湿式脱硫系统智能化改造架构及关键技术

Intelligent transformation architecture and key technologies of wet flue gas desulfurization system

燃煤机组智能化改造是提升机组能效和工业绿色转型的必然选择,从实际需求和工程视角出发,对湿式脱硫系统智能化改造的总体框架和关键技术进行设计。首先,对智能控制系统(ICS)网络框架的结构组成进行阐述。其次,基于ICS框架,设计了信息物理融合模型与先进控制算法相结合的优化控制策略,并基于直接能量平衡(DEB)策略设计吸收塔pH值优化控制策略;同时,使用信息物理融合优化结果指导分析智能评价系统;利用数据孪生技术和机理模型结合实现系统智能预警和故障诊断;统计典型故障,建立专家系统且结合数据驱动实现及时故障追踪。最后,采用可视化人机交互系统对脱硫系统指标实时展示,构建ICS+数字孪生+机器学习+可视化一体化脱硫系统,为实现自趋优、自学习、自恢复、自组织、自适应的智慧脱硫系统提供依据。

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 information-physical 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 information-physical 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 real-time 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,self-learning,self-recovering,self-organizing and self-adaptive intelligent desulfurization system.