Using power-law rate expression parameters for discrimination among mechanistic models in rate data regression

Langmuir-Hinshelwood (or Hougen-Watson) type rate expressions are most often used in modeling reaction rate data. In cases when there are tens of possible rival models, effective discrimination between them requires that most of the inadequate models be discarded as early as possible in the discrimination process. In this paper, the relationship between power-law and Hougen and Watson type rate expressions was studied It was found that there is a clear mathematical connection between the two types of rate expressions. This connection can be utilized in order to discriminate between feasible and infeasible models, using only the numerical values of the power-law rate expression parameters. This way most of the inadequate mechanisms can be discarded after fitting the data to a single (power-law) model. The 95% confidence intervals of the parameters have proven to be key statistical variables in determining the adequacy of both power-law and mechanistic models. Using the data and results of Hougen and Watson (1947), it is shown that they rejected valid mechanisms and accepted invalid ones because they did not take into account the confidence intervals on the parameters.