Novel project and operations management strategies to adapt cyclical mineral industries to Industry 4.0 and the circular economy

将岩石转化为中间可销售产品的采矿作业具有高度复杂和相互依存的价值链。采矿活动之间错综复杂的优先关系进一步复杂化。采矿部门还具有周期性、资源退化、废物和尾矿量高、市场动态复杂、矿场特有的制约因素和高度不确定性等结构性特征。与这种复杂性和采矿业的结构问题相关的主要挑战通常是缺乏数据，从而导致预测不佳和技术风险。许多关于项目和运营管理的决策都是在不确定的情况下做出的。Industry 4.0提出了革命性的新方法，以提高工业流程的性能、定制化和灵活性。传感器、遥感、无线通信、雷达、云计算、自动化、机器人和物联网等领域的数字化提供了海量数据，从根本上改变了项目和运营管理方式。同时，循环经济的概念渗透到工业世界，鼓励进一步减少对环境的影响和废物/尾矿量。工业4.0也可以应对循环经济带来的挑战。采矿业现在有重要的机会最大限度地提高效用和创造价值，并最大限度地减少对环境的影响。该研究项目将借助数字化获得的大数据，通过数据分析、机器学习和在线优化来探索前沿进展。大数据分析将使我们能够对数据进行表征，从而提取新的信息。利用表征数据，对设备、矿体、工艺进行预测。在挖掘系统中，将对挖掘过程和进程间流中的瓶颈、死锁、异常、故障、异常情况和冲突进行诊断和预测。处理后的数据和/或预测将用于实时优化挖掘系统。在接下来的五年里，将培养两名孟晚舟和两名博士生，为专门从事大数据分析、机器学习和在线优化的新一代采矿工程师做出贡献。这项研究将有助于加拿大采矿业掌握新的工业革命，并获得遵循工业4.0和循环经济理念的新项目和运营管理工具。因此，采矿作业的生产率、安全性、环境合规性、绩效和效率都将得到提高。综上所述，该研究将提出新的矿山生产计划、矿山建设优化、矿体表征、环境和安全管理、维护计划和过程控制方法。

A mining operation of converting rock to an intermediate saleable product possesses a highly complicated and interdependent value chain. Intricate precedence relationships among mining activities further complicate. The mining sector also has structural characteristics of cyclicality, resource degradation, high waste and tailing quantities, complex market dynamics, mine-specific constraints, and high uncertainty. The major challenge associated with this complication and the structural problems of the mining industry is generally the lack of data that lead to poor predictions and technical risks. Many decisions regarding project and operations management are made under uncertainty. The Industry 4.0 proposes revolutionary new ways to increase the performance, customization and flexibility of industrial processes. Digitalization through sensors, remote sensing, wireless communication, radars, cloud computing, automation, robotics and internet of things provides a massive volume of data and changes project and operation management styles radically. Meanwhile, the circular economy concepts penetrate the industrial world, encouraging a further reduction in environmental impacts and waste/tailing quantities. The Industry 4.0 also can deal with the challenges posed by the circular economy. The mining industry has now significant opportunities to maximize utility and value creation, and minimize environmental impact. This research program will explore cutting-edge advancements through data analytics, machine learning and online optimization with the aid of "big data" obtained from digitalization. Big data analytics will allow us to characterize data such that new information is extracted. Using the characterized data, the predictions on equipment, orebody and process will be made. In a mining system, bottlenecks, deadlocks, anomalies, faults, exceptional circumstances, and conflicts within the mining process and inter-process streams will be diagnosed and prognosed. The processed data and/or the predictions will be used to optimize the mining system in real-time. During the next five years, two MEng and two Ph.D. students will be trained, contributing to a new generation of mining engineers who specialize in big data analytics, machine learning and online optimization. This research will help the Canadian mining industry to grasp the new industrial revolution and acquire new project and operations management tools that follow Industry 4.0 and circular economy concepts. Hence, the productivity, safety, environmental compliance, performance, and efficiency of mining operations will be increased. In conclusion, the research will propose new mine production scheduling; mine construction optimization; orebody characterization; environmental and safety management; maintenance planning and process control approaches.