Sensmet Optimises Process Water Management in Closed-loop Processes

Sensmet Optimises Industrial Process Water Management in Closed-loop Processes

Sensmet has kickstarted collaboration with the Oulu Mining School (OMS) Research Centre. OMS has a pilot platform dedicated to R&D of minerals processing and it can be used to simulate various processes and conditions at real mines. The mining industry needs new and better water measurement technologies to decrease freshwater usage and to improve the process yield to compensate for lowering quality of the ores that are available. Sensmet targets to optimise process water recycling by studying how real-time big water data from multiple sources can be employed to save energy and chemicals, stabilise process water conditions and to improve the end-product quality.

Sensmet µDOES® Multi-metal Water Analyser can perform quantitative and simultaneous analysis of multiple metals. The sensitivity and automated operation of the Sensmet advanced analyser technology opens totally new opportunities to improve water management in mining. This was recently proved as Sensmet won the water monitoring category of the Mining Hub competition organised in Brazil.

For the first concept demonstration, Sensmet team prepared one of its advanced µDOES® Multi-metal Water Analysers to acquire real-time data on nine different metals (Ba, Ca, Fe, K, Mg, Mn, Na, Sr, Zn) in the incoming water and in the wastewater of the OMS froth flotation process.

“Sensmet’s fast, turn-key water quality solution for piloting is a big benefit. Our easy plug-and-play installation enables quick access to valuable new process data for process monitoring and optimisation,” Sensmet CTO Kalle Blomberg von der Geest describes.

Sensmet Analyser integrated in the OMS pilot process.

“The collaboration with University of Oulu is a great possibility to prove the capability of our μDOES® technology. We can capture the elemental composition of process water in real-time. The accuracy of the data generated by Sensmet µDOES® allows to define the baseline, which can be correlated using statistical and machine learning models. The possibilities for forecasting are stronger than ever before owing to the sheer amount of physicochemical parameters we are able to extract, “ Sensmet Data Scientist Markus Rauhalahti sums up.

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