Micro-kinetic simulation

Figure 16 Steady state CO2 production on (5.5 nm) Pd particles supported on an alumina thin film grown on NiAl( 1 1 0), as a function of Xqo and for various temperatures (Pco + Po2 — 1 x It)-6 mbar). (a) Experiment (b) micro-kinetic simulation (from Ref. [46]).

Figure 25 Micro-kinetic simulation of the CO + NO reaction on a Pd/MgO model catalyst, (a) Steady state production of C02 as a function temperature at Pco = 5 x 10 s Torr and various NO pressures, (b) Steady state coverage of NO, CO and O as a function of sample temperature for Pco = 7 no = 5 x 1CT8 Torr (from Ref. [167]).

Let us, however, focus on the chemical and micro-kinetic sense of modeling. As we mentioned several times, the success in kinetic simulations that is sparking further progress in gas processing is associated with the development of models meeting certain requirements. Among the latter, the requirement of fullness is crucial. 

Even though the authors could not avoid some adjustment of selected kinetic parameters, what is explicable taking into account the extraordinary complexity of the system. As a result, they succeeded in reproducing in their simulations some important features of the real system and validated their micro-kinetic model against high-pressure spatially resolved experimental data for catalytic partial oxidation of methane. 

Surface segregation of Pd in Pd—Rh catalysts suppresses NOx reduction [61]. De Sarkar and Khanra studied the segregation difference between Pd—Rh and Pt—Rh nanoparticles, and the influence of sulfur in fuel on CO oxidation and NO. They used Monte-Carlo (MC) simulation to predict the surface composition of PtsoRhso and PdsoRhso particles (2406 atoms for 4nm particles). TTiey used a micro-kinetic model to compare the activities of both soHds for reactions of CO -i- O2, CO -I- NO and CO -1- NO -1- O2, and found that Pt and Pd segregate to the particles surface, especially in the Pd catalyst, which is clearly better for CO oxidation, while Pt—Rh is a better catalyst for NO reduction. For both reactions, sulfur poisons the Pd—Rh catalyst more than the Pt—Rh catalyst [62]. 

Due to the kinetic nature of LBE, phenomenon or physics that involves molecular interaction can easily be applied and hence makes LBM a good tool for micro-Znanofluidics simulation. Fewer sets of discrete velocities and particle density distribution function in phase space are used in LBM as opposed to a continuous velocity or distribution function in the complete functional phase space of the Boltzmann equation. 

Early devices fbr thermogravimetric analysis were limited in precision and convenience when compared with DTA or DSC equipment. Now devices for simultaneous DSC and TG are on the market which can operate at high temps in reactive atms permitting the simulation of high temp reactions on a micro scale. The use of TG for the study of reaction kinetics was described in Sect 5.3.3. An exptl study of the sublimation of ammonium perchlorate was published by Jacobs and Jones (Ref 25). Similar techniques should find application in the study of other propint systems. The product gases have been collected for further analysis using gas chromatography and mass spectrometry... 

At present, the most known and widely used kinetic model for light hydrocarbon oxidation and combustion is GRI-Mech (Smith et al.), which has been developed since the 1990s by the Gas Research Institute. The project was aimed at the development of the detailed micro-chemical mechanism that can be used for simulations of natural gas ignition and combustion. Now the model is optimized for two fuels, methane and natural gas. Recommended temperature and pressure ranges are 1,000-2,500 K and 1.3 x 10 3-1 MPa, respectively the fuel-to-oxidant equivalence ratio in premixed compositions is 0.1-5. Although the scheme includes some reactions of C2-C3 hydrocarbons and oxygenates participating in methane (natural gas) combustion, it is not recommended to use GRI-Mech for simulations of their oxidation (combustion) as the main initial fuels, since the model is not optimized for these purposes. 

Initially the model was compiled using both experimentally measured and theoretically calculated kinetic parameters. Then, the results of simulations were compared with the data of multiple experiments and sensitivity analysis was employed to select the parameters, which should be corrected for the better agreement between experimentally observed and simulated kinetic behavior. The computation routine can perform the modification of each kinetic parameter within the range of its initial uncertainty. Such an approach gives a serious cause for criticism, since the discrepancies with experimental data are eliminated (or minimized) by changing the values of multiple parameters. First, this makes all of them correlated. Next, an independent correction of just one parameter in the model, or just a slight modification of the micro-chemical scheme leads to the readjustment of the whole system of kinetic parameters. This is in a certain sense equal to the solution of the inverse kinetic task, which, as we mentioned above, is an ill-conditioned problem. 

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