The predictive machine learning model of a hydrated inverse vulcanized copolymer for effective mercury sequestration...

Inverse vulcanized polysulfides (IVP) are promising sulfur-enriched copolymers with unconventional properties irresistible for diverse applications like Hg²⁺ remediation. Nevertheless, due to their inherent hydrophobic nature, these copolymers still offer low Hg²⁺ uptake capacity. Herein, we reported the synthesis of IVP by reacting molten sulfur with 4-vinyl benzyl chloride, followed by their functionalization using N-methyl D-glucamine (NMDG) to increase the hydration of the developed IVP. The chemical composition and structure of the functionalized IVP were proposed based on FTIR and XPS analysis. The functionalized IVP demonstrated a high mercury adsorption capacity of 608 mg/g (compared to <26 mg/g for common IVP) because of rich sulfur and hydrophilic regions. NMDG functionalized IVP removed 100 % Hg²⁺ from a low feed concentration (10-50 mg/l). A predictive machine learning model was also developed to predict the amount of mercury removed (%) using GPR, ANN, Decision Tree, and SVM algorithms. Hyperparameter and loss function optimization was also carried out to reduce the prediction error. The optimized GPR algorithm demonstrated high R² (0.99 (training) and 0.98 (unseen)) and low RMSE (2.74 (training) and 2.53 (unseen)) values indicating its goodness in predicting the amount of mercury removed. The produced functionalized IVP can be regenerated and reused with constant Hg²⁺ uptake capacity. Sulfur is the waste of the petrochemical industry and is abundantly available, making the functionalized IVP a sustainable and cheap adsorbent that can be produced for high-volume Hg²⁺ remediation. ENVIRONMENTAL IMPLICATION: This research effectively addresses the removal of the global top-priority neurotoxic pollutant mercury, which is toxic even at low concentrations. We attempted to remove the Hg²⁺ utilizing an inexpensive adsorbent developed by NMDG functionalized copolymer of molten sulfur and VBC. A predictive machine learning model was also formulated to predict the amount of mercury removal from wastewater with only a 0.05 % error which shows the goodness of the developed model. This work is critical in utilizing this low-cost adsorbent and demonstrates its potential for large-scale industrial application.