Gradiant, a provider of water and wastewater treatment solutions, recently highlighted partnerships with SLB (formerly Schlumberger), Rio Tinto and an unnamed Australian global mining company, with a focus on reducing carbon and water footprints. 

The projects are in the United States and Western Australia and target the recovery of valuable metals such as lithium, nickel and cobalt. US-based Gradiant’s work with SLB, first announced in October 2022, integrates Gradiant’s technologies to concentrate lithium solution with SLB’s direct lithium extraction (DLE) and production technology process – allowing reduced time-to-market and environmental footprint for lithium extraction. The solution, according to Gradiant, enhances the impact of the sustainable lithium extraction process by enabling high levels of lithium concentration in a fraction of the time required by conventional methods while reducing carbon emissions, energy consumption, and capital costs compared to thermal-based methods.

For Rio Tinto, Gradiant will deliver a new facility in Western Australia to replace aging facilities, employing the company’s proprietary RO Infinity membrane technologies and SmartOps Digital AI into existing mining operations. Gradiant has introduced two chemical-free technologies into operations to minimize chemical consumption and waste discharge.

Gradiant’s RO Infinity and SmartOps technologies will also be used to concentrate complex wastewater from nickel and cobalt production at a new facility in Western Australia for a global mining company, resulting in cost savings associated with lower carbon and water footprints compared to conventional technologies.

“Mining is a uniquely complex industrial sector with challenges of remote locations, large volumes of waste, wide fluctuations in water quality, and the high-value end-product that demands relentless design and operations efficiencies,” said Prakash Govindan, COO of Gradiant. “The real opportunity for water technology in the mining industry is resource recovery in wastewater coupled with machine learning AI.”