ISSN print edition: 0366-6352 ISSN electronic edition: 1336-9075 Registr. No.: MK SR 9/7 Published monthly

Process optimization and modeling of Pb(II) ions adsorption on a new and reusable biocomposite material using experimental design approach

Muharrem Ince, Olcay Kaplan Ince, and Hevidar Alp Kavlo

Department of Chemical Technologies, Graduate Education Institute, Munzur University, Tunceli, Türkiye

E-mail: olcaykaplan@munzur.edu.tr

Received: 5 September 2023  Accepted: 1 June 2024

Abstract:

This research paper aimed to establish the optimal conditions for lead (Pb(II)) ions from industrial wastewater (IW) using response surface methodology and central composite design (CCD) approach. A fixed-bed column study was designed by using a novel and reusable biocomposite material, Amanita vaginata (A. vaginata) immobilized on Amberlite XAD-4 resin (A&XAD-4@AV), to find out biosorption behavior of Pb(II) ions. Various experimental variables such as optimum pH value, biocomposite material amount, flow rate, and Pb(II) ions initial concentration were investigated, which significantly affected the Pb(II) ions removal efficiency. The maximum Pb(II) removal efficiency of A&XAD-4@AV biocomposite material was determined as 11.2 mg g⁻¹ under optimum conditions. The reusability of A&XAD-4@AV biocomposite material was found to be suitable. For checking model adequacy, a regression model was derived using CCD to predict the responses and analysis of variance (ANOVA) and lack of fit test was performed. It was observed that the quadratic model, which was controlled and proposed, originated from experimental design data. The ANOVA showed a high coefficient of determination (R² = 0.9896, R²_Adj = 0.9800, R²_Pred = 0.9633). Finally, developed and optimized method was applied effectively to wastewater for determining Pb(II) ions levels. The current study indicated that the prepared A&XAD-4@AV is an efficient material to remove of Pb(II) ions from IW.

Keywords: A&XAD-4@AV biocomposite; Response surface methodology; Fixed-bed column; Regression model

Full paper is available at www.springerlink.com.

DOI: 10.1007/s11696-024-03545-9

Chemical Papers 78 (11) 6417–6429 (2024)