Genetic Algorithm Based Nonlinear Optimization of Adsorption Processes

Abstract

In this study, for different adsorption processes, nonlinear isotherm, kinetic model and thermodynamic parameters were calculated using genetic algorithm-based optimization method. Nonlinear equations were directly used as the model parameters can change to give false results when they are transformed to linear forms. Fifteen isotherms and two kinetic models were considered. All the experimental data was taken from the literature. Methylene green, thiram, and phenol red were used as the adsorbates and silica gel and chemically activated coal mining waste with potassium carbonate (K2CO3) or zinc chloride (ZnCl2) were used as the adsorbents. For three different temperatures, the Root Mean Square Error (RMSE) values were obtained between calculated and the experimental data. The biggest RMSE values were obtained as 5.23 x 10-1 for Freundlich isotherm at 45 °C and the smallest RMSE value was obtained as 3.19 x 10-4 for Halsey isotherm at 35°C. For the kinetic study, Lagergren and Particle Internal Diffusion models were applied to the experimental data for three different initial concentrations and it was shown that Lagergren pseudo-first-order Kinetic Model fits better to experimental data. Thermodynamic calculations were made for two different initial pH values and four different temperatures. The Arrhenius factor (A) and Arrhenius activation energies (Ea) (kJ/mol) were also calculated.

Keywords

• adsorption
• genetic algorithm
• isotherms
• kinetics
• thermodynamics