BACKGROUND
Seashell waste (SW) is rich in biogenic calcium carbonate and potentially can substitute geological sources in various applications, such as the separation of heavy metals and radionuclides from contaminated solutions. This study aims to compare SW sorption efficiency towards different chemical species (Cu2+, Zn2+, Pb2+ and Sr2+) and to evaluate the effects of various factors based on the experimental data and modeling approach.
RESULTS
The reaction of SW with aqueous metal solutions is a combination of several processes that result in metal retention, Ca2+ release, and changes in pH. SW demonstrates variable selectivity for investigated cations, depending on their concentrations and reaction times. Maximum sorption capacities declined in the order Zn2+ > Pb2+ ≈ Sr2+ > Cu2+. The model based on general regression neural network (GRNN) architecture was developed, which enabled prediction of removal efficiency taking into account the process specific, metal specific parameters and their non‐linear interactions. Initial concentration and covalent radius of a cation exhibit the highest, while the initial pH the lowest significance.
CONCLUSION
Ecological problems caused by SW accumulation in coastal areas could be mitigated by mastering technologies for their practical utilization. The results obtained facilitate the understanding of cationic pollutants removal by SW in the range of experimental conditions, while the GRNN approach demonstrates advantages in modeling complex sorption processes. © 2017 Society of Chemical Industry