Removal of divalent nickel from aqueous solution using blue-green marine algae: adsorption modeling and applicability of various isotherm models

• Published in: Separation science and technology Vol. 54; no. 6; pp. 943 - 961
• Main Authors: Ramadoss, Ramsenthil, Subramaniam, Dhanasekaran
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 13.04.2019; Taylor & Francis Ltd
• Subjects: Adsorption; Algae; Aqueous solutions; blue-green marine algae; Capacity; Copper; Design parameters; Divalent nickel; Experimental data; Heavy metals; Ion concentration; Ions; isotherm models; Isotherms; Metal concentrations; Metal ions; modeling; Modelling; Nickel; Parameter identification; pH effects; Process parameters; Removal
• ISSN: 0149-6395; 1520-5754
• DOI: 10.1080/01496395.2018.1526194

Adsorption of Ni(II) onto blue-green marine algae (BGMA) is investigated under batch condition. Under optimum experimental conditions, the initial Ni(II) metal ion concentration is varied from 25 to 250 ppm and the maximum adsorption capacity of BGMA is found to be 42.056 mg/g. The optimum pH, biomass loading, and an agitation rate on maximum removal of Cu(II) ion are found to be 6, 2 g, and 120 rpm, respectively. 24 h of contact time is allowed to achieve equilibrium condition. All the experiments are carried out at room temperature. The equilibrium experimental data infer that the isotherm is L-shaped. It is the indication of no strong competition between solvent and Ni(II) to occupy the active sites of BGMA. Also, it indicates that the BGMA has a limited sorption capacity for adsorption of Ni(II). The experimental data are tested with various isotherm models; subsequently, the mechanism of adsorption is identified and the characteristic parameters for process design are established. Fritz-Schlunder-V isotherm model is highly significant in establishing the mechanism of adsorption of Ni(II) under the conditions employed in this investigation followed by Freundlich. The q max of 41.89 mg/g obtained by this model indicates its relevance more precisely with experimental data.