GRSG 34th Conference 2023

Title: Application of wide-range infrared spectroscopy for coal mine waste characterization

Author: Oscar Kamps

Abstract:

The current climate change crisis was the reason for the European Union to develop Europe’s Green Deal containing a vision to end the dependence on fossil fuels (Fetting, 2020). This reduction of fossil fuel energy makes coal mining redundant and results in the closure of these mines. The post-mining landscape is characterized by open pits and large areas with waste dumps at the surface. Mine waste consists of all rocks that are not of economic interest and are dumped as landfills. The problem with this mine waste is that it is a heterogeneous material having various geotechnical and geochemical concerns. For these reasons, it is important to characterize the material and better understand the mine waste composition. Rather than sampling and taking detailed lab measurements, it would be preferable to have portable and real-time sensors that can be used to characterize the mine waste composition. This study focuses on the integration of infrared spectroscopic data such as Fourier Transform Infrared Spectroscopy (FTIR) and short-wave infrared (SWIR) with geochemical data of X-ray fluorescence (XRF).

This should result in a better understanding of the mine waste and how mineralogy and geochemistry relate. The benefit of infrared spectroscopy is that it could potentially be extrapolated to remote sensing imaging systems from ground, air or space. Methods The samples come from different mines where the lignite deposits are deposited in fluviatile and estuarine environments in the Eocene. The lignite is found in several seams with interlayers of sedimentary deposits of clay, silt and sand. We have used different machine-learning techniques to test the mineralogical and geochemical composition of various mines and mine waste sites. Both the infrared and XRF data are integrated using support vector machine regression (SVR). Thereby we tested multiple data pre-processing methods and selected different subsets of the wavelength ranges.

We will build regression models based on measurements from samples taken from specific lithologies. These models will be applied to the measurements of the waste dump samples. This way we can test the model accuracy based on the nature of the samples, measuring mode (i.e. field- or lab measurements) and influence of pre-processing the samples. Results Initial results have shown that the regression model accuracy is very much dependent on what element is modelled. Also, what wavelength range used for the modelling has a high impact on the model accuracy. So far, the modelling has been performed on the mine waste samples measured in the field. It is expected that the next measurements that will be taken in the lab on pre-processed samples collected from specific lithologies will improve the accuracy of the model. By the time of the conference, the data should be collected and the model results can be presented.

The current climate change crisis was the reason for the European Union to develop Europe’s Green Deal containing a vision to end the dependence on fossil fuels (Fetting, 2020). This reduction of fossil fuel energy makes coal mining redundant and results in the closure of these mines. The post-mining landscape is characterized by open pits and large areas with waste dumps at the surface. Mine waste consists of all rocks that are not of economic interest and are dumped as landfills. The problem with this mine waste is that it is a heterogeneous material having various geotechnical and geochemical concerns. For these reasons, it is important to characterize the material and better understand the mine waste composition.

Rather than sampling and taking detailed lab measurements, it would be preferable to have portable and real-time sensors that can be used to characterize the mine waste composition. This study focuses on the integration of infrared spectroscopic data such as Fourier Transform Infrared Spectroscopy (FTIR) and short-wave infrared (SWIR) with geochemical data of X-ray fluorescence (XRF). This should result in a better understanding of the mine waste and how mineralogy and geochemistry relate. The benefit of infrared spectroscopy is that it could potentially be extrapolated to remote sensing imaging systems from ground, air or space. Methods The samples come from different mines where the lignite deposits are deposited in fluviatile and estuarine environments in the Eocene. The lignite is found in several seams with interlayers of sedimentary deposits of clay, silt and sand. We have used different machine-learning techniques to test the mineralogical and geochemical composition of various mines and mine waste sites. Both the infrared and XRF data are integrated using support vector machine regression (SVR). Thereby we tested multiple data pre-processing methods and selected different subsets of the wavelength ranges. We will build regression models based on measurements from samples taken from specific lithologies. These models will be applied to the measurements of the waste dump samples. This way we can test the model accuracy based on the nature of the samples, measuring mode (i.e. field- or lab measurements) and influence of pre-processing the samples. Results Initial results have shown that the regression model accuracy is very much dependent on what element is modelled. Also, what wavelength range used for the modelling has a high impact on the model accuracy. So far, the modelling has been performed on the mine waste samples measured in the field. It is expected that the next measurements that will be taken in the lab on pre-processed samples collected from specific lithologies will improve the accuracy of the model. By the time of the conference, the data should be collected and the model results can be presented.
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