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.

Keywords:

• Arsenate
• layered double hydroxides
• competing cations and anions
• multi-component adsorption
• Artificial neural network

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.