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Optimization of cesium adsorption by Prussian blue using experiments and gene expression modeling

Authors
Nguyen, Mai NgocYaqub, MuhammadKim, SeongbeomLee, Wontae
Issue Date
Jun-2021
Publisher
ELSEVIER
Keywords
Adsorption; Cesium; Gene expression programming; Kinetics; Prussian blue
Citation
JOURNAL OF WATER PROCESS ENGINEERING, v.41
Journal Title
JOURNAL OF WATER PROCESS ENGINEERING
Volume
41
URI
https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/19349
DOI
10.1016/j.jwpe.2021.102084
ISSN
2214-7144
Abstract
Water-soluble and insoluble Prussian blue (PB) named PB-sol and PB-insol are synthesized to investigate the removal of cesium (Cs+) from an aqueous solution. During synthesis, different molar ratios of potassium hexacyanoferrate and ferric chloride are tested for adsorption of Cs+. The effect of synthesized PBs on the pH of deionized water (DI) and surface water is also studied. The effect of aqueous solution pH, competitive ions, and initial concentration of Cs+ on adsorption by PB-sol and PB-insol are explored separately as well as in combination with zeolite. Moreover, the adsorption kinetics are discussed by applying pseudo 1st and pseudo 2nd order kinetic models, and gene expression programming (GEP) model is developed for the first time to optimize the process. The PB-sol and PB-insol led to a lower pH of the aqueous solution. PB-insol/zeolite showed maximum adsorption of 91.8 mg g (-1) at an initial Cs+ concentration of 100 ppb in the surface water, whereas PB-sol, PBinsol, and PB-sol/zeolite demonstrated comparatively lower results. An increasing trend with higher initial concentrations is observed, while high pH and the absence of competing ions also favored the Cs+ adsorption. A significant decrease is noticed in presence of both monovalent and divalent ions (K+, Na+, Ca2+, and Mg2+). The kinetics are better described by the pseudo 2nd order kinetics model because of the physiochemical adsorption. Moreover, the developed GEP model presented reasonably good results for PB-sol and PB-insol by tracing the input-output relationship with R-2 = 0.799 and 0.886, respectively, for the testing dataset.
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