Prediction of Soluble Al2O3 in Calcined Kaolin Using Infrared Spectroscopy and Multivariate Calibration
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. Ba...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-03-01
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| Series: | Minerals |
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| Online Access: | http://www.mdpi.com/2075-163X/8/4/136 |
| _version_ | 1811295658139713536 |
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| author | Adriana Guatame-Garcia Mike Buxton |
| author_facet | Adriana Guatame-Garcia Mike Buxton |
| author_sort | Adriana Guatame-Garcia |
| collection | DOAJ |
| description | 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 R 2 = 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. |
| first_indexed | 2024-04-13T05:36:37Z |
| format | Article |
| id | doaj.art-6749b339e1d34f58b3b2eaf6737d6f16 |
| institution | Directory Open Access Journal |
| issn | 2075-163X |
| language | English |
| last_indexed | 2024-04-13T05:36:37Z |
| publishDate | 2018-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Minerals |
| spelling | doaj.art-6749b339e1d34f58b3b2eaf6737d6f162022-12-22T03:00:16ZengMDPI AGMinerals2075-163X2018-03-018413610.3390/min8040136min8040136Prediction of Soluble Al2O3 in Calcined Kaolin Using Infrared Spectroscopy and Multivariate CalibrationAdriana Guatame-Garcia0Mike Buxton1Resource Engineering Section, Department of Geoscience and Engineering, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The NetherlandsResource Engineering Section, Department of Geoscience and Engineering, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The NetherlandsIn 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 R 2 = 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.http://www.mdpi.com/2075-163X/8/4/136soluble Al2O3calcined kaolinmultivariate calibrationsupport vector regressionpartial least squaresinfrared spectroscopy |
| spellingShingle | Adriana Guatame-Garcia Mike Buxton Prediction of Soluble Al2O3 in Calcined Kaolin Using Infrared Spectroscopy and Multivariate Calibration Minerals soluble Al2O3 calcined kaolin multivariate calibration support vector regression partial least squares infrared spectroscopy |
| title | Prediction of Soluble Al2O3 in Calcined Kaolin Using Infrared Spectroscopy and Multivariate Calibration |
| title_full | Prediction of Soluble Al2O3 in Calcined Kaolin Using Infrared Spectroscopy and Multivariate Calibration |
| title_fullStr | Prediction of Soluble Al2O3 in Calcined Kaolin Using Infrared Spectroscopy and Multivariate Calibration |
| title_full_unstemmed | Prediction of Soluble Al2O3 in Calcined Kaolin Using Infrared Spectroscopy and Multivariate Calibration |
| title_short | Prediction of Soluble Al2O3 in Calcined Kaolin Using Infrared Spectroscopy and Multivariate Calibration |
| title_sort | prediction of soluble al2o3 in calcined kaolin using infrared spectroscopy and multivariate calibration |
| topic | soluble Al2O3 calcined kaolin multivariate calibration support vector regression partial least squares infrared spectroscopy |
| url | http://www.mdpi.com/2075-163X/8/4/136 |
| work_keys_str_mv | AT adrianaguatamegarcia predictionofsolubleal2o3incalcinedkaolinusinginfraredspectroscopyandmultivariatecalibration AT mikebuxton predictionofsolubleal2o3incalcinedkaolinusinginfraredspectroscopyandmultivariatecalibration |