Data Resource Presentation

This section describes the format used to characterize the information in the data resources presented in Sections 4.3 through 4.8. The following data elements are used to present information in the resources summaries  

Title The name of the data resource as shown on the data source, as described in literature explaining data base services, or as conventionally cited when referencing a CPQRA or PRA. 

Sponsorlauthor The organization or individual(s) responsible for performing or funding the collection and analysis of the information in the resource. 

Number An identifying number based on the resource eategory and its sequential plaee-ment within the category in the range of 4.3-x to 4.8-x. 

Industry The industry from which the data originated, such as chemical process, power, nuclear, and offshore oil. 

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Appendix C—Matrix of Data Elements in Each Data Resource Presents the user with a more detailed summary of the data elements available from each data resource in Chapter 4. 

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. 

Seetion 4.1 deseribes how data resourees were ehosen for inelusion in this book. Section 4.2 describes the format used to present the information on the data resources, and Sections 4.3 through 4.8 present these data resources. Eaeh resouree section is preceded by an index of the resources presented in the section. 

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 ... 

Number and type of record The number of data points or tables of data presented in the resource or the number of events the data set reflects where available, the form in which the data are presented, such as failure rates or availability data, confidence intervals or error factors the raw data source used, sueh as surveys, plant records, tests, or judgment. 

Data boundary The equipment types for whieh data are presented in the resource (such as pipelines, valves or instruments) plant type (coal gasification facility, nuclear or ammonia plant, for example). 

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. 

Data reference number Refers to Table 5.1, which cites the data resources used to provide the failure rates presented in the data cell. 

The definition of the chemical processing industries (CPI) used in this table is the one used by Data Resources and Chemical Engineering in compiling their statistics on these industries. For several of the industries listed, only a part is considered to be in the CPI and data are presented for this part only. A list of the Standard Industrial Classification codes used to define the CPI for this table is given in Appendix C. 

The data requirements for approval of a drug are fairly uniform across the world, but there are major differences in the formats in which these data are presented in the dossiers to various regulatory authorities. This has meant ineffective use of effort and manpower. In order to reduce the time and resources needed to compile applications and to facilitate electronic submissions, regulatory reviews and communications and exchange of information between regulatory authorities, the International Conference... 

SAS The SAS Institute support organization has recently published resources for both validation (21) and actual IQ/OQ guidance (22). This should certainly be reviewed as a plausible starting point for the OQ. The same advice regarding analyses from standards organizations (15) applies. Please note that both S-Plus and SAS provide a wide range of capabilities for model creation, data analysis, presentation, and interfacing to databases and other software. It is incumbent on the user community to identify, at least initially, the capabilities that will be utilized in the user requirements documentation. Such software-specific capability should then be appropriately tested in the OQ. 

Data manipulation software is an important component of data management systems. As well as a vital component of the build process of integrated data resources, they are used in their APIs and user interfaces. In addition, Web services and the workflow software provide a means of finking applications with data resources to create complex workflows. These workflows may be used by biologists as stand-alone analysis packages. Again, we present here a review of the most currently used packages rather than a comprehensive list. 

In this section a selection of experimental data is presented which demonstrate that different experimental technique partly overlap or complement each other in their range of application. Cases of partial overlap should not be interpreted as needless developments but more as additional resources to obtain information on dynamics of adsorption. On the one hand, experimental data of the same surfactant solution are performed to demonstrate the agreement between different experimental techniques. Many examples of experiments with different techniques are given in a recent paper by Miller et al. (1994b, d). 

Looking at the different types of plastics and their applications large differences in the share of bio-based materials can be found. In 2010, the German Eederal Agency for Renewable Resources presented data for the German market in the year 2007, which was analysed in three different sectors thermoplastic and thermoset resins, elastomers, and man-made fibres (Figure 1.2). 

Commission on Professionals in Science and Technology. 1997. Professional Women Minorities A Total Human Resources Data Compendium. Washington, D.C. CPST. A comprehensive reference book of data on human resources presented in nearly 400 tables and charts, with breakouts by sex and race/ethnidty. Also included are data on enrollments, degrees, and the general, acadanic, and federal workforce by field and subfield (from The Commission on Professionals in Science and Technology (CPST) http //www.cpst.org/). 

Currently the online database consists of reaction details for 11 neutral species reacting with a broad selection of atomic metal ions, as well as a table of selected plasma matrix ion reactions. Also, various tables of thermodynamic data and physical property data are presented. The universal resource locator (URF) for the database Web page is http //www.chem.yorku.ca/profs/ bohme/research/rese arch.html. 

Although AES and XPS are widely regarded as mature surface-analytical tools, their applications are extremely varied and there can be many challenges to obtaining reliable information on the sample or process of interest We have presented information on data resources for AES and XPS with emphasis on databases available from NIST and information to determine the surface sensitivity of AES and XPS measurements. The key material parameter is the IMFP. Owing to elastic scattering of the signal electrons, other parameters needed for particular applications (the EAL for film-thickness measurements and quantitative analyses and the MED and ID for statements of surface sensitivity) need to be determined for the particular samples and conditions of measurement. Two NIST databases,... 

Detailed economics of individual industrial processes, including SCP processes, are usually regarded as confidential, out of fear that publication may lend advantage to competitors. In addition, economy of scale rule generally applies (that is as the production capacity increases, the cost of the product decreases), so that direct comparisons can only be made between systems of similar capacity. Some economic data on SCP processes have been published and are presented in the Resource Material at the end of this chapter. You should appreciate that the data are outdated by more than a decade, during which time substrate costs will have varied relative to each other, and technology will have improved. This means that the comparative costs presented in Table 4.13, for example, may not be now as presented there. Nevertheless the data presented do provide an outline of the economics of SCP production. The processes referred to in the Resource Material are not necessarily those mentioned in the text and so you may find some differences in detail. 

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. 

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