Synthesis of heated aluminum oxide particles impregnated with Prussian blue for cesium and natural organic matter adsorption: Experimental and machine learning modeling
- Authors
- Yaqub, Muhammad; Nguyen, Mai Ngoc; Lee, Wontae
- Issue Date
- Feb-2023
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Keywords
- Adsorption; Cesium; Gene expression programming; Heated aluminum oxide particles; Natural organic matter; Prussian blue
- Citation
- CHEMOSPHERE, v.313
- Journal Title
- CHEMOSPHERE
- Volume
- 313
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/21531
- DOI
- 10.1016/j.chemosphere.2022.137336
- ISSN
- 0045-6535
1879-1298
- Abstract
- Heated aluminum oxide particles impregnated with Prussian blue (HAOPs-PB) are synthesized for the first time using different molar ratios of aluminum sulfate and PB to improve the adsorption of cesium (133Cs+) and natural organic matter (NOM) from an aqueous solution. The Cs+ adsorption from various aqueous solutions, including surface, tap and deionized water by synthesized HAOPs-PB, is investigated. The influencing factors such as HAOPs-PB mixing ratio, pH and dosage are studied. In addition, pseudo 1st and 2nd order is tested for adsorption kinetics study. A machine learning model is developed using gene expression programming (GEP) to evaluate and optimize the adsorption process for Cs+ and NOM removal. Synthesized adsorbent showed maximum adsorption at a 1:1 M ratio of aluminum sulfate and PB in DI, tap, and surface water. The pseudo 2nd order kinetics model described the Cs + adsorption by HAOPs-PB more accurately that indicating physiochemical adsorption. Adsorption of Cs+ showed an increasing trend with higher HAOPs-PB concentration, while high pH also favored the adsorption. Maximum NOM adsorption is found at a higher HAOPs-PB dosage and a neutral pH value. Furthermore, the proposed GEP model shows outstanding performance for Cs+ adsorption modeling, whereas a modified-GEP model presents promising results for NOM adsorption prediction for testing dataset by learning the relationship between inputs and output with R2 values of 0.9348 and 0.889, respectively.
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Collections - Department of Environmental Engineering > 1. Journal Articles
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