Estimation of the kinetic parameters from experimental data obtained from the start-up of an industrial tubular reactor. This requires dynamic simulation of a tubular reactor, comprising a set of partial differential equations. [Pg.632]

Methods for the determination of kinetic models from experimental data including model discrimination, parameter estimation and design of experiments. [Pg.632]

Besides the set of experimental points used to estimate the kinetic parameters, 43 additional data were obtained for inlet temperatures ranging from 460-500 °C and the previously selected values of yAo and Q. Fig. 3 shows the influence of the inlet temperature on the methane conversion for three different yAo values. The solid lines represent the predictions of Model A, which includes the mass transfer resistances. This Model predicts the experimental behavior with high accuracy even for higher inlet temperatures than those used to lit the kinetic parameters. Fig. 3 [Pg.629]

The kinetic and equilibrium parameters included in Equation (5) are estimated from experimental data with regression analysis. Simulation of the model with the estimated parameters revealed that Equation (5) can predict the experimental trends in the acid-catalyzed esterification correctly. [Pg.258]

The inventory of commercially available software packages aimed at kinetic parameter estimation from experimental data showed that all the packages need improvement in order to become really good and user friendly. Especially the quality of the statistics, the number of useful statistical tools and several user-friendliness aspects need significant improvement. [Pg.636]

The models in chemical kinetics usually contain a number of unknown parameters, whose values should be determined from experimental data. Regression analysis is a powerful and objective tool in the estimation of parameter values. The task in regression analysis can be stated as follows the value of the dependent variable (y) is predicted by the model a function (/), contains independent variables (x) and parameters (/ ). The independent variable is measured experimentally, at different conditions, i.e. at different values of the independent variables (x). The goal is to find such numerical values of the parameters (/ ) that the model gives the best possible agreement with the experimental data. Typical independent variables are reaction times, concentrations, pressures and temperatures, while molar amounts, concentrations, molar flows [Pg.431]

It ould also be noted that, in practice, it is not always possible to estimate kinetic parameters from I-E data. Some redox couples 0/R have kinetic parameters such that the rate of electron transfer is, under all experimental conditions, fast compared with the prevailing rate of mass transport in such circumstances the electron transfer at the surface will appear to be in equilibrium and the ratio of surface concentrations can be calculated from the Nernst equation. Such [Pg.10]

Table 1 presents several examples of unsteady-state kinetics models. These models are presented in the form of rate dependencies for catalytic reaction stages and side processes. The parameters of the models, such as reaction rate constants and activation energies, are given in references (Table 1) and were determined mainly from experimental data using transient response techniques. For the reaction of CO oxidation over a supported platinum catalyst, the kinetic gas theory was applied for estimating the adsorption constants. [Pg.492]

Here Kj is the adsorption equilibrium constant of species j. which can be a function of potential. In this case estimation of the best fit of kinetic parameters Qj, m, a, k°, Kj, Ef, requires the use of nonlinear regression techniques (84a). Although experimental data can fit an equation similar to Eq. (16), mechanistic deductions from such information alone should be restrained. It can be shown that more than one mechanism can be devised, the rate expressions of which cannot be statistically discriminated (84a). [Pg.237]

The main objectives of this chapter are to (1) review the different modeling techniques used for sorption/desorption processes of organic pollutants with various solid phases, (2) discuss the kinetics of such processes with some insight into the interpretation of kinetic data, (3) describe the different sorption/ desorption experimental techniques, with estimates of the transport parameters from the data of laboratory tests, (4) discuss a recently reported issue regarding slow sorption/desorption behavior of organic pollutants, and finally (5) present a case study about the environmental impact of solid waste materials/complex [Pg.171]

Being generally weakly nonlinear, adsorption systems make good candidates for investigation by means of nonlinear FR. As it will be shown further in the text, the nonlinear FR method enables identification of the kinetic mechanism and estimation of both equilibrium and kinetic parameters from the same experimental data. [Pg.284]

The final discussion of the book focused on a perhaps surprising feature of kinetic systems, namely, the similarity of their local sensitivity functions. It was shown that for certain conditions, there can be strict relations among these functions, such as having similar shapes. The origin of these relations was discussed in Chap. 8, and their consequence oti the uniqueness of models and on the estimation of their parameters from experimental data was explored. These important features of the local sensitivity functions have been detected in combustion and systems biology models. [Pg.358]

The observed transients of the crystal size distribution (CSD) of industrial crystallizers are either caused by process disturbances or by instabilities in the crystallization process itself (1 ). Due to the introduction of an on-line CSD measurement technique (2), the control of CSD s in crystallization processes comes into sight. Another requirement to reach this goal is a dynamic model for the CSD in Industrial crystallizers. The dynamic model for a continuous crystallization process consists of a nonlinear partial difference equation coupled to one or two ordinary differential equations (2..iU and is completed by a set of algebraic relations for the growth and nucleatlon kinetics. The kinetic relations are empirical and contain a number of parameters which have to be estimated from the experimental data. Simulation of the experimental data in combination with a nonlinear parameter estimation is a powerful 1 technique to determine the kinetic parameters from the experimental [Pg.159]

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