Development of Sensors for Advanced Mining Systems

该研究计划旨在通过传感器的应用开发先进的采矿系统。 传统的挖掘是静态的，基于计划的，主观的和耗时的。 在常规采矿中，矿石资源是根据在资源开发的早期阶段对岩芯进行分析所产生的相对少量的数据来确定的。 然后应用地质统计学建立矿体模型和采矿计划，以实现从开采到运营的整个生命周期。研究表明，在传统的采矿系统中，有价值的矿石往往被送到废物堆，而废物则被误报给加工厂;对一个大型露天铜矿的案例研究表明，废物堆中30%的岩石符合矿石质量等级。 * 在先进的采矿系统中，传感器将安装在材料处理设备上，如铲子、输送机和库存，在那里它们将收集数据，这些数据将使用校准算法转换为有意义的信息，用于真实的时间决策。 研究表明，在矿石被处理时表征矿石的传感器可以改善生产调度、品位控制和加工厂控制设置等操作活动，使其更加灵活和客观。 传感器允许在真实的时间内同时做出决策，从而创建一个集成的动态挖掘和处理系统。先进采矿系统的使能技术是能够表征矿石重要特性的传感器系统。 该研究旨在推进传感器系统，以量化影响生产量、金属生产和资源利用等运营目标的矿石属性。 感兴趣的矿石性质包括岩性/矿石类型、硬度、破碎度、品位和杂质的存在。了解矿石的异质性是研究的一个重要焦点，因为它表明矿石对基于传感器的采矿系统的适应性;如果没有诸如品位、硬度或岩性等属性的异质性，就没有机会进行分类。 面临的挑战是开发能够准确地将传感器响应与矿石性质相关联的算法。将使用一系列传感器，包括X射线荧光，X射线透射，激光诱导击穿光谱和电磁。 传感器响应将通过开发可使用统计建模（多变量回归）或人工智能（遗传算法、神经网络）方法生成的算法与矿石性质相关。稳健的算法应基于传感器响应与矿石中矿物的物理和化学性质之间的关系。

The research program is aimed at developing advanced mining systems through the application of sensors. Conventional mining is static, plan based, subjective and time consuming. In conventional mining, the ore resources is defined based on relatively small amounts of data resulting from the analysis of drill core during the early stages of resource development. Geo-statistics are then applied to create the ore body model and mine plan for the life to the operation. Studies have shown that in conventional mining systems valuable ore is often sent to the waste dump and waste misreports to the process plant; one case study on a large open pit copper mine indicated that 30% of the rock in the waste dump was of ore quality grade. ***In advanced mining systems, sensors would mounted on material handling equipment such as shovels, conveyors and stockpiles where they would collect data that would be converted into meaningful information using calibrated algorithms for real time decision making. Research has shown that sensors that characterize ore as it is being handled can improve operation activities such as production scheduling, grade control and process plant control settings making them more flexible and objective orientated. Sensors allow simultaneous decision making in real time thereby creating an integrated dynamic mining and processing system.***The enabling technology for the advanced mining systems are the sensor systems that can characterize important properties of the ore. The research is aimed at advancing the sensor systems to quantify properties of the ore that affect operational goals such as throughput, metal production and resource utilization. Ore properties of interest include lithology/ ore type, hardness, fragmentation, grade and the presence of impurities. Understanding the ore heterogeneity is an important focus of the research because it indicates the amenability of the ore to a sensor based mining system; without heterogeneity of properties such as grade, hardness or lithology, there is no opportunity for classification. The challenge is to develop algorithms that can accurately relate the sensor response to the ore property. A range of sensors will be used including x-ray fluorescence, x-ray transmission, laser induced breakdown spectroscopy and electromagnetic. The sensor responses will be related to ore properties through the development of algorithms that can be generated using statistical modelling (multivariable regression) or Artificial Intelligence (Genetic Algorithms, Neural Network) approaches. Robust algorithms should be grounded in the relationship between the sensor response and the physical and chemical properties of the minerals in the ore.