Response surface methodology and artificial neural network for prediction and validation of bisphenol a adsorption onto zeolite imidazole framework

Mahmad, A. and Zango, Z.U. and Noh, T.U. and Usman, F. and Aldaghri, O.A. and Ibnaouf, K.H. and Shaharun, M.S. (2023) Response surface methodology and artificial neural network for prediction and validation of bisphenol a adsorption onto zeolite imidazole framework. Groundwater for Sustainable Development, 21.

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Abstract

Zeolite imidazole frameworks (ZIFs) have demonstrated good capacity in the adsorption of molecules. This work reported the highly porous ZIF�8 with a specific Bruner�Emmett�Teller (BET) area and pore volume of 1299 m2/g and 0.60 m3/g, respectively, for the effective removal of bisphenol A (BPA) from the aqueous medium. The experiments were designed using response surface methodology (RSM), according to Box�Behnken design (BBD), comprising four factors; BPA concentrations, ZIF�8 dosages, pH, and contact time. The model fitting was justified by the analysis of variance with the statistical model F and p�values of 6.360 and 0.0007, respectively, thus, achieving the highest removal efficiency of 99.93. The artificial neural network (ANN) was employed for the experimental validation, and the optimum topography was obtained at node 10. Thermodynamically, the process was described as exothermic and spontaneous, with overall changes of enthalpy (�H°) and entropy (�S°) of 9.557 kJ/mol and 0.0142 J/mol/K, respectively. The ZIF�8 has demonstrated good reusability for several adsorption cycles. Thus, ZIF�8 could be adopted as potential material for BPA removal from the environmental waters. © 2023 Elsevier B.V.

Item Type: Article
Additional Information: cited By 12
Uncontrolled Keywords: Adsorption; Neural networks; Phenols; Reusability; Surface properties; Topography, Aqueous media; Bis-phenol a; Bisphenol A; Bisphenols-A; Effective removals; Imidazol; Pore volume; Porous zeolites; Response-surface methodology; Zeolite imidazole framework, Zeolites, adsorption; artificial neural network; enthalpy; entropy; pollutant removal; prediction; response surface methodology; thermodynamics; zeolite
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 04 Jun 2024 14:10
Last Modified: 04 Jun 2024 14:10
URI: https://khub.utp.edu.my/scholars/id/eprint/18602

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