Machine Learning-Assisted Prediction and Exploration of the Homogeneous Oxidation of Mercury in Coal Combustion Flue Gas.

Mercury emission from coal combustion flue gas is a significant environmental concern due to its detrimental effects on ecosystems and human health. Elemental mercury (Hg) is the dominant species in flue gas and is hard to immobilize. Therefore, it is necessary to comprehend the reaction mechanisms of Hg oxidation, namely, Hg to oxidized mercury (Hg), for mercury immobilization. In spite of extensive research on homogeneous Hg oxidation, universal accurate prediction models and unified explanations are lacking. In this study, for the first time, quantitative prediction models were developed for the Hg oxidation percentage with machine learning (ML) using flue gas compositions and operating conditions as inputs. Gradient boosting regression models showed optimal performance (test ≥ 0.85). ML-aided feature analysis results exhibited that Cl, HCl, Hg, temperature, and HBr were the top five critical factors affecting mercury homogeneous oxidation. Halogen gas promoted Hg oxidation at temperatures around 250 °C, while Hg, SO, and quench rates were not conducive to Hg oxidation. High reaction rate coefficients for the Hg/Cl and Hg/Br reactions verified the ML interpretive results and revealed the major mercury homogeneous oxidation mechanisms. Models developed here may play important roles in understanding Hg oxidation and optimizing flue gas Hg immobilization technologies.