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 (Hg0) is the dominant species in flue gas and is hard to immobilize. Therefore, it is necessary to comprehend the reaction mechanisms of Hg0 oxidation, namely, Hg0 to oxidized mercury (Hg2+), for mercury immobilization. In spite of extensive research on homogeneous Hg0 oxidation, universal accurate prediction models and unified explanations are lacking. In this study, for the first time, quantitative prediction models were developed for the Hg0 oxidation percentage with machine learning (ML) using flue gas compositions and operating conditions as inputs. Gradient boosting regression models showed optimal performance (test R2 ≥ 0.85). ML-aided feature analysis results exhibited that Cl2, HCl, Hg0, temperature, and HBr were the top five critical factors affecting mercury homogeneous oxidation. Halogen gas promoted Hg0 oxidation at temperatures around 250 °C, while Hg0, SO2, and quench rates were not conducive to Hg0 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 Hg0 oxidation and optimizing flue gas Hg immobilization technologies.