Physical Data Model Specifications

In this section the physical data model of the proposed CAPE-SAFE within PEEE will be specified in more details. 

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Figure 8-2 shows the relationships between the different entities of the physical data model that represents the plant physical structure. In this figure, plant equipment class specifications are represented in equipmentspec , while the equipment records are represented in equipment . Similarly for spare parts, itemspec is used to specify the class specifications while item is used to describe the spare parts instances i.e. objects. The specifications of the class hierarchy are represented in classstructure , classspec , and assetattribute . 

B. Equipment specifications Identifies the equipment by manufacturer name and model number. Provides the physical data and design information associated with the equipment. 

The linear equilibrium isotherm adsorption relationship (Eq. 11) requires a constant rate of adsorption, and is most often not physically valid because the ability of clay solid particles to absorb pollutants decreases as the adsorbed amount of pollutant increases, contrary to expectations from the liner model. If the rate of adsorption decreases rapidly as the concentration in the pore fluid increases, the simple Freundlich type model (Eqs. 8 and 9) must be extended to properly portray the adsorption relationship. Few models can faithfully portray the adsorption relationship for multicomponent COM-pollutant systems where some of the components are adsorbed and others are desorbed. It is therefore necessary to perform initial tests with the natural system to choose the adsorption model specific to the problem at hand. From leaching-column experimental data, using field materials (soil solids and COMs solutions), and model calibration, the following general function can be successfully applied [155] ... 

With the general polynomial equation discussed above, the value of the first coefficient, a, represents the intercept of the line with the y-axis. The b coefficient is the slope of the line at this point, and subsequent coefficients are the values of higher orders of curvature. A more physically significant model might be achieved by modelling the experimental data with a special polynomial equation a model in which the coefficients are not dependent on the specific order of equation used. One such series of equations having this property of independence of coefficients is that referred to as orthogonal polynomials. 

A comparison between PCR and PLS is difficult because the quality of results is rather close (at least for a small set of data), but some specific advantages of the PLS can be drawn. The intermediate results (eigenvectors) are related to the initial physical data (looks like particular spectra). Moreover, the calibration step is more robust (if the data set is representative), and PLS can thus be used for the study of complex mixtures. Some known drawbacks are the computational time, the need for a large calibration set (representative) and some difficulties in understanding and explaining the resulting model. 

Physical Characterization. The specific BET surface areas of all aerogels were determined from nitrogen adsorption data with a commercial Autosorb-1 instrument (Quantachrome Corp.). Powder X-ray diffraction patterns were obtained with a Rigaku D/Max diffractometer with Cu Ka radiation. Raman spectra were obtained with the 514.5-nm line of a Spectra Physics Model 2050-5W argon ion laser (12). Samples were dried in an in situ cell at 383 K for 2 h in flowing oxygen before Raman measurements. 

Recently, however, limited use of best estimate plus uncertainty analysis methods has been undertaken. This is consistent with the international trend toward use of such methods. In this approach, more physically realistic models, assumptions, and plant data are used to yield analysis predictions that are more representative of expected behavior. This requires a corresponding detailed analysis of the uncertainties in the analysis and their effect on the calculated consequences. Typically, the probability of meeting a specific numerical safety criterion, such as a fuel centerline temperature limit, is evaluated together with the confidence limit that results from the uncertainty distributions associated with governing analysis parameters. The best estimate plus uncertainties approach addresses many of the problematic issues associated with conservative bounding analysis by... 

In fact, one of the objectives of the book is to introduce nonexpert readers to modem computational spectroscopy approaches. In this respect, the essential basic background of the described theoretical models is provided, but for the extended description of concepts related to theory of molecular spectra readers are referred to the widely available specialized volumes. Similarly, although computational spectroscopy studies rely on quantum mechanical computations, only necessary aspects of quantum theory related directly to spectroscopy will be presented. Additionally, we have chosen to analyze only those physical-chemical effects which are important for molecular systems containing atoms from the first three rows of the periodic table, while we wiU not discuss in detail effects and computational models specifically related to transition metals or heavier elements. Particular attention has been devoted to the description of computational tools which can be effectively applied to the analysis and understanding of complex spectroscopy data. In this respect, several illustrative examples are provided along with discussions about the most appropriate computational models for specific problems. 

A model of a reaction process is a set of data and equations that is believed to represent the performance of a specific vessel configuration (mixed, plug flow, laminar, dispersed, and so on). The equations include the stoichiometric relations, rate equations, heat and material balances, and auxihaiy relations such as those of mass transfer, pressure variation, contac ting efficiency, residence time distribution, and so on. The data describe physical and thermodynamic properties and, in the ultimate analysis, economic factors. 

In its simplest form, a model requires two types of data inputs information on the source or sources including pollutant emission rate, and meteorological data such as wind velocity and turbulence. The model then simulates mathematically the pollutant s transport and dispersion, and perhaps its chemical and physical transformations and removal processes. The model output is air pollutant concentration for a particular time period, usually at specific receptor locations. 

Cause-consequence analysis serx es to characterize tlie physical effects resulting from a specific incident and the impact of these physical effects on people, the environment, and property. Some consequence models or equations used to estimate tlie potential for damage or injury are as follows Source Models, Dispersion Models, Fire Explosion Models, and Effect Models. Likelihood estimation (frequency estimation), cliaractcrizcs the probability of occurrence for each potential incident considered in tlie analysis. The major tools used for likelihood estimation are as follows Historical Data, Failure sequence modeling techniques, and Expert Judgment. 

An artificial neural network based approach for modeling physical properties of nine different siloxanes as a function of temperature and molecular configuration will be presented. Specifically, the specific volumes and the viscosities of nine siloxanes were investigated. The predictions of the proposed model agreed well with the experimental data [41]. 

Dimensional analysis techniques are especially useful for manufacturers that make families of products that vary in size and performance specifications. Often it is not economic to make full-scale prototypes of a final product (e.g., dams, bridges, communication antennas, etc.). Thus, the solution to many of these design problems is to create small scale physical models that can be tested in similar operational environments. The dimensional analysis terms combined with results of physical modeling form the basis for interpreting data and development of full-scale prototype devices or systems. Use of dimensional analysis in fluid mechanics is given in the following example. 

Richards, et. al. s idea is to use a genetic algorithm to search through a space of a certain class of cellular automata rules for a local rule that best reproduces the observed behavior of the data. Their learning algorithm (which was applied specifically to sequential patterns of dendrites formed by NH4 Br as it solidifies from a supersaturated solution) starts with no a-priori knowledge about the physical system. R, instead, builds increasingly sophisticated models that reproduce the observed behavior. 

To this point, we have emphasized that the cycle of mobilization, transport, and redeposition involves changes in the physical state and chemical form of the elements, and that the ultimate distribution of an element among different chemical species can be described by thermochemical equilibrium data. Equilibrium calculations describe the potential for change between two end states, and only in certain cases can they provide information about rates (Hoffman, 1981). In analyzing and modeling a geochemical system, a decision must be made as to whether an equilibrium or non-equilibrium model is appropriate. The choice depends on the time scales involved, and specifically on the ratio of the rate of the relevant chemical transition to the rate of the dominant physical process within the physical-chemical system. 

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