THE USE OF MONTE-CARLO SIMULATIONS TO EVALUATE KINETIC DATA AND ANALYTIC APPROXIMATIONS

Experimental kineticists are always faced with the problem of reducing kinetic data to extract physically meaningful information. A particul arly vexing problem arises when different models reproduce the data bu t yield different values for the physical parameters. For over forty-f ive years Monte Carlo simulation techniques have been used to study th e statistical behavior of parameters extracted from data. Not only do these simulations provide realistic uncertainties, correlation coeffic ients, and confidence envelopes, but they also provide insight into th e nature of the model. These insights may be obtained by viewing two-d imensional scatter plots of the fractional changes of the parameters a nd one-dimensional histograms of the distributions of the changes in t he parameters. Monte Carlo simulations are illustrated with examples f rom OH + CH4 --> CH3 + H2O and the high-pressure rate coefficient for methyl-methyl association. A more complex problem involves models for pressure-dependent rate coefficients in the falloff region. We have mo deled methyl-methyl association with five of the most current analytic approximations for behavior in the falloff region. Ail of these repro duce the data to within their uncertainties. However, when Monte Carlo techniques are applied the correlations between the parameters and th e nonlinear nature of their behavior become evident. We postulate that the statistical behavior of the parameters of a model may be used to distinguish one model from another and, thereby, identify those analyt ic approximations that hold promise for further investigation and util ization. Finally, the recent advent of highspeed workstations implies that Monte Carlo simulations should become a routine part of the analy sis of kinetic data. (C) 1997 John Wiley & Sons, Inc.