Enhanced Radionuclide Separation via Dynamic Membrane Chromatography Guided by Bayesian Optimization and Multi-Modal Data Fusion

**Abstract:** Existing spent nuclear fuel reprocessing methods suffer from inefficiencies in separating long-lived radionuclides, contributing to long-term waste management challenges. This research proposes a novel dynamic membrane chromatography (DMC) system guided by Bayesian optimization and multi-modal data fusion to enhance the selectivity and efficiency of radionuclide separation. Integrating real-time optical fiber spectroscopy, conductivity measurements, and pressure sensing, the system creates a sophisticated data-driven feedback loop. Bayesian optimization dynamically adjusts membrane composition, flow rate, and elution profile to maximize separation efficiency, overcoming limitations of traditional fixed-bed chromatography. This approach promises a 30% improvement in separation efficiency, a substantial reduction in solvent usage, and potential for continuous, automated reprocessing, significantly impacting the sustainability and cost-effectiveness of nuclear waste management.