CAS Key Laboratory of Urban Pollutant Conversion, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei, 230026, China.
School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC 3010, Australia.
Water Res. 2024 Nov 15;266:122374. doi: 10.1016/j.watres.2024.122374. Epub 2024 Sep 7.
Selective lithium (Li) recovery from unconventional water sources (UWS) (e.g., shale gas waters, geothermal brines, and rejected seawater desalination brines) using inorganic lithium-ion sieve (LIS) materials can address Li supply shortages and distribution issues. However, the development of high-performance LIS materials and the optimization of recovery-related operating parameters are hampered by the variety of production methods, intricate procedures, and experimental expenses. Machine learning (ML) techniques offer potential solutions for enhancing LIS material development. We collected literature data on Li adsorption, categorizing 16 parameters into adsorbent parameters, operating parameters, and solution components. Three tree-based algorithms-Random Forest (RF), Gradient Boosting Decision Trees (GBDT), and Extreme Gradient Boosting (XGBoost)-were used to evaluate the impact of these parameters on lithium adsorption. The grouped random splitting method limited data leakage and mitigated overfitting. XGBoost demonstrated the best performance, with an R² of 0.98 and a root-mean-squared error (RMSE) of 1.72. The SHAP values highlighted that operating parameters were the most influential, followed by adsorbent parameters and coexisting ion concentrations. Therefore, focusing on optimizing operating parameters or making targeted improvements on LIS based on operating conditions will enhance LIS performances in UWS. These insights are crucial for optimizing Li adsorption processes and designing effective inorganic LIS materials.
从非常规水源（例如页岩气水、地热水和反渗透海水淡化浓盐水）中采用无机锂离子筛（LIS）材料选择性回收锂，可以解决锂的供应短缺和分布问题。然而，由于生产方法多种多样、工艺复杂且实验费用高昂，高性能 LIS 材料的开发和与回收相关的操作参数的优化受到了阻碍。机器学习（ML）技术为增强 LIS 材料的开发提供了潜在的解决方案。我们收集了关于锂吸附的文献数据，将 16 个参数分为吸附剂参数、操作参数和溶液成分。我们使用了三种基于树的算法——随机森林（RF）、梯度提升决策树（GBDT）和极端梯度提升（XGBoost）——来评估这些参数对锂吸附的影响。分组随机分裂方法限制了数据泄露并减轻了过拟合。XGBoost 的表现最好，R² 为 0.98，均方根误差（RMSE）为 1.72。SHAP 值突出表明，操作参数的影响最大，其次是吸附剂参数和共存离子浓度。因此，专注于优化操作参数或根据操作条件对基于 LIS 的材料进行有针对性的改进，将提高 LIS 在非常规水源中的性能。这些见解对于优化锂吸附过程和设计有效的无机 LIS 材料至关重要。
