The intelligent retrofit of coal-fired power generation units is an inevitable choice for improving energy efficiency and promoting green industrial transformation. Based on practical requirements and engineering perspectives

this article designs the overall framework and key technologies for the intelligent retrofitting of wet flue gas desulfurization systems. First

the structural components of the intelligent control system(ICS) network framework are discussed. Next

based on the ICS framework

an optimized control strategy combining informationphysical fusion models and advanced control algorithms is designed

as well as an optimized control strategy for the absorption tower pH value based on the direct energy balance(DEB) approach. Simultaneously

the informationphysical fusion optimization results guide the analysis of the intelligent evaluation system. Using data twin technology and mechanism models

intelligent early warning and fault diagnosis for the system are achieved. By analyzing typical faults

an expert system is established

combined with data-driven techniques for real-time fault tracking. Finally

the article points out that a visualization-based human-machine interaction system is used for realtime display of desulfurization system indicators

constructing an integrated desulfurization system that combines ICS

digital twins

machine learning and visualization. This provides a basis for realizing a self-optimizing

selflearning

self-recovering

self-organizing and self-adaptive intelligent desulfurization system.