Pyrolysis computer modeling

At present there is no small-scale test for predicting whether or how fast a fire will spread on a wall made of flammable or semiflammable (fire-retardant) material. The principal elements of the problem include pyrolysis of solids char-layer buildup buoyant, convective, tmbulent-boundary-layer heat transfer soot formation in the flame radiative emission from the sooty flame and the transient natme of the process (char buildup, fuel burnout, preheating of areas not yet ignited). Efforts are needed to develop computer models for these effects and to develop appropriate small-scale tests. 

Schulten, H.-R. Leinweber, P. Characterization of Humic and Soil Particles by Analytical Pyrolysis and Computer Modeling. J. Anal. Appl. Pyrolysis 1996, 38, 1-53. 

Schulten, H.-R., and Leinweber, P. (1996). Characterization of humic and soil particles by analytical pyrolysis and computer modeling. J. Appl. Anal. Pyrolysis 38,1-53. 

Schulten, H.-R., Leinweber, P., and Schnitzer, M. (1998). Analytical pyrolysis and computer modelling of humic and soil particles. In IUPAC Environmental Analytical and Physical Chemistry Series, Vol. 4 Environmental Particles Structure and Surface Reactions of Soil Particles, Huang, P. M., Senesi, N, and Buffle, J., eds., John Wiley Sons, Chichester, England, Chapter 8, pp. 281-324. 

Figure 1 shows a computational framework, representing many years of Braun s research and development efforts in pyrolysis technology. Input to the system is a data base including pilot, commercial and literature sources. The data form the basis of a pyrolysis reactor model consistent with both theoretical and practical considerations. Modern computational techniques are used in the identification of model parameters. The model is then incorporated into a computer system capable of handling a wide range of industrial problems. Some of the applications are reactor design, economic and flexibility studies and process optimization and control. 

Ross, L. L., Shu, W. R., Computer Modeling of Hydrocarbon Pyrolysis for Olefins Production, in "Thermal Hydrocarbon Chemistry", Adv. Chem. Series,... 

Ross and Shu [38], discussing the computer modelling of hydrocarbon pyrolysis for olefin production, classify reaction models in four categories in order of increasing sophistication empirical, semi-kinetic, stoichiometric and mechanistic. Most concepts of this classification are included in Table 3 with, however, a more classical meaning of the word stoichiometry. 

In order to extrapolate the laboratory results to the field and to make semiquantitative predictions, an in-house computer model was used. Chemical reaction rate constants were derived by matching the data from the Controlled Mixing History Furnace to the model predictions. The devolatilization phase was not modeled since volatile matter release and subsequent combustion occurs very rapidly and would not significantly impact the accuracy of the mathematical model predictions. The "overall" solid conversion efficiency at a given residence time was obtained by adding both the simulated char combustion efficiency and the average pyrolysis efficiency (found in the primary stage of the CMHF). 

Figure 10 shows the predictions of the computer model for the two reference coals and the CSD and PFD SRC. A temperature of 2500 F and 20% excess air were used for this case. The intercepts of the curves on the Y-axis indicate the pyrolysis (devolatilization) efficiencies of the fuels. The plots show the combustion efficiencies of the CSD and PFD SRC, and the WSB coal approach 100% in 2 seconds residence time, whereas, the marginal KHB coal attains about 97% combustion efficiency. 

Chapters 7-9 deal with the process aspects of pyrolysis to produce epbba. The first discusses the use of aerospace technology to simulate an unconventional process. The second discusses the results of recent attempts to develop computer models for large scale pyrolysis of hydrocarbons and the third discusses recent process and furnace design advances. 

Computer Modeling of Hydrocarbon Pyrolysis for Olefins Production... 

Computer modeling of hydrocarbon pyrolysis is discussed with respect to industrial applications. Pyrolysis models are classified into four groups mechanistic, stoichiometric, semi-kinetic, and empirical. Selection of modeling schemes to meet minimum development cost must be consistent with constraints imposed by factors such as data quality, kinetic knowledge, and time limitations. Stoichiometric and semi-kinetic modelings are further illustrated by two examples, one for light hydrocarbon feedstocks and the other for naphthas. The applicability of these modeling schemes to olefins production is evidenced by successful prediction of commercial plant data. 

For convenience, computer modeling of hydrocarbon pyrolysis may be categorized into four types. In order of decreasing degree of sophistication, these are mechanistic, stoichiometric, semikinetic, and empirical. A brief description of each follows. 

A similar computational modelling approach has been shown to be useful, for example, in studying the mechanism of low-temperature oxidation of alkanes (4), pyrolysis of alkanes (5-7), other gas-phase reactions (8), the formation of photochemical smog (9,10), and peroxide decomposition (11), among others. It is not uncommon to begin with all possible species and by permutation and combination derive a complete set of reactions, and then eliminate a subset by chemical... 

The combination of analytical pyrolysis, molecular modeling, and computational chemistry has also been stressed in investigating the structure of HS. It was reported that computational chemistry which allows to draw, construct and optimize in 3D space biomacromolecules, e.g., aquatic and terrestrial humic substances, with precise bond distances, bond angles, torsion angles, nonbonded distances, hydrogen bonds, charges, and chirality is a powerful tool, and molecular visualization and simulation can also be used to further understand the structure and dynamics of humic and dissolved organic matter. 

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