Human-AI Synergy in Adaptive Active Learning for Continuous Lithium Carbonate Crystallization Optimization

• URL: http://arxiv.org/abs/2507.19316v1
• Date: Fri, 25 Jul 2025 14:30:37 GMT
• Title: Human-AI Synergy in Adaptive Active Learning for Continuous Lithium Carbonate Crystallization Optimization
• Authors: Shayan S. Mousavi Masouleh, Corey A. Sanz, Ryan P. Jansonius, Cara Cronin, Jason E. Hein, Jason Hattrick-Simpers,
• Abstract summary: This study introduces a Human-in-the-Loop (HITL) assisted active learning framework to optimize the continuous crystallization of lithium carbonate.<n>Our results demonstrate the framework's ability to rapidly adapt to new data, significantly improving the process's tolerance to critical impurities.<n>This breakthrough makes the exploitation of low-grade, impurity-rich lithium resources feasible, potentially reducing the need for extensive pre-refinement processes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As demand for high-purity lithium surges with the growth of the electric vehicle (EV) industry, cost-effective extraction from lower-grade North American sources like the Smackover Formation is critical. These resources, unlike high-purity South American brines, require innovative purification techniques to be economically viable. Continuous crystallization is a promising method for producing battery-grade lithium carbonate, but its optimization is challenged by a complex parameter space and limited data. This study introduces a Human-in-the-Loop (HITL) assisted active learning framework to optimize the continuous crystallization of lithium carbonate. By integrating human expertise with data-driven insights, our approach accelerates the optimization of lithium extraction from challenging sources. Our results demonstrate the framework's ability to rapidly adapt to new data, significantly improving the process's tolerance to critical impurities like magnesium from the industry standard of a few hundred ppm to as high as 6000 ppm. This breakthrough makes the exploitation of low-grade, impurity-rich lithium resources feasible, potentially reducing the need for extensive pre-refinement processes. By leveraging artificial intelligence, we have refined operational parameters and demonstrated that lower-grade materials can be used without sacrificing product quality. This advancement is a significant step towards economically harnessing North America's vast lithium reserves, such as those in the Smackover Formation, and enhancing the sustainability of the global lithium supply chain.

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