Automation in sensing and raw material characterization - a conceptual framework

Abstract

The use of sensor technologies for material characterization is rapidly growing and innovative advancement is observed. However, the use of sensor combinations for a raw material characterization in mining is very limited and automation of the material identification process using a combined sensor signal is not defined. Potential sensor technologies for raw material characterization were evaluated based on the applicability and technological maturity. To ensure a rapid implementation of the Real-time mining (RTM) project concept, mature technologies such as Red Green Blue (RGB) imaging, Visible Near Infrared (VNIR) hyperspectral imaging, Short Wave Infrared (SWIR) hyperspectral imaging, Fourier-Transform Infrared Spectroscopy (FTIR), Laser Induced Breakdown Spectroscopy (LIBS) and Raman were selected. Each selected technology was assessed for automation in sensing and applicability (for characterization of the test case materials). Based on the results the sensor data were further considered for data fusion. The proposed sensor combinations approach encompasses three levels of data fusion: low-level, mid-level and high-level. The data of the different sensors are fused together in order to acquire a wide range of mineral properties within each lithotype and an improved classification and predictive models. The preferred level of data fusion and preferred sensor data combinations will be used to develop a multi-variate statistical interpretation rule which relates combination of sensors signals with raw material properties. Thus a tool which integrates the combined sensor signal with materials properties will be developed and used to automate the material characterization process.