Real-Time Integration of Sensor Data for Improved Grade Control

"Advanced data acquisition and process modelling technology provides online data and decision support about different aspects of the resource extraction process. Real-time approaches have recently been applied to utilize the value of this information for improved production control in mineral resource extraction in different operational set ups including coal, polymetallic ore and gold deposits. The key concept of these approaches promotes the change in paradigm from discontinuous intermittent process monitoring to a continuous process and quality management system in the resource extraction process. The framework includes a real-time feedback control loop that rapidly links online material characteristics data acquired during extraction at the mining face, during material handling and processing with a sequentially up-datable resource model. This will allow near real-time optimization of decisions related to long-term planning, short-term sequencing and production control. The contribution reviews recent developments in the three main constituents sensor based material characterisation, rapid updating of grade control models and simulation based optimization for improved production control. Technological readiness is assessed and critical gaps for further technology development are identified. The value added is illustrated by means of selected case studies. INTRODUCTION With the depletion of known and established mineral reserves, the decrease in head grades and the continuing demand of mineral raw materials by the modern society, future exploitation will move towards extraction of deposits under geologically more complex conditions. Complex deposits are characterized by a low continuity in grade, presence of deleterious elements such as arsenic and high irregularity in the geometry of the ore boundaries. The profitable exploitation of such deposits becomes significantly more challenging. To be able to better utilize such unconventional mineral resources of the future, smart autonomous mining systems are needed, which integrate local exploration of spatially varying material characteristics in–situ and have the ability to follow and extract the pay zones in the ore body while minimizing dilution (Buxton and Benndorf, 2013). The main barriers to overcome for the successful economic exploitation are: • effective grade control which will maximize resource potential along the whole value chain, • minimization of handling zero-value material introduced by dilution, thus • reducing unnecessary expenditure of energy and financial resources and • management and control of the geological uncertainty due to limited information available,"