Catalogue垃圾焚燒AI技術(shù)應(yīng)用背景Backgroundof

AITechnology Applicationin

Waste-to-Energy一二三四AI技術(shù)應(yīng)用關(guān)鍵要素KeyElementsofAITechnology

ApplicationAI技術(shù)應(yīng)用方案ImplementationplanforAI

technology運(yùn)行情況比較ComparisonofOperationConditionswith

AI公司簡(jiǎn)介Company

Profile影響人類(lèi)發(fā)展的工業(yè)技術(shù)革命浪潮第一次工業(yè)技術(shù)革命蒸汽輪機(jī)、鐵路運(yùn)輸、機(jī)械化時(shí)代、 英國(guó)主導(dǎo)十八世紀(jì)六十年代第二次技術(shù)革命電力、內(nèi)燃機(jī)

電氣化時(shí)代 歐美主導(dǎo)十九世紀(jì)七十年代第三次技術(shù)革命計(jì)算機(jī)、信息化、原子能、航天技術(shù)美國(guó)主導(dǎo)二十世紀(jì)中第四次技術(shù)革命人工智能、大數(shù)據(jù)、量子計(jì)算

中美主導(dǎo)二十一世紀(jì)初一．垃圾焚燒人工智能技術(shù)應(yīng)用背景Background of AI Technology Application國(guó)內(nèi)生活垃圾焚燒行業(yè)發(fā)展經(jīng)過(guò)十多年的高速發(fā)展，投產(chǎn)垃圾焚燒項(xiàng)目超過(guò)千個(gè)，當(dāng)前面臨地方環(huán)保排放標(biāo)準(zhǔn)不斷提標(biāo)、產(chǎn)能過(guò)剩、國(guó)補(bǔ)退坡、垃圾處理費(fèi)欠費(fèi)嚴(yán)重和設(shè)備老化等許多問(wèn)題和瓶頸。AsthedomesticWaste-to-Energyhasdevelopedrapidlyformorethantenyears,morethanonethousandWaste-to-Energyprojectshavebeenputintooperation,currentlyfacingthealotofdifficultiesand

bottlenecks.迫切需要國(guó)內(nèi)垃圾焚燒企業(yè)通過(guò)智能化技術(shù),提質(zhì)增效和提高精細(xì)化管理水平，增強(qiáng)企業(yè)可持續(xù)發(fā)展競(jìng)爭(zhēng)力。Thereisanurgentneedfordomesticwaste-to-energyenterprisestoenhancetheirefficiency,improvetheirrefinedmanagementlevelandstrengthentheirsustainabledevelopmentcompetitivenessthroughAIinnovative

technologies.ChallengesFacedby

Waste-to-EnergyEmission

standardsarebecomingmorestringent.OvercapacitySubsidy

reduction,severearrearsinhandling

feesEquipmentagingoverseasprojects人工智能AI技術(shù)應(yīng)用于垃圾焚燒行業(yè)的優(yōu)勢(shì)：TheadvantagesofapplyingAItechnologytotheWaste-to-Energy1、能持續(xù)不斷對(duì)垃圾焚燒過(guò)程進(jìn)行尋優(yōu)和最佳運(yùn)行工況控制。由于垃圾成分復(fù)雜多變、運(yùn)行管理水平差異等原因，存在燃燒不充分、偏燒、結(jié)焦和煙氣排放較難控制等問(wèn)題，而AI技術(shù)則能通過(guò)精確控制焚燒參數(shù)和CEMS煙氣排放指標(biāo)，實(shí)現(xiàn)垃圾高效燃燒、環(huán)保排放和長(zhǎng)周期穩(wěn)定運(yùn)行的目標(biāo)，并可有效提高蒸發(fā)量、節(jié)省廠(chǎng)用電率、降低環(huán)保耗材和減少操作人員，降本增效和提質(zhì)增效的經(jīng)濟(jì)效益明顯。Itcancontinuouslyoptimizethewasteincinerationprocessandcontrolthe

optimaloperatingconditions.AItechnologycanachievethegoalsofefficientwastecombustion,environmentallyfriendlyemissions,andlong-termstableoperationbypreciselycontrollingincinerationparametersandCEMSfluegasemissionindicators.Itcanalsoeffectivelyincreaseevaporation,saveplantelectricityconsumption,reduceenvironmentalprotectionmaterials,anddecreasethenumberofoperators.Theeconomicbenefitsofcostreductionandefficiencyimprovementaswellasqualityimprovementandefficiencyenhancementareobvious.2、具有強(qiáng)大的大數(shù)據(jù)算法和數(shù)據(jù)處理能力。通過(guò)與垃圾焚燒廠(chǎng)現(xiàn)有的DCS和PLC結(jié)合，能夠精準(zhǔn)地監(jiān)測(cè)和控制焚燒全過(guò)程的運(yùn)行狀態(tài)，快速分析并預(yù)測(cè)可能出現(xiàn)的故障，從而提前預(yù)警并采取預(yù)防措施，確保焚燒長(zhǎng)周期、安全穩(wěn)定和環(huán)保運(yùn)行。Ithaspowerfulbigdataalgorithmsanddataprocessingcapabilities.ByintegratingwiththeexistingDCSandPLCoftheWaste-to-Energyplant,itispossibletoaccuratelymonitorandcontroltheoperationstatusoftheentireincinerationprocess.Itcanquicklyanalyzeandpredictpotentialfaults,therebyissuingearlywarningsandtakingpreventivemeasuresinadvancetoensurelong-term,safe,stable,andenvironmentallyfriendlyoperationofthe

incineration.3、與現(xiàn)有通過(guò)機(jī)理驅(qū)動(dòng)的ACC燃燒控制技術(shù)比較，AI技術(shù)采用數(shù)據(jù)驅(qū)動(dòng)方式、具有強(qiáng)大的自學(xué)習(xí)和自?xún)?yōu)化提升能力。通過(guò)大量運(yùn)行經(jīng)驗(yàn)和數(shù)據(jù)對(duì)AI算法模型的訓(xùn)練和優(yōu)化，AI系統(tǒng)能夠快速提升對(duì)垃圾焚燒全過(guò)程運(yùn)行規(guī)律和操作技能的學(xué)習(xí)認(rèn)知和智能控制水平，進(jìn)而實(shí)現(xiàn)更精準(zhǔn)更及時(shí)的預(yù)測(cè)、控制和經(jīng)濟(jì)運(yùn)行優(yōu)化，使得AI技術(shù)在垃圾焚燒行業(yè)具有廣闊的應(yīng)用前景并可創(chuàng)造顯著的經(jīng)濟(jì)社會(huì)效益。ComparedwiththeexistingACCcombustioncontroltechnologydrivenbymechanismsmodel,AItechnologyadoptsadata-drivenapproachandhasstrongself-learningandself-optimizationcapabilities.ThroughthetrainingandoptimizationofAIalgorithmmodelsbasedonalargeamountofoperationalexperienceanddata,theAIsystemcanrapidlyenhanceitslearning,cognition,andintelligentcontrolleveloftheentireprocessoperationrulesandoperationalskillsofwasteincineration,therebyachievingmoreaccurateandtimelyprediction,control,andeconomicoperationoptimization.ThismakesAItechnologyhavebroadapplicationprospectsintheWaste-to-Energyindustryandcancreatesignificanteconomicandsocial

benefits.采用大模型預(yù)訓(xùn)練和現(xiàn)場(chǎng)推理優(yōu)化。Adopt

large

model

pre-training

andon-siteinference

optimization.算力：垃圾焚燒綠電也將成為算力中心能源。GreenelectricityfromWaste-to-Energywillalsobecometheenergysourceforcomputing

centers.算據(jù)：垃圾焚燒技術(shù)和運(yùn)行管理經(jīng)驗(yàn)和數(shù)據(jù)指導(dǎo)模型訓(xùn)練。Thewasteincinerationtechnology,operationmanagementexperienceanddataareusedtoguidethemodel

training.算法：多模態(tài)、機(jī)器學(xué)習(xí)、深度學(xué)習(xí)和大模型架構(gòu)Algorithms:Multimodality,MachineLearning,DeepLearning,andModelArchitectures二．AI技術(shù)應(yīng)用關(guān)鍵要素KeyelementsofAITechnology

Application垃圾焚燒人工智能技術(shù)AI technology二．AI技術(shù)應(yīng)用關(guān)鍵要素lementsofAITechnology

Application1、領(lǐng)導(dǎo)負(fù)責(zé)、組織落實(shí) Leadershiptakescharge.,organizational

implementation制定了《綠色動(dòng)力數(shù)字化行動(dòng)方案(2023-2028)》相關(guān)要求，并成立了由集團(tuán)主要領(lǐng)導(dǎo)掛帥的數(shù)字化轉(zhuǎn)型發(fā)展與建設(shè)領(lǐng)導(dǎo)小組，緊密?chē)@“管理信息化、業(yè)務(wù)數(shù)字化、運(yùn)營(yíng)智能化、集團(tuán)一體化”的實(shí)施路徑，在北京通州項(xiàng)目首先開(kāi)展垃圾焚燒數(shù)字化智慧化技術(shù)研發(fā)和應(yīng)用工作，確保2024年數(shù)字化轉(zhuǎn)型工作的質(zhì)量、實(shí)效性、合規(guī)性，為集團(tuán)和行業(yè)數(shù)字化智慧化產(chǎn)業(yè)應(yīng)用提供樣本和示范。Theleadershipattachesgreatimportanceandthe

organizationimplements.The ' Digital Transformation Action Plan (2023-2028)' has beenformulated, and a leading group for digital transformation development andconstruction headed by the main leaders of the group has been established.2、豐富的垃圾焚燒技術(shù)研發(fā)和運(yùn)行管理經(jīng)驗(yàn)指導(dǎo)AI模型訓(xùn)練

.TherichresearchanddevelopmentofwasteincinerationtechnologyandtheexperienceofoperationmanagementareusedtoguidethetrainingoftheAI

model.綠色動(dòng)力自主研發(fā)的200~1000噸/套多驅(qū)動(dòng)逆推式焚燒爐系列采用標(biāo)準(zhǔn)化、模塊化和三維化設(shè)計(jì)，并通過(guò)CFD計(jì)算機(jī)模擬設(shè)計(jì)和低氮燃燒技術(shù)優(yōu)化，結(jié)合自主研發(fā)的二惡英在線(xiàn)預(yù)警控制技術(shù)，具有結(jié)構(gòu)緊湊、維護(hù)簡(jiǎn)便、燃燒充分、熱效率高和有害物質(zhì)排放較低等特點(diǎn)；已在20多個(gè)垃圾焚燒項(xiàng)目投產(chǎn)應(yīng)用。Dynagreenindependentlydevelopedseriesincineratorswithacapacityof200to1,000tonsperset，whichadoptsstandardized,modularized,andthree-dimensional

design.Through

CFD designandlownitrogencombustiontechnologyoptimization,combinedwithindependentlydevelopeddioxinonlineearlywarningcontroltechnology,hasbeenputintoproductionapplicationinmorethan20wasteincineration

projects.集團(tuán)投產(chǎn)的近40個(gè)垃圾焚燒項(xiàng)目，已有10個(gè)垃圾焚燒項(xiàng)目被評(píng)為國(guó)家級(jí)AAA，最早投產(chǎn)項(xiàng)目運(yùn)行時(shí)間已有超16年，項(xiàng)目分布在全國(guó)各地；自主研發(fā)的200~1000噸爐排爐已應(yīng)用到20多個(gè)垃圾焚燒項(xiàng)目，積累了先進(jìn)的垃圾焚燒技術(shù)研發(fā)和運(yùn)維經(jīng)驗(yàn)，為垃圾焚燒人工智能技術(shù)應(yīng)用提供了豐富的指導(dǎo)經(jīng)驗(yàn)和海量訓(xùn)練數(shù)據(jù)。Amongthenearly40waste-to-energyprojectsputintooperationbytheDYNAGREEN,tenprojectshavebeenratedasnationalAAAbyCAUES.Theearliestputintooperationprojecthasbeeninoperationfornearly16years.Theprojectsaredistributedalloverthecountryandhaveaccumulatedadvancedexperienceintheresearchanddevelopmentofwasteincinerationtechnologyandoperationandmaintenance.Itprovidesrichguidingexperienceandmassivetrainingdatafortheapplicationofartificialintelligencetechnologyin

waste-to-energy.3、采用先進(jìn)的智能控制技術(shù)和人工智能大數(shù)據(jù)算法。AdoptadvancedAIcontroltechnologyandartificialintelligencebigdata

algorithms.當(dāng)今是數(shù)字化智能化高速發(fā)展的時(shí)代，AI人工智能技術(shù)趨于成熟，應(yīng)用也更加廣泛。公司2023年開(kāi)始牽頭合作研發(fā)適合中國(guó)特色的生活垃圾焚燒發(fā)電廠(chǎng)數(shù)字化智能化系統(tǒng)，并在北京通州垃圾焚燒項(xiàng)目率先應(yīng)用，成為行業(yè)垃圾焚燒人工智能應(yīng)用的標(biāo)桿和典范，為垃圾焚燒行業(yè)人工智能技術(shù)應(yīng)用提供樣本和參考。Todayisaneraofrapiddevelopmentindigitalizationandintelligence,withAItechnologymaturinganditsapplicationsbecomingmorewidespread..ThecompanybegantotaketheleadincollaboratingwithresearchanddevelopmentofadigitalintelligentsystemsuitableforChina'scharacteristicsin2023,andfirstapplieditintheTongzhouwaste-to=energyprojectinBeijing.IthasbecomeabenchmarkandmodelforAIapplicationinthedomisticwasteincinerationindustry,providingsamplesandreferencesfortheapplicationofAItechnologyinthewasteincineration

industry.垃圾焚燒全過(guò)程AI控制。采用AI人工智能大數(shù)據(jù)、專(zhuān)家?guī)臁C(jī)器學(xué)習(xí)和深度學(xué)習(xí)等先進(jìn)創(chuàng)新技術(shù)與原有控制相結(jié)合，引入到垃圾焚燒、煙氣處理和垃圾庫(kù)管理全生產(chǎn)過(guò)程中，達(dá)到智慧管控。AIcontroloftheentiregarbageincinerationprocess.ByintegratingadvancedinnovativetechnologiessuchasAIbigdata,expertdatabase,machinelearning,anddeeplearningwiththeexistingcontrolsystem,theyareintroducedintotheentireproductionprocessofgarbageincineration,fluegastreatment,andwasterepositorymanagementto

achieveintelligent

control.多模態(tài)AI控制。在原有的檢測(cè)儀器和控制設(shè)備基礎(chǔ)上，增加圖像和視頻識(shí)別、聲音采集、紅外線(xiàn)成像和更精準(zhǔn)的控制儀表閥門(mén)等智能設(shè)備，并打通全廠(chǎng)垃圾焚燒全過(guò)程DCS、PLC與AI數(shù)字系統(tǒng)的通訊聯(lián)系，實(shí)現(xiàn)更高效精準(zhǔn)的智能控制。MultimodalAIcontrolbasedontheoriginaldetectioninstrumentsandcontrolequipment,intelligentdevicessuchasimagerecognition,infraredimaging,andmoreprecisecontrolinstrumentsandvalvesareadded.Moreover,thecommunicationconnectionbetweentheDCS,PLCandAIdigitalsystemsthroughouttheentirewasteincinerationprocessintheentireplantisestablishedtoachievemoreefficientandprecise

control三．AI技術(shù)應(yīng)用方案AI Application Solution1、數(shù)智化AI系統(tǒng)架構(gòu) AI System ArchitectureTheentireprocessflowofwaste

incineration垃圾庫(kù)熱值智能管控垃圾吊智能控制IntelligentControlofGarbage

Cranes垃圾焚燒智能控制

AI

Control

of渣吊智能控制Incineration煙氣智能預(yù)測(cè)與控制 AIControl

offlue

gas2、垃圾焚燒AI智能控制

AI硬件部署圖AIHardwareDeploymentDiagram自動(dòng)控制輸出Data

collectionAI

analyseAI

learningAutomaticcontrol

output2、垃圾焚燒AI智能控制-智能控制架構(gòu)IntelligentControl

ArchitectureMainparameterspredictionMulti-VariableReinforcement

LearningAbnormalworkingconditionidentificationandperception大數(shù)據(jù)和經(jīng)驗(yàn)引入 Bigdata

andexperienceintroduction大模型學(xué)習(xí)訓(xùn)練LargeModel

Learningand

Training在線(xiàn)調(diào)試優(yōu)化Online

DebuggingOptimization在線(xiàn)應(yīng)用OnlineApplication七天AILearningTraining

ProcessHistoricaloperating

data歷史運(yùn)行數(shù)據(jù)-2、垃圾焚燒AI智能控制 -AI學(xué)習(xí)訓(xùn)練過(guò)程Self-learning自學(xué)習(xí)AIself-regulationand

optimization-2、垃圾焚燒AI智能控制

-AI自調(diào)節(jié)和尋優(yōu)Real-timemonitoringofmultimodal

parametersAIPredictingEquipment

FailureOptimizeloadandtemperature

curvesAIIntelligent

Control2、垃圾焚燒AI智能控制-核心算法和尋優(yōu)Corealgorithmand

optimization在垃圾焚燒發(fā)電過(guò)程中，以數(shù)據(jù)為核心，通過(guò)AI技術(shù)自適應(yīng)、自學(xué)習(xí)和自調(diào)節(jié)，實(shí)現(xiàn)智能焚燒智能控制。Intheprocessofwaste-to-energy,operationdataisatthecore.ThroughAItechnology,itadaptsself-learningandself-regulationtoachieveintelligentincinerationand

control.垃圾焚燒優(yōu)化算法整體架構(gòu)，主要包含多變量、多模態(tài)強(qiáng)化學(xué)習(xí)引擎、主要運(yùn)行參數(shù)預(yù)測(cè)引擎及異常工況感知引擎，通過(guò)其協(xié)作控制，實(shí)現(xiàn)對(duì)給料、爐排、一二次風(fēng)量的優(yōu)化控制，實(shí)現(xiàn)鍋爐負(fù)荷、爐溫、氧量/CO的關(guān)鍵經(jīng)濟(jì)指標(biāo)的穩(wěn)定。Theoverallarchitectureofthegarbageincinerationoptimizationalgorithmmainlyincludesamulti-variable,multimodalreinforcementlearningengine,amainoperatingparameterpredictionengine,andanabnormalworkingconditionperceptionengine.Throughtheircollaborativecontrol,itachievesoptimizedcontroloverfeedstock,gratemovement,primaryandsecondaryairvolume,therebystabilizingkeyeconomicindicatorssuchasboilerload,furnacetemperature,andoxygen

content/CO.2、垃圾焚燒AI智能控制 -火焰圖像識(shí)別深度學(xué)習(xí)算法通過(guò)火焰圖像識(shí)別，提取火焰強(qiáng)度、形狀、位置等特殊相量，識(shí)別爐排的著火區(qū)域、燃燒狀態(tài)、偏燒情況、料層厚度情況?；鹧鎴D像識(shí)別將每列爐排畫(huà)面分成三個(gè)區(qū)域進(jìn)行識(shí)別；進(jìn)行火焰亮度的識(shí)別，反映爐排上火焰的著火強(qiáng)度，最終對(duì)爐排的燃燒狀態(tài)進(jìn)行識(shí)別，并給出相應(yīng)的控制策略。Throughflameimagerecognition,extractspecialscalarssuchasflameintensity,shape,position,etc.,toidentifytheignitionareaofthegrate,combustionstate,deviationfromnormalburning,andwastelayerthicknessongrate.FlameImageRecognitionDeepLearning

AlgorithmAI+MPC多模態(tài)多變量融合控制AI+MPCMultimodalMultivariableFusion

ControlACC方案：經(jīng)典PID或馬丁On-Off法或日立造船算法的方法，以機(jī)理驅(qū)動(dòng)為主，往往都達(dá)無(wú)法實(shí)現(xiàn)精準(zhǔn)的自學(xué)習(xí)、自?xún)?yōu)化控制效果和多變量控制。Mechanism-drivensuchasACCwithoutself-learningandself-optimizationcontrol

effects.AI控制方案：引入相關(guān)聯(lián)所有動(dòng)態(tài)參數(shù)（如主蒸汽流量/汽溫、爐溫、氧量/CO、一二次風(fēng)等）融合到AI預(yù)測(cè)的多變量模型預(yù)測(cè)控制（AI-MPC）中，以控制機(jī)理+數(shù)據(jù)驅(qū)動(dòng)為主，根據(jù)AI預(yù)測(cè)模型預(yù)測(cè)未來(lái)趨勢(shì)，在滿(mǎn)足MV/CV約束的前提下，求解最優(yōu)控制指令，有效提升控制性能。 AIsolution:Basedoncontrolmechanisms+data-driven,predictfuturetrendsaccordingtoAIprediction

models.AI+MPC典型算法 TypicalAI+MPC

algorithmsPrediction

engineInput

featuresPredictiveEngine

LibraryOptimalPrediction

ModelMainsteam

flowpredictionMainsteamtemperatureprediction3、煙氣智慧預(yù)測(cè)與控制系統(tǒng)3大關(guān)鍵污染物：threekeypollutants二噁英（Dioxins）；二氧化硫（SO2）、氮氧化物（NOx）目的/目標(biāo)： Objective/Goal形成超前的監(jiān)測(cè)預(yù)測(cè)值，引入控制系統(tǒng)的前饋，與產(chǎn)生報(bào)警；Formulate

advanced

monitoringandpredictionvalues,andgenerate

alarms;產(chǎn)生控制指令，實(shí)現(xiàn)超前控制，提高控制的穩(wěn)定性。Generatecontrolinstructionstoachieveanticipatorycontroland

improve

thestabilityofthe

system.FluegasIntelligentPredictionandControl

SystemRunreal-time

metricsEmission

IndicatorsmodelGuide

operation3、煙氣智慧預(yù)測(cè)與控制系統(tǒng)

-二噁英在線(xiàn)預(yù)測(cè)與控制DioxinOnlinePredictionand

Control公司自主研發(fā)的二噁英在線(xiàn)預(yù)測(cè)與控制技術(shù)，是國(guó)內(nèi)首創(chuàng)技術(shù)，并獲得發(fā)明專(zhuān)利授權(quán)。Ourcompany'sindependentlydevelopeddioxinonlinepredictionandcontroltechnologyisadomesticallypioneeringtechnology,whichhasbeenauthorizedforinvention

patent.模型訓(xùn)練：利用當(dāng)前國(guó)內(nèi)外對(duì)二噁英生成與消除的機(jī)理文獻(xiàn)建立數(shù)學(xué)模型，再結(jié)合我集團(tuán)大量生產(chǎn)數(shù)據(jù)及相關(guān)試驗(yàn)檢測(cè)數(shù)據(jù)下進(jìn)行模型線(xiàn)下訓(xùn)練仿真，利用大數(shù)據(jù)的多點(diǎn)線(xiàn)性回歸、相關(guān)性隨機(jī)森林、多因素決策樹(shù)等算法，實(shí)現(xiàn)內(nèi)部參數(shù)系數(shù)。Establish

a

mathematical

model

and

combine

the

large

production

dataandrelevanttestdetectiondataforofflinetrainingsimulation.Utilizealgorithmsinbigdatatoachieveinternalparameter

coefficients.在線(xiàn)應(yīng)用：通過(guò)DCS和PLC實(shí)時(shí)采集鍋爐及煙氣參數(shù)，結(jié)合運(yùn)行工況過(guò)程，實(shí)時(shí)計(jì)算二噁英生成數(shù)據(jù)預(yù)測(cè)值，并參與二噁英抑制的控制。 Real-timecollectionofboilerandfluegasparametersthroughDCSandPLC,combinedwiththeoperatingconditionsprocess,tocalculatethereal-timepredictionvalueofdioxingenerationdata,andparticipateinthecontrolofdioxin

inhibition.校準(zhǔn)優(yōu)化：采用垃圾焚燒全過(guò)程、多工況、變參數(shù)的定向測(cè)試方式，并結(jié)合二噁英實(shí)際檢測(cè)值進(jìn)行校正，以提高其可靠性。

CalibrationOptimizationtoimproveits

reliability。工藝顯示預(yù)測(cè)詳細(xì)數(shù)據(jù)顯示預(yù)測(cè)曲線(xiàn)顯示3、煙氣智慧預(yù)測(cè)與控制系統(tǒng)-二噁英在線(xiàn)預(yù)測(cè)與控制DioxinOnlinePredictionand

Control3、煙氣AI智慧預(yù)測(cè)與控制系統(tǒng)

--煙氣排放智慧預(yù)測(cè)與控制訓(xùn)練成生NOx和SO2監(jiān)測(cè)的預(yù)測(cè)模型，然后算法經(jīng)過(guò)多參尋優(yōu)，建立SO2、NOx排放量的實(shí)時(shí)預(yù)測(cè)分析能力，最終形成預(yù)測(cè)值。TrainapredictivemodelforNOxandSO2

monitoring通過(guò)設(shè)定國(guó)標(biāo)、地標(biāo)或廠(chǎng)標(biāo)目標(biāo)限值，控制最優(yōu)的環(huán)保耗材投入量、協(xié)調(diào)AICS控制等，從而穩(wěn)定的控制煙氣凈化處理的工藝參數(shù)。Stable

controlofprocessparametersforflue

gas控制

Control自學(xué)習(xí)Self-learningpurification

treatment.SO2的預(yù)測(cè)與控制：燃料中含硫物質(zhì)的含量、焚燒溫度及空氣供給量相關(guān).SO2Predictionand

ControlNOx的預(yù)測(cè)與控制：與焚燒爐溫度、空氣供給量及垃圾成分相關(guān).

PredictionandControlof

NOxIntelligentPredictionandControlofFlueGas

Emission4、垃圾庫(kù)數(shù)智化管理 -垃圾庫(kù)三維建模與數(shù)字化GarbageStorageIntelligent

Management垃圾庫(kù)管理的好壞，對(duì)垃圾焚燒過(guò)程至關(guān)重要。建立一套含有垃圾倉(cāng)堆料高度、垃圾屬性（來(lái)源地及時(shí)間、種類(lèi)等）、發(fā)酵時(shí)域、焚燒特性（返回?zé)嶂?、SO2、HCl）、吊車(chē)工作狀態(tài)等相關(guān)智能管控平臺(tái)，智能推送垃圾管理信息，指導(dǎo)或控制垃圾吊工作，有利于燃燒、負(fù)荷、煙氣指標(biāo)穩(wěn)定的控制。Themanagementofthegarbagedepotiscrucialto

theprocessofgarbageincineration. Establishan

intelligentcontrolplatformcontainingtheheightof

garbage

storage,garbageattributes(sourceandtime,type,etc.),fermentationduration,combustioncharacteristics(returnthermalvalue,SO2,HCl),craneworkingstatus,

etc.Intelligentpushgarbagemanagementinformationtoguideorcontroltheoperationofthegarbagecrane,whichisconducivetostablecontrolofcombustion,load,

and

fluegas

indicators.4、垃圾庫(kù)數(shù)字化管理--垃圾庫(kù)三維建模與網(wǎng)格化GarbageStorageIntelligent

Management1）垃圾庫(kù)三維建模。通過(guò)激光掃描的數(shù)據(jù)與行車(chē)位置數(shù)據(jù)，形成垃圾庫(kù)的三維圖形，實(shí)時(shí)動(dòng)態(tài)、直觀(guān)地展示垃圾的原始屬性垃圾原始屬性（入庫(kù)時(shí)間、來(lái)源、種類(lèi)、高度等）、燃燒屬性（發(fā)酵度、投入爐數(shù)據(jù)、焚燒返回?zé)嶂?、焚燒返回?zé)煔庵笜?biāo)等主要參數(shù)）、物料狀態(tài)和吊車(chē)狀態(tài)。1）3Dmodelingofthegarbagedepot.Bycombininglaserscanningdatawithvehiclepositiondata,

athree-dimensionalmodelofthegarbagestorageisformed.Thisallowsforreal-time

and

dynamicvisualizationoftheoriginalattributesofthegarbage,combustionproperties(includingfermentationdegree,inputfurnacedata,returnheatvaluefromincineration,smokeemissionindicators,etc.),materialstatus,andcrane

status.2）垃圾庫(kù)網(wǎng)格化管理。庫(kù)區(qū)分成m*n的網(wǎng)格，每個(gè)網(wǎng)格寄存垃圾原始屬性（高度、種類(lèi)等）與燃燒屬性（發(fā)酵度、熱值、SO2/HCL等）、投料信息等，形成物料點(diǎn)云圖，重現(xiàn)垃圾倉(cāng)內(nèi)的垃圾當(dāng)前屬性。2)Garbagestoragegridmanagement.Thedepotisdividedintoanm*ngrid,whereeachgridregisterstheoriginalpropertiesofgarbage(height,type,etc.)andcombustionproperties(fermentationdegree,calorificvalue,SO2/HCL,etc.),aswellasfeedinginformation.This

formsamaterialpointcloudmapthatreproducesthecurrentattributesofthegarbageinthe

bin.4、垃圾庫(kù)數(shù)字化管理 -垃圾庫(kù)三維建模與數(shù)字化GarbageStorageIntelligent

Management基于垃圾庫(kù)網(wǎng)格化數(shù)據(jù)的入爐垃圾熱值控制。 采用無(wú)監(jiān)督學(xué)習(xí)及深度學(xué)習(xí)算法結(jié)合方式，提取入爐垃圾固有屬性與焚燒返回?zé)嶂禈?gòu)建垃圾燃燒屬性，建立熱值推算模型，更新時(shí)域發(fā)酵度系數(shù)；再輸出至燃燒系統(tǒng)，調(diào)整燃燒控制，以降低入爐垃圾熱值波動(dòng)對(duì)運(yùn)行參數(shù)穩(wěn)定性的影響。Calorificvaluecontrolofwasteenteringthefurnacebasedongriddataofwastedepot。Toreducetheimpactoffluctuationsinthecalorificvalueofwasteenteringthefurnaceonthestability

ofoperatingparameters.。定制化控制策略，清單化任務(wù)。有了垃圾倉(cāng)的三維和網(wǎng)格化建模后，可以形成分區(qū)管理，定義各區(qū)功能，如堆放發(fā)酵區(qū)n、投料區(qū)、工業(yè)料區(qū)、陳腐料區(qū)。根據(jù)每區(qū)功能，設(shè)定一定時(shí)段內(nèi)的垃圾吊車(chē)工作任務(wù)，以數(shù)字化技術(shù)模式輸出任務(wù)清單，根據(jù)垃圾庫(kù)特性自行進(jìn)行排列任務(wù)清單優(yōu)先級(jí)。Customized

control

strategy,

inventory-based

tasks。According

to

the

function

of

waste

depoteacharea,setupacertainperiodoftimeforgarbagecraneworktasks,outputtasklistindigitaltechnologymode,andarrangethepriorityofthetasklistaccordingtothecharacteristicsofthegarbage

storage.5、垃圾焚燒全過(guò)程數(shù)智技術(shù)融合一體基于垃圾焚燒智慧控制技術(shù)、煙氣預(yù)測(cè)控制技術(shù)、垃圾庫(kù)管控技術(shù)和冷端優(yōu)化關(guān)系相互融合，信息貫通、互通協(xié)作。

Based

on

theintegrationofintelligentcontroltechnologyforincineration,fluegasandwastestoragemanagement,controlinformationisinterconnectedand

collaborative.智慧焚燒控制返回垃圾熱值預(yù)測(cè)值，引入到垃圾庫(kù)管理，從而進(jìn)一步有利于入爐垃圾熱負(fù)荷控制和燃燒穩(wěn)定。通過(guò)煙氣預(yù)測(cè)預(yù)警系統(tǒng)高控制精準(zhǔn)度。燃燒后熱值與煙氣指標(biāo)送至垃圾庫(kù)管理中，形成垃predictedvalueofgarbagecalorificvalue,whichisintroducedintothegarbagestoragemanagement,thereby

further

facilitatingthecontroloffurnaceloadandcombustion

stability.IntegrationofAIcontrolthroughouttheentireincineration

processIntelligentcontroltechnologyfor

incinerationGarbageStorageIntelligentcontrolIntelligent

的煙氣預(yù)測(cè)值，指導(dǎo)燃燒，讓控制系統(tǒng)實(shí)現(xiàn)超前控制，從而大