Predicting the performance of lithium adsorption and recovery from unconventional water sources with machine learning

• Published in: Water research (Oxford) 2024-11, Vol.266, p.122374, Article 122374
• Main Authors: Xu, Ziyang, Ding, Yihao, Han, Soyeon Caren, Zhang, Changyong
• Format: Article
• Language: English
• Subjects: Adsorption; Electrosorption; Lithium - chemistry; Lithium recovery; Machine Learning; Prediction; Unconventional water sources; Water Purification - methods; Water Supply
• ISSN: 0043-1354; 1879-2448; 1879-2448
• DOI: 10.1016/j.watres.2024.122374

•Grouped random splitting for DLM mitigates overfitting effects.•XGBoost has the best predictive ability for Li adsorption using LIS.•Operating parameters have significant influences on Li adsorption using LIS.•Machine learning can play an important role on optimizing Li adsorption from UWS. 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. [Display omitted]