Automated measurement systems in mine water management and mine workings – A review of potential methods

There is limited research on the application of digital technologies at mine sites for treating and managing polluted mine water. This review will identify underlying models, theories and frameworks applied to the mines, and used to “smartly” treat and manage mine water. The aim is to provide informed details about these technologies in order to move the mine water management sector to the Industrial-Internet of Things (IIoT). The Internet of Things (IoT), Wireless Sensor Networks (WSN), artificial intelligence, swarming drones and automation are the most widely used smart technologies in current industries, especially the mining industry. In recent years, automation has seen substantial growth in the mines, for example, self-driving trucks. In addition, a lot of work that usually was done by humans can be performed better and safer by machines and drones. WSN are used for automated measurement systems, especially for collecting flow data and physico-chemical parameters for managing and treating mine water. All of these are operated through the main smart technology which is the IoT. It is expected that these technologies will positively affect the mining industry in the long run, making it possible for the mines to reach their expected full-scale production more easily. In this study, latest digital technologies to optimise mine water management and state-of-the-art mines are reviewed and described. In addition, the advantages of using these digital technologies are investigated. [Display omitted] •Smart mine water management requires continued advancement of the Industrial-Internet of Things technologies.•Full mine production demands continuous data collection and increased data analysis for optimized mine water management.•Mines are moving into more comprehensive solutions for data management using highly advanced analytical techniques.•Data lakes are more suitable for predicting future mine water scenarios than data warehouses.