SRK Consulting issued a brief in which the firm states that, following recent tailings dam failures such as the one at Brumadinho, companies are looking at ways to incorporate automated sensors to generate big, real-time data to better monitor and manage these facilities.
“The need for knowing more about tailings dam conditions – and in real-time – has become a major focus within the mining sector, demanding a step-change in the way we collect, process and interpret data,” said Lyzandra Boshoff, principal engineering geologist at SRK.
“As part of these efforts, SRK has been rolling out initiatives using automated vibrating wire piezometers (VWPs) on tailings facilities.”
In Boshoff’s view, VWP networks, which use logging systems that can send data wirelessly to cloud-based databases and then visualised and analysed in real-time, provide improved monitoring of the associated pore pressure regime within a tailings facility. These are vital aspects of the integrity and stability of the structure.
In general and prior to the existence of cloud-based technology, seepage and pore pressure were tracked by manual standpipe piezometers whose performance, while accurate, depends on the quality of installation and aftercare – and manual data collection is subject to human error.
“This means spending considerable effort for relatively little data, which may often not exactly reflect the current situation by the time the information reaches the engineer for analysis,” the geologist said. “Even the automated sensors using vibrating wire technology tended to rely on manual data collection from the logging devices connected to the sensors.”
According to Boshoff, the datasets generated by VWP networks can be significantly large, depending on the frequency at which data is collected, yet, the visualization tools available, make the data easier to process.
“For the first time, we can see and correlate in real-time what we have always predicted using models and assumptions,” she said.
“Harnessing the power of big data, we can now test our assumptions and substantially raise the confidence of our observations. With the exponential growth in the application of technology in this field, more data is being generated and is available to be harnessed and interpreted.”