Quantitative risk analysis example

Health and Safety Outcome Measures Besides the NFDL IR measure used above in the quantitative risk analysis example, other frequency-based IR measures include ... 

Inventory in transfer lines can be a major risk. For example, a quantitative risk analysis of a chlorine storage and supply system... 

Fault tree analysis (FTA) and event tree analysis (ETA) are the methods most commonly applied quantitatively. Since they only address the likelihood of undesired events, these methods are often combined with consequence severity calculations in a quantitative risk analysis, as described by CCPS (1999b). Layer of protection analysis (LOPA) uses a semiquantitative, order-of-magnitude approach. It is documented with worked examples in CCPS (2001b). 

The methodology outlined in this chapter follows that in Cuidelines for Chemical Process Quantitative Risk Analysis (CCPS, 2000). NFPA 550 Cu/de to the Fire Safety Concept Tree provides another example of fire risk assessment. There are three keys to a successful fire risk assessment ... 

At this point in the example, the eompany-wide reviews eompleted in Chapters 2 and 3 are replaeed with a first-level qualitative risk analysis foeused on the transportation aetivities of a single XYZ Cheinieal faeihty. To eontinue the example, a single XYZ Chemical facility is selected. This facility will continue to be evaluated as the level of analysis detail increases from qualitative to semi-quantitative in this chapter to quantitative risk analysis (Chapter 5). As the level of detail increases, the analysis will be directed at specific questions that remain following each level of analysis. This facility will continue to be the focus of Chapter 6 where the security vulnerabihty of the hazardous materials in transit is evaluated. Chapter 7 where risk reduction options are evaluated, and Chapter 8 where the ongoing management of risk continues in the future. 

XYZ Chemical Example—Semi-Quantitative Risk Analysis... 

Due to the potential impacts and safety record of some of the carriers, corporate commissioned a QRA of all pesticide tank truck operations that were escalated as a higher-risk issue from the semi-quantitative risk analysis. The focus of this example is on a single origin-designation pair from the Asian facility. 

Figure 7.3 shows another example that compares a base case level of operational risk with two risk reduction options using quantitative risk analysis techniques. This F-N curve from Chapter 5 illustrates that both options reduce the likelihood and the potential maximum consequence(s). However, Option 2 results in the greatest risk reduction benefits of lower maximum impacts and lower frequencies. Since both options result in risk reduction, as compared to the baseline, an additional cost-benefit analysis would be required to determine which (if either) of these two options should be considered for implementation. 

In addition to the risk reduction benefits, the costs of risk mitigation options need to be evaluated. Due to the uncertainties associated with semi-quantitative and quantitative risk analysis results, a relative risk comparison, as compared to absolute measures of risk and benefits, is recommended. To conduct this type of relative comparison, incremental risk analysis can be used to evaluate the cost effectiveness of risk mitigation options, or determine the optimal combination of risk mitigation options. Figure 7.4 illustrates example results of this type of analysis, and uses the options from the F-N curve in Figure 7.3 as the basis for comparison. 

Thanks in large part to work done in several of the programs in technology and policy, today modern policy analytic work is much improved, both in terms of the way in which problems are framed and the analytical tools that are employed, than was the case 30 years ago. For example, techniques such as decision analysis, the systematic characterization and analysis of uncertainty, and methods in quantitative risk analysis, that were pioneered in several of these programs, are now almost ubiquitous. 

HSE assessments have a long tradition within the oil-and gas industry. These assessments use a wide range of methodologies, from the strict quantitative methods such as QRA (Quantitative Risk Analysis) and FMECA (Failure Mode Effect and Criticality Analysis) to the more qualitative methods such as HAZOP (HAZard OPerability analysis). Most methods combine qualitative and quantitative data and approaches. For example, an FMECA basically uses generic failure data, expert judgments are likewise important. 

In most quantitative risk analysis methods (Uijt de Haag 2006, purple book 1999), persons present in the hazardous area are assumed to be exposed for a fixed amount of time. Assumptions for fixed exposure times are 30 minutes for a toxic exposure and 20 seconds for exposure to heat radiation. Furthermore, persons are assumed to stay on the same place. The reahty is different in case of an emergency, every person capable of escape will try to rescue himself. In case of a toxic release it is possible that a safe location (for example inside a building) is reached within the prescribed 30 minutes. On the other hand, in case of fire in crowded places, it can be expected that people are unable to escape within 20 seconds. 

A bottleneck is a narrowing of the evacuation route, which causes a decrease of the evacuation velocity and may cause a congestion. Examples of bottlenecks are doors, stairs, aisles. With respect to the self-rescue model, where self-rescue of outdoor situations is considered, focus is on crowded locations, such as railway stations or public events. Within a quantitative risk analysis the location of the bottleneck is determined and (a part of) the population grid is assigned to be captured inside an area in front of a bottleneck, which means that the persons inside this (part of the) population grid, need to escape through the bottleneck, before being able to continue their escape (see Fig. 5). 

In this paper we have a special focus on health care applications. Decisions in health care are influenced by a growing complexity, better knowledge and higher demands from the patients, hospitals and society as a whole. Every day there are a lot of difficult decisions to be made in health care, and the need for decision-support is large. Risk assessments provide such decision support and recently we have seen several attempts to use such assessments in this sector (Marx Slonim 2003, Wreathall Nemeth 2004, Battles Kanki 2004, IHI 2008, Dhillon 2003). Our main interest here is the Probabilistic Risk Analysis (PRA)/Quantitative Risk Analysis (QRA), which aims at presenting a comprehensive quantitative description of the risk associated with the activity. These analyses are used to analyse complex systems where there is a lack of data to accurately predict the future performance of the system, for example a complex surgical operation. 

We will use an example to illustrate the main features of the semi-quantitative risk analysis approach. The aimofthe analysis is to estabhsh a risk picture covering all the dimensions (A, C, C, U, P, S, K). 

ANSI/ISA-84.00.01-2004-1, Clause 8, and ANSI/ISA-84.01-1996 address the Hazard and Risk Analysis (H RA). Both mandate the need for H RA, but neither defines how to specifically execute the H RA or to identify process risk. Rather than providing specific requirements, ANSI/ISA-84.01-1996 referred to OSHA 1910.119 and the CCPS books. Guidelines for Hazard Evaluation Procedures, Guidelines for Chemical Process Quantitative Risk Analysis, and Guidelines for Safe Automation of Chemical Processes, for guidance on determining the SIS requirements. ANSI/ISA-84.01-1996, Annex A, also provided examples of H RA techniques commonly used in 1996. 

CPQRA is a probabilistic methodology that is based on the NUREG procedures. The term chemical process quantitative risk analysis (CPQRA) is used throughout this book to emphasize the features of this methodology as practiced in the chemical, petrochemical, and oil processing industries. Some examples of these features are... 

The original CCPS book Guidelines for Quantitative Risk Analysis (1989) CPQRA Guidelines) contained a long chapter on consequence analysis. When CCPS decided to update the CPQRA Guidelines in 1995, preparing a second edition for publication, there were major revisions and additions to the material on consequence analysis. These revisions included more detail on many of the consequence models, additional and updated models which reflect the current state of the art, a more complete presentation of the fundamentals of many of the models, and more worked examples. Spreadsheet solutions for all of the worked examples have also been provided. 

Fault tree analysis is based on a graphical, logical description of the failure mechanisms of a system. Before construction of a fault tree can begin, a specific definition of the top event is required for example the release of propylene from a refrigeration system. A detailed understanding of the operation of the system, its component parts, and the role of operators and possible human errors is required. Refer to Guidelines for Hazard Evaluation (CCPS, 1992) and Guidelines for Chemical Process Quantitative Risk Assessment (CCPS, 2000). 

The deviation scenarios found in the previous step of the risk analysis must be assessed in terms of risk, which consists of assigning a level of severity and probability of occurrence to each scenario. This assessment is qualitative or semi-quantitative, but rarely quantitative, since a quantitative assessment requires a statistical database on failure frequency, which is difficult to obtain for the fine chemicals industry with such a huge diversity of processes. The severity is clearly linked to the consequences of the scenario or to the extent of possible damage. It may be assessed using different points of view, such as the impact on humans, the environment, property, the business continuity, or the company s reputation. Table 1.4 gives an example of such a set of criteria. In order to allow for a correct assessment, it is essential to describe the scenarios with all their consequences. This is often a demanding task for the team, which must interpret the available data in order to work out the consequences of a scenario, together with its chain of events. 

A systems hazards analysis (SHA) is a systematic and comprehensive search for and evaluation of all significant failure modes of facility systems components that can be identified by an experienced team. The hazards assessment often includes failure modes and effects analysis, fault tree analysis, event tree analysis, and hazards and operability studies. Generally, the SHA does not include external factors (e.g., natural disasters) or an integrated assessment of systems interactions. However, the tools of SHA are valuable for examining the causes and the effects of chemical events. They provide the basis for the integrated analysis known as quantitative risk assessment. For an example SHA see the TOCDF Functional Analysis Workbook (U.S. Army, 1993-1995). 

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