Consequence Estimation

For any specific incident Uiere will be an infinite number of incident outcome cases that can be considered. There is also a wide degree of consequence models which can be applied. It is imporumt, Uierefore, to understand Uie objective of Uie study tin order to limit Uie nmnber of incident outcome cases to those which satisfy that objective. An example of variables Uiat can be considered is as follows  

Quality, magnitude, and duraUon of the release Dispersion parameters -Wind speed -Wind direction -Weather stability 

Following an accident, tlie effects on individuals able to escape or remain in a shelter (or equivalent) differ from tliose for people in Uie open. Factors to consider in relation to building types and human beliavior include  

The nature of the hazard considering both intensity and duration. Shelters vary in tlic degree of protection provided. For tliermal tmd toxic hazards, shelters can liave a beneficial effect. However, for explosions, tlie hazard maybe greater because of the possibility of the building collapsing. 

The nature of the hazard considering its degree of toxicity and its warning properties. A release of carbon monoxide provides no warning while a release of some amine nornuilly provides a strong odor at concentrations well below hanuful levels. 

Following an accident, tlie effects on individuals able to escape or 

Factors to consider in relation to building types and human beliavior include  

---

Consequence Estimation References Guidelines for- Use of Vapor Cloud... 

Consequence Estimation Given that an incident (release of material or energy) has been defined, the consequences can be estimated. The general logic diagram in Fig. 26-6 illustrates these calculations for the release of a volatile hazardous substance. 

Sometimes the expected consequences of an accident alone may provide you with sufficient information for decision-making purposes. Conventionally, the form of these estimates will be dictated by the purpose (concern) of the study (safety, economics, etc.). Absolute consequence estimates are best estimates of the impacts of an accident and, like frequency estimates, may have considerable uncertainty. Table 4 contains examples of typical consequence estimates obtained from QRA. These examples point to the difficulty in comparing various safety and economic results on a common basis—there is no common denominator. 

Managers can use QRA to study small-scale, as well as large-scale, problems. For example, a QRA can be performed on a small part of a process, such as a storage vessel. Depending upon the study objectives, a complete QRA (both frequency and consequence estimates are made) could require as little as a few days to a few weeks of technical effort. On the... 

Emergency If the QRA involves consequence estimation, the team will proba-... 

Hazard identification builds the foundation on which subsequent quantitative frequency and/or consequence estimates are made. Many companies have been using the hazard identification techniques listed in Figure 7 for... 

Like frequency estimates, consequence estimates can have very large uncertainties. Estimates that vary by orders of magnitude can result from (1) basic uncertainties in chemical/physical properties, (2) differences in average vs. time-dependent meteorological conditions, and/or (3) uncertainties in the release, dispersion, and effects models. Some... 

Once frequency and consequence estimates are generated, the risk can be evaluated in many ways. It is essential that the large number of fre-quency/consequence estimates from a QRA be integrated into a presentation format that is easy to interpret and use. The presentation format you select will depend on the purpose of the QRA and the risk measure of interest. 

Another way of interpreting absolute risk estimates is through the use of benchmarks or goals. Consider a company that operates 50 chemical process facilities. It is determined (through other, purely qualitative means) that Plant A has exhibited acceptable safety performance over the years. A QRA is performed on Plant A, and the absolute estimates are established as calibration points, or benchmarks, for the rest of the firm s facilities. Over the years, QRAs are performed on other facilities to aid in making decisions about safety maintenance and improvement. As these studies are completed, the results are carefully scrutinized against the benchmark facility. The frequency/consequence estimates are not the only results compared—the lists of major risk contributors, the statistical risk importance of safety systems, and other types of QRA results are also compared. As more and more facility results are accumulated, resources are allocated to any plant areas that are out of line with respect to the benchmark facility. 

Emergency response personnel If the QRA involves consequence estimation, the team will probably need data on the emergency response procedures and capabilities. For example, how quickly could the neighborhood be evacuated, what fraction of the release would be neutralized, and can firefighters extinguish a blaze in that location Expect to commit a few staff-days of effort over the life of the project. 

High react is o> and extreme imucity nl these compounds have limited the ainouni of experimental work on physical properties Consequently. estimation methods have been used extensively to po vide data over a wide temperature range... 

Which of the three criteria is chosen is very much dependent upon the characteristics of the system. If the controlled variable response exhibits large deviations (i.e. there are large errors), then use of the ISE function will lead to the determination of controller parameters which will give the best control characteristics. As the errors are squared, minimisation of the ISE will be particularly effective. Use of the IAE function will suppress small errors better than will the ISE criterion as it treats errors of all magnitudes in a uniform manner. The ITAE function should be employed where there are persistent errors since this will lead to an amplification of the effect (and thus a weighted influence on the consequent estimation of the controller parameters) of the latter at large values of f. 

To solve this problem, some assumptions should be made on the relationship between the error of the regression line and the concentration. As a rule, one assumes that the error of the regression line is proportional to the concentration. The variance function Var(X) is obtained by plotting the standard error vs. the concentration. The function is consequently estimated with the least-squares method Var(X) = Sl = (c -T d cone)2. An alternative approach is described in the ISO 11483-2 standard, which uses an iterative procedure to estimate the variance function [18]. 

---