Machine learning-driven membrane material development for lithium recovery performance optimization

WEI Jiaqi; CHEN Hao; ZHANG Jiahui; CHENG Xue; ZHANG Xuehong; LI Haixiang; ZHENG Junjian

doi:10.13205/j.hjgc.202606003

Volume 44 Issue 6

Jun. 2026

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2026 > 44(6): 20-29

WEI Jiaqi, CHEN Hao, ZHANG Jiahui, CHENG Xue, ZHANG Xuehong, LI Haixiang, ZHENG Junjian. Machine learning-driven membrane material development for lithium recovery performance optimization[J]. ENVIRONMENTAL ENGINEERING , 2026, 44(6): 20-29. doi: 10.13205/j.hjgc.202606003

Citation:

WEI Jiaqi, CHEN Hao, ZHANG Jiahui, CHENG Xue, ZHANG Xuehong, LI Haixiang, ZHENG Junjian. Machine learning-driven membrane material development for lithium recovery performance optimization[J]. ENVIRONMENTAL ENGINEERING , 2026, 44(6): 20-29. doi: 10.13205/j.hjgc.202606003

Citation:

WEI Jiaqi, CHEN Hao, ZHANG Jiahui, CHENG Xue, ZHANG Xuehong, LI Haixiang, ZHENG Junjian. Machine learning-driven membrane material development for lithium recovery performance optimization[J]. ENVIRONMENTAL ENGINEERING , 2026, 44(6): 20-29. doi: 10.13205/j.hjgc.202606003

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Machine learning-driven membrane material development for lithium recovery performance optimization

doi: 10.13205/j.hjgc.202606003

1. College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541004, China;
2. School of Life and Environmental Sciences, Guilin University of Electronic and Technology, Guilin 541004, China;
3. Guangxi Key Laboratory of Theory and Technology for Environmental Pollution Control, Guilin 541004, China

Received Date: 2026-01-31
Accepted Date: 2026-03-06
Rev Recd Date: 2026-03-01

Available Online: 2026-07-06

Abstract

Abstract

Membrane separation technology， characterized by its superior separation efficiency， inherently low energy footprint， and exceptional operational flexibility， has emerged as a compelling and sustainable solution for the strategic recovery of lithium. Despite its immense potential， the precise and selective extraction of lithium ions from multifaceted and chemically demanding matrices， such as hypersaline salt lake brines and highly acidic spent battery leachates， encounters formidable technical bottlenecks that are difficult to overcome using conventional means. Traditional membrane development paradigms are increasingly hindered by the inherent inefficiencies of empirical trial-and-error approaches and the deep-seated difficulty in achieving a synergistic optimization of competing performance metrics， particularly the pervasive permeability-selectivity trade-off. To systematically address these limitations， this review provides a comprehensive delineation of cutting-edge machine learning （ML）-based frameworks. These methodologies encompass the high-throughput rational screening of high-performance membrane materials， the complex inverse design of synthesis protocols， and the high-fidelity prediction of multi-stage separation performance under diverse conditions. The review further elucidates the pivotal role of advanced ML algorithms in deciphering intricate structure-activity relationships at the molecular level， surmounting historical performance ceilings， and providing transformative， data-driven guidance for the bottom-up fabrication of next-generation functional membranes. Furthermore， the review critically assesses the prevailing challenges within this rapidly evolving interdisciplinary nexus， notably the acute paucity of high-quality standardized datasets， the persistent nature of model interpretability， and the restricted generalizability of laboratory-scale models across complex industrial landscapes. Finally， strategic future research trajectories are proposed， emphasizing the integration of physics-informed hybrid intelligent models， the establishment of robust open-source global databases， and the implementation of holistic， full-process system optimizations. These advancements are essential to bridging the current gap between laboratory-scale innovation and the large-scale industrial deployment of ML-based lithium recovery technologies.
- membrane-based water treatment,
- machine learning,
- lithium recovery,
- membrane material development,
- performance optimization

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References(59)

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