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Remediation of acidic effluents from Uranium-Contaminated soil using coffee Residue Biochar: A Combined experimental and Machine learning approach

Title
Remediation of acidic effluents from Uranium-Contaminated soil using coffee Residue Biochar: A Combined experimental and Machine learning approach
Authors
Jun B.; Chae S.H.; Kim D.; Son C.; Kim T.; Hong S.; Yoon Y.; Chon K.; Rho H.
Ewha Authors
윤여민
SCOPUS Author ID
윤여민scopus
Issue Date
2025
Journal Title
Separation and Purification Technology
ISSN
1383-5866JCR Link
Citation
Separation and Purification Technology vol. 366
Keywords
Acidic Effluent; Biochar; Machine Learning; Uranium Adsorption; Effluents; Radioactive Wastes; Remediation; Soil Pollution; Uranium Compounds; Adsorption Capacities; Adsorption Performance; Coffee Residue; Contaminated Soils; Experimental Learning; Machine Learning Approaches; Machine-learning; Radioactive Waste Disposal
Publisher
Elsevier B.V.
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
Abstract
Uranium contamination resulting from nuclear waste disposal poses significant environmental and health risks, necessitating the development of effective remediation strategies. In this study, we investigate the use of biochar-based adsorbents derived from coffee waste for the removal of U(VI) from acidic effluent produced by real uranium-contaminated soil through acid leaching, providing a novel solution to real-world contamination. Two biochars—pristine and ZnFe-modified—were synthesized, and their U(VI) adsorption performance was evaluated through a comprehensive analysis of their physicochemical properties. The biochar's adsorption performance was assessed under diverse experimental conditions, examining its isotherm, kinetic, and thermodynamic characteristics. Additionally, its behavior was analyzed in the presence of background cations, anions, and humic acid within a real acidic effluent containing U(VI). Despite the significantly larger surface area of ZnFe-modified biochar (1218.4 m2/g vs. 40.8 m2/g for pristine biochar), pristine biochar exhibited superior U(VI) adsorption capacity at pH 4. This enhancement was attributed to its negative surface charge, which promotes electrostatic interactions with the positively charged U(VI) ions. Conversely, the positive surface charge of ZnFe-modified biochar hindered U(VI) adsorption efficiency under similar conditions. Machine learning models, including Random Forest, XGBoost, and LightGBM, were employed to predict adsorption capacity and analyze key operational parameters. SHapley Additive exPlanations analysis identified initial uranium concentration, exposure time, and pH as the most critical factors. These findings underscore the novelty of using real uranium-contaminated soil effluent and highlight pristine biochar as an effective, sustainable material for U(VI) removal. © 2025 Elsevier B.V., All rights reserved.
DOI
10.1016/j.seppur.2025.132844
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공과대학 > 환경공학과 > Journal papers
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