Innovative dynamic short-term, medium-term and long-term mine planning strategies incorporating new automation and data analytics technologies

采矿业面临着独特的挑战，金属价格波动近400%，矿石品位多年来不断下降。勘查阶段采集的矿体样品稀少，容易产生随机误差。此外，金属价格、贴现率和成本等财务参数总是在变化，很难预测。因此，长期和中期业务决策是在缺乏足够信息的情况下作出的。这带来了高度的不确定性，导致了意想不到的后果，需要能够根据未来情况变化的灵活的长期计划和为所有可能性而设计的有先见之明的中期计划。另一方面，随着自动化设备和工业物联网(IIoT)技术等新来源的大数据流入，有机会以更高的精度制定短期计划。然而，来自多个来源的丰富信息和自主设备的广泛使用对采矿提出了新的挑战，如异常检测、将这些信息纳入短期矿山规划和考虑极端条件。拟议研究计划的长期计划是调整机器学习和优化方面的进展，并利用采矿设备的IIoT传感器制定新的动态、适应性强的采矿规划战略，在存在长期不确定性和短期传感器数据的情况下做出更明智的决定，以便在矿价波动和品位剥夺的情况下维持运营并增加利润。这一目标在短期内将重点放在两个问题上：(1)建立理解和处理不确定性的规划方法：查明不确定性的来源及其对地雷计划的综合影响。利用这一深入了解，根据矿体品位分布、成本和商品价格的不同情景，定义新的长期矿山规划方法。(2)解决矿山自动化的潜在问题并将其集成到矿山规划中：使用数据分析来验证、集成和分析来自不同传感器的数据，并利用最新的机器学习技术将这些信息用于短期矿山规划。通过开发处理误读、错误通信和中断的方法，说明通信和传感器故障的原因。在拟议的研究计划中，将培养2名博士、3名硕士和2名本科生。此外，将制定新的战略，以更好地适应不确定性和矿山自动化，预计将对加拿大和国外的采矿业产生重大影响。

Mining industry faces unique challenges where the metal prices fluctuate by almost 400% and the grade of ore declines over the years. The orebody samples collected in the exploration stage are sparse and prone to random errors. Also, the financial parameters such as metal price, discount rate and costs are always changing and difficult to predict. Consequently, long-term and medium-term operational decisions are made in the absence of sufficient information. This brings high levels of uncertainty, which leads to unexpected consequences, creating a need for flexible long-term plans that are able to change, based on future circumstances and prescient medium-term plans that are designed for all possibilities. On the other hand, with the inflow of big data from new sources, such as automated equipment and the industrial internet of things (IIoT) technology, there is an opportunity to make short-term plans with higher precision. However, the abundance of information from many sources and extensive usage of autonomous equipment pose new challenges in mining such as anomaly detection, integrating of this information into short-term mine planning and considering extreme conditions. The long term plan of the proposed research plan is to adapt the progress in machine learning and optimization and to capitalize on IIoT sensors of mining equipment to develop new dynamic, adaptable mine planning strategies that make more informed decisions in the presence of uncertainty for the long term and sensor data for the short term in order to sustain the operations and increase the profit despite ore price fluctuations and grade deprivation. This goal is addressed in the short term by focusing on two issues: (1) Establishing planning approaches that understand and handle uncertainty: Identify the sources of uncertainty and their combined effects on the mine plan. Using this in-depth understanding, define new approaches to long-term mine planning accounting for different scenarios of grade distribution of the orebody, costs and commodity prices. (2) Solving potential problems of mine automation and integrating it into mine planning: Use data analytics to validate, integrate and analyze the data coming from the different sensors and use this information in short-term mine planning with state-of-the-art machine learning techniques. Account for communication and sensor failures by developing methodologies to handle misreadings, miscommunications and interruptions. In the proposed research plan, 2 PhD, 3 Master's and 2 Undergraduate students will be trained. Furthermore, novel strategies for better adaptation to uncertainty and mine automation will be developed, which are expected to have a significant impact for the mining industry in Canada and abroad.