Plant data base

Perry, S. G., Paumier, J. O., and Burns, D. J., Evaluation of the EPA Complex Terrain Dispersion Model (CTDMPLUS) with the Lovett Power Plant Data Base, pp 189-192 in "Preprints of Seventh Joint Conference on Application of Air Pollution Meteorology with AWMA," Jan. 14-18,1991, New Orleans, American Meteorological Society, Boston, 1991. Bums, D. ]., Perry, S. G., and Cimorelli, A. ]., An advanced screening model for complex terrain applications, pp. 97-100 in "Preprints of Seventh Joint Conference on Application of Air Pollution Meteorology with AWMA," Jan. 14-18, 1991, New Orleans, American Meteorological Society, Boston, 1991. 

Heats of reactions were estimated from heats of formations and chemical compositions of feed and product using standard procedures. For REY catalysts, we estimated approximately 130 Btu/lb heat of reaction. The heat of reaction was close to 200 Btu/lb for USY catalysts. These values are in close agreement with reported data (21)- The activation energies for different catalyst types were estimated from our extensive pilot plant data base, and found to be a weak function of catalyst type. The adsorption constants and other kinetic parameters used in these simulations were fitted to a large in-house data base. Typical parameter values are reported in Tables III and V. The kinetic parameters (k-, and Aj) are a strong function of catalyst used, whereas the adsorption parameters were found to be relatively insensitive. One could estimate these parameters even from a limited data base as illustrated below for Catalyst D. 

FIGURE 2. Plant Data Base multi-windows updating system. 

If, during the verification process, ERICE needs the value of a parameter that is not contained in the Plant Data Base, it asks the operator for such value. 

Figure 18.7 for a system defined as stainless steel piping in BWRs. It is seen that the dispersion of the plant data based on such a general definition of the system makes it impossible to predict accurately any future trends. 

In addition, NDT plays an important part in industrial maintenance. During plant shutdowns for instance, many thousands of ultrasonic wall thickness measurements are taken on piping, vessels, furnace tubes etc. All these thickness readings have to go into extensive data bases, and this process is, thanks to modem computers and data loggers, ever more automated. 

Numeric-to-numeric transformations are used as empirical mathematical models where the adaptive characteristics of neural networks learn to map between numeric sets of input-output data. In these modehng apphcations, neural networks are used as an alternative to traditional data regression schemes based on regression of plant data. Backpropagation networks have been widely used for this purpose. 

Implementation Issues A critical factor in the successful application of any model-based technique is the availability of a suitaole dynamic model. In typical MPC applications, an empirical model is identified from data acquired during extensive plant tests. The experiments generally consist of a series of bump tests in the manipulated variables. Typically, the manipulated variables are adjusted one at a time and the plant tests require a period of one to three weeks. The step or impulse response coefficients are then calculated using linear-regression techniques such as least-sqiiares methods. However, details concerning the procedures utihzed in the plant tests and subsequent model identification are considered to be proprietary information. The scaling and conditioning of plant data for use in model identification and control calculations can be key factors in the success of the apphcation. 

The first two examples show that the interaction of the model parameters and database parameters can lead to inaccurate estimates of the model parameters. Any use of the model outside the operating conditions (temperature, pressures, compositions, etc.) upon which the estimates are based will lead to errors in the extrapolation. These model parameters are effec tively no more than adjustable parameters such as those obtained in linear regression analysis. More comphcated models mav have more subtle interactions. Despite the parameter ties to theoiy, tliey embody not only the uncertainties in the plant data but also the uncertainties in the database. 

The external events PSA was based on standard methods used for commercial reactor PSAs, Fire risk was estimated from commercial nuclear power plant data combined with industrial fire information. The seismic hazard was evaluated using a combination of the EPRI and LLNL ( UREG/CR-.3250) databases. Wind hazards were analyzed by EQE, Inc., using NRC-based nicihodulogy. 

Williams, J. C., 1989, A Data-Based Method for Assessing and Reducing Human Error to Improve Operational Performance, Proceedings of the 1988 IEEE Fourth Conference on Human Factors and Power Plants, Monterey, CA, June 5-9, pp 436-450, IEEE. 

They attend to and control different technical plants and systems, and they have to deal with all processes required. In data-based plants, controllers usually communicate with one or several local or central computers. 

Chapter 7—Failure Rate Data Transfer Provides a form to facilitate the transfer of plant-specific data to the CCPS Data Base or to combine it with other generic data. 

When plant-specific data are required. Chapter 6 discusses how to collect and treat the data so that the resulting failure rates can be used in a CPQRA or be combined with the data in the CCPS Generic Failure Rate Data Base. Chapter 7 provides a form that can be used to transfer these data to CCPS s Generic Failure Rate Data Base. 

However, the data that are contributed to a generic failure rate data base are rarely for identical equipment and may represent many different circumstances. Generic data must be chosen carefully because aggregating generic and plant-specific data may not improve the statistical uncertainty associated with the final data point, owing to change in tolerance. 

Drago, J. P., Borkowski, R. J., Pike, D. H., and Goldberg F. F. The In-Plant Reliability Data Base for Nuclear Power Plant Components Data Collection and Methodology Report. NUREG/ CR-2641, ORNL/TM-9216, January 1985. 

Development of an Improved Liquified Natural Gas Plant Failure Rate Data Base... 

SAIC Data Base Nudear Plant maintenance and repair records by system and component type selected basic event failure rates and unavailability Data mainly for pumps, valves, diesels, batteries, chargers, and heat exchangers 76. 

Tils In-Plant Reliability Data Base lor Nudear Plant Components... 

The German Gesellschaft fur Reaktorsicherheit (GRS) has a private arrangement with Rheinische Westalisches Elekrizitatswerke (RWE) to compile reliability data from an operating power plant, Biblis B. The data base contains failure rate, maintenance, and operational event data. External event data (floods, earthquake, fire, etc.) are compiled through a separate utility-sponsored data base. The data base provides information on repair and maintenance, and equipment performance. 

The Swedish Thermal Power Reliability Data System (ATV) is maintained and managed by the Swedish State Power Board at Stockholm, Sweden. Engineering and reliability data have been collected from both nuclear and nonnuclear power generating plants. Nuclear data collection began in 1973. Collection of reliability data began in 1976. Over 30,000 events have been recorded in the data base. 

All data recorded in the data base have been acquired from plant records. Statistical reductions of data for generation of reports or specific end use are available. Data are currently collected from four operating plants (eight units). Time clocks have been installed on components, to record actual exposure time. Event data are available on a broad variety of safety and commercial grade components including pumps, valves, transformers, diesels, filters, tanks (vessels), and heat exchangers. 

The main objective of the In-Plant Reliability Data System (IPRDS) was to develop a comprehensive and component-specific data base for PRA and other component reliability-related statistical analysis. Data base personnel visited selected plants and copied all the plant maintenance wor)c requests. They also gathered plant equipment lists and plant drawings and in some cases interviewed plant personnel for Information on component populations and duty cycles. Subsequently, the maintenance records were screened to separate out the cases of corrective maintenance applying to particular components these were reviewed to determine such things as failure modes, severity, and, if possible, failure cause. The data from these reports were encoded into a computerized data base. 

Three reports have been issued containing IPRDS failure data. Information on pumps, valves, and major components in NPP electrical distribution systems has been encoded and analyzed. All three reports provide introductions to the IPRDS, explain failure data collections, discuss the type of failure data in the data base, and summarize the findings. They all contain comprehensive breakdowns of failure rates by failure modes with the results compared with WASH-1400 and the corresponding LER summaries. Statistical tables and plant-specific data are found in the appendixes. Because the data base was developed from only four nuclear power stations, caution should be used for other than generic application. 

A Statistical Analysis of Nuclear Power Plant (Pump and Valve) Failure Rale Variability me Preliminary Results Nuclear All IPRDS data base records for the pumps and valves selected for analysis The set of valves and pumps selected for analysis from the IPRDS data base 104. 

The LER data base served as the primary source of DG failure data, while a data base for DG successes was formed from nuclear plant licensees responses to a USNRC questionnaire (Generic Letter 84-15). Estimates of DG failure on demand were calculated from the LER data, DG test data, and response data from the questionnaire. The questionnaire also provided data on DG performance during complete and partial LOSP and in response to safety injection actuation signals. Trends in DG performance are profiled. The effects of testing schedules on diesel reliability are assessed. Individual failures are identified in an appendix. 

The study performed by Burns and Roe (BSR) shows that valve failures constitute the component category most responsible for the shutdown of PWR and BWR plants. This Investigation, contracted with SNL for DOE, identified the principal types and causes of valve failures that led to plant trips for the period from 12/72 to 12/78. The primary sources of data for the report were searches of the data base, the monthly Gray Books, Nuclear Power Experience publications, as well as discussions with utilities, valve manufacturers, and suppliers. 

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