In the production of calcined kaolin, the on-line monitoring of the calcination reaction is becoming more relevant for the generation of optimal products. In this context, this study aimed to assess the suitability of using infrared (IR) spectroscopy as a potential technique for the on-line characterization of the calcination of kaolin. The transformation of kaolin samples calcined at different temperatures were characterized in the short-wave (SWIR) spectra using the kaolinite crystallinity (Kx) index and the depth of the water spectral feature (1900D). A high correlation between the standard operational procedure for the quality control of calcined kaolin and the Kx index was observed (r = -0.89), as well as with the 1900D parameter (r = -0.96). This study offers a new conceptual approach to the use of SWIR spectroscopy for the characterization the calcination of kaolin, withdrawing the need of using extensive laboratory techniques.
Diatomite, a rock formed by the accumulation of opaline diatom frustules, is a preferred raw material for the manufacturing of filters. Its uniqueness relies on the high porosity and inertness of the frustules. The presence of carbonates in some diatomite ores hinders these properties. The purpose of this study was to identify the type of carbonates and their association with the ore in a diatomite deposit, and to assess the suitability of determining the quality of the ore using techniques with potential for in-pit implementation. For this, run-of-mine samples were analysed using environmental scanning electron microscopy (ESEM) and infrared spectroscopy. The ESEM images showed that carbonate is present as cement and laminae. The infrared data revealed that the carbonate minerals correspond to aragonite and calcite, and that their occurrence is linked to the total amount of carbonate in the sample. By using a portable spectral instrument that uses diffuse reflectance, it was possible to classify the spectra of the ore samples based on the carbonate content. These results indicate that infrared technology could be used on-site for determining the quality of the ore, thus providing relevant information to assist the optimisation of mining and beneficiation activities.
In the production of calcined kaolin, the soluble Al 2 O 3 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 Al 2 O 3 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 Al 2 O 3 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. Keywords: soluble Al 2 O 3 ; calcined kaolin; multivariate calibration; support vector regression; partial least squares; infrared spectroscopy 2 (Al 2 O 3 • 2 SiO 2) 980 • C −−−→ 2 Al 2 O 3 • 3 SiO 2 + SiO 2 (amorphous) (2) 3 (2 Al 2 O 3 • 3 SiO 2) >1100 • C − −−−− → 3 (Al 2 O 3 • 2 SiO 2) + 5 SiO 2 (amorphous) (3)
Many scientific journals have committed to advancing diversity, equity, and inclusion, but publish articles counter to this goal. We propose actions the scientific community should take to move research and the publication process toward more rigorous and socially-just standards.
TRIM4Post-Mining is a H2020/RFCS-funded project that brings together a consortium of European experts from industry and academia to develop an integrated information modelling system. This is designed to support decision making and planning during the transition from coal exploitation to a revitalized post-mining landscape, enabling infrastructure development for agricultural and industrial utilization, and contributing to the recovery of energy and materials from coal mining dumps. The smart system will be founded upon a high-resolution spatiotemporal database, utilizing state-of-the-art multi-scale and multi-sensor monitoring technologies that characterize dynamic processes in coal waste dumps related to timely, dependent deformation and geochemical processes. It will integrate efficient methods for operational and post-mining monitoring, comprehensive spatiotemporal data analytics, feature extraction, and predictive modelling; this will allow for the identification of potential contamination areas and the forecasting of geotechnical risks and ground conditions. For the interactive exploration of alternative land-use planning scenarios—in terms of residual risks, technical feasibility, environmental and social impact, and affordability—up-to-date data and models will be embedded in an interactive planning system based on Virtual Reality and Augmented Reality technology, forming a TRIM—a Transition Information Modelling System. This contribution presents the conceptual approach and main constituents, and describes the state-of-the-art and detailed anticipated methodological approach for each of the constituents. This is supported by the presentation of the first results and a discussion of future work. An anticipated second contribution will focus on the main findings, technology readiness and a discussion of future work.
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