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Interpol

For systems containing four components, most previous attempts for calculating LLE use geometrical correlations of ternary data (Branckner, 1940), interpolation of ternary data (Chang and Moulton, 1953), or empirical correlations of ternary data (Prince, 1954 Henty, 1964). These methods all have two... [Pg.71]

Experimental values were interpolated from the data of Brown et al. (1964). ... [Pg.92]

The primary purpose for expressing experimental data through model equations is to obtain a representation that can be used confidently for systematic interpolations and extrapolations, especially to multicomponent systems. The confidence placed in the calculations depends on the confidence placed in the data and in the model. Therefore, the method of parameter estimation should also provide measures of reliability for the calculated results. This reliability depends on the uncertainties in the parameters, which, with the statistical method of data reduction used here, are estimated from the parameter variance-covariance matrix. This matrix is obtained as a last step in the iterative calculation of the parameters. [Pg.102]

Twenty percent is too low, since S A cf is positive at the end of year 5. Thirty percent is too large, since SAdcf is negative at the end of year 5, and is the case with 25 percent. The answer must be between 20 and 25 percent. Interpolating on the basis of 2 Adcf, the DCFRR = 23 percent. [Pg.425]

The factor enabling interpolation of reduced properties of a pure compound or mixture between two reduced properties calculated on two reference fluids merits attention in order to understand its meaning. [Pg.113]

This method utilizes essentially the concept developed by Fitzer in 1955. According to the principle of three-parameter corresponding states, the compressibility factor z, for a fluid of acentric factor w, is obtained by interpolating between the compressibilities Zj and Z2 for the two fluids having acentric factors w, and (p -... [Pg.119]

At low temperature and pressure, the conductivity of a pure hydrocarbon is obtained by linear interpolation between two known conductivities ... [Pg.134]

The functions giCdT) and g2([Pg.166]

The can be Interpolated using Table 4.18 and the fugacities calculated by the Soave model. [Pg.169]

The European regulations have set SO2 emission limits for industrial combustion systems. They range from 1700 mg/Nm for power generation systems of less than 300 MW and to 400 mg/Nm for those exceeding 500 MW between 300 and 500 MW, the requirements are a linear interpolation (Figure 5.24). To give an idea how difficult it is to meet these requirements, recall that for a fuel having 4% sulfur, the SO2 emissions in a conventional boiler are about 6900 mg/Nm this means that a desulfurization level of 75% will be necessary to attain the SO2 content of 1700 mg/Nm and a level of 94% to reach 400 mg/Nm. ... [Pg.256]

One way of calculating the IRR is to plot the NPV against discount rate, and to extrapolate/ interpolate to estimate the discount rate at which the NPV becomes zero, as in the Present Value Profile in Figure 13.16. The alternative method of calculating IRR is by... [Pg.323]

The data volume, which can be imported as data block or single slices, can be cutted or rebinned (e.g. if the data set is very large) and interpolated (e.g. interpolating intermediate slices between measured CT cuts in the case of 2D-CT). [Pg.495]

The critical surface tension concept has provided a useful means of summarizing wetting behavior and allowing predictions of an interpolative nature. A schematic summary of 7 values is given in Fig. X-10 [123]. In addition, actual contact angles for various systems can be estimated since )3 in Eq. X-38 usually has a value of about 0.03-0.04. [Pg.367]

There are many large molecules whose mteractions we have little hope of detemiining in detail. In these cases we turn to models based on simple mathematical representations of the interaction potential with empirically detemiined parameters. Even for smaller molecules where a detailed interaction potential has been obtained by an ab initio calculation or by a numerical inversion of experimental data, it is usefid to fit the calculated points to a functional fomi which then serves as a computationally inexpensive interpolation and extrapolation tool for use in fiirtlier work such as molecular simulation studies or predictive scattering computations. There are a very large number of such models in use, and only a small sample is considered here. The most frequently used simple spherical models are described in section Al.5.5.1 and some of the more common elaborate models are discussed in section A 1.5.5.2. section Al.5.5.3 and section Al.5.5.4. [Pg.204]

Sometimes it may be convenient to use an even simpler interpolation fomuila that coimects the different rate coefficient limits [4]... [Pg.849]

In simple relaxation (the fixed approximate Hessian method), the step does not depend on the iteration history. More sophisticated optimization teclmiques use infonnation gathered during previous steps to improve the estimate of the minunizer, usually by invoking a quadratic model of the energy surface. These methods can be divided into two classes variable metric methods and interpolation methods. [Pg.2336]

The coefficients p. are chosen so that, on a quadratic surface, the interpolated gradient becomes orthogonal to all Aq. This condition is equivalent to minimizing the energy in the space spaimed by the displacement vectors. In the quadratic case, a further simplification can be made as it can be shown that all p. with the... [Pg.2337]

An alternative, and closely related, approach is the augmented Hessian method [25]. The basic idea is to interpolate between the steepest descent method far from the minimum, and the Newton-Raphson method close to the minimum. This is done by adding to the Hessian a constant shift matrix which depends on the magnitude of the gradient. Far from the solution the gradient is large and, consequently, so is the shift d. One... [Pg.2339]

The full dynamical treatment of electrons and nuclei together in a laboratory system of coordinates is computationally intensive and difficult. However, the availability of multiprocessor computers and detailed attention to the development of efficient software, such as ENDyne, which can be maintained and debugged continually when new features are added, make END a viable alternative among methods for the study of molecular processes. Eurthemiore, when the application of END is compared to the total effort of accurate determination of relevant potential energy surfaces and nonadiabatic coupling terms, faithful analytical fitting and interpolation of the common pointwise representation of surfaces and coupling terms, and the solution of the coupled dynamical equations in a suitable internal coordinates, the computational effort of END is competitive. [Pg.233]

The first technique is very intuitive. Out of the few proteins that could be crystallized in a number of different conformations, adenylate kinase is probably the best-studied example. By combining nine observed crystal structures and interpolating between them, a movie was constructed that visualized a hypothetical path of its hinge-bending transition (jVonrhein et al. 1995]). [Pg.67]

G. Benettin and A. Giorgilli. On the Hamiltonian interpolation of near to the identity symplectic mappings with applications to symplectic integration algorithms. J. Stat. Phys. 74 (1994)... [Pg.115]


See other pages where Interpol is mentioned: [Pg.2]    [Pg.187]    [Pg.385]    [Pg.426]    [Pg.196]    [Pg.218]    [Pg.99]    [Pg.173]    [Pg.212]    [Pg.487]    [Pg.235]    [Pg.514]    [Pg.1811]    [Pg.2183]    [Pg.2256]    [Pg.2337]    [Pg.2337]    [Pg.2337]    [Pg.2337]    [Pg.2337]    [Pg.2340]    [Pg.2350]    [Pg.220]    [Pg.222]    [Pg.222]    [Pg.256]    [Pg.218]    [Pg.278]    [Pg.310]   
See also in sourсe #XX -- [ Pg.68 ]




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A Guide to the GPDC Interpolation Charts

Absorbance interpolated

Activity interpolation

Adaptive axial interpolation

Application Guidelines for Using the GPDC Interpolation Charts

Approximations, Interpolations, Perturbations

B. Spline interpolation

Bi-linear interpolation

Bilinear interpolation

Central differences. Interpolation

Characteristics of interpolation methods

Color interpolation

Cubic interpolation

Cubic spline interpolation

D-INTERPOLATION OF VIRIAL COEFFICIENTS

Data interpolation

Differentiation of a Lagrange Interpolation Polynomial

Dimensional interpolation

Domb-Barrett interpolation formula

Double interpolation

Ensemble effects in adsorption - the interpolation principle

Errors interpolation

Evaluation of Interpolation Methods

Finite Difference Methods and Interpolation

Finite element interpolation

Finite element method linear interpolation

Flood-packings interpolation

Function inverse polynomial interpolation

Function inverse rational interpolation method

Gauss interpolation formula

Generalized pressure drop correlation Interpolation charts

Global and Radial Interpolation

Global interpolations

Graphic interpolation

Graphical interpolation

Gregory-Newton Interpolation

Gregory-Newton interpolation formula

HETP Prediction—Data interpolation

Hyperbolic interpolation method

International Interpol)

Interpolant

Interpolate

Interpolate

Interpolated Distance Weighted

Interpolated VTST

Interpolated optimized corrections

Interpolated optimized corrections method

Interpolated optimized energies

Interpolated single point energies

Interpolated variational transition state theory

Interpolating degree

Interpolating functions

Interpolating polynomials

Interpolating schemes

Interpolation

Interpolation Degree

Interpolation Differentiation

Interpolation Hermite

Interpolation Hermite method

Interpolation Lagrange method

Interpolation Newton

Interpolation Newton method

Interpolation Procedure

Interpolation Schemes

Interpolation Stirling

Interpolation algorithm

Interpolation and Fractal Concentration-Area (C-A) Method

Interpolation and Quadrature

Interpolation and extrapolation

Interpolation and finite differences

Interpolation backward Gregory-Newton

Interpolation formula

Interpolation forward Gregory-Newton

Interpolation function

Interpolation higher-order

Interpolation inverse

Interpolation linear, table lookup with

Interpolation methods

Interpolation methods characteristics

Interpolation methods procedures

Interpolation model

Interpolation moving least squares

Interpolation nearest

Interpolation of Equally Spaced Points

Interpolation of Global

Interpolation of Unequally Spaced Points

Interpolation of potential

Interpolation operations

Interpolation or Extrapolation

Interpolation order

Interpolation parabolic

Interpolation principle

Interpolation radial

Interpolation support points

Interpolation surfaces, reaction paths

Interpolation surfaces, reaction paths techniques

Interpolation theory

Interpolation with spline functions

Interpolation, Approximation and Galerkin Method

Interpolation, extrapolation and estimation procedures

Interpolation, regula falsi

Interpolative couplings

Interpolative method

Interpolative reasoning

Inverse Polynomial Interpolation Method

Inverse Rational Interpolation Method

Isochronous interpolation

Isoparametric interpolation

LCB interpolation

Lagrange Interpolation and Numerical Integration Application on Error Function

Lagrange interpolating condition

Lagrange interpolating polynomial

Lagrange interpolation

Lagrange interpolation formula

Lagrange interpolation function

Lagrange interpolation polynomial

Lanczos interpolation

Linear interpolation

Linear interpolation method

Linear interpolation-based scheme

Linear inverse interpolation

Lookup with linear interpolation

MATLAB data interpolation

MIDW interpolation

Maximum probability interpolant

Method integro-interpolation

Methods Based on Interpolation Between Reactant and Product

Mixed interpolation

Molecular dynamics potential energy surfaces interpolation

Mullers Method or Parabolic Interpolation

Multi-structure interpolation methods chain, locally updated planes, self-penalty walk, conjugate peak refinement and nudged elastic band

Multidimensional interpolation

Multidimensional interpolation approach

Multifractal interpolation method

Newton backward interpolation formula

Newton-Cotes interpolation formula

Newton’s interpolation

Newton’s interpolation formula

Noise Reduction and Time Domain Interpolation

Normalized interpolated peak height

Numerical Interpolation

Numerical methods interpolation

Optimal interpolation

Optimization interpolation

Piecewise cubic spline interpolation

Piecewise linear interpolation

Polynomial and Lagrange Interpolation

Polynomial interpolation

Polynomial interpolation method

Polynomial root interpolation

Prediction by Data Interpolation

Predictions compared with interpolation

Pressure Drop Prediction by Interpolation

Proportional errors Interpolation

Quadratic interpolation

Quasi-interpolating degree

Radial Basis Function Interpolator

Regression interpolation

Resampling and Interpolation

Retention index interpolation using temperature

Saddle points interpolation methods

Scattered data interpolation

Shepard interpolation

Shepard interpolation method

Ship Evolutionary Trajectory Planning Method with Application of Polynomial Interpolation

Simple interpolation

Simplex interpolation

Smoothing and Interpolation

Spatial interpolation

Spline interpolating

Spline interpolating splines

Spline interpolation

Stirlings Interpolation

String interpolation

Time course interpolation

VTST with interpolated corrections

Vapor compositions, interpolation

Variable interpolation

Velocity interpolation

Waites interpolation equations

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