High-capacity uranium adsorption by pure and doped hydroxyapatite from post-leaching solutions: study of effect factors and machine learning approach

Abstract This work explores hydroxyapatite-based adsorbents as a high-performance, environmentally friendly adsorbent for recovering uranium from acidic post-leaching solutions (PLS). Hydroxyapatite (HAp) and aluminum-doped hydroxyapatite (Al-HAp) were synthesized via wet precipitation method. A Central Composite Design (CCD) was employed to assess the influence of experimental parameters, such as initial pH (1.07–1.8), uranium concentration (0.5–3.1 g l –1 ), and adsorbent mass (0.06–0.14 g), on the adsorption efficiency. Adsorbents were characterized using x-ray diffraction (XRD), x-ray fluorescence (XRF), Brunauer–Emmett–Teller (BET) surface area analysis, and scanning electron microscopy (SEM). Response Surface Methodology (RSM) and Machine Learning (ML) approaches were employed to evaluate predictive performance. XRD analysis indicated an increase in the lattice c/a ratio upon aluminum doping. BET analysis showed an enhancement in surface area and pore volume when doping. Isotherm modeling showed high correlation for the Langmuir model with maximum adsorption capacities of 1036.27 mg g –1 for HAp and 1084.60 mg g –1 for Al-HAp. ML models showed higher accuracy than RSM, with R 2 values reaching 0.99 for training and validation, and 0.98 for testing. These findings highlight the potential of aluminum-doped hydroxyapatite as a highly efficient and environmentally friendly adsorbent for uranium recovery from acidic PLS, offering a promising alternative to ions exchange (IX) and solvent extraction (SX) processes.