Digital twin of minerals processing operations for an advanced monitoring and supervision: froth flotation process case study

In the dynamic landscape of modern manufacturing, the pursuit of efficiency, reliability, and optimal performance has prompted the integration of cutting-edge technologies. Among these, digital twins (DT) have emerged as transformative tools, offering a virtual representation of physical processes, systems, and equipment. This paper delves into the pivotal role of digital twins in advancing the monitoring and supervision of manufacturing processes, focusing specifically on process digital twin (PDT) and its application in the domain of froth flotation in minerals processing. Our data-driven digital twin, replicating the behavior of a flotation cell, was developed using a combination of industrial and simulation data anchored by Artificial Neural Networks. This approach provides precise process emulation of the flotation process. Industrial evaluations of the AI model within the Digital Shadow demonstrated an overall 94% accuracy in estimating insightful information regarding the flotation cell operations with 2 s in response time. This research significantly contributes to the practical implementation of digital twins in industrial processes, highlighting their potential to revolutionize process control and enhance efficiency in the industrial sector.