Abstract
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.
Highlights
Biosorption employs inactivated materials of biological origin as sorbents to sequester toxic pollutants such as heavy metal ions from waste streams [1,2,3]
The performance of the genetic algorithm (GA) was gauged relative to Gauss-Newton-Levenberg-Marquardt nonlinear regression as well as linear regression in cases where models can be linearized
To avoid the use of optimization methods, practitioners often select models that can be transformed to linearized forms so that model parameters can be obtained by linear regression
Summary
Biosorption employs inactivated materials of biological origin as sorbents to sequester toxic pollutants such as heavy metal ions from waste streams [1,2,3]. Additional examples include simplified mass transfer models which are used to describe the kinetics of biosorption in batch contactors and continuous flow models which are used to characterize the breakthrough behavior in fixed bed columns packed with biosorbents Some of these models can have mechanistic relevance under some circumstances, they are often used in an empirical way to correlate the process information represented by a body of discrete data points generated from experimentation. A cursory examination of the recent biosorption literature reveals that the more popular models tend to be those that can be linearized to allow the estimation of parameters by means of linear regression Examples of such models include the aforementioned Langmuir isotherm equation, the Freundlich isotherm equation, the pseudo-first order and second order kinetic equations, and the Bohart-Adams or bed-depth-service-time (BDST) fixed bed equation. The performance of the GA is compared with that of Gauss-Newton-Levenberg-Marquardt nonlinear regression as well as ordinary linear regression in cases where models can be linearized
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
