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

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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.