The AWMS consists of a network of sensors that can be installed at the existing observation wells of hydraulic structures at tailings facilities. It is designed to monitor the hydraulic structure's key parameters - such as water levels in open reservoirs, water pressure in wells and soil temperature - and provide early warnings if there is any deviation from the safe parameters.
The company noted several benefits of switching to automated monitoring, such as further reducing the risk of emergencies and improving the operational safety of the structures. It also facilitates collecting data on the operations which may be analysed in the future, as the technology allows data to be collected faster and more frequently than from manual monitoring. The manufacturing execution system integration also allows the collected data to be available to every data expert at Alrosa.
As part of the first implementation stage, the AWMS was installed and commissioned at a tailings facility at the Aikhal division's processing plant #14 in November 2020. For the second stage, the AWMS will be installed at two tailings facilities at the Udachny division's processing plant #12 in 2021, and at two tailings facilities at the Nyurba and Mirny divisions in 2022. Next, the system will be extended to the remaining hydraulic structures at the Mirny, Udachny, Aikhal and Nakyn divisions.
Alrosa estimates the total investment of the two stages to be approximately RUB 100 million (US$1.3 million).
Konstantin Kolegov, deputy chief engineer for hydraulic structures at Alrosa, said: "Industrial and environmental safety is our top priority. The company's dedicated units gather the hydraulic structure's data according to the schedule. The AWMS minimises the human error factor and reduces the risk of emergencies at hydraulic structures, including tailings dumps and hydraulic power systems, as it can send real-time alerts of changes in key parameters. Moreover, the system, which was first introduced last year at Aikhal division, will help accumulating and analysing data on the condition of hydraulic structures to optimise their performance."