Chemical parameters, variability

Situation There are two vendors for a particular bulk chemical who meet all written specifications. The products are equally useful for the intended reaction as far as the chemical parameters are concerned both comply in terms of one physical parameter, the size distribution of the crystals, but on the shop floor the feeling prevails that there is a difference. Because the speed of dissolution might become critical under certain combinations of process variables, the chemical engineers would favor a more finely divided raw material. On the other hand, too many fine particles could also cause problems (dust, static charging). 

Fig. 5 Main contamination sources identified by PCA for sediments, fish, and suface water in the Ebro River basin, and explained variances for each principal component. Variable identification. Organic compounds in sediments 1, summatory of hexachlorocyclohexanes (HCHs) 2, summa-tory of DDTs (DDTs) 3, hexachlorobenzene (HCB) 4, hexachlorobutadiene (HCBu) 5, summatory of trichlorobenzenes (TCBs) 6, naphthalene 7, fluoranthene 8, benzo(a)pyrene 9, benzo(b) fluoranthene 10, benzo(g,h,i)perylene 11, benzo(k)fluoranthene 12, indene(l,2,3-cd)pyrene. Organic compounds in fish 1, hexachlorobenzene (HCB) 2, summatory of hexachlorocyclohexanes (HCHs) 3, o,p-DDD 4, o,p-DDE 5, o,p-DDT 6, p,p-DDD 7, />,/>DDE 8, />,/>DDT 9, summatory of DDTs (DDTs) 10, summatory of trichlorobenzenes (TCBs) 11, hexachlorobutadiene (HCBu) 12, fish length. Physico-chemical parameters in water 1, alkalinity 2, chlorides 3, cyanides 4, total coliforms 5, conductivity at 20°C 6, biological oxygen demand 7, chemical oxygen demand 8, fluorides 9, suspended matter 10, total ammonium 11, nitrates 12, dissolved oxygen 13, phosphates 14, sulfates 15, water temperature 16, air temperature...

In most natural situations, physical and chemical parameters are not defined by a unique deterministic value. Due to our limited comprehension of the natural processes and imperfect analytical procedures (notwithstanding the interaction of the measurement itself with the process investigated), measurements of concentrations, isotopic ratios and other geochemical parameters must be considered as samples taken from an infinite reservoir or population of attainable values. Defining random variables in a rigorous way would require a rather lengthy development of probability spaces and the measure theory which is beyond the scope of this book. For that purpose, the reader is referred to any of the many excellent standard textbooks on probability and statistics (e.g., Hamilton, 1964 Hoel et al., 1971 Lloyd, 1980 Papoulis, 1984 Dudewicz and Mishra, 1988). For most practical purposes, the statistical analysis of geochemical parameters will be restricted to the field of continuous random variables. 

Other independent variables (changes in temperature or medium composition) should produce effects analogous to pressure and therefore induce phase changes and shifts in association equilibria. Combination of these different variables could be used to investigate protein-subunit interactions and conformational changes, to determine the fundamental physical-chemical parameters of these changes. 

Nevertheless, these studies show a development towards a multivariate point of view, as the high number of chemical parameters produced by modern analytical instrument requires. The spread of computing systems of all sizes and the circulation of their respective software have allowed the great amount of computing deriving from the large number of measured variables and their relationships to be dealt with effectively. 

In this example, the relation between 19 chemicals and 23 physicochemical parameters was examined ( ). PLS, unlike canonical correlation, permits use of more chemical parameters than stimuli. The twenty-three physicochemical variables included molecular weight, functional groups, Raman frequencies and Laffort parameters (see ( )) The Laffort parameters are alpha (an apolar factor proportional to molvolume), rho (a proton receptor factor), epsilon (an electron factor) and pi (a proton donor factor). 

The possibility of describing chemical structures numerically with the aid of physico-chemical parameters and indicator variables puts us in the position to determine similarity or dissimilarity of chemical compounds more objectively. Chemical compounds can be represented as points in an n-dimensional space whose coordinates are formed by the parameters which are used to characterize the compounds. This space is therefore called parameter space. The distance of two... 

This indicates that a general geothermal pattern has been established in the total column and that rapidly circulating warmer water has only local effects on the clay mineralogy. The mineralogy of these different types of semi-permeable rocks corresponds, on a depth-temperature basis, very closely with that found in pelitic shale rocks of other studies. It is likely therefore that high permeability gives a noticeably different set of chemical parameters (intensive variables) to a rock whereas medium to low permeability can be assimilated to a "closed" system where rock and fluid are effectively part of the same physicochemical unit. 

The above use of "stable coexisting minerals" is of course based upon the fundamental consideration that the chemical system is "closed" that is, the chemical components K, Si and OH are "inert", their relative proportions, mass, in the system determines the phases formed. This can be assumed valid for many argillaceous sediments and rocks. However, in some geological environments, aqueous solutions containing alkalis and hydrogen ions in various concentrations (whose activities, therefore, are variables but constant throughout a given system) react with kaolinite or other minerals to influence its stability under otherwise constant physical and chemical parameters. 

The procedure used to define an equilibrium model is to (1) define all the variables and (2) define independent equilibria as a function of phase equilibria. The variables are defined as the chemical parameters typically measured in water chemistry. For the major constituents and some of the more important minor constituents, these are calcium, magnesium, sodium, potassium, silica, sulfate, chloride, and phosphate concentrations as well as alkalinity (usually carbonate alkalinity) and pH. To this list we would also add temperature and pressure. The phase equilibria are defined by compiling well-known equilibria between gas-liquid phases and solid-liquid equilibria for the solids commonly found forming in nature in sedimentary rocks. Within this framework, one can construct different equilibrium models depending upon the mineral chosen actual data concerning the formation of specific minerals therefore must be ascertained to specify a particular model as valid. 

Defining the rate of the different processes in terms of the state variables and physico-chemical parameters, and introducing equations that reflect the physical and chemical relations between the states at the different phases. 

Writing mass-, heat-, energy-, and/or momentum-balance equations to obtain the model equations that relate the system input and output to the state variables and the physico-chemical parameters. These mathematical equations describe the state variables with respect to time and/or space. 

The equations of the developed model need to be solved for certain inputs, certain design objectives, and given physico-chemical parameters in order to predict the output and, for design purposes, the variation of the state variables within the boundaries of the system. In order to solve the model equations we have two tasks ... 

Information about excipients is useful in the initial planning and interpretation of the excipient compatibility results. Important factors to consider for excipients include their physical-chemical properties. The Handbook of Pharmaceutical Excipients lists important information on structure, moisture content, melting point, pH, solubility, and equilibrium moisture at variable relative humidity for individual excipients (27). An example of relevant physical-chemical parameters for some select excipients is detailed in Table 1. A spectroscopic review of excipients (28) has been completed, and extensive reviews of some of the most common types of excipients (i.e., carbohydrate based) are published (29). 

The projection of variables similarity also contains semiquantitative information about the distribution of a given chemical parameter in the space of the sampling locations. 

Foam exhibits higher apparent viscosity and lower mobility within permeable media than do its separate constituents.(1-3) This lower mobility can be attained by the presence of less than 0.1% surfactant in the aqueous fluid being injected.(4) The foaming properties of surfactants and other properties relevant to surfactant performance in enhanced oil recovery (EOR) processes are dependent upon surfactant chemical structure. Alcohol ethoxylates and alcohol ethoxylate derivatives were chosen to study techniques of relating surfactant performance parameters to chemical structure. These classes of surfactants have been evaluated as mobility control agents in laboratory studies (see references 5 and 6 and references therein). One member of this class of surfactants has been used in three field trials.(7-9) These particular surfactants have well defined structures and chemical structure variables can be assigned numerical values. Commercial products can be manufactured in relatively high purity. 

The limitation of the use of one atmosphere foaming experiments to rank order the predicted surfactant performance in permeable media rather than in quantitatively or semi-quantitatively predicting the actual performance of the surfactants under realistic use conditions has already been mentioned. Multiple correlation analysis has its greatest value to predicting the rank order of surfactant performance or the relative value of a physical property parameter. Correlation coefficients less than 0.99 generally do not allow the quantitative prediction of the value of a performance parameter for a surfactant yet to be evaluated or even synthesized. Despite these limitations, multiple correlation analysis can be valuable, increasing the understanding of the effect of chemical structure variables on surfactant physical property and performance parameters. 

A somewhat simpler method than the hydrothermal process to produce submi-cron-size BT powders is a novel techniqne called the ACS process. We examine here some of the chemical experimental variables affecting particle morphology in the process. Here we define the ambient condition as near room temperature and pressure. To study the influence of experimental parameters on the properties of final BT prodncts, a series of BT samples have been prepared as described in Table 7.1. 

To investigate the influence of chemical parameters alone on the collision efficiency factor, all physical variables were kept constant. To determine the ability of Brownian motion to form separable aggregates, no stirring was applied. 

Because bioluminescence in marine surface waters (upper 100 m) is primarily due to small plankton, it can he successfully characterized by relatively simple photometer systems. The two basic types of bioluminescence detectors are an open type that views directly out into the seawater and a closed type that views a closed volume through which seawater is pumped. The bioluminescence variability is an interdependent phenomenon often associated with changes in physical and chemical parameters. For example, ocean frontal regions are almost always associated with enhanced levels of bioluminescence. Bioluminescence spectral content and signal kinetics often indicate the type of organisms present. 

Because biological and chemical variations in the upper ocean are not random, but rather interdependent phenomena often associated with specific physical events (e. g., upwelling, divergence, convergence, and stratification), correlations should exist between the occurrence of bioluminescence and other physical and chemical parameters. Our prime objective was to determine if correlations could be established between the occurrence of bioluminescence and the distribution of other oceanographic variables. Specifically, we wanted to physically characterize bioluminescence in the marine environment, determine how bioluminescence can be used to characterize planktonic communities in situ, and determine the relationships between the spatial and temporal distribution of planktonic bioluminescence with physical, chemical, and biological variables in the open ocean. 

In order to improve the spatial and temporal variability of water quality along Orlice River, the portable UV instrument and some chemical test kits (NH4 measurement) have been deployed increasing the frequency of physico-chemical parameter measurements. 

The testing activities have been deployed in the city of Daugavpils (Latvia) in order to carry out transects profiles of the Lake to estimate the spatial variability of the physico-chemical parameters and biogens in the lake (Figure 2.4.7). Indeed, no monitoring data were available for this lake and the main objective of the field trials was to achieve a first set of data. 

Vertical survey. Analysis of data from the vertical surveys shows that the depth effect was not significant, and was eliminated from the sampling programme for the estimation of the spatial and temporal variability. The variations in the physico-chemical parameters, measured with the multiparameter probe, with depth are presented (for positions 1-1, 1-3 and 1-5 along transect I) in Figure 4.5.8 for illustrative purposes. In... 

Assumption 7 We assume that the column is operated under constant conditions, e.g., imder constant temperature, pressure, mobile phase flow rate, so that all the physico-chemical parameters remain constant e.g., diffusion coefficients). In writing Eqs. 2.1b to 2.Id, it was assumed that the porosity remains constant, which is not always true (see later. Section 2.1.6). If the phase ratio, the mobile phase velocity, and /or the axial dispersion coefficient are not constant but depend on the space variable or on the solute concentration, it is easy to modify Eq. 2.2 by leaving the corresponding term under the appropriate differential operator. 

The IfM also participated in the first international multiship research programme named Cooperative Synoptic Investigation of the Baltic (cf. Matthaus, 1987). Scheduled for August 1964, this activity was organized by CBO under the leadership of Erich Bruns. Its main objectives aimed at the variabilities in dynamic, physical, and chemical parameters in the Baltic Proper caused by meteorological processes. The data were published in the ICES Oceanographic Data Lists (Anonymous, 1968a). 

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