Real-time dynamic modelling of industrial WFGD process using an intelligence-based multi-model approach

The burning of fossil fuels is responsible for a large share of global electricity generation, leading to the emission of various atmospheric pollutants, such as sulfur dioxide (SO₂). Due to the significant release of SO₂ from coal combustion, wet flue gas desulfurization (WFGD) technologies are widely utilized in coal-powered plants. The design of WFGD modeling systems is essential for enhancing and managing the desulfurization process. However, WFGD processes in industrial settings are complex, featuring non-linear behavior, time delays, and dynamic uncertainties driven by environmental changes, making effective dynamic modeling a daunting task. This study presents an innovative $\text{FGD}$ modeling system that combines machine learning, multi-model approaches, and dynamic neural model to address these challenges. The system achieves high accuracy in predicting SO₂ emission concentration, even with fluctuating process dynamics. The proposed modeling system’s effectiveness and practicality are validated through an examination of a real-world WFGD process. Moreover, its flexible structure, real-time capability, and exceptional performance highlight its broad applicability across many sectors.