Data Resource Selection

The selection of data resources was a three-step proeess  

Titles of potential resourees were obtained by eonducting a literature search and an industry survey. Simultaneous literature searches were condueted by CCPS and SAIC. CCPS eoneentrated on obtaining CPI data resources while SAIC used a literature search conducted for the nuclear power reliability eommunity. These literature searches used in-house eompany, engineering, and public libraries and recommendations from members of the user eommunity. At the same time, a questionnaire was sent to professionals who eonduct CPQRAs. The survey requested information on the data resourees used by the companies and whether they had plant-speeific data that could be used by CCPS. Members of the CCPS Equipment Reliability Data Subcommittee were also asked to eompile lists of data resources with which they were familiar and which they had used for reliability or risk analyses. As a result, an extensive but not necessarily eomplete list of data resource titles was assembled. Any resources uncovered after the publisher s eutoff date and not reviewed have been included in Appendix D. 

The effort to collect CPI and general reliability data resources is considered by CCPS to be an ongoing projeet. Users of this book are encouraged to assist in this process by recommending additional resources to CCPS that ean be used for subsequent editions of this book. 

Subcommittee members selected those resources for further study whieh had titles suggesting that the resouree might contain equipment failure rate data. Copies of these resourees were then obtained and read. 

Summaries of the data resourees considered useful were prepared. Useful data was defined as information that was publicly availdWe, scientifically collected, had statistical merit, and could be used for CPQRAs. A list of rejected resources was retained to identify references for supplemental reading and to avoid review duplication when the anticipated second edition of this book is developed. In total, 72 resources were accepted, and over 200 references were rejected. 

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Plaspec Material Selection Database Data Resources, Inc. Dialog, STN detailed engineering and design data, chemical descriptions, and trade names for over 11,500 grades of plastics materials... 

Chapter 4—Data Bases, Sources, and Studies Summarizes and characterizes several generic data resources available to risk analysts and process engineers in the CPI. It includes a discussion of the resource search and selection process and the presentation format for the information on resources. 

Chapter 5—CCPS Generic Failure Rate Data Base Contains tables of generic process equipment reliability data that are structured by the CCPS Taxonomy. The data are extracted from data resources in Chapter 4. The chapter includes a discussion of the selection, treatment, and presentation of the data in the Tables. 

The selected data resources were sorted into the six categories, each presented in a section of this chapter. Resources are numbered consecutively within each category. The sections and categories are ... 

To help the reader select the appropriate data resource, an index precedes Sections 4.3 through 4.8. The index provides the source number within the section and the following set of data elements for each source title, industry, number and type of records, and data boundary. Appendix C contains additional information about the data elements presented in each data resource. It can also be used to help identify the resources which may provide data for a CPQRA. A discussion of the Appendix C Matrix and an explanation of data elements indexed is presented. After examining Appendix C and the pattern of data elements contained in the data resources, it is evident that equipment reliability data have been published in a variety of formats, often without any apparent effort to conform to a recognized standard for data specification. The CCPS Taxonomy and the raw data collection requirements in Chapter 6 present the basis for reliability data specification in future literature. 

This chapter contains tables of generic equipment failure rate data for some of the CPI equipment types listed in Appendix A, the CCPS Taxonomy, or in Appendix B, the Equipment Index. Section 5.1 on data selection explains how data were selected from resources and lists which resources in Chapter 4 were used to provide data. 

SAIC provided much of the data used in this book from its proprietary files of previously analyzed and selected information. Since these data were primarily from the nuclear power industry, a literature search and industry survey described in Chapter 4 were conducted to locate other sources of data specific to the process equipment types in the CCPS Taxonomy. Candidate data resources identified through this effort were reviewed, and the appropriate ones were selected. Applicable failure rate data were extracted from them for the CCPS Generic Failure Rate Data Base. The resources that provided failure information are listed in Table 5.1 with data reference numbers used in the data tables to show where the data originated. 

Guide to Selected Publicly Available Sleep-Related Data Resources (ed. NIH, National Heart, Lung and Blood Institute. National Center on Sleep Disorders Research), 2006. 

Usually, a hundred random initial structures are calculated to explore the conformational space and to eliminate the initial structure dependency. Subsequently, the final structures that equally satisfy the NMR data are selected (in most of the cases, by the target function) to compose an NMR structure ensemble (see Section 6.5). Actually, the number of initial structures and the size of ensemble had not been systematically determined or validated, but had been just limited by computational resources. A systematic validation for the number and size is discussed in Section 7. 

The SRS core consists of several programs which use the meta definition layer to allow the resources to be queried, data from these resources to be returned and, in the case of applications, other programs run on selected data. This includes the creation of indices from the data resources. It is the creation of these indices which makes the querying of the databases quick. 

Bioinformatics is a relatively new discipline that is concerned with the collection, organisatic and analysis of biological data. It is beyond our scope to provide a comprehensive overvie of this discipline a few textbooks and reviews that serve this purpose are now available (s the suggestions for further reading). However, we will discuss some of the main rnethoc that are particularly useful when trying to predict the three-dimensional structure and fum tion of a protein. To help with this. Appendix 10.1 contains a limited selection of some of tf common abbreviations and acronyms used in bioinformatics and Appendix 10.2 lists sorr of the most widely used databases and other resources. 

Measurement Selection The identification of which measurements to make is an often overlooked aspect of plant-performance analysis. The end use of the data interpretation must be understood (i.e., the purpose for which the data, the parameters, or the resultant model will be used). For example, building a mathematical model of the process to explore other regions of operation is an end use. Another is to use the data to troubleshoot an operating problem. The level of data accuracy, the amount of data, and the sophistication of the interpretation depends upon the accuracy with which the result of the analysis needs to oe known. Daily measurements to a great extent and special plant measurements to a lesser extent are rarelv planned with the end use in mind. The result is typically too little data of too low accuracy or an inordinate amount with the resultant misuse in resources. 

When more in-depth analysis of environmental fate is required, the analyst must select the modeling procedure that is most appropriate to the circumstances. In general, the more sophisticated models are more data, time, and resource intensive. 

Maximum usefulness and focus on end use. Remember that the PSM assessment phase is a means to an end the design and installation of a workable PSM system within your company. This means you may want to gather information that might otherwise not be included in comparable studies, audits, or reviews, e.g., data concerning resource allocations and requirements. If so, these considerations should be factored into both selection of your assessment method and the specific design of the tools you select. 

The selection of data from the resources available required decisions about the acceptable quality of the data and applicability of the data to the CCPS Taxonomy. Data from a resource was rejected by SAIC and the CCPS Subcommittee when ... 

Note SAIC has selected some data from resources 8.1 through 8.15 to construct its proprietary data files for use in performing PRAs. Relevant data from these files was used to construct the CCPS Generic Failure Rate Data Base. Accordingly, all usable data points contained in the resources used by SAIC may not be in the Data Tables in this book. 

Thus, tlie focus of tliis subsection is on qualitative/semiquantitative approaches tliat can yield useful information to decision-makers for a limited resource investment. There are several categories of uncertainties associated with site risk assessments. One is tlie initial selection of substances used to characterize exposures and risk on tlie basis of the sampling data and available toxicity information. Oilier sources of uncertainty are inlierent in tlie toxicity values for each substance used to characterize risk. Additional micertainties are inlierent in tlie exposure assessment for individual substances and individual exposures. These uncertainties are usually driven by uncertainty in tlie chemical monitoring data and tlie models used to estimate exposure concentrations in tlie absence of monitoring data, but can also be driven by population intake parameters. As described earlier, additional micertainties are incorporated in tlie risk assessment when exposures to several substances across multiple patliways are suimned. 

The lEP provides the monitoring fi amework to characterize the status and trends of Delta aquatic ecosystems. The lEP strives to provide information on the many factors that affect ecological resorrrces in the Delta. Key near-term goals for the lEP include (1) collecting and analyzing data needed to understand factors controlling the distribution and abundance of selected fish and wildlife resources, (2)... 

The user is able to customize the transform setup resource file to include as many custom scripts or unit transforms as desired for a particular application. In addition, the user can improvise operations on the fly. If the analyst decides to try plotting the data as a function of a reciprocal log, he takes the log and then reciprocal of the x-axis. If he decides to make this a regular option, he can enter it in the transform setup list under any name desired. Next time the transform option is called he will be able to select that operation by choosing it from the list under the name saved under. 

Increased computational resources allow the widespread application of fundamental kinetic models. Relumped single-event microkinetics constitute a subtle methodology matching present day s analytical techniques with the computational resources. The singleevent kinetic parameters are feedstock invariant. Current efforts are aimed at mapping catal) t properties such as acidity and shape selectivity. The use of fundamental kinetic models increases the reliability of extrapolations from laboratory or pilot plant data to industrial reactor simulation. 

There are various uncertainties in all the data influencing the selection of a set of equipment uncertainties in recipe parameters, product specifications, processing times and size factors, equipment availability, product requirements, and resource availability. Data needed for the evaluation of processing times and equipment sizes are never 100% reliable. The market situation when the plant is started up will certainly be different from the situation at the time of the definition of a production program for the plant. Unpredictable process disturbances may also occur. 

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