Prediction of Soluble Al2O3 in Calcined Kaolin Using Infrared Spectroscopy and Multivariate Calibration

Abstract

In the production of calcined kaolin, the soluble Al2O3 content is used as a quality control criterion for some speciality applications. The increasing need for automated quality control systems in the industry has brought the necessity of developing techniques that provide (near) real-time data. Based on the understanding that the presence of water in the calcined kaolin detected using infrared spectroscopy can be used as a proxy for the soluble Al2O3 measurement, in this study, a hand-held infrared spectrometer was used to analyse a set of calcined kaolin samples obtained from a production plant. The spectra were used to predict the amount of soluble Al2O3 in the samples by implementing Partial Least Squares regression (PLS-R) and Support Vector Regression (SVR) as multivariate calibration methods. The presence of non-linearities in the dataset and the different types of association between water and the calcined kaolin represented the main challenges for developing a good calibration. In general, SVR showed a better performance than PLS-R, with Root Mean Squared Error of the cross-validation (RMSECV) = 0.046 wt.% and R2 = 0.87 for the best-achieved prediction. This accuracy level is adequate for detecting variation trends in the production of calcined kaolin which could be used not only as a quality control strategy but also for the optimisation of the calcination process.