Development of Sensors for Advanced Mining Systems

该研究计划旨在通过传感器的应用来开发先进的采矿系统。传统的采矿是静态的、基于计划的、主观的、耗时的。在常规采矿中，矿石资源的定义是基于在资源开发的早期阶段对钻芯进行分析所产生的相对少量的数据。在此基础上，应用地质统计学方法建立了矿体模型和开采寿命计划。研究表明，在传统采矿系统中，有价值的矿石往往被送到废石场，废物误报给加工厂；一个大型露天铜矿的案例研究表明，废石场30%的岩石属于矿石质量等级。在先进的采矿系统中，传感器将安装在铲子、传送带和库存等材料处理设备上，它们将在那里收集数据，并使用校准算法将这些数据转换为有意义的信息，以便进行实时决策。研究表明，在处理矿石时，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。传感器可以实时地同时做出决策，从而创建一个集成的动态采矿和处理系统。先进采矿系统的使能技术是能够表征矿石重要性质的传感器系统。这项研究旨在推动传感器系统的发展，以量化影响产量、金属产量和资源利用率等运营目标的矿石属性。感兴趣的矿石属性包括岩性/矿石类型、硬度、碎屑、品位和杂质的存在。了解矿石的不均一性是这项研究的一个重要重点，因为它表明了矿石对基于传感器的采矿系统的适应性；如果没有品位、硬度或岩性等属性的不均一性，就没有机会进行分类。挑战是开发能够准确地将传感器响应与矿石属性相关联的算法。将使用一系列传感器，包括X射线荧光、X射线传输、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)方法生成的算法来与矿石属性相关。稳健的算法应该建立在传感器响应与矿石中矿物的物理和化学性质之间的关系上。

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.