Optimization-Driven Synergistic CO2 Capture Using a Polyacrylamide-Based Hydrogel with Enhanced Stability and Regenerability

Abstract

The urgent need for efficient, cost-effective, and regenerable materials for CO₂ capture is key to addressing climate change. A novel hydrogel composed of polyacrylamide, alpha-olefin sulfonate, and chromium (III) acetate was synthesized and investigated for its CO₂ adsorption performance for multiple cycles. The inclusion of AOS enhanced gel’s surface activity and porosity, while Cr(III) acetate served as cross-linker, providing structural stability and active CO₂ binding sites. Regeneration was achieved by mild heating at 50–60 °C, and the material was reused for 5 cycles, retaining a CO₂ adsorption capacity of 0.60 mmol g⁻¹. Hydrogel maintained excellent mechanical integrity and adsorption capacity, highlighting its suitability for sustainable CO₂ capture applications. To further enhance performance, an artificial neural network (ANN) based optimization framework was employed to identify optimal gel compositions. The predictive capabilities of Levenberg-Marquardt (LM), particle swarm optimization (PSO), and genetic algorithm (GA) assisted ANN models were compared. Among these, the ANN-PSO model exhibited superior predictive accuracy, achieving a correlation coefficient of 89.73%, outperforming ANN-GA (84.16%) and ANN-LM (75.77%). The strong agreement between experimental and predicted results highlights the effectiveness of ANN-PSO as a powerful tool for optimizing hydrogel-based CO₂ capture systems.