Issue 11, 2025

Human-AI synergy in adaptive active learning for continuous lithium carbonate crystallization optimization

Abstract

As the demand for high-purity lithium surges, primarily fueled by the adaptation of the electric vehicle (EV) industry, the need for cost-effective extraction and purification technologies intensifies. The Smackover Formation in southern Arkansas, recently identified as one of the world's largest lithium resources, offers vast potential. This formation is part of a broader array of lithium resources across North America, many of which possess lower-grade lithium compared to renowned sources like South American brines. These alternative formations, while presenting significant opportunities, require innovative purification techniques to make their exploitation economically viable. Continuous crystallization is a promising method to produce battery-grade lithium carbonate from these lower-grade sources. Yet, the optimization of this process is challenging due to its complex parameter space, often constrained by scarce data. This study introduces a Human-in-the-Loop (HITL) assisted active learning framework aimed at adapting and optimizing the continuous crystallization process of lithium carbonate. By integrating human expertise with data-driven insights, this approach significantly accelerates the optimization of lithium extraction from challenging sources. Our results demonstrate the framework's ability to rapidly adapt to new data, improving the process's tolerance to critical impurities, such as magnesium, by industry practices at a few hundred ppm, and extending it to handle contamination levels as high as 6000 ppm. This makes the use of low-grade lithium resources contaminated with such impurities feasible, potentially reducing overhead processes. By leveraging artificial intelligence, we not only refined the operational parameters but also demonstrated a potentially reduced need for extensive pre-refinement, promoting the use of lower-grade materials without sacrificing product quality. This advancement marks a significant step towards economically harnessing North America's lithium reserves, particularly those in the Smackover Formation, thereby contributing to the sustainability of the lithium supply.

Graphical abstract: Human-AI synergy in adaptive active learning for continuous lithium carbonate crystallization optimization

Supplementary files

Article information

Article type
Paper
Submitted
28 Jun 2025
Accepted
21 Sep 2025
First published
01 Oct 2025
This article is Open Access
Creative Commons BY license

Digital Discovery, 2025,4, 3078-3091

Human-AI synergy in adaptive active learning for continuous lithium carbonate crystallization optimization

S. M. Masouleh, C. A. Sanz, R. P. Jansonius, C. Cronin, J. E. Hein and J. Hattrick-Simpers, Digital Discovery, 2025, 4, 3078 DOI: 10.1039/D5DD00285K

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements