Removal of Acid Dye from Aqueous Solutions with Adsorption onto Modified Wheat Bran – Modeling with Artificial Neural Networks

Abstract

In this study, the removal of 2-Naphthol Orange dye known as Acid Orange 7 (AO7) from aqueous solutions was studied using a modified adsorbent of wheat bran. The efficiency of neural networks in predicting biological uptake was also investigated. The effect of initial concentration, adsorbent dose, pH, velocity, and mixing time in a discontinuous reactor on the adsorption of 2-naphthol orange dye was investigated. Part of the laboratory results was amplified by a feed-forward-back-propagation neural network, and another part was simulated to measure the accuracy of the model. The transmission function and a number of hidden layer neurons were optimized. Optimum conditions were obtained at an initial concentration of 250 mg/L, an adsorbent dose of 0.07 g/L, a pH of 3, a mixing rate of 200 rpm, and a mixing time of 120 min, with a maximum removal rate of 99% and a maximum adsorption capacity of 92.64 mg/g. The dye adsorption kinetics and adsorption equilibrium (isotherm) were found to follow the quasi-second order and Freundlich model, respectively. In the designed neural network, the best transfer function in the sigmoid tangent function's hidden and output layers and the number of optimal neurons was determined to be 13. The model output had a proper correlation with the target vector (R = 0.986). Also, the simulations performed by the neural network model were in good agreement with the experimental results.