Design Optimization Methodologies for Multi-stage Samplers in Mining Applications

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In the mining industry, reliable accounting of mineral production is essential to sound corporate governance, particularly as the figures generated by metal accounting systems feed directly into the financial accounts of mining companies. In many instances, metal accounting requires accurate measurement of metal content in slurry flows, which are fluid mixtures of solid particles in a liquid medium (e.g. water) found in many mineral processing plants as a convenient way of transporting and handling solid particles (e.g. extracted minerals) in bulk. The goal of this research project is to develop methods and algorithms to optimize the design of slurry sampling systems to improve their precision and reduce sampling bias, thus providing more accurate and cost-effective accounting of mineral content in slurry flows. The potential impact of these developments on the mining industry is astounding, as just a fraction of a percent improvement in the accuracy of the sampling system can impact millions of dollars in revenues. Successful completion of the project would also help Canadian firms to develop and/or maintain their leadership in the global market for mining services and equipment.