Novel project and operations management strategies to adapt cyclical mineral industries to a new industry age and circular economy

工业4.0预示着一场革命，并提出了提高工业流程性能和灵活性的新方法。同时，循环经济理念渗透到工业世界，鼓励进一步减少对环境的影响和废物量。工业4.0和循环经济对工业运营提出了新的生产和管理挑战，以实现效用和价值创造的最大化。*采矿业不能对这些挑战无动于衷。首先，采矿业务包括一条价值链，涉及将岩石或其他原材料转化为中间可销售产品。在这个链条中，流程控制和相互依赖关系极其复杂。采矿部门还具有周期性、资源退化、复杂的市场动态、矿场特有的制约因素和高度不确定性等结构性特征。这些流程之间错综复杂的优先关系使运营管理进一步复杂化，每个运营的内部挑战也是如此。最后，稀疏的数据会导致糟糕的预测和技术风险。*这项研究计划借助数字化(例如传感器、自动化、无线通信、雷达、机器人、云计算和物联网)和追踪人类行为的认知科学获得的“大数据”，通过机器学习和人工智能提出尖端进展。这项研究还将扩展到将人的因素、人体工效学、人机关系、决策分析和系统工程纳入整个认知工程的矿山运维计划，以了解影响人类智能、过程和设备之间相互作用的机制。其他目标是实时在线优化，以及反应性和主动性优化策略。*在整个五年计划中，将培训两名孟晚舟和两名博士生，为新一代挖掘工程师做出贡献，他们专门从事数据分析、建模和通过机器学习和认知工程进行优化。*这项研究提案的结果将是采矿业新的矿山运营和项目管理战略。因此，通过更准确和详细的设计；弹性计划和调度；矿山优化；矿体表征；环境和安全管理；设备预测；以及遵循工业4.0和循环经济概念的可靠性、可用性和维护方法，将提高加工厂和机器的效率。*这项研究将帮助加拿大采矿业掌握新的工业革命，并获得新的工具，以提高采矿作业的生产率、环境合规性、绩效和效率。

The Industry 4.0 heralds a revolution and proposes new ways to increase the performance and flexibility of industrial processes. Meanwhile, circular economy concepts penetrate the industrial world, encouraging a further reduction in environmental impacts and waste quantities. The Industry 4.0 and circular economy pose new production and management challenges to industrial operations to maximize utility and value creation. ***The mining industry must not remain indifferent to these challenges. First, a mining operation comprises a value chain involved in the conversion of rock or other raw material to an intermediate saleable product. Within the chain, process control and interdependencies are extremely complicated. The mining sector also has structural characteristics of cyclicality, resource degradation, complex market dynamics, mine-specific constraints, and high uncertainty. Intricate precedence relationships among these processes further complicate operation management, as do internal challenges of each operation. Finally, sparse data lead to poor predictions and technical risks. ***This research program proposes cutting-edge advancements through machine learning and artificial intelligence with the aid of “big data” obtained from digitalization (e.g., sensors, automation, wireless communication, radar, robotics, cloud computing, and the internet of things) and from cognitive science, which traces human behavior. The research will also be extended to incorporate human factors, ergonomics, human-computer relations, decision analysis, and systems engineering into mine operation and maintenance planning throughout cognitive engineering in such a way as to understand mechanisms affecting interactions among human intelligence, processes, and equipment. Additional goals are real-time online optimization, as well as reactive and proactive optimization strategies. ***Throughout this five-year program, two MEng and two Ph.D. students will be trained, contributing to a new generation of mining engineers who specialize in data analytics, modeling, and optimization through machine learning and cognitive engineering. ***The outcome of this research proposal will be novel mine operation and project management strategies for the mining industry. Hence, process plant and machine efficiencies will be increased through more accurate and detailed design; resilient planning and scheduling; mine optimization; orebody characterization; environmental and safety management; equipment prognostics; and reliability, availability, and maintenance approaches that follow Industry 4.0 and circular economy concepts. ***This research will help the Canadian mining industry to grasp the new industrial revolution and acquire new tools to increase the productivity, environmental compliance, performance, and efficiency of mining operations.