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研究建立火电厂数据挖掘和关键目标寻优的智能系统，通过稳定工况划分、运行参数聚类、关键目标对标寻优等流程挖掘历史工况中有价值的信息，找到与当前实时运行工况近似的目标值及运行参数，可为优化运行提供开环建议。在锅炉效率关键目标的对标寻优实际工程应用中，系统发掘出在相近的MV（操作变量）、DV（扰动变量）参数情况下，实时工况炉效为92.13%而历史标杆工况炉效为93.34%，提出该工况下烟气氧量设定值由5.5%降低至4%仍可平稳运行，达到了运行提效、节约发电煤耗的目的。
Abstract：This paper studies and establishes an intelligent system of data mining and key target optimization for thermal power plant. Through the process of stable operating condition division,operation parameter clustering and key target benchmarking optimization,valuable information in historical operating conditions is mined to find the target value and operation parameters similar to the current real-time operating conditions to provide open-loop suggestions for optimal operation. In the practical engineering application of benchmarking optimization of key targets of boiler efficiency,the system found that under similar MV(manipulation variable)and DV(disturbance variable)parameters,the boiler efficiency under real-time condition is 92.13% and that under historical benchmark condition is 93.34%. It is proposed that the setting value of oxygen in flue gas can be reduced from 5.5% to 4% under this condition. As a result,the boiler operation efficiency is improved and the coal consumption of power generation is saved.
[1] MALLAPATY S.How china could be carbon neutral by mid-century[J].Nature,2020,586(7830):482-483.
[2] WEI Y M,HAN R,WANG C,et al. Self-preservation strategy for approaching global warming targets in the post-Paris Agreement era[J]. Nature Communications,2020,11(1):1624
[3]王斯一，吕连宏，罗宏.“十四五”及未来我国应对气候变化目标指向及战略路径研究[J].环境保护，2020,48(20):51-55.
[4]寇静娜，张锐.疫情后谁将继续领导全球气候治理——欧盟的衰退与反击[J].中国地质大学学报（社会科学版），2021,21(1):87-104.
[5]袁亮，张农，阚甲广，等.我国绿色煤炭资源量概念、模型及预测[J].中国矿业大学学报，2018,47(1):1-8.
[6]平新乔，郑梦圆，曹和平.中国碳排放强度变化趋势与“十四五”时期碳减排政策优化[J].改革，2020(11):37-52.
[7]卢勇，徐向东.锅炉变工况运行优化监控系统的实现[J].动力工程，2003,23(2):2325-2328.
[8]李蔚，任浩仁，盛德仁，等.300 MW火电机组在线能耗分析系统的研制[J].中国电机工程学报，2002,22(11):153-155.
[9]徐杰彦.火力发电厂机组优化运行与辅机节能改造研究[D].南京：东南大学，2004.
[10]李鑫鑫.基于历史寻优的火电机组运行优化研究[D].北京：华北电力大学，2017.
[11] FAYYAD U,PIATETSKYSHAPIRO G,SMYTH P.Knowledge Discovery and Data Minin:Towards a Unifying Framework[C]. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining,Portland,1996.
[12]唐坚，尹二新，路光杰，等.大数据技术在火电厂SCR脱硝系统中的应用[J].电力大数据，2020(2):32-37.
基本信息:
中图分类号:TM621;TP311.13
引用信息:
[1]虞仕杰,蒋赢凯,尹贵豪,等.燃煤电厂大数据挖掘和关键目标寻优智能系统研究[J],2022,41(02):86-91.
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2021-08-09
2021
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