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。传统的采矿是静态的、有计划的、主观的、耗时的。在常规采矿中，在资源开发的早期阶段，通过对钻芯的分析得出的相对少量的数据来确定矿石资源。然后应用地质统计学方法建立矿体模型，并制定矿区开采计划。研究表明，在传统的采矿系统中，有价值的矿石经常被送到垃圾场，而废物被误报到加工厂；某大型露天铜矿的实例研究表明，排土场中30%的岩石具有矿质品位。***在先进的采矿系统中，传感器将安装在诸如铲子、传送带和储存库等物料搬运设备上，在那里它们将收集数据，这些数据将使用校准算法转化为有意义的信息，以便进行实时决策。研究表明，在矿石处理过程中对其进行表征的传感器可以改善生产调度、品位控制和工艺工厂控制设置等操作活动，使其更加灵活和客观。传感器允许同时进行实时决策，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，以量化影响吞吐量、金属生产和资源利用等操作目标的矿石特性。感兴趣的矿石属性包括岩性/矿石类型、硬度、破碎度、品位和杂质的存在。了解矿石的非均质性是研究的一个重要焦点，因为它表明了矿石对基于传感器的采矿系统的适应性；没有诸如品位、硬度或岩性等性质的非均质性，就没有机会进行分类。挑战在于开发能够准确地将传感器响应与矿石属性联系起来的算法。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。传感器响应将通过算法的开发与矿石属性相关，这些算法可以使用统计建模（多变量回归）或人工智能（遗传算法、神经网络）方法生成。稳健的算法应以传感器响应与矿石中矿物的物理和化学性质之间的关系为基础。

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.