Evaluation of sensor technologies for on-line raw material characterization in “Reiche Zeche” underground mine - outcomes of RTM implementation

Abstract

The increasing advances in sensor technology have resulted in greater availability of sensor data for a wide range of applications. One such application is raw material characterization in mining operations. Sensor technologies operate over certain range of the electromagnetic spectrum and provide information on several aspects of material properties. The sensitivity and the material properties the instrument detects and measures varies from sensor to sensor. The purpose of this study was to synthesize and evaluate the use of sensor technologies for characterization of a polymetallic sulphide deposit in “Reiche Zeche” underground mine. This paper discusses the material characterization methodology using sensor technologies, demonstrates how it fits within the Real-Time Mining (RTM) framework, identifies the interface for both software and hardware requirements and defines the gaps and limitations of application of sensors. It provides a brief overview of the use of sensor and data fusion for material characterization to convey a high-level context in raw material characterization. The sensor technologies considered in this study include RGB imaging, visible–near infrared (VNIR), short wave infrared (SWIR), mid-wave infrared (MWIR), long-wave infrared (LWIR) and Raman spectroscopy.

The required information from sensor data in mining operations is not limited to grade control applications. Information on co-occurring minerals or elements are also important for definition of requirements in mineral processing, to identify indirect proxies of elements/minerals of interest, to understand the formation of minerals, to define requirements for blasting parameters, to improve safety and to define requirements for environmental monitoring of toxic material. In view of these points, there is a need for combinations of sensors to achieve a near complete description of material composition and properties. The methodological approaches developed for information extraction from each sensor data and fused data are presented. This includes both direct mineral fingerprinting and indirect proxies using spectral data. The efficient sensor data processing methods and the acquired results from the use of individual sensor and the fused data are summarized. Overall, the acquired results from the use of each sensor technology and the data fusion approach significantly contributed to an improvement of data quality and illustrate the efficiency of use of sensors in the mining industry. However, some of the observed limitations include lack of system robustness, a need for test case specific mineral libraries, the need for development of an integrated principled tool for efficient data collection, processing and knowledge generation. Going forward, automated material characterization is possible with robust system design (exemplified by portable and ruggedized system) and efficient software (test case specific mineral libraries) that can be developed using a combined sensor signal.