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机器学习驱动的膜材料开发用于锂回收性能优化

魏嘉琪,  陈豪,  张佳慧,  程雪,  张学洪,  李海翔,  郑君健

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文章导航 > 环境工程  > 2026 >  44(6): 20-29
魏嘉琪, 陈豪, 张佳慧, 程雪, 张学洪, 李海翔, 郑君健. 机器学习驱动的膜材料开发用于锂回收性能优化[J]. 环境工程, 2026, 44(6): 20-29. doi: 10.13205/j.hjgc.202606003
引用本文: 魏嘉琪, 陈豪, 张佳慧, 程雪, 张学洪, 李海翔, 郑君健. 机器学习驱动的膜材料开发用于锂回收性能优化[J]. 环境工程, 2026, 44(6): 20-29. doi: 10.13205/j.hjgc.202606003
shu
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
shu

机器学习驱动的膜材料开发用于锂回收性能优化

doi: 10.13205/j.hjgc.202606003

  • 1. 桂林理工大学 环境科学与工程学院, 广西 桂林 541004;

  • 2. 桂林电子科技大学 生命与环境科学学院, 广西 桂林 541004;

  • 3. 广西环境污染控制理论与技术重点实验室, 广西 桂林 541004

基金项目: 

国家自然科学基金面上项目（524700292）；广西重点研发项目（桂科FN2600640067）；广西研究生教育创新计划项目（YCBZ202516）

详细信息
    作者简介:

    魏嘉琪(1998-),女,博士研究生,主要研究方向为膜法污水处理与资源回收。weijiaqihere@163.com

    通讯作者:

    李海翔(1984-),男,博士,教授,主要研究方向为膜法污水处理与资源回收。lihaixiang0627@163.com

    郑君健(1990-),男,博士,副研究员,主要研究方向为膜法污水处理与资源回收。zhengjunjianglut@163.com

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

  • 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

    摘要
  • 摘要: 膜分离技术具有分离效率高、能耗低、易于控制等优势，在锂回收领域具有良好应用。针对盐湖卤水及电池浸出液等复杂体系中锂资源选择性分离的严峻挑战，传统膜技术受限于材料开发效率低下、性能难以协同优化等瓶颈，从机器学习（ML）辅助的膜材料理性筛选、合成工艺逆向设计以及分离性能精准预测等核心方法出发，阐述其在解析构效关系、突破渗透-选择性权衡、指导新型膜材料创制等方面的关键应用。探讨了当前该交叉领域面临的挑战，包括高质量数据集缺乏、模型的可解释性与泛化能力不足，难以规模化推广应用，并提出了混合智能模型、标准化数据库构建、全流程系统优化等未来潜在研究方向。
    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.
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  • 收稿日期:  2026-01-31
  • 录用日期:  2026-03-06
  • 修回日期:  2026-03-01
  • 网络出版日期:  2026-07-06

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