Several fouling mitigation techniques depend on the capacity of predicting fouling rates. Therefore, the identification of accurate fouling rate models is an important task. Crude fouling rates are usually evaluated through empirical or semiempirical models. In both alternatives, there are parameters that must be determined through laboratory or process data. In this context, the article presents an analysis of the parameter estimation problem involving fouling rate models. A proposed procedure for addressing this problem is described through the development of a computational routine called HEATMODEL. An important aspect of this study is focused on the obstacles associated to the search for the optimal set of parameters of the Ebert and Panchal models and its variants. This optimization problem may present some particularities that complicate the utilization of traditional algorithms. In the article, the performance of a conventional optimization algorithm (Simplex) is compared with a more modern numerical technique (a hybrid genetic algorithm) using real data from a Brazilian refinery. The results indicated that, due to the complexity of the parameter estimation problem, the Simplex method may be trapped in poor local optima, thus indicating the importance of the utilization of global optimization techniques for this problem.