequencies are determined from probabilistic risk assessment (PRA) analyses. The pressure, temperature and heat transfer coefficient time histories at the vessel inner surface are determined from thermal hydraulic analyses for the events identified by the PRA analyses. These time histories are used together with probabilistic fracture mechanics (PFM) analysis to calculate the conditional probability of RPV failure. Discussion of the methodology used to perform the PRA analyses and define the transient events and associated frequencies, and the thermal hydraulic analyses used to define the event pressure and temperature time histories are outside the scope of this chapter. Consequently, the remainder of this chapter focuses on the PFM evaluation assumptions and procedures. 

The output from this phase is a technically and economically well established Nuclear Island design and a preliminary non-site specific safety analysis report, accompanied by initial probabilistic risk assessment studies. 

At the same date also a preliminary cost evaluation was performed, and a preliminary probabilistic risk assessment and a safety analysis limited to mdn accidental scenarios were carried out. 

The NRC used probabilistic risk assessment (PRA) analysis in NUREG/CR-5102 (Reference 2) to evaluate three older operating pressurized water reactors for the effectiveness of proposed requirement changes in reducing the risk of an interfacing system LOCA, and for calculating the contribution of the ISL to the overall core damage frequency estimate. 

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