Bioleaching of waste-derived rare earth elements: An integrated approach with meta-analysis and predictive analytics for scale-up - Rehman, Hamid; Debik, Eyup; Ulucan-Altuntas, Kubra; Manav-Demir, Neslihan; Canci, Baris; Barros, Rocio; Iqbal, Mazhar; Rehman, Wasif ur; Mohanty, Sanjay K.; Khan, Aqib Hassan Ali (ELSEVIER, 2025)
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 (beta = 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.
Keywords: Bioleaching; REE recovery; Machine learning; Waste management; Metals; Organic acids; RECOVERY; OPTIMIZATION; Engineering