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Data used

PHREEQC2 is distributed with different databases. The database Minteq.dat was chosen for the modelling of the column experiments. Constants of the protonation reactions for all oxoanions are included in this database. It also contains constants of various soluble phosphate and chromate species (complexes with all major cations and anions). The formation of soluble arsenate complexes is not considered because this seems not to be necessary (compare Cullen and Reimer, 1989). [Pg.218]

To model the sorption of oxoanions onto the solid surface with PHREEQC2, it is necessary to define the reactive surface by a set of surface complexation constants and parameters describing the availability of surface sites. [Pg.218]

The artificial water used in the column experiments contained various ions which are intended to react with the surface of iron hydroxides. To obtain a realistic model, PHREEQC2 has to be used with a database which not only considers surface complexation constants for the sorption of the studied oxoanions but for aU reactive ions of the artificial water. It is assumed that only chloride and nitrate do not react with the iron hydroxide surface (Dzombak and Morel, 1990). [Pg.218]

The constants for the surface complexation of calcium, sulphate, phosphate and arsenate are included in the file minteq.dat. This data was not sufficient for the modelling of the column experiments performed and had to be augmented. Surface complexation constants for magnesia and chromate for amorphous iron hydroxide were taken from Dzombak and Morel (1990) (see Table 12.3). Van Geen (1994) showed that also carbon dioxide has to be considered for the modelling of adsorption. Carbon dioxide is not mentioned in Dzombak and Morel (1990). The database used contains complexation constants derived from data of Van Geen et al. (1994) and were reoptimized by Dr. C. A. J. Appelo (Amsterdam) for use with PHREEQC2 and amorphous iron hydroxide. This data was transferred from the database file PHREEQC.dat to Minteq.dat. [Pg.218]

Silicate can potentially also affect the surface charge of iron hydroxide (Goldberg, 1985). Silicate is not a primary constituent of the artificial water but it may be dissolved from the quartz sand. The concentration of silicate in equilibrium with Si02 (amorphous) calculated by PHREEQC2 is about 1 mmol/L H4Si04 in case of the artificial water at pH 6. Experimentally determined surface complexation constants for the adsorption of silicate on iron hydroxide were not available. Instead estimates made by linear free-energy relationships from Dzombak and Morel (1990) were implemented in [Pg.218]

4 STEP-BY-STEP EXAMPLE FOR APPLICATION OF THE MODEL 11.4.1 Data Used [Pg.421]


The accuracy of our calculations is strongly dependent on the accuracy of the experimental data used to obtain the necessary parameters. While we cannot make any general quantitative statement about the accuracy of our calculations for multicomponent vapor-liquid equilibria, our experience leads us to believe that the calculated results for ternary or quarternary mixtures have an accuracy only slightly less than that of the binary data upon which the calculations are based. For multicomponent liquid-liquid equilibria, the accuracy of prediction is dependent not only upon the accuracy of the binary data, but also on the method used to obtain binary parameters. While there are always exceptions, in typical cases the technique used for binary-data reduction is of some, but not major, importance for vapor-liquid equilibria. However, for liquid-liquid equilibria, the method of data reduction plays a crucial role, as discussed in Chapters 4 and 6. [Pg.5]

Figure 4-14. Predicted liquid-liquid equilibria for a typical type-II system shows good agreement with experimental data, using parameters estimated from binary data alone. Figure 4-14. Predicted liquid-liquid equilibria for a typical type-II system shows good agreement with experimental data, using parameters estimated from binary data alone.
The subsequent representations are probably reliable within the range of data used (always less broad than 200° to 600°K), but they are only approximations outside that range. The functions are, however, always monotonic in temperature, to provide appropriate corrections when iterative programs choose temperature excursions outside the range of data. [Pg.138]

NOTE - r NG GIl ES THE TENPERArURE RANGE tKl OF THE EXPERIMENTAL DATA USED TO FIT THE CONSTANTS CONSTANTS FOR NCNCONDENSABLES CCOMPONENTS 1-B) MERE DETERMINED FROM A GENERALIZED CORRELATION FOR THE HYPOTHETICAL REFERENCE FUGACITY. [Pg.154]

MAIN PROGRAM AND DRIVER FOR FITTING BINARY VLF DATA USING METHOD EASED ON THE MAXIMUM LIKELIHCOO PRINCIPLE ONLY CONTROL VARIABLES APE READ IN THIS ROUTINE. [Pg.229]

Usually well logs are only one type of data used to establish a correlation. Any meaningful interpretation will need to be supported by palaeontological data (micro fossils) and... [Pg.136]

Computer assisted operations (CAO) involves the use of computer technology to support operations, with functions ranging from collection of data using simple calculators and PCs to integrated computer networks for automatic operation of a field. In the extreme case CAO can be used for totally unmanned offshore production operations with remote... [Pg.280]

Figure Bl.9.9. Comparison of the distance distribution fiinction p(r) of a RNA-polymerase core enzyme from the experimental data (open circle) and the simulation data (using two different models). This figure is duplicated from [27], with pennission from Elsevier Science. Figure Bl.9.9. Comparison of the distance distribution fiinction p(r) of a RNA-polymerase core enzyme from the experimental data (open circle) and the simulation data (using two different models). This figure is duplicated from [27], with pennission from Elsevier Science.
Figure Bl.18.10. Scaimmg microscope in reflection the laser beam is focused on a spot on the object. The reflected light is collected and received by a broad-area sensor. By moving the stage, the object can be scaimed point by point and the corresponding reflection data used to construct the image. Instead of moving the stage, the illuminating laser beam can be used for scaiming. Figure Bl.18.10. Scaimmg microscope in reflection the laser beam is focused on a spot on the object. The reflected light is collected and received by a broad-area sensor. By moving the stage, the object can be scaimed point by point and the corresponding reflection data used to construct the image. Instead of moving the stage, the illuminating laser beam can be used for scaiming.
Table 3-4. Seven physiocheinical property data used to characterize each reaction center. Table 3-4. Seven physiocheinical property data used to characterize each reaction center.
Figure 5.4. Plot of the apparent second-order rate constant, kapp (= kotJ[5.2]i) versus the concentration of surfactant for the Diels-Alder reaction of S.lg with 5.2 in CTAB solution at 25 C. The inset shows the treatment of these data using Equation 5.6. From slope and intercut P j can be calculated (see Table 5.2). Figure 5.4. Plot of the apparent second-order rate constant, kapp (= kotJ[5.2]i) versus the concentration of surfactant for the Diels-Alder reaction of S.lg with 5.2 in CTAB solution at 25 C. The inset shows the treatment of these data using Equation 5.6. From slope and intercut P j can be calculated (see Table 5.2).
Empirical methods, such as group additivity, cannot be expected to be any more accurate than the uncertainty in the experimental data used to parameterize them. They can be much less accurate if the functional form is poorly chosen or if predicting properties for compounds significantly different from those in the training set. [Pg.121]

This book is the result of a number of years experience in the compiling and editing of data useful to chemists. In it an effort has been made to select material to meet the needs of chemists who cannot command the unlimited time available to the research specialist, or who lack the facilities of a large technical library which so often is not conveniently located at many manufacturing centers. If the information contained herein serves this purpose, the compiler will feel that he has accomplished a worthy task. Even the worker with the facilities of a comprehensive library may find this volume of value as a time-saver because of the many tables of numerical data which have been especially computed for this purpose. [Pg.1289]

In this experiment students standardize a solution of HGl by titration using several different indicators to signal the titration s end point. A statistical analysis of the data using f-tests and F-tests allows students to compare results obtained using the same indicator, with results obtained using different indicators. The results of this experiment can be used later when discussing the selection of appropriate indicators. [Pg.97]

Hypothetical Data Used to Study Procedures for Method Blanks... [Pg.128]

To develop an empirical model for a response surface, it is necessary to collect the right data using an appropriate experimental design. Two popular experimental designs are considered in the following sections. [Pg.676]

The data used to construct a two-sample chart can also be used to separate the total variation of the data, Otot> into contributions from random error. Grand) and systematic errors due to the analysts, Osys. Since an analyst s systematic errors should be present at the same level in the analysis of samples X and Y, the difference, D, between the results for the two samples... [Pg.689]

Table 7.6 Values of Fj as a Function of fj for the Methyl Acrylate (Mi)-Vinyl Chloride (M2) System (Data used in Example 7.5)... Table 7.6 Values of Fj as a Function of fj for the Methyl Acrylate (Mi)-Vinyl Chloride (M2) System (Data used in Example 7.5)...
Evaluate Ej - E by means of an Arrhenius plot of these data using a/( 1 - a) as a measure of kj/k. Briefly justify this last relationship. [Pg.500]

Evaluate for these polymers from these data. Use the value of 1q... [Pg.654]

Data are U.S. EPA official criteria where available National Academy of Sciences (NAS) data used where EPA data not available. [Pg.289]

Drinking Water Health Advisories for Pesticides, Office of Drinking Water, U.S. Environmental Protection Agency, Lewis Pubhshets, Chelsea, Mich., 1989. Includes data used for evaluating 1-day, 10-day, and longer-term health advisories for 50 pesticides which have a potential for being found in drinking water, with specific references as sources of information. [Pg.153]

Rules of matrix algebra can be appHed to the manipulation and interpretation of data in this type of matrix format. One of the most basic operations that can be performed is to plot the samples in variable-by-variable plots. When the number of variables is as small as two then it is a simple and familiar matter to constmct and analyze the plot. But if the number of variables exceeds two or three, it is obviously impractical to try to interpret the data using simple bivariate plots. Pattern recognition provides computer tools far superior to bivariate plots for understanding the data stmcture in the //-dimensional vector space. [Pg.417]


See other pages where Data used is mentioned: [Pg.212]    [Pg.176]    [Pg.1787]    [Pg.2788]    [Pg.121]    [Pg.123]    [Pg.322]    [Pg.683]    [Pg.740]    [Pg.156]    [Pg.135]    [Pg.49]    [Pg.322]    [Pg.631]    [Pg.721]    [Pg.39]    [Pg.627]    [Pg.323]    [Pg.408]    [Pg.58]    [Pg.150]    [Pg.510]    [Pg.78]    [Pg.323]    [Pg.327]    [Pg.422]    [Pg.162]    [Pg.224]   
See also in sourсe #XX -- [ Pg.92 ]




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Case studies using real production data

Combined refinement using different diffraction data

Comparison of Classification Methods Using High-Dimensional Data

DSC data, using

Data Analysis Using Absorption Probability Density (Example Guanidinium Nitroprusside)

Data Generation Using Optical In-line Spectroscopy

Data Reconciliation Using Nonlinear Programming Techniques

Data Used for Model Parameterization and Validation

Data acquisition and use

Data acquisition and use generally

Data filters, using multiple

Data matrix used for modelling

Data model using

Data of Frequently Used Substances

Data, using effectively

Derivation of Internally Consistent Data Bases Using Linear Programming

Design Methods for Plastics using Deformation Data

Determination of Kinetic Parameters Using Data Linearization

Dimensionless Groups Used to Plot Rheological Data

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Emission data for the exhaust gas from fettling, using various dedusting techniques

Environmental Protection Agency risk assessment data used

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Foundation design using penetration resistance data

GENERIC AND SELECTED BRAND DATA COMMONLY USED MEDICINAL HERBS

How to use the BDE data tables

Identification Using Data-Dependent Analysis

Location of H Atoms Using X-Ray Data

Making Use of Experimental Data

Model Building using Crystallographic Data

Modeling of Bitumen Oxidation and Cracking Kinetics Using Data from Alberta Oil Sands

Modelling from Noisy Step Response Data Using Laguerre Functions

Operational data for the production of a similar cast iron compressor casing, using various methods

Optical Data Storage using Dyes

Parameter Estimation Using Binary Critical Point Data

Parameter Estimation Using the Entire Binary Phase Equilibrium Data

Pesticides use data

Photochemical quantum yields using chromatographic data

Prediction of CYP Inhibition Using In vitro Data

Probit Analysis Models Used for Fitting Response Data

Protein Identification by PMF Tools Using MS Data

Published data, use

Quantification of Analytical Data via Calibration Curves in Mass Spectrometry Using Certified Reference Materials or Defined Standard Solutions

Radioactive metabolic data using

Refinement using neutron diffraction data

Refinement using x-ray diffraction data

Representing data using histograms

Results using concentration data

Risk assessment data used

Self-evaluation using data effectively

Sensitivity Analysis using Platts Data

Slope Factor Using Animal Data

Some Useful Data Tables

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Statistical test data usefulness

Stirred Tank Modeling Using Experimental Data

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Use of Mechanistic Data in Cancer and Genetic Risk Assessments (Specific Considerations)

Use of Metastable Ion and CID Data

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Use of Physical Properties with Kinetic Data

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Use of Thermodynamic Data

Use of data during different project stages

Use of data in design

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Use of urinary excretion data

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Useful Data

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Useful Property Data for Corresponding States Estimates

Usefulness of Operating Reliability Data

Using Experimental Data

Using Experimental Data as Restraints

Using Failure Rate Data

Using Magnetic Fields and Storing Data

Using Recent Observed Data to Improve Forecasts

Using Scanty and Fragmentary Data

Using major element data

Using radiogenic isotope data

Using stable isotope data

Using trace element data

Vapor pressure data, use

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