Arsenate removal using chitosan-coated bentonite via fixed-bed system: a process integration by fuzzy optimization

Cybelle Concepcion Morales Futalan; Khyle Glainmer Nagtalon Quiton; Angelo Earvin Sy Choi

doi:10.1007/s11356-024-33301-1

Arsenate removal using chitosan-coated bentonite via fixed-bed system: a process integration by fuzzy optimization

Environ Sci Pollut Res Int. 2024 Apr 15. doi: 10.1007/s11356-024-33301-1. Online ahead of print.

Authors

Cybelle Concepcion Morales Futalan¹, Khyle Glainmer Nagtalon Quiton², Angelo Earvin Sy Choi³

Affiliations

¹ Institute of Civil Engineering, University of the Philippines Diliman, 1101, Quezon City, Philippines.
² School of Chemical, Biological, and Materials Engineering and Sciences, Mapúa University, 1002, Intramuros, Manila, Philippines.
³ Department of Chemical Engineering, De La Salle University, 2401 Taft Avenue, 0922, Malate, Manila, Philippines. angelo.choi@dlsu.edu.ph.

PMID: 38622419
DOI: 10.1007/s11356-024-33301-1

Abstract

Groundwater contamination is a global concern that has detrimental effect on public health and the environment. Sustainable groundwater treatment technologies such as adsorption require attaining a high removal efficiency at a minimal cost. This study investigated the adsorption of arsenate from groundwater utilizing chitosan-coated bentonite (CCB) under a fixed-bed column setup. Fuzzy multi-objective optimization was applied to identify the most favorable conditions for process variables, including volumetric flow rate, initial arsenate concentration, and CCB dosage. Empirical models were employed to examine how initial concentration, flow rate, and adsorbent dosage affect adsorption capacity at breakthrough, energy consumption, and total operational cost during optimization. The ε-constraint process was used in identifying the Pareto frontier, effectively illustrating the trade-off between adsorption capacity at breakthrough and the cost of the fixed-bed system. The integration of fuzzy optimization for adsorption capacity and its total operating cost utilized the global solver function in LINGO 20 software. A crucial equation derived from the Box-Behnken design and a cost equation based on energy and material usage in the fixed-bed system was employed. The results from identifying the Pareto front determined boundary limits for adsorption capacity at breakthrough (ranging from 12.96 ± 0.19 to 12.34 ± 0.42 μg/g) and total operating cost (ranging from 955.83 to 1106.32 USD/kg). An overall satisfaction level of 35.46% was achieved in the fuzzy optimization process. This results in a compromise solution of 12.90 μg/g for adsorption capacity at breakthrough and 1052.96 USD/kg for total operating cost. Henceforth, this can allow a suitable strategic decision-making approach for key stakeholders in future applications of the adsorption fixed-bed system.

Keywords: Arsenate; Bentonite; Chitosan; Fixed-bed system; Fuzzy optimization; Pareto front.