Quantified risk analysis

These special analyses are prepared from the quantifiable risk analysis and a total risk scenario can be presented which depicts the estimated incident effects. An example is shown in Table 8. 

Dynamic event tree analysis method is a quantifiable risk analysis approach that treats time-dependent evolution of plant hardware states, process variable values, and... 

A Safety Case does not have to be quantified. Indeed, only half the European countries stipulate the use of Quantified Risk Analysis (QRA) in their Safety Case legislation (Rasche 2001). Nevertheless, it is difficult to demonstrate that risks have been reduced to the target ALARP threshold without some type of quantification. 

Usually the process industry level of risk for a particular facility is based on one of two parameters. The average risk to the individual i.e., Fatality Accident Rate (FAR) or Potential Loss of Life (PLL) or the risk of a catastrophic event at the facihty, a Quantified Risk Analysis (QRj ). The risk criteria can be expressed in two manners. Risk per year (annual) or facihty risk (lifetime). For purposes of consistency and familiarity, all quantifiable risks are normally specified as aimually. Where value analysis is appHed for cost comparisons of protection options, a lifetime risk figure is normally used to calculate the cost-benefit value. 

Assembly Variability Risks Analysis is key to better understanding the effeets of a eomponent s assembly situation on variability by quantifying the risks that various assembly operations inherently exhibit. By identifying eomponents with high assembly risks and potentially high failure eosts, further design effort is highlighted and performed in order to identify the assoeiated toleranees for the eomponent s optimal fit and funetion. 

This study investigated risks to the public from serious accidents which could occur at the industrial facilities in this part of Essex, U.K. Results are expressed as risk to an individual and societal risk from both existing and proposed installations. Risk indices were also determined for modified versions of the facilities to quantify the risk reduction from recommendations in the report. Nine industrial plants were analyzed along with hazardous material transport by water, road, rail and pipeline. The potential toxic, fire and explosion hazards were assessed for flammable liquids, ammonia, LPG, LNG, and hydrogen fluoride (HE). The 24 appendices to the report cover various aspects of the risk analysis. These include causes and effects of unconfined... 

The most important part of the approach adopted in this book is concerned with the analysis of dangerous reactions , in this chapter a forecast analysis of risks that can be caused by an interaction between two or several chemicals, or between chemicals and materials, is proposed. It will be a general approach, and its purpose is to develop a range of tools in order to forecast and quantify risks due to substance reactivity in deliberate or accidental contact. 

Logic Model Methods The following tools are most commonly used in quantitative risk analysis, but can also be useful qualitatively to understand the combinations of events which can cause an accident. The logic models can also be useful in understanding how protective systems impact various potential accident scenarios. These methods will be thoroughly discussed in the Risk Analysis subsection. Also, hazard identification and evaluation tools discussed in this section are valuable precursors to a quantitative risk analysis (QRA). Generally a QRA quantifies the risk of hazard scenarios which have been identified by using tools such as those discussed above. 

The facility is subjected to a process hazard analysis commensurate to the level of hazard the facility represents (i.e., Checklist, PHA, HAZOP, What-If review, Event Tree, FMEA, etc.). The results of these analyses are fully understood and acknowledged by facility management. Where high risk events are identified, quantifiable risk estimation and effects of mitigation measures should be evaluated and applied if productive. 

Evaluation of the cleanliness of the equipment involves measuring the residue left on a known area of the equipment. This is done by marking off a known area and then swabbing the area with a good solvent for the residue. Laboratory measurements quantify the amount of residue in the swabbed area after cleaning so that the quantity of residue left in the equipment can be estimated. If the estimate of the residue is below the maximum calculated from the risk analysis above, then the equipment has been adequately cleaned. 

Smith and Cohen did not perform a detailed risk analysis to quantify the boundaries of the different waste classes. However, as an example, if concentration is used as the measure of radioactivity in waste, the following 239Pu-equivalent concentrations (concentrations for which the hazard would be equivalent to that of 239Pu) were suggested for use in quantifying the different waste classes (1) BRC waste would be any waste which, after 10 y of decay, has an equivalent concentration less than 40 MBq m 3 (2) low-level waste would be any non-BRC waste which, after 100 y of decay, has an equivalent... 

Quantitative Risk Analysis (QRA) models the events, incidents, consequences, and risks, and produces numerical estimates of some or all of the frequencies, probabilities, consequences, or risks.38 55 QRA can be done at a preliminary level or a detailed level, and in all cases may or may not quantify all events, incidents, consequences, or risks.56 QRA is the art and science of developing and understanding numerical estimates of the risk associated with a facility or an operation. It uses highly sophisticated but approximate tools for acquiring risk understanding. 

Methods such as HAZOP, FMEA, and quantitative risk analysis that are used to analyze and quantify process hazards... 

A modified method of quantifying risk through the use of systems analysis has been adopted by the process industries. The technique is known as LOPA (layers of protection analysis) provides an order of magnitude estimation of risk (details of the method are provided in Chapter 15). 

In accounting explicitly for the variance - the uncertainty associated with the exposure levels and the toxic concentrations - Bamthouse and Suter (1986) described a probabilistic method for quantifying risk to aquatic species, the analysis of extrapolation error (AEE). Based on the assumption that environmental and effective concentrations are log-normally distributed (Figure 9.2), the overlap of the corresponding probability density functions provides the measure of risk. 

FayerweathepW.E. Collins, J.J Schnatter, A.R. Hearne, ET. Menning, R.A Reynefl, D.R Quantifying Uncertainty in a Risk Assessment Using Human Data. Risk Analysis, vol. 19, n. 6, 1999. 

Its approach is based on the assumption that reliability analysis cannot give evidence to all risks for workers, especially inside SMEs because of their typical features as regards organization, layout and processing. So, its main target is to provide workers consciousness about risks and the necessary devices for their own safety following this approach, it s clear that data related to system faults are inadequate to quantify risks. 

Figure 3. Flood risk analysis quantifying loss of life (after Jonkman 2007)... 

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