基于数据分析的锅炉协同减污优化研究现状  

Research Status of Collaborative Pollution Reduction Optimization of Boilers Based on Data Analysis

作　　者：李运泉 江志铭[1,3] 白凯杰 冀光普 邓聪 Li Yunquan;Jiang Zhiming;Bai Kaijie;Ji Guangpu;Deng Cong(Guangdong Institute of Special Equipment Inspection and Research Shunde Branch,Foshan 528000,China;School of Mechanical&Automotive Engineering,South China University of Technology,Guangzhou 510641,China;National Industrial Boiler Quality Inspection and Testing Center(Guangdong),Foshan 528000,China)

机构地区：[1]广东省特种设备检测研究院顺德检测院,广东佛山528000 [2]华南理工大学机械与汽车工程学院,广东广州510641 [3]国家工业锅炉质量检验检测中心(广东),广东佛山528000

出　　处：《科学技术创新》2022年第32期5-8,共4页Scientific and Technological Innovation

基　　金：工业锅炉能效测试与评价关键技术研究(佛山市科技计划项目,项目编号:2020001005418)。

摘　　要：以电厂燃煤锅炉为研究对象,针对现有电厂燃煤锅炉脱硫除尘技术,分析了国内外的研究现状及多目标优化进程,在此基础上,构建了基于数据分析的燃煤锅炉协同减污优化方案。根据电厂锅炉实时运行数据,利用机器学习等技术手段,对数据进行分析,充分考虑各子系统之间的耦合关系,在提高污染物去除效率的同时,提高运营商的投资回报。Taking coal-fired boilers in power plants as the research object,the current research status and multi-objective optimization process at home and abroad are analyzed for the existing coal-fired boiler desulfurization and dust removal technology in power plants,based on which a collaborative pollution reduction optimization scheme for coal-fired boilers based on data analysis is constructed.Based on the real-time operation data of power plant boilers,the data is analyzed by using machine learning and other technical means,and the coupling relationship between subsystems is fully considered to improve the pollutant removal efficiency while increasing the return on investment of operators.

关 键 词：燃煤锅炉 协同 减污 优化 

分 类 号：TM621.8[电气工程—电力系统及自动化]