Effect of coexisting ions on adsorptive removal of arsenate by Mg-Fe-(CO3) LDH: multi-component adsorption and ANN-based multivariate modeling

Abstract

The adsorptive removal of a pollutant from water is significantly affected by the presence of coexisting ions with various concentrations. Here, we have studied adsorption of arsenate [As(V)] by calcined Mg–Fe–(CO3)–LDH in the presence of different cations (Mg2+, Na+, K+, Ca2+, and Fe3+) and anions (CO3 2‒, Cl‒, PO4 3‒, SO4 2‒, and NO3 ‒) with their different concentrations to simulate the field condition. The experimental results indicated that Ca2+, Mg2+ , and Fe3+ have a synergistic effect on removal efficiency of As(V), whereas PO4 3‒ and CO3 2‒ ions have a significant antagonistic impact. Overall, the order of inhibiting effect of coexisting anions on adsorption of As(V) was arrived as NO3 –˂Cl–<SO4 2–<CO3 2 –<PO4 3 –. Among them, competitive adsorption of phosphate with arsenic at different initial phosphate concentrations was found to be responsive to formulate a binary adsorption system. We have also developed a modified non-competitive Langmuir and Langmuir–Freundlich models; however, the modified competitive Langmuir model was arrived to be the most adequate model for this binary system. An Artificial Neural Network based multivariate prediction model was developed, delineating the impact of coexisting ions on the adsorption system. The proposed method may appropriately demonstrate the overall system and exhibited a significantly adequate prediction model with high R2, high F-value, and low error values.