Multielement models

Figure 4.143. Circuits proposed for modeling the impedance spectrum of polycrystalline sodium )8-alumina (a) Easy path model according to Lilley and Strutt [1979] (b) Multielement model according to De Jonghe [1979].

Favier, J.F. Abbaspour-Fard, M.H. Kremmer, M. Raji, A.O. (1999) Shape representation of axisymmetrical, non-spherical particles in discrete element simulation using multielement model particles. Engineering Computations 16,467 80. 

Perrin, D. D., Agarwal, R. P. Multielement-multigand equilibria a model for biological systems, in Metal Ions in Biological Systems, (ed.) Sigel, H., Vol. 2,. p. 167, New York— Basel Marcel Dekker, Inc. 1973... 

In this study we have employed the simultaneous collection of atmospheric particles and gases followed by multielement analysis as an approach for the determination of source-receptor relationships. A number of particulate tracer elements have previously been linked to sources (e.g., V to identify oil-fired power plant emissions, Na for marine aerosols, and Pb for motor vehicle contribution). Receptor methods commonly used to assess the interregional impact of such emissions include chemical mass balances (CMBs) and factor analysis (FA), the latter often including wind trajectories. With CMBs, source-strengths are determined (1) from the relative concentrations of marker elements measured at emission sources. When enough sample analyses are available, correlation calculations from FA and knowledge of source-emission compositions may identify groups of species from a common source type and identify potential marker elements. The source composition patterns are not necessary as the elemental concentrations in each sample are normalized to the mean value of the element. Recently a hybrid receptor model was proposed by Lewis and Stevens (2) in which the dispersion, deposition, and conversion characteristics of sulfur species in power-plant emissions... 

All detection limits are given in micrograms per liter and were determined using elemental standards in dilute aqueous solution. All detection limits are based on a 98% confidence level (3 S.D.). All atomic absorption (Model 5100) detection limits were determined using instrumental parameters optimized for the individual element, including the use of system 2 electrodeless discharge lamps where available. ICP emission (Optima 3000) detection limits were obtained under simultaneous multielement conditions with a radial plasma. Detection limits using an axial plasma (Optima 3000 XL) are typically improved by 5-10 times. 

The 30-mm sediment slices of the segmented cylindrical cores obtained from box coring at the seven stations were dried, pulverized, and thoroughly mixed to yield a uniform sample for analysis. Sediment from each of these slices was analyzed by two independent methods. The first method used a Perkin-Elmer model 5000 atomic absorption spectrophotometer (AA) for the elements Fe, Mn, Ti, Pb, Zn, Cu, Cr, Ni, Co, Hg, and Cd (9). The second method utilized a Philips PW 1410 X-ray fluorescence spectrometer for the analysis of elements Fe, Mn, Ti, Ca, K, P, Si, Al, Mg, Na, Pb, Zn, Cu, Cr, V, and Ba (10). The AA analysis was chosen because of the known accuracy and sensitivity to a wide spectrum of elements. The XRF analysis was chosen for its accuracy and similar nondestructive mode of analysis equivalent to the shipboard XRF analysis. Good agreement between the AA and the XRF values was felt to be imperative because the Philips XRF equipment was to be used in the land-based multielement analysis of the CS -collected sediment samples. 

Results for elements in aerosol samples which are obtained by multielement techniques from data sets from which information about the sources of the components can be extracted (Gordon 1980). Such methods which make use of data obtained at receptor points are called receptor models. The most important receptor models are chemical mass balances (CMB), enrichment factors, time series correlation, multivariate models and spatial models (Cooper and Watson 1980 Gordon 1988). Dispersion modeling has also been used to explain the... 

In the early 1970s, Simonits proposed the development of a standardization method using universal fc-factors. In this method, the essential information for a gamma ray emitted by any nuclide produced by neutron activation would be contained in a universal constant, the ko factor, and all factors depending on the specific irradiation and counting conditions would be calculated by models. The inventors of the ko method envisioned that for each sample analyzed at least one neutron flux monitor would be co-irradiated and counted, and all other parameters of the models would be measured once and only remeasmed when irradiation conditions changed. Thus, multielement analysis could be performed with the same amount of work needed for single-element analysis. 

In parallel with the large amount of work done to study the mechanism and operating features of methanol fuel cells with proton-conducting membranes, operating models of such fuel cells started to appear in the mid-1990s, first as laboratory-type small single-element fuel cells and finally, in the form of multielement batteries of relatively large power. 

Following this, in the next step, authors plan to develop the availability model for the case of multielement system with delay time. This will complete the research work of authors connected with maintenance analytical modelling for technical systems with time delay. 

A reasonable question arises why problems unsolvable by the known methods are readily settled by stoiehiographie methods Let us eonsider how the DD method ean reveal phase composition of a model multielement multiphase object for which ordy its gross elemental composition is known, whereas data on its phase eomposition earmot be obtained, for example, due to amorphous strueture of the object. The model eonsists of three elements (wt. %) A (45.5), B (21.2) and C (33.3), which form the imknown munber of phases (5 in this model) with imknown stoiehiometry and quantitative eontent. All ealeulations were based on the model of redueing spheres. The stoichiometric composition of five phases, radii of their spheres as well as rate eonstants and induction periods of dissolution processes were chosen randomly. The dissolution process was simulated by a dynamie regime with the solvent concentration increasing linearly with time at a constant temperature. Note that the initial data for stoiehiographie calcidation of the simulation data were represented only by the data on qualitative composition of elements A, B and C in the object of analysis, whereas all other parameters specified in the model were considered as the unknown quantities. Thus, the DD method had to reveal the presence of individual phases in the sample and then identify them and find their quantitative content. 

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