Using the industrial Internet of Things (IIoT) and advanced diagnostics systems that draw new data from sensors in mines can provide deeper insight into operations and improve control, says industrial automation multinational Endress & Hauser Group mining industry manager Jenish Gheewala.
Data is one of the most important resources available to mines and gathering additional data from sensors can boost optimisation. However, using existing sensors is crucial, as installing new sensors in mines is mostly unfeasible and will increase the maintenance burden, he says.
Endress & Hauser’s research and development department, of which Gheewala was a member, has demonstrated how advanced diagnostics systems – analytical systems that can measure process and machine parameters during operation – can draw more data from existing sensors to provide detailed and real-time information for mines.
“Data is an asset in the fourth industrial revolution. Do not speculate. Collect data to know your process better,” he adds.
The physical principles behind sensors do not change; however, the calculating power and algorithms that can now be run by the devices and analytics engines can identify aberrations and anomalies of sensor data in the plant, process or devices. Better-quality information leads directly to better control, says Gheewala.
Advanced diagnostics also classifies and manages the data for process diagnostics and device diagnostics, he says.
“Mineral recovery begins in the flotation unit. However, metallurgists do not always believe that the pH sensors are operating accurately and point out that ‘drift’ of data obtained from the sensor, owing to wear or damage, can negatively impact on recovery,” notes Gheewala.
The pH sensors are subjected to harsh conditions and either the glass or the electrode can be damaged. This is where metrological data on the condition of the sensor can provide the visibility of the condition of the sensor and its effect on the output, enabling metallurgists to maintain the process within tight parameters and thereby improve recovery.
“Detailed information about the sensor and the process conditions cannot be gathered with an analogue output and requires a digital connection. “The diagnostic information about the sensor increases its reliability and means that the metallurgist knows the conditions in the process and can manage it within tighter tolerances, which also helps to reduce the chemicals consumed,” he explains.
The use of advanced diagnostics to monitor the pH sensors reduces the maintenance hours spent on the flotation unit from 2 200 h/y to 240 h/y, equating to a 90% reduction in maintenance costs and improved reliability of the sensor.
Similarly, IIoT diagnostic data can be used to optimise maintenance, as such data from sensors provides the necessary timing of calibration, and the information can be published in the mining company’s cloud to make it accessible to all relevant employees and divisions.
“Most devices can measure many additional parameters that can be analysed to inform operations. Many plants and mines already have upgraded networks and access points that can be used to collect additional data. “The diagnostic data can then be used to generate analytics outputs that are useful and relevant, help users to solve specific and technical problems and optimise processes and systems, as well as reduce costs,” he concludes.