Process equipment reliability database

CCPS G-7. Process Equipment Reliability Database. American Institute of Chemical Engineers, Center for Chemical Process Safety, New York. 

The AIChE (American Institute of Chemical Engineers) CCPS (Center for Chemical Process Safety) Process Equipment Reliability Database (PERD) initiative has rigorously identified and documented failure modes for instrument loops, which encompass control, indication, alarm, and automatic protection (Ref. 3). An excerpt from those lists shows a comparison of failure modes applicable to BPCS versus SIS (Figure 2-2). The ability of operations personnel to detect these failure modes is also quite different. 

One effort in the right direction is the PERD (Process Equipment Reliability Database) initiative (Ref. 10) from the Center for Chemical Process Safety (CCPIS) of the AIChE (www.aiche.org/ccps/perd/). That... 

Another challenge is the continual effort necessary to track changes to the device, so that it can be determined when prior-use evidence is no longer applicable. An owner/operator may only have a limited number of devices in a specific application, which prevents the calculation of any statistically viable failure rates. This is why it is important for owners/operators to participate in industry organizations, such as OREDA, NAMUR, and CCPS Process Equipment Reliability Database (PERD). 

One of the most popular failure rate databases is the OREDA database (Ref. 4). OREDA stands for "Offshore Reliability Data." This book presents detailed statistical analysis on many types of process equipment. Many engineers use it as a source of failure rate data to perform safety verification calculations. It is an excellent reference for all who do data analysis. 

The results of simulation models are strongly affected by the particular assumptions made in the plant model and by the model parameters. This is the reason why these parameters should be fitted to reliable data describing simplified systems, for example, binary mixtures. Regressing parameters to observations on the complex target system is usually not feasible, as the influence of the assumptions made on the performance of the equipment (e.g., tray efficiency in distillation) is not sufficiently isolated from the influence of the model parameters. The experimental data for the isolated subsystems are the key for a successful process model. They contain all the information about the process model, the model parameters are only a representation. If new data become available, the model can be upgraded by extending the database and perform a new regression. 

Life cycle assessment of SOFC technology is still uncommon due to the relatively early stage in technical development. However, several studies have been performed since the end of the 1990s. Since there is a lack of standard commercial equipment that could serve as a basis and reference point for analysis, LCA studies mostly refer to hypothetical concepts and/or extrapolate from laboratory and early market prototypes to commercial units. While the first studies had only little access to operation data at aU (for the fuel cell system itself but also for production processes), the main effort was set in the assessment of inventory data using assumptions, simplifications, and correlations [79, 80]. The main outcomes of these studies were the identification of weak points and the setting of benchmarks for further development. With more information about fuel cells available today and a simultaneous advancement in LCA methodology, the studies became more reliable and detailed, regarding system description [81] as well as the assessment of environmental impacts coimected with inputs and outputs [82]. Especially the extensive data of these two studies found their way to commercial databases for LCA [83] and thereby became available to LCA practitioners. In 2005, the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU)... 

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