Klassifikationsverfahren zur Materialerkennung : Grenzschichterkennung mittels laserinduzierter Fluoreszenz in mineralischen Lagerstätten am Beispiel der Braunkohlegewinnung

Abstract

The analytical tool of the optical laser induced fluorescence (LIF) method offers the opportunity to recognize different materials and to detect boundaries. This study shows the methodology how to extract significant features from the fluorescence spectrum using systematic search algorithms. An optimization of the mining process and consequently a better quality of the extracted material are the direct results of such a material recognition. In contrast to the traditional approaches, which often use the principle of "trial & error", this systematic way allows for the best recognition of the mined materials. Measurements taken in RWE Power's Hambach open pit lingnite mine form the basis of this paper. Using material specific fluorescence characteristics, the different kind of material should be distinguished. The avoidance of unscheduled and uncontrolled dilution during the mining process results in an optimization of the overall deposit extraction by means of a reduction of safety barriers. The large numbers of features, which can be extracted from the emission spectrum, pose a problem to both layout and dimensions of a robust LIF analyzer, which can be employed in the mining industry. Therefore the selection of suited feature is crucial. Apart from the Gaussian classification approach k-nearest neighbor procedures and the method of linear regression are applied to realize the material classification. Conventional sequential search algorithms and the "Sequential Forward Floating Selection (SFFS)“ are utilized. The extraction of significant LIF features is based on the "Wrapper-2" approach. As a result of analysis of the number of material classes one can observe that the success of the material recognition is depended on depth of segmentation. The more detailed alias the more classes the less satisfying the success. Regarding the classification methods none of the three approaches seems to be superior when taking the success criterion and the needed computing time into account. The basic lesson learnt from this work is the statement that using an optimized method for the feature selection and the material classification effective boundary detection is possible for the Hambach mine's section in scope. It results from the utilization of classification algorithms which allow for an uncomplicated assigment of LIF measurements to quality relevant material classes. Starting from the presented methodology of feature extraction and material classification this work lays the foundation of a systematic analysis of LIF measurement for effective future application of the LIF technology. Furthermore it offers a starting point for future research activities and shows the potential for further optimization in the field of LIF analytics application for material classification in the mining industry