600MW超临界机组在线燃烧优化系统的设计与实现  

Design and Implementation of the Online Combustion Optimization System for a 600MW Supercritical Unit

作　　者：王莉 WANG Li(Nanjing Vocational College of Science and Technology,Nanjing 211500,Jiangsu,China)

机构地区：[1]南京科技职业学院,江苏南京211500

出　　处：《热处理技术与装备》2025年第2期79-82,共4页Heat Treatment Technology and Equipment

摘　　要：随着人工智能与大数据分析技术的发展,将传统工业与人工智能、大数据等前沿技术相融合,以促进产业的绿色化、智能化发展,并提升企业生产质量与效率,已成为未来发展的必然趋势。在某电厂600 MW超临界机组控制系统中嵌入智能控制平台,并与DCS控制系统实现了通信对接,完成了逻辑切换。此外,基于智能控制平台实现了优化算法。该在线燃烧优化系统通过在线检测仪表收集锅炉燃烧的实时数据,运用粒子群优化算法,结合历史燃烧数据对优化模型进行训练,从而获得最优目标函数。同时,系统对二次风门等关键参数进行了精细化调整,并构建了闭环反馈机制,旨在降低NOx排放量并提升锅炉燃烧效率。With the development of artificial intelligence and big data analysis technology,integrating traditional industries with cutting-edge technologies such as artificial intelligence and big data to promote the green and intelligent development of the industries,and enhance the production quality and efficiency of enterprises has become an inevitable trend in the future.The intelligent platform is embedded in the control system of 600 MW supercritical unit of a power plant,and the communication with DCS control systemis realized,and the logical switch is completed.Additionally,the optimization algorithm is implemented on the intelligent control platform.The online combustion optimization system collects realtime data of boiler combustion through online detection instruments,uses the particle swarm optimization algorithm,and combines historical combustion data to train the optimization model,thereby obtaining the optimal objective function.At the same time,the system makes fine adjustments to key parameters such as secondary air dampers and builds a closed-loop feedback mechanism,aiming to reduce NOx emissions and improve boiler combustion efficiency.

关 键 词：智能控制平台 燃烧效率 粒子群算法 

分 类 号：TM621[电气工程—电力系统及自动化]