Likelihood categories

Qualitative frequency evaluation involves defining broad categories of event frequency, which can be used to assess the likelihood of occurrence of a specific incident outcome (consequence). These categories cover a full spectmm of frequencies, from those representing events that are likely to those that are highly unlikely. Definitions of likelihood categories vary, but Table 5.4 presents a typical list and definitions. 

As discussed in Section 5.3, site-specific conditions, including previous incident history, must be considered when assigning likelihood categories. The assessment team must have a thorough working knowledge of plant systems and practices to make a reasonable judgment of event likelihood. 

Most deviations (82%) belong to the high likelihood category, i.e. they are frequently re-occurring deviations. Most of these deviations (71 %) have low consequences implying that they are signs indicating events that are not directly related to safety consequences. 

Using the consequence and likelihood categories, risk matrix, and risk evaluation criteria, the team reviewed three release scenarios (small, medium, and large) for the segments identified for each of the chemical movements. The result of the semi-quantitative risk estimation for this facility s hazardous material transportation operation is detailed in Table 4.12. From this results table, the following are determined ... 

The resulting matrix is a simple way to communicate and evaluate risk in a repeat-able and consistent manner. At the point of evaluating risk each unique severity category can be paired with a unique likelihood category and this combination points to a non-unique clinical risk category. Once embedded, this simple grid acts... 

Awareness of bias - Bias is discussed in Sect. 15.2. Given that the selection of a severity and likelihood category involves a judgement call, there is an opportunity for bias to contaminate the evaluation. Should an organisation have an agenda for the clinical risk evaluation to have a particular outcome, it can be tempting to express a hazards in terms of a special case. It is therefore essential... 

Note the position within the listings of Even chance could happen in the Likelihood category and Annually in the Frequency of exposure category. A computation appears later in which these same ratings are used. 

Figure 2.37 presents the HRI matrix concept for establishing the level of potential mishap risk presented by a hazard. It can be seen from this figure that the HRI matrix concept essentially involves one matrix and three tables. The HRI matrix is the main component, which is based upon the combination of the hazard/mishap likelihood on one axis and hazard/mishap severity on the other axis. The hazard/mishap likelihood category is determined from the criteria stated in the Likelihood Table and the hazard/mishap severity category is determined from the criteria stated in the Severity Table. The Risk Level Table ranks each hazard into one of four risk levels (high, serious, medium, or low) based on the particular HRI matrix indices designated for the particular level. 

The tendency is greatest, however, where pressures are close to atmospheric and "superheat" relative to atmosphere is least. Pipestill atmospheric towers and cat unit fractionators tend to fall in this category. Some operators consider that the likelihood is great that calculated condensation (dew) will coalesce to droplets which will gravitate (rain) when the partial pressure of condensibles at the dew point exceeds 1/3 atmosphere. With this factor and environmental protection in mind, some plants have diverted such releases into closed systems. Generally, however, this has not been of sufficient concern, and such releases have been treated as though they were all vapor. 

Performance-influencing factors are general conditions which increase or decrease the likelihood of specific forms of error. They can be broadly grouped into the following categories ... 

This analysis is applied to each operation at the particular level of the HTA being evaluated. In most cases the analysis is performed at the level of a step, for example. Open valve 27B. For each operation, the analyst considers the likelihood that one or more of the error types set out in classification in Figure 5.7 could occur. This decision is made on the basis of the information supplied by the PIF analysis, and the analyst s knowledge concerning the types of error likely to arise given the nature of the mental and physical demands of the task and the particular configuration of PIFs that exist in the situation. The different error categories are described in more detail below ... 

Ideally, data bases will have been developed within a company such that predetermined PIFs are associated with particular categories of task. If this is not the case, the analyst decides on a suitable set of PIFs. In this example, it is assumed that the main PIFs which determine the likelihood of error are time stress, level of experience, level of distractions, and quality of procedures. (See Section 5.3.2.6.)... 

It is the purpose here to briefly review the state of the art of the most important electrochemical methods for medical applications, and report on the status and viability of currently emerging research. To accomplish this, electrochemical methods have been divided into four basic categories. The first two categories (Sect. 2 and 3) represent the relatively mature contribution of electrochemistry to medical diagnostics. Sections four and five deal largely with developments in electrochemistry which have not yet achieved commercialization, but which have the greatest likelihood of future success. There are, of course, some minor areas of research which have been intentionally omitted because of space limitations. Much of this work can be found in the references provided in the text. 

The number of Sis, present in today s chemical process industry is overwhelming as discussed by Tixier (Tixier et al., 2002). These indicators are categorized in several ways in literature, for example pro-active versus reactive indicators. Many of these categories are not unambiguous. Some authors, like Kletz (Kletz, 1998) define proactive as prior to the operational phase of an installation while other authors, like Rasmussen et al. (Rasmussen et al., 2000), define pro-active as prior to an accident. In this thesis two categories of indicators are used, i.e. pro-active and reactive indicators. Here the definition of Rasmussen (Rasmussen et al., 2000) is adopted, who defined pro-active indicators as indicators before an accident and reactive indicators as indicators after an accident. Moreover, the pro-active indicators are divided into predictive and monitoring indicators. The monitoring indicators use actual events as a measure for the likelihood, while the predictive indicators predict the likelihood. 

Deviations belonging to the category high likelihood and low consequence are rarely taken into account when constructing pro-active Sis, even though it is this... 

The most commonly used techniques for estimating trees for sequences may be grouped into three categories (1) distance methods, (2) maximum parsimony, and (3) maximum likelihood based methods. There are other methods but they are not widely used. Further, each of these categories covers many variations and even distinct methods with different properties and assumptions. These methods have often been divided different ways (different from the three categories here) such as cladistic versus phenetic, character-based versus non-character-based, method-based versus criterion-based, and others. These divisions may merely reflect particular predjudices by the person making them and can be artificial. 

Generally, compartment fire simulation models predict the fire development in a compartment under varying conditions. These types of simulations are useful for estimating tenability criteria, thermal insult to the compartment, and the likelihood of fire spread from one compartment to another. These types of models can be further subdivided into three categories based on their approach to simulating the fire environment the zone model, the field model, and the post-flashover model. 

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