ScholarWorks@동국대학교

초록

Bentonite, mainly composed of montmorillonite, has unique physicochemical properties, such as a high swelling capacity, low hydraulic conductivity, and high cation exchange capacity. The properties of bentonite significantly depend on its montmorillonite content, making quantifying montmorillonite essential for evaluating bentonite. Traditional methods such as X-ray diffraction analysis often encounter difficulties due to the structural and elemental variability of clay minerals. In contrast, spectroscopy can provide a fast and cost-effective alternative with the benefits of straightforward preprocessing and measurement. This study aimed to develop calibration models for predicting montmorillonite content in bentonite using visible and near-infrared (Vis–NIR) spectral features combined with partial least squares regression (PLSR) analysis. Quartz, feldspar, Ca-bentonite (KCa-B), and Na-bentonite (GNa-B) were used in this study. Montmorillonites (KCa-M and GNa-M) were extracted from their respective bentonites. Binary and ternary mixtures of these minerals were then prepared and analyzed spectrally in the 350–2500 nm range. Correlations between montmorillonite content and spectral features were derived using PLSR, with evaluation via the leave-one-out cross-validation method. The resulting model demonstrated high accuracy with R2 and RMSE values of 0.917 and 8.6 wt% for Ca-montmorillonite and 0.936 and 7.5 wt% for Na-montmorillonites, respectively. Independent validation confirmed the effectiveness of the model. Furthermore, adjustments for humidity based on Vis–NIR spectral variations can potentially enhance the precision of the prediction. The study highlights the potential of Vis-NIR spectroscopy as a reliable tool for predicting montmorillonite content in bentonite. © 2024 The Authors

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