Artificial Neural Network (ANN) Approach for Modeling of Sr(II) Adsorption from Aqueous Solution by Natural Calcium-based Materials

摘要

Background and Objective: Nuclear pollution-causing strontium affects almost all life forms in the surrounding environment. This study aimed to investigate and explore the possibility of using eggshell powder (ES) and natural hydroxyapatite (HAp) derived from chicken bone for adsorption of Sr(II) ions from aqueous solutions. Materials and Methods: Eggshells and leg bones of the chicken were collected from the local market of Turkey. The Sr(II) removal from aqueous solutions by natural calcium based materials were studied in a batch mode. Adsorption isotherms (Langmuir, Freundlich and Dubinin-Radushkevich (D-R)) and thermodynamic parameters were calculated and obtained data were evaluated. The artificial neural network s (ANN) are carried out for prediction of adsorption efficiency for the removal of Sr(II) ions from aqueous solution by eggshell (ES) and bone ash-hydroxyapatite (BA-HAp). Results: Experimental results indicated that adsorption of Sr(II) was highly pH dependent and strontium removal at relatively high at pH 5 for BA-HAp and pH 6 for ES. The Freundlich and D-R equations are in quite an agreement with the equilibrium isotherm for these adsorbents. Thermodynamic studies indicated that the adsorption is physical, which is consistent with the results of the isotherm models. Conclusion: The natural calcium based adsorbents seems to be a suitable low-cost material for removing strontium ions from aqueous solutions. In addition, ANN model is an effective technique in modeling, estimation and prediction of adsorption process for Sr(II) ions onto ES and BA-HAp.