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

使周期性矿产行业适应工业 4.0 和循环经济的新颖项目和运营管理策略

• 批准号: RGPIN-2019-04763
• 负责人: Kumral, Mustafa
• 金额: $ 2.26万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715127
• 关键词: Novel; project; operations; management; strategies

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.

将岩石转换为中间可销售产品的采矿操作具有高度复杂且相互依存的价值链。采矿活动之间的复杂优先关系更加复杂。采矿部门还具有周期性，资源退化，高废物和尾部量，复杂的市场动态，特定于矿山特定的约束和高不确定性的结构特征。与这种并发症以及采矿业的结构性问题相关的主要挑战通常是缺乏导致预测和技术风险不佳的数据。关于项目和运营管理的许多决定都是在不确定性下做出的。 行业4.0提案革命性的新方法来提高工业流程的绩效，定制和灵活性。通过传感器，遥感，无线通信，雷达，云计算，自动化，机器人技术和物联网的数字化提供了大量的数据，更改项目和操作管理样式。同时，循环经济概念渗透到工业世界中，鼓励进一步减少环境影响和废物/尾巴数量。行业4.0还可以应对循环经济带来的挑战。采矿业现在有很大的机会来最大化效用和价值创造，并最大程度地减少环境影响。 该研究计划将借助从数字化获得的“大数据”来通过数据分析，机器学习和在线优化探索最先进的进步。大数据分析将使我们能够表征数据，从而提取新信息。使用特征数据，将对设备，矿体和过程进行预测。在采矿系统中，将诊断和进步在采矿过程和过程间流中的瓶颈，僵局，异常，特殊情况以及冲突。处理后的数据和/或预测将用于实时优化采矿系统。 在接下来的五年中，两名孟和两位博士学位。学生将接受培训，为新一代的采矿工程师做出贡献，他们专门研究大数据分析，机器学习和在线优化。这项研究将帮助加拿大采矿行业掌握新的工业革命，并获取遵循行业4.0和循环经济概念的新项目和运营管理工具。因此，将提高采矿业务的生产率，安全性，环境合规性，绩效和效率。总之，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。