RT info:eu-repo/semantics/article
T1 Bioleaching of waste-derived rare earth elements: An integrated approach with meta-analysis and predictive analytics for scale-up
A1 Rehman, Hamid
A1 Debik, Eyup
A1 Ulucan-Altuntas, Kubra
A1 Manav-Demir, Neslihan
A1 Canci, Baris
A1 Iqbal, Mazhar
A1 Barros García, Rocío
A1 ur Rehman, Wasif
A1 Mohanty, Sanjay K
A1 Khan, Aqib Hassan Ali
K1 Bioleaching
K1 REE recovery
K1 Machine learning
K1 Waste management
K1 Metals
K1 Organic acids
K1 Aprendizaje automático
K1 Machine learning
K1 Gestión de residuos
K1 Refuse and refuse disposal
AB This review provides a comprehensive, data-driven perspective on rare earth element (REE) recoveries fromvarious waste streams by bioleaching, integrating mechanistic insights, microbial performance data, advancedstatistical and machine learning tools. A total of 77 observations across 10 waste types were analyzed viaBayesian meta-analysis, yielding an average REE recovery of 56.2 % (95 % credible interval: 51.1–61.0 %).Among the waste types, coal fly ash and electronic waste (e-waste) demonstrated the highest recoveries (76 %and 89 %, respectively). Fungi, particularly Aspergillus and Penicillium, performed better than bacteria, despitebeing less commonly used in bioleaching studies. Fungal-only systems typically achieved 60–76 % recovery,whereas values above 85 % were reported when fungal bioleaching was combined with chemical or physicalpretreatments. Acidophilic bacteria exhibited the highest recovery efficiency among the bacterial species (66 %).The microbial consortia (combinations of fungi and bacteria) achieved up to 76 % recovery efficiency due tosynergistic interactions. Importantly, many of the highest recoveries (≥90 %) reported in the literature refer tobase metals such as Cu, Ni, and Zn, which are more easily solubilized than REEs; harmonizing claims requiresdistinguishing organism-only effects from organism + pretreatment strategies, and base metal recoveries fromREE recoveries. Structural equation modeling (SEM) revealed that factors such as pH, type of waste, and processparameters, played key roles in determining REE recovery success. Among these, process variables (e.g. pH andpulp density) had the strongest direct influence (β = 0.895). Machine learning models, including support vectormachine regression (SVMR) and K-nearest neighbor regression (KNNR), further highlight the importance ofmetal content, process parameters, and microbial presence. These models performed well, with R² values of 0.87for SVMR and 0.787 for KNNR. Overall, this integrated approach demonstrates the potential for scaling-upbioleaching processes. By combining biological insights with predictive analytics, this integrated frameworkdemonstrates strong foundation for industrial-scale REE recovery and supports shifting toward a more circularand sustainable economy
PB Elsevier
SN 2590-1230
YR 2025
FD 2025-12
LK https://hdl.handle.net/10259/11737
UL https://hdl.handle.net/10259/11737
LA eng
NO This project has received funding from the European Union’s Horizon Europe research and innovation Program under the Marie Skłodowska-Curie grant Actions agreement No 101126655. The project is also partially supported in part by a research grant from the Scientific and Technological Research Council of Türkiye (TÜBİTAK) under the grant number 123C459
DS Repositorio Institucional de la Universidad de Burgos
RD 28-may-2026