Backwashing behavior and hydrodynamic performances of granular activated carbon blends

Abstract Ozone-biological activated carbon (O3-BAC) process has been proved to be an efficient and cost-effective technology in advanced treatment of drinking water. However, O3-BAC raises strict requirements in adsorption, hydrodynamic and regeneration performances, which one single activated carbon could hardly all-sided meet. Blending activated carbons seems to be an appropriate and economically feasible method to deal with the issue. Thus, the uniformity and stability of activated carbon blends during water treatment, especially in backwashing process are of great importance. In this paper, cyclic experiments of downward adsorption and upward backwash on 11 typical commercial granular coal-based activated carbons and their blends were carried out in column test. Hydrodynamic performances such as bed expansion rate and bed pressure drop were measured. The uniformity and stability of activated carbon blends were investigated by determining iodine number of samples collected from different heights of activated carbon bed. Then, both traditional regression methods and back-propagation neural network model were utilized to predict superficial velocity at 30% bed expansion rate and maximum bed pressure drop of activated carbon blends. The results indicate that water backwashing process has no effect on the composition proportion of activated carbon blends, and slightly changes the particle distribution of activated carbon bed regarding pore structure and adsorption capacity. A three-layer back-propagation neural network model for superficial velocity at 30% bed expansion rate yields mean relative errors of 2.17%, which is much lower than that given by traditional regression methods such as 5.53% (weighted average), 4.08% (linear) and 4.06% (polynomial). Moreover, the back-propagation neural network model for maximum bed pressure drop yields mean relative errors of 1.37%, which is much lower than that given by traditional regression methods such as 4.31% (weighted average), 4.28% (linear) and 4.22% (polynomial). The non-linear relationships can be accurately identified by the back-propagation neural network model.