A Trend‐Based Two‐Stage Framework for Tin (Sn) Recovery From Waste PCBs via Bioleaching and Thermally Activated Bentonite Adsorption

This study proposes a two‐stage, trend‐based framework for tin (Sn) recovery from waste printed circuit boards (PCBs) by integrating Acidithiobacillus ferrooxidans –mediated bioleaching with thermally activated bentonite adsorption. Bioleaching performance was optimized using a central composite design–based response surface methodology (CCD‐RSM), which identified an operational window of 20°C, a 0.25‐μm particle size, a 15–20‐g·L −1 pulp density, and 21 days, yielding a maximum relative tin depletion trend of approximately 97.7%. Model robustness was supported by strong agreement between predicted and experimental responses ( R 2 = 0.9042), a high desirability value (0.845), and low deviation under optimized conditions (±1.07%). Beyond conventional regression optimization, an error deviation analysis was introduced to define microbial stability windows and saturation thresholds, thereby providing a reliability‐oriented analytical layer for process evaluation. Subsequent reclamation of tin from PCB‐derived bioleachates using thermally activated bentonite achieved a maximum relative adsorption trend of approximately 97.86% under optimized laboratory conditions (80°C, 4‐g adsorbent, 0.05‐mm particle size, 4 h). Adsorption behavior was attributed to surface complexation and ion‐exchange interactions enhanced by thermal activation. Scanning electron microscopy coupled with energy‐dispersive x‐ray spectroscopy (SEM–EDX) was employed as a surface‐sensitive, semiquantitative technique to validate relative tin depletion and surface enrichment trends rather than absolute recovery efficiencies. Accordingly, all numerical values are interpreted strictly as comparative indicators. The high‐temperature PCB preconditioning step (500°C–1000°C) and elevated adsorption temperature were applied solely as laboratory‐scale process‐intensification measures and are not intended to represent scalable industrial operating conditions. By integrating CCD‐RSM trend optimization, deviation‐based reliability analysis, and clay‐based adsorption into a unified framework, this study advances a statistically consistent, low‐chemical‐intensity pathway for sustainable e‐waste valorization.