Multivariate explainable machine learning assists in redefining high-performance phosphate removal by layered double hydroxides

Peng Zhang; Yafei Li; Silu Huo; Peng Lin; Dezhi Fang; Sile Hu; Bowen Li; Zikang Xu; Xinyuan Qiu; Kexun Li; Hao Wang

doi:10.1016/j.watres.2025.124098

Multivariate explainable machine learning assists in redefining high-performance phosphate removal by layered double hydroxides

Water Res. 2025 Jun 24:285:124098. doi: 10.1016/j.watres.2025.124098. Online ahead of print.

Authors

Peng Zhang¹, Yafei Li², Silu Huo³, Peng Lin⁴, Dezhi Fang⁴, Sile Hu⁴, Bowen Li⁴, Zikang Xu⁴, Xinyuan Qiu⁴, Kexun Li⁵, Hao Wang⁶

Affiliations

¹ College of Environmental Science and Engineering, MOE Key Laboratory of Pollution Processes and Environmental Criteria, Nankai University, Tianjin 300350, China; Hubei Key Laboratory of Mineral Resources Processing and Environment, School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China.
² College of Environmental Science and Engineering, MOE Key Laboratory of Pollution Processes and Environmental Criteria, Nankai University, Tianjin 300350, China. Electronic address: liyafeinku@mail.nankai.edu.cn.
³ New Quality Productive Forces Promotion Center, Ministry of Science and Technology, Beijing 100862, China.
⁴ College of Environmental Science and Engineering, MOE Key Laboratory of Pollution Processes and Environmental Criteria, Nankai University, Tianjin 300350, China.
⁵ College of Environmental Science and Engineering, MOE Key Laboratory of Pollution Processes and Environmental Criteria, Nankai University, Tianjin 300350, China. Electronic address: likx@nankai.edu.cn.
⁶ College of Environmental Science and Engineering, MOE Key Laboratory of Pollution Processes and Environmental Criteria, Nankai University, Tianjin 300350, China; State Key Laboratory of Simulation and Regulation of Water Cycles in River Basins, China Institute of Water Resources and Hydropower Research, Beijing 100038, China.

PMID: 40580836
DOI: 10.1016/j.watres.2025.124098

Abstract

The excessive discharge of phosphorus can trigger eutrophication, thereby posing significant threats to water quality and ecosystem health. Layered double hydroxides (LDHs) are considered promising adsorbents for phosphate removal due to their unique layered structures and tunable properties. However, the full realization of dephosphorization performance of LDHs is determined by multiple factors, including structural features, synthesis conditions, and operational parameters. This complex interplay renders the optimization of their design and application a formidable challenge. Herein, an optimized multilevel nested random forest (MNRF) model was proposed to systematically analyze, predict, and enhance the phosphate adsorption performance of LDHs. This approach not only enabled precise prediction of phosphate adsorption capacity (PAC) and phosphate removal efficiency (PRE), but offered a comprehensive assessment of features importance from diverse perspectives. Through multivariate interpretability analysis using tree-based diagnostics with multi-metric disassembly of implied trees, Shapley values, partial dependence plots, and individual conditional expectations, we identified the key adsorbent properties and reaction parameters that determine the dephosphorization performance of LDHs. Decisive structural features include metal type, synthesis temperature, and synthesis time, while critical operational parameters include initial concentration, dosage, and pH. Experimental validation further confirmed the model's predictions, highlighting that the Mg-Al LDH prepared under model-guided conditions is effective in scenarios requiring high phosphate uptake capacity, achieving a PAC of 98.32 mg g^-1. Meanwhile, the Ca-Fe LDH synthesized following the model's guidance is suitable for the deep treatment of medium-to-low phosphate concentrations, demonstrating a PRE exceeding 93 %. This study offers an innovative design and optimization guide for redefining high-performance LDHs via machine learning, enhancing phosphate removal performance and advancing sustainable water treatment techniques.

Keywords: Adsorption; Machine learning; Phosphate adsorption; Phosphate removal.