Artificial Neural Network, Equilibrium, Kinetics and Thermodynamics Modeling of Reactive Orange 12 Dye Using Rice Husk - Ghaedi, A.M.; Vafaei, A.; Hafezi, S.; Mohagheghian, M. (Asian Journal of Chemistry, 2012)
The aim of this work was to study artificial neural network (ANN), equilibrium, kinetics, mechanism modeling of reactive orange 12 using rice husk. A batch system was employed for all experiments. The optimum operating conditions for adsorption reactive orange 12 dye using rice husk were 3 g adsorbent dose, 150 μm particle size, 10 mg/L initial concentration of dye, 2 h contact time, 35 ºC temperature and pH 3. A three layer back propagation artificial neural network (BP-ANN) model with a tangent sigmoid function (tansig) at hidden layer with 10 neurons, a linear transfer function (pureline) at output layer and the Levenberg-Marquardt algorithm (LMA) was used to predict the adsorption efficiency (%) of reactive orange 12 using rice husk. The optimal ANN model was found with a minimum mean square error (MSE) of 0.00011 and a determination coefficient (R2) of ca. 0.966 between the network outputs and experimental data. Langmuir isotherm was found to be optimum isotherm (R2 = 0.993) as compared with Freundlich, Temkin and Dubinin-Radushkevich (D-R) isotherms. The pseudo second order model (R2 = 0.9993) affected the kinetics better than pseudo first order model (R2 = 0.9652). The positive value of DHº (32.94 J/mol) displays the endothermic adsorption. The rate of adsorption was controlled by both boundary layer and pore diffusion. Coefficient of pore diffusion (D = 1.23 × 10-8 cm2/s) confirms the boundary layer diffusion (mass transfer) as the controlling step in the dye adsorption process.