Resumen
In biosorption research, a fairly broad range of mathematical models are used to correlate discrete data points obtained from batch equilibrium, batch kinetic or fixed bed breakthrough experiments. Most of these models are inherently nonlinear in their parameters. Some of the models have enjoyed widespread use, largely because they can be linearized to allow the estimation of parameters by least-squares linear regression. Selecting a model for data correlation appears to be dictated by the ease with which it can be linearized and not by other more important criteria such as parameter accuracy or theoretical relevance. As a result, models that cannot be linearized have enjoyed far less recognition because it is necessary to use a search algorithm for parameter estimation. In this study a real-coded genetic algorithm is applied as the search method to estimate equilibrium isotherm and kinetic parameters for batch biosorption as well as breakthrough parameters for fixed bed biosorption. The genetic algorithm is found to be a useful optimization tool, capable of accurately finding optimal parameter estimates. Its performance is compared with that of nonlinear and linear regression methods.