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。勘探阶段采集的矿体样品稀少，容易出现随机误差。此外，金融参数，如金属价格，贴现率和成本总是变化的，难以预测。因此，长期和中期业务决定是在缺乏充分信息的情况下作出的。这带来了高度的不确定性，导致意想不到的后果，从而需要根据未来的情况制定能够改变的灵活的长期计划，以及为所有可能性设计的有预见性的中期计划。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，有机会制定更高精度的短期计划。然而，来自许多来源的丰富信息和自主设备的广泛使用给采矿带来了新的挑战，例如异常检测，将这些信息整合到短期采矿规划中以及考虑极端条件。 拟议研究计划的长期计划是适应机器学习和优化的进展，并利用采矿设备的IIoT传感器开发新的动态，适应性强的矿山规划策略，在长期存在不确定性和短期传感器数据的情况下做出更明智的决策，以便在矿石价格波动和品位剥夺的情况下维持运营并增加利润。在短期内，这一目标将通过重点关注两个问题来实现： (1)建立理解和处理不确定性的规划方法：确定不确定性的来源及其对采矿计划的综合影响。利用这种深入的理解，确定长期采矿规划的新方法，考虑矿体品位分布、成本和商品价格的不同情况。 (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，并通过最先进的机器学习技术在短期矿山规划中使用这些信息。通过开发方法来处理误读、误解和中断，从而解决通信和传感器故障。 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，还将制定新的战略，以更好地适应不确定性和矿山自动化，预计这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.