Mine Digital Twin For Value Chain Optimization

该计划的总体目标是开发一个数字孪生平台，将整个采矿价值链的数据汇集在一起，并实现数据驱动的决策。信息的流动和决策沿着采矿链通常在很长的时间跨度内以不连续的方式发生。此外，由于有关存款的知识的不确定性及其材料特性的固有异质空间分布，实际矿山生产性能（生产的矿石品位和数量）和提取过程效率往往偏离预期。将数字技术和实时数据捕获和分析嵌入到采矿业务中可以为整个采矿价值链的转型创造机会。使用地质、工程和资产信息构建的物理环境的数字模型可以使用来自传感器和位置感知系统的数据不断更新。自动化和数据分析的最新进展使我们相信这是可能的，本研究计划旨在解决这一问题。在机器学习进步的推动下，目标是开发一套数据同化和分析算法，这将使矿山生命周期各个阶段的各种类型的数据得以整合。拟议的研究计划将围绕两个相互关联的主题进行开发：1）量化跨多个来源和各种数据类型和结构的可用地质，采矿和处理数据的上下文关系; 2）开发矿山数字孪生模型;一个集成框架，用于管理数据，执行签名分析以从原始数据中提取显著特征和不确定性，查找数据集之间的对应关系，并帮助用户可视化极端规模的数字矿山数据。 这种集成的数据同化-机器学习方法将允许解决不均匀的空间和时间数据分布和冗余，以便模型可以摄取大量，异构和潜在不完整的数据集。新开发的数字孪生模型将接受培训，以分析历史生产数据，并发现哪些操作参数对操作性能影响最大。这将使我们能够通过试验“杠杆”来模拟未来。它将使我们能够在一个虚拟的资源有限的环境中拉动“杠杆”，模拟未来可能出现的情况，并确定最佳生产战略。 这个项目的影响主要是方法上的，允许其他人在我们的发现的基础上解决以前无法解决的问题。此外，该计划将为三名博士提供数字技术和数据科学方面的广泛培训。学生和PDF。在该计划中接受培训的学生将能够利用通过该项目学到的新思想和技术，并能够将其用于加拿大及其采矿业的利益。

The overall goal of this program is to develop a digital twin platform that will bring together data across the mining value chain and enable data-driven decision making. The flow of information and the decision-making along the chain of mining typically occurs in a discontinuous fashion over long time spans. Also, due to the uncertain nature of the knowledge about the deposit and its inherently heterogeneous spatial distribution of material characteristics, actual mine production performance (produced ore grades and quantity) and extraction process efficiency often deviate from expectations. Embedding digital technologies and real-time data capture and analytics into mining operations can create an opportunity for transformation across the mining value chain. A digital model of the physical environment, constructed using geological, engineering, and asset information, can be continuously updated with data from sensors and location-aware systems. Recent advances in automation and data analytics lead us to believe that this is possible and this research program aims to address this. Motivated by advances in machine learning, the objective is to develop a suite of algorithms for data assimilation and analysis, which will enable integration of diverse types of data from all phases of a mine lifecycle. The proposed research program will be developed around two interrelated themes: 1) Quantification of contextual relationships for available geological, mining, and processing data across multiple sources and various data types and structures; and 2) Development of a mine digital twin; an integrated framework that manages data, performs signature analysis to extract the salient features and uncertainty from the raw data, finds correspondences across datasets, and aids the user in visualizing extreme-scale digital mine data. This integrated data assimilation-machine learning approach will allow for uneven spatial and temporal data distributions and redundancies to be addressed so that models can ingest massive, heterogeneous, and potentially incomplete data sets. Newly developed digital twin will be trained to analyze historical production data and discover what operational parameters have the most significant impact on the operational performance. This will allow us to simulate the future by experimenting with `levers'. It will enable us to pull `levers' in a virtual resource-constrained environment to simulate possible future scenarios and define optimal production strategies. The impact of this program would be mostly methodological, allowing others to build on our findings and tackle previously unreachable problems. Also, the program will provide extensive training in digital technologies and data science for three Ph.D. students and one PDF. Students trained in this program will be positioned to capitalize upon new ideas and technologies learned through this project and be able to use them for the benefit of Canada and its mining industry.