STTR Phase I: Reducing Mining Waste and Energy Using a Spectral Imaging Tracking System

这项小型企业技术转让第一阶段项目将涉及开发一种创新的光谱图像系统，以跟踪采矿作业中的岩石材料流动。准确跟踪物料流动将允许更智能的选矿电路和优化爆破，显着减少浪费和能源。这涉及与采矿有关的两个主要环境影响：环境足迹的大小和使用的大量能源。跟踪系统将基于不同位置的矿石的光谱成像，例如爆破后，破碎和研磨的各个阶段，以及库存和仓之前和之后。该系统将根据矿山中不同矿石类型的独特光谱特征，在这些位置连续跟踪矿石类型。通过将推扫帚高光谱成像与颗粒描绘算法相结合，并开发一种跟踪岩石类型和材料体积的跟踪算法，可以实现精确的材料跟踪。第一阶段的工作将包括使用矿山岩石样本在模拟采矿环境中进行光谱实验室测试，开发处理综合光谱/颗粒描绘数据的算法，开发跟踪算法，以及确定原型系统的最佳硬件和软件。这个项目更广泛的影响/商业潜力将是通过在加工过程中采用的解决办法减少矿物作业对环境的影响和减少能源消耗。所提出的技术为改进矿物加工提供了新的机会，可以针对遇到的每种特定岩石类型定制技术。物料类型的跟踪机制将使作业人员能够调整矿石类型的设置，以有效地最大化采收率，同时减少非生产活动。因此，对能源、材料和解决方案的需求将在整个过程中减少。取得的重大效益将是采矿作业的实际足迹总体减少。这项技术还将促进对现有业务的进一步投资，而不是创建新的绿地项目。该系统提供的新信息和数据将使人们对影响矿物提取的物理、机械和化学性质有更深入的了解。这一努力将反过来推动大学的进一步研究，这将进一步了解这一过程和技术的进步。最后，高光谱成像技术的进步将跨越到其他应用和行业，将进一步研究和调查。

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.