This Small Business Technology Transfer Phase I project will involve the development of an innovative spectral image system to track rock material flows through mining operations. Accurately tracking material flows will allow smarter mineral processing circuits and optimized blasting, significantly reducing waste and energy. This addresses two of the central environmental impacts associated with mining: the size of the environmental footprint, and the large amount of energy used. The tracking system will be based on spectral imaging of the ore at various locations, such as after blasting, during the various stages of crushing and grinding, and before and after stockpiles and bins. The system will continuously track ore type at these locations, based on the unique spectral signature of different ore types in a mine. Accurate material tracking will be accomplished by integrating push-broom hyperspectral imaging with particle delineation algorithms, and by developing a tracking algorithm that tracks both rock type and material volumes. The Phase I work will involve spectral laboratory testing in simulated mining environments using mine rock samples, the development of algorithms to process the integrated spectral/particle delineation data, the development of a tracking algorithm, and the determination of optimal hardware and software for a prototype system.
The broader impact/commercial potential of this project will be a reduction in the environmental impact of mineral operations and a reduction in energy consumption, via solutions applied during processing. The proposed technology presents new opportunities to improve mineral processing to customize the techniques to each specific rock type encountered. The tracking mechanisms for material types will enable operators to adjust settings for the ore type to effectively maximize the recovery while reducing unproductive activities. As a result, requirements for energy, materials, and solutions will be reduced throughout the process. The significant benefit achieved will be an overall reduced physical footprint of mining operations. This technology will also promote further investment into existing operations as opposed to the creation of new green-field projects. The new information and data provided by this system will allow more insight on the physical, mechanical and chemical properties that affect mineral extraction. This effort will in turn drive additional research by universities which will further understanding of the process and the advancement of the technology. Finally, the advancement of hyperspectral imaging technologies will cross-over to other applications and industries that will further research and investigation.
This Small Business Innovation Research (SBIR) Phase I project included the research and development of a hyperspectral camera system to identify different ore types processed by mineral operations. Also, a simulation model was developed to analyze how different ore types affect costs and production. Two primary environmental impacts associated with mining are the large amount energy used, and the large amount of waste rock that is produced. Rock crushing and grinding in particular utilizes a very large amount of energy, and a large amount of waste rock is produced from processing ores that only contain a very small fraction of the desired mineral. One of the goals of the research is to develop technologies that can optimize the mining process and thereby considerably reduce the amount of energy used and the amount of waste produced. This research focuses on the use of a hyperspectral imaging system to track material flows, starting with the rock after blasting and continuing through the crushing and grinding circuit. First of all, by tracking material flows, the rock fragmentation due to blasting in different parts of the mine can be accurately determined and used to optimize blasting, crushing and grinding. Secondly, by tracking material flows, the material going into the mineral liberation circuit at any given time can be accurately determined and used to increase mineral recovery and reduce waste. The research included a series of laboratory tests with a range of spectral cameras and a range of rock types from six different mining operations. Tests were conducted to determine the practicality of using spectral systems in a mining environment. This included experimenting with different lighting conditions, dust conditions, and utilizing an actual conveyor belt. Finally, a simulation model was developed that can be used to vary the many parameters and determine the accuracy and usefulness of the proposed spectral tracking system. The societal benefit will be from reducing the environmental impact from mineral operations and from reducing energy and solutions applied during processing. The proposed technology presents new opportunities for improving mineral processing based upon unique product types. As a result, less energy, materials and solutions will be applied to the process. Also, the overall physical footprint of mining operations would be reduced as operators would be able to invest into existing operations while reducing new green-field construction. Understanding different rock types will allow operators to not overuse blasting product that further destabilizes steep mining walls. Less damage to the surrounding work environment will lead to fewer wall collapses and reductions in human injury or loss. The new information and data will provide more insight on the physical, mechanical and chemical properties that affect mineral processing. Additional research by universities would be initiated with new understanding of the process and the advancement of the technology. Commercial applications would then be created to address the identified issues.
