2026-05-28T09:08:57Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/117372026-05-28T00:05:35Zcom_10259_6168com_10259_5086com_10259_2604col_10259_6169

Rehman, Hamid 8ccea297-d25f-4b8a-a38b-fae36c3fecfe 600 Debik, Eyup 9894ea07-4c46-4839-acf1-b7d10c818796 600 Ulucan-Altuntas, Kubra 8bf47932-74f4-445c-b48d-443a65ac34ad 600 Manav-Demir, Neslihan 572c9c2e-7784-443c-8b03-e8730f3875e9 600 Canci, Baris ce27c2a2-9fdd-4f7e-9b21-fcc2f2f0a92e 600 Iqbal, Mazhar 32fbecb4-7e60-4f8a-826d-dd70eb453ea2 600 Barros García, Rocío 787 600 0000-0002-0392-8504 ur Rehman, Wasif cc9f842d-f1e2-44ae-a566-a237fa7751e4 600 Mohanty, Sanjay K 95ce923f-b595-4b1b-aca8-28dded2fa7cd 600 Khan, Aqib Hassan Ali 830 600 0000-0002-2256-4742 2026-05-27T08:52:11Z 2026-05-27T08:52:11Z 2025-12 2590-1230 https://hdl.handle.net/10259/11737 10.1016/j.rineng.2025.107720 This review provides a comprehensive, data-driven perspective on rare earth element (REE) recoveries from various waste streams by bioleaching, integrating mechanistic insights, microbial performance data, advanced statistical and machine learning tools. A total of 77 observations across 10 waste types were analyzed via Bayesian 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, despite being 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 physical pretreatments. 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 to synergistic interactions. Importantly, many of the highest recoveries (≥90 %) reported in the literature refer to base metals such as Cu, Ni, and Zn, which are more easily solubilized than REEs; harmonizing claims requires distinguishing organism-only effects from organism + pretreatment strategies, and base metal recoveries from REE recoveries. Structural equation modeling (SEM) revealed that factors such as pH, type of waste, and process parameters, played key roles in determining REE recovery success. Among these, process variables (e.g. pH and pulp density) had the strongest direct influence (β = 0.895). Machine learning models, including support vector machine regression (SVMR) and K-nearest neighbor regression (KNNR), further highlight the importance of metal content, process parameters, and microbial presence. These models performed well, with R² values of 0.87 for SVMR and 0.787 for KNNR. Overall, this integrated approach demonstrates the potential for scaling-up bioleaching processes. By combining biological insights with predictive analytics, this integrated framework demonstrates strong foundation for industrial-scale REE recovery and supports shifting toward a more circular and sustainable economy 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 application/pdf eng Elsevier Results in Engineering. 2025, V. 28, 107720 https://doi.org/10.1016/j.rineng.2025.107720 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Bioleaching REE recovery Machine learning Waste management Metals Organic acids Aprendizaje automático Machine learning Gestión de residuos Refuse and refuse disposal Bioleaching of waste-derived rare earth elements: An integrated approach with meta-analysis and predictive analytics for scale-up info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Results in Engineering 28 107720 THUMBNAIL Rehman-RE_2025.pdf.jpg Rehman-RE_2025.pdf.jpg IM Thumbnail image/jpeg 4891 https://riubu.ubu.es/bitstream/10259/11737/4/Rehman-RE_2025.pdf.jpg 10d13366548e3ff6ded9babd3215ad9c MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 1362 https://riubu.ubu.es/bitstream/10259/11737/3/license.txt 5d013bfa6e473ff0db22cd82a4d71a70 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 908 https://riubu.ubu.es/bitstream/10259/11737/2/license_rdf 0175ea4a2d4caec4bbcc37e300941108 MD5 2 ORIGINAL Rehman-RE_2025.pdf Rehman-RE_2025.pdf application/pdf 6327820 https://riubu.ubu.es/bitstream/10259/11737/1/Rehman-RE_2025.pdf 33ec85dac71578fa64ad7bc411c12a1e MD5 1 10259/11737 oai:riubu.ubu.es:10259/11737 2026-05-28 02:05:35.029 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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