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1、ABSTRACTABSTRACTIntelligentalgorithmwasappliedtomulti-objectiveoptimizationofboilercombustionisoneoftheimportantmeasuresforenergyconservationandemissionsreduction,inthispaper,basedonavarietyofintelligentalgorithm,multi-objectiveoptimizationwascarriedoutontheboilercombustion.Fir
2、stofall,takinga660MWcoal-firedboilerastheresearchobject,usingBPartificialneuralnetworktoestablishtheboilercombustionpredictionmodelinthecaseofa660MWload,with23boilercombustionoperationparametersastheinput,theboilerthermalefficiencyandNOxemissionsfortheoutput.Therangeofmodeltrai
3、ningerroris-1.110-9and1.3310-9,theabsolutevalueofNOxemissioncalibrationsampleaverageerroris3.0398%,theabsolutevalueoftheboilerthermalefficiencycalibrationsampleaverageerroris0.1129%,thepredictionmodelhashigheraccuracyandgoodgeneralization.Basedonthepredictionmodelsofthecombus
4、tion,boilercombustionoptimizationmodelissetupusinggeneticalgorithmoptimizationalgorithm,amongthem,theweightcoefficientmethodisusedtotransformedthemulti-objectiveoptimizationproblemintoasingleobjectiveoptimizationproblem,anddifferentweightratio(0.1-0.9、0.2-0.8、0.3-0.7、0.4-0.6、0.
5、5-0.5、0.6-0.4、0.7-0.3、0.8-0.2)wereoptimizedrespectively.Theoptimizingresultsunderdifferentweightratioisdifferent,astheweightratioofNOxemissionsandthethermalefficiencyoftheboilerfrom1-9graduallyincreaseto8-2,thevalueofNOxemissionsby176mg/m3declinegraduallyto111mg/m3,theboilercom
6、bustionheatlossgraduallyrisefrom4.24%(Theboilerthermalefficiencyis95.76%)to6.05%(Theboilerthermalefficiencyis93.95%).AllofitssolutionsetformtheParetosolutionset,andpresentstheconcaveParetofrontier.Thefourthchapterintroducedthemulti-objectiveevolutionaryalgorithmbasedondecomposi
7、tiontotheboilercombustionmulti-objectiveoptimization,Chebyshevdecompositionstrategyisusedforboilercombustionoptimization,theoptimizedNOxemissionsintherangeof112mg/m3~183mg/m3,boilerheatlossis4.3%~5.8%,comparedwiththetraditionalgeneticalgorithm,theoptimizationeffectisabitpoor,an
8、deachhaveadvantagesandIV万方数据ABSTRACTdisadvantagesofbot