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RESEARCH PAPER · Separation and Purification Technology · 2025

Applications of machine learning in heavy metal adsorption modeling: A review

Shuyan Zhao, Jiayi Guo, Yang Shan Tang, Yanbo Zhou

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Applications of machine learning in heavy metal adsorption modeling: A reviewOriginalFrom Prediction to Synthesis: A Combined Machine Learning and Density Functional Theory Approach for Designing High-Performance Biochar and Decoding Codeine Adsorption0Sustainable wastewater treatment: Mechanistic, environmental, and economic insights into biochar for synthetic dye removal37Metal-organic frameworks for CO2 capture: Tailoring structure and function through modification strategies29Machine learning-assisted simulated annealing for deciphering multi-factor coupling mechanisms in complex wastewater adsorption systems with experimental validation2Tree-based machine learning model to predict the performance of heavy metal removal by sulfate-reducing bacteria1AI-Based Expert System Design for Optimisation of Heavy Metal Adsorption on Waste-Derived Adsorbents1Flyash for sustainable wastewater treatment: Origin, environmental concerns, and removal of dyes and heavy metals2Progress and perspective of machine learning in capacitive deionization2A 1T-MoS 2 /FBC composite electrode for efficient removal of Cr( vi ) through capacitive deionization: performance, mechanism, and BP neural network modeling0Adsorptive cleanup of hazardous heavy metals from wastewater using engineered porous graphene/graphitic-based MOFs: a review of performance, technology readiness level, SWOT analysis, and future perspectives2Critical review of adsorption technologies for heavy metal removal: mechanistic, computational, and policy perspectives0Generalizable and interpretable machine learning framework for arsenic adsorption prediction on metal-organic frameworks (MOFs)0From prediction to synthesis: A combined machine learning and density functional theory approach for designing high-performance biochar and decoding codeine adsorption7Heavy Metal Sequestration of Pb²⁺, Cr6+, and Cu²⁺ by Cerium–Samarium Bimetallic RE-Tetrazole MOF/Alginate Beads (Ce–Sm–Tz@Alginate Beads): A Multivariate and Explainable AI Framework Using PCA and LIME2Revealing the key factors of synergistic transport of microplastics and heavy metals in groundwater: Batch experiments, density functional theory calculations, molecular dynamics simulations, and machine learning modeling3From inert graphite tailings to active silicate: Mineral-genetic engineering enables a high-efficiency hierarchical porous capturer for heavy metals0Prediction and interpretation of pesticide behavior in acidic soil based on XGBoost-SHAP0A closed-loop machine-learning framework for rational design of manganese-based nano-adsorbents3Machine learning assisted prediction of impurity stability across multiple configurations in α-MoO30A modified HSDM coupling dynamic total salt concentration for simulating lithium loss in the fixed-bed washing process of aluminum-based adsorbents0Fabrication of a Congo Red/Chitosan@Melamine sponge composite for efficient U(VI) removal: Adsorption behavior, mechanistic insights, and machine learning-assisted performance modeling1Machine Learning-Driven Optimization for Predicting Biochar Adsorption Performance Toward Pb(II) and Cd(II)1Electrochemical Lithium Recovery and Magnesium Hydroxide Coproduction from High-Mg 2+ /Li + Salt-Lake Brine2A review of machine learning applications in soil heavy-metal remediation: material design, process optimization, and future challenges1Deciphering the multivariable interactions of MOFs in heavy metal adsorption via explainable machine learning for sustainable and resource-efficient adsorbent development2Carbonaceous adsorbents for per- and polyfluoroalkyl substances removal from water, synthesis, adsorption performance, mechanisms, regeneration, and computational insights2Applications of Machine Learning in the Research of Heavy Metal(loid)s-Related Risk: A Scoping Review of Methodology0Multi-objective environmental-economic optimization of rare metal recovery from phosphogypsum: Techno-economic and life cycle assessment driven by machine learning0Fabrication of a Congo red/Chitosan@Melamine sponge composite for efficient U(VI) removal: adsorption behavior, mechanistic insights, and machine learning-assisted performance modeling0Predicting hazardous Sb(III)/Sb(V) adsorption on biochar via an interpretable ML-DFT integrated BiMeSorb framework with experimental validation0Machine learning in clarifying the coupled effects of pore structure and nitrogen density on desalination capacity for capacitive deionization1A systematic review of machine learning in aqueous pollutant adsorption, from prediction to mechanistic interpretation and adsorbent design0Integrated experimental and computational investigation of Rhodamine B adsorption onto Moroccan stevensite clay: Kinetics, isotherms, thermodynamics, DSD optimization, DFT, and Monte Carlo analysis1Design of experiments in adsorptive removal of aqueous pollutants: Methodological practices and best-practice recommendations0Efficient removal of Ni, Al, and As from water using Micrococcus yunnanensis: batch experiments and explainable machine learning0Machine learning prediction of frontier orbital energies in graphene and silicon carbide quantum dots0Modeling equilibrium adsorption isotherms of human immunoglobulin G onto fibroin microparticles using machine learning and physics-informed neural networks0Comparative study on Cu²⁺ adsorption behavior of raw and immobilized Fe₃O₄: Machine learning modeling and SHAP-based interpretability analysis0A Decade of Machine Learning for Adsorption of Emerging Contaminants: A Systematic Review of Algorithms, Performance, and Methodological Transparency0High-Value Utilization of Phosphate Tailings: Phosphorus Recovery and In-Situ Synthesis of Layered Double Hydroxides for Stable Immobilization of Heavy Metals0Super-stable mineralization materials for long-term remediation of heavy metal contaminated soil: Triple criteria, research progress and design of materials0Evidence boundaries in machine learning-assisted adsorption of emerging contaminants: From prediction to mechanistic interpretation and water-treatment applications0

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