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

这个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。准确跟踪材料流动将允许更智能的选矿电路和优化的爆破，显著减少浪费和能源。这涉及与采矿有关的两个核心环境影响：环境足迹的大小和大量能源的使用。跟踪系统将基于矿石在不同位置的光谱成像，例如在爆破后、在粉碎和研磨的不同阶段期间以及在库存和仓储之前和之后。该系统将根据矿山不同矿石类型的独特光谱特征，连续跟踪这些地点的矿石类型。通过将推扫式高光谱成像与粒子描绘算法相结合，并开发一种跟踪岩石类型和材料体积的跟踪算法，将实现准确的材料跟踪。第一阶段的工作将包括利用矿山岩石样品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。该项目更广泛的影响/商业潜力将是减少采矿作业对环境的影响，并通过在加工过程中应用的解决方案减少能源消耗。这项拟议的技术为改进矿物加工提供了新的机会，以根据所遇到的每一种特定岩石类型定制技术。材料类型的跟踪机制将使操作员能够调整矿石类型的设置，以有效地最大限度地提高回收率，同时减少非生产性活动。因此，在整个过程中，对能源、材料和解决方案的需求将会减少。所获得的重大好处将是采矿作业的实体足迹总体上减少。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能够更深入地了解影响矿物提取的物理、机械和化学特性。这一努力将反过来推动大学进行更多的研究，从而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将进一步研究和调查的应用和行业。

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.