Empirical Kinetic Modeling Approach

Dimitriades, B., and M. Dodge, Eds., Proceedings of the Empirical Kinetic Modeling Approach (EKMA) Validation Workshop, EPA Report No. EPA-600/9-83-014, August 1983. 

This section will begin by looking at how thermodynamic and kinetic modelling has been combined to understand time-temperature-transformation diagrams in steels. The woric, for the most part, is semi-empirical in nature, which is forced upon the topic area by difficulties associated with the diffusional transformations, particularly where nucleation aspects have to be considered. The approaches have considered how best to predict the time/temperature conditions for austenite to... 

The kinetic modeling nomenclature arises from the incorporation of chemical kinetic submodels in EKMA. The empirical term comes from the use of observed 03 peaks in combination with the model-predicted ozone isopleths to develop control strategy options. Thus, the approach historically was to use the model to develop a series of ozone isopleths using conditions specific for that area. The second highest hourly observed 03 concentration and the measured... 

Empirical approaches are useful when macroscale HRR measurements are available but little or no information is available regarding the thermophysical properties, kinetic parameters, and heats of reaction that would be necessary to apply a more comprehensive pyrolysis model. Although these modeling approaches are crude in comparison with some of the more refined solid-phase treatments, one advantage is that all required input parameters can be obtained from widely used bench-scale fire tests using well-established data reduction techniques. As greater levels of complexity are added, establishing the required input parameters (or material properties ) for different materials becomes an onerous task. 

M. Bisi, C. Nicolella, E. Palazzi, M. Rovatti and G. Ferraiolo HCl formation via pyrolytic degradation of polyvinyl chloride (PVC) an empirical approach to kinetic modeling, Chem. Engng. Technol, 17, 67-72 (1994). 

This type of model is compatible with complex feedstocks (21,22), where limited understanding of the mechanisms involved and the size of the reaction network preclude stoichiometric modeling. Its range of applicability depends not only on the extent of data base used to estimate model parameters, but on the degree to which its empirical framework reflects true kinetics. This approach will be illustrated later. 

Computer modeling of hydrocarbon pyrolysis is central to an optimum design of reactors for olefins production. Approaches to pyrolysis model development can be classified into four groups mechanistic, stoichiometric, semikinetic, and empirical. Selection of approaches to meet minimum development cost must be consistent with constraints imposed by such factors as data quality, kinetic knowledge, and time limitations. 

Undoubtedly, improved numerical simulations of the variation in the low-temperature reactivity amongst alkanes and their isomers will emerge. Perhaps, more importantly there will be reduced numerical models, obtained by the methods discussed in Chapter 4, which encapsulate the appropriate kinetic dependences in a fundamental way. The difficulty with a number of current approaches to reduced kinetic modelling [217] is that, although some of the elements of (74) may be identified in the reduced schemes, the implementation of the schemes relies too heavily on empirically derived kinetic parameters. 

An example of this approach is shown with carrots (19). Seventeen cultivars of carrots were sliced transversely and blanched 4 min at 212F or 165F, then canned and processed 23, 30,45,60,75 min at 250F. The firmness of the phloem tissue and xylem tissue (core) were separately measured by a puncture test. Figure 5 shows log firmness versus process time for the cultivar Dominator Sunseed together with the equations for the lines of best fit and the correlation coefficients R. Similar plots were obtained for the other sixteen cultivars. Sufficient data was obtained to measure substrate "b" and the thermal firmness value. No data were collected to measure substrate "a" because it is not needed. However, the firmness at zero process time was measured and these points are plotted on the ordinate. The correlation coefficients range from r = 0.98 to r = 1.00 which shows the excellent fit of the empirical data to the kinetic model. 

Due to the industrial importance of deactivation, various kinetic models that account for deactivation have been advanced in the literature. Probably the most frequently used approach, is based on different empirical and semi-empirical equations. However, increasingly, the nature of deactivation is considered as a constituent part of the reaction scheme. This approach provides more possibilities for elucidating deactivation mechanisms, which should be an essential part of any new catalyst development. 

Smith et al., 2010) classifies the reaction kinetic models in microkinetic approach and the empirical method. 

The epoxy-amine system is obviously more difficult to treat than the epoxy-anhydride system. The experimental conditions are more stringent (temperature interval of more than 200°C) and the empirical kinetic rate equation is probably not accurate enough. However, the proposed approach allows the model to be refined without too much difficulty. 

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