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

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.