The application of nonlinear regression in catalysis typically involves the following steps:
Model Selection: Choosing an appropriate mathematical model that represents the reaction mechanism. This could be based on theoretical considerations or empirical data. Parameter Estimation: Using nonlinear regression techniques to estimate the parameters of the chosen model. This often involves iterative methods such as the Levenberg-Marquardt algorithm. Model Validation: Comparing the model predictions with experimental data to validate its accuracy. This may involve cross-validation techniques and goodness-of-fit tests.
