CN108956876B - 一种烟气在线连续监测系统的测量时延修正方法 - Google Patents
一种烟气在线连续监测系统的测量时延修正方法 Download PDFInfo
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- CN108956876B CN108956876B CN201810763844.XA CN201810763844A CN108956876B CN 108956876 B CN108956876 B CN 108956876B CN 201810763844 A CN201810763844 A CN 201810763844A CN 108956876 B CN108956876 B CN 108956876B
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- 238000005259 measurement Methods 0.000 title claims abstract description 30
- 239000000779 smoke Substances 0.000 title claims abstract description 25
- 238000005070 sampling Methods 0.000 claims abstract description 168
- UGFAIRIUMAVXCW-UHFFFAOYSA-N carbon monoxide Chemical compound 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[O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims abstract description 116
- 239000003546 flue gas Substances 0.000 claims abstract description 116
- 239000003344 environmental pollutant Substances 0.000 claims abstract description 33
- 231100000719 pollutant Toxicity 0.000 claims abstract description 33
- 230000001537 neural Effects 0.000 claims abstract description 25
- 238000000034 method Methods 0.000 claims abstract description 16
- 238000004458 analytical method Methods 0.000 claims abstract description 15
- 238000004364 calculation method Methods 0.000 claims abstract description 11
- OKTJSMMVPCPJKN-UHFFFAOYSA-N carbon Chemical compound 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[C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 24
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- 239000007789 gas Substances 0.000 claims description 16
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0036—Specially adapted to detect a particular component
- G01N33/0037—Specially adapted to detect a particular component for NOx
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Computing arrangements based on biological models using neural network models
- G06N3/08—Learning methods
Abstract
本发明提供了一种烟气在线连续监测系统的测量时延修正方法,包括以下三个部分:空间和分析结合的时延计算方法的提出,预测模型的建立以及针对不同采样断面下的不同污染物浓度进行的预测。通过测量各烟气采样管中的烟气流速和采样管实际长度,综合考虑采样仪器参数以及其他因素的影响计算采样管内的测量时延;通过给定的输入和输出量,建立神经网络预测模型;采用迭代的方法来预测污染物采样值,将上一时刻的输出量作为下一时刻输入量的一部分,与预测过程一同更新;通过选取不同污染物历史采样值作为模型输入量,预测输出当前时刻真实的污染物浓度值。本发明提出的测量时延修正方法能够提高烟气排放连续监测系统对污染物监测的实时性和准确性。
Description
技术领域
本发明属于烟气污染监测技技术领域,具体地说是涉及一种烟气在线连续监测系统的测量时延修正方法。
背景技术
随着我国经济的不断发展,对环保的要求也在逐步提高。为保护大气环境,保证我国重点污染源的烟气排放工业能够朝着绿色化、无污染的方向发展,各相关企业在烟气排放口处安装了烟气在线连续监测设备,实时监测烟气排放情况,避免对空气造成污染。
“烟气在线连续监测系统”(CEMS)是一种通过采样、分析方式或直接测量方式,测定烟气中二氧化硫、氮氧化物等污染物浓度和含氧量的设备,在电厂、冶金、矿冶、石化、环境监测等行业得到广泛的使用。该系统取代人工连续采样的监测方式,提高了烟气数据监测的及时性和准确性。
但由于烟气在线连续监测系统中的烟气采样设备工作时,从采集样气到被测样气进入监测仪表内进行测量和分析需要经过采样管等一系列装置,从而导致监测有一定延迟,其监测结果并非当前实时值;且由于每个监测分析仪表分别安装在不同位置,采集流程不同,导致不同采集过程的时间延迟量也有所不同。上述问题严重影响了CEMS对于被监测系统整体运行状态的监测精度和评估能力。
为保证CEMS系统监测数据的实时性和准确性,通过预测模型对烟气采样时延进行修正具有重要意义。
发明内容
为了克服现有技术存在的不足,本发明提供了一种基于深度学习的超低排放环保岛烟气在线连续监测系统(CEMS)的测量时延修正方法,通过神经网络预测模型来修正由于被测样气经过采样装置到达分析仪表消耗的一定时间所带来的监测误差,实现检定装置对被测烟气的实时同步监测,保证在线监测数据的实时性和有效性,实现烟气在线连续监测系统更加准确、客观的现场检定,为后续制定超低排放环保岛污染物脱除方案提供更可靠的数据。
一种烟气在线连续监测系统的测量时延修正方法,包括下述步骤:
(1)空间和分析结合的时延计算方法的提出:测量各烟气采样管中的烟气流速和采样管实际长度,计算被测烟气通过采样装置到达分析仪表所需的时间,即烟气在线连续监测系统对采样烟气的测量时延;
(2)预测模型的建立:通过被测烟气历史采样值,根据不同污染物选取相应输入量,通过深度学习建立神经网络预测模型;
(3)针对不同采样断面下的不同污染物浓度进行的预测:利用步进滚动的迭代预测方法来预测被测烟气中污染物的当前实时浓度。
作为优选,步骤(1)中,获得烟气在线连续监测系统对采样烟气测量时延的方法具体包括以下几个步骤:
步骤L1:确定超低排放环保岛各污染物采样位点,结合管路参数测量超低排放环保岛各采样管的实际长度和管径;
步骤L2:结合仪器参数和采样泵参数,通过采样流量并综合考虑采样气体的温度、压力、湿度因素的影响,计算被测烟气在采样管中的流速;
步骤L3:计算被测烟气经过各采样管到达分析仪所需的时间,即需要修正的采样时延。
作为优选,步骤(2)中,建立神经网络预测模型的方法具体包括以下几个步骤:
步骤T1:根据对不同污染物进行分析,得到影响其生成和转化的参数,以及系统中各参数的历史采样值,确定深度神经网络预测模型的输入量和输出量;
步骤T2:采集数据并进行预处理和修正,确定深度神经网络预测模型的训练样本大小;
步骤T3:划分数据,一部分作为训练集,用于确定深度神经网络预测模型中的权值;选取一部分作为检验集,用于验证和评价模型预测精度;
步骤T4:用处理好的数据建立深度神经网络训练模型并评估模型性能,根据模型收敛性调整参数。
作为优选,步骤T1中,选取能够先于污染物浓度被检测的参数和没有时间延迟的信号和参数,作为神经网络预测模型的修正依据。
作为优选,步骤T4中,采用梯度下降法作为神经网络模型训练方法。
作为优选,步骤(3)中,步进滚动的迭代预测方法具体包括以下几个步骤:
步骤S1:采用过去一个时间周期内的烟气历史采样值作为第一次迭代预测过程中的输入量,通过预测模型输出一个预测值;
步骤S2:第二次迭代预测过程中,步骤S1中的预测值与烟气历史采样值的一部分相结合形成新的输入量,通过预测模型输出一个预测值;
步骤S3:之后每一次的输入量形成过程都采用与步骤S2相同的方式完成,根据上一步输出的预测值来更新下一次的输入量;
步骤S4:当完成对下一个时间周期的全部预测时,本次迭代预测过程停止。
作为优选,对于热电厂超低排放智能环保岛中的烟气在线连续监测系统,其采样
点包括SCR(选择性催化还原)系统脱硝前烟气采样点,WFGD(湿法烟气脱硫)系统脱硫前采
样点,WFGD系统脱硫后采样点,WESP(湿式电除尘器)系统除尘前采样点和WESP系统除尘后
采样点,通过采样管在采样点出口刻度L 1 和进入CEMS分析仪时的进口刻度L 2 之差,计算出采
样实际长度L=|L 1 -L 2 |;根据采样管型号和参数得到管内径为d;根据采样泵参数得平均采样
流量为Q;通过下式计算出采样管内烟气的平均流速:;
在采样仪器能够测得烟气动压和烟气静压的情况下,通过以下气体流速计算公式对计算得到的烟气平均流速进行适当修正:
其中,v’为原始流速,K p 为皮托管系数,ρ’为计算气体密度,p 1 为烟气动压,p 2 为烟气静压,p 0 为设定静压(当地大气压,可固化为101.325KPa),1.205为标准大气压下(101.325KPa),20摄氏度时的空气密度;
系统分析仪分析污染物浓度耗时为△t;由此可得烟气在线连续监测系统对采样
烟气的测量时延为:。
作为优选,烟气在线连续监测系统对SCR脱硝前烟气的采样,采集不同负荷和不同煤种下的特征量,包括锅炉负荷、给煤量、总风量、入口原烟气NOx浓度、入口原烟气氧量、入口温度。
作为优选,所述烟气在线连续监测系统的测量时延修正方法具体包括下述步骤:
(1)空间和分析结合的时延计算方法的提出:
步骤L1:SCR系统脱硝前采样位点的确定,通过观测得到脱硝前采样管在采样点出口刻度L 1 和进入CEMS分析仪时的进口刻度L 2 ,得采样管长度为L,采样管型号并查询到对应的管内径d;
步骤L2:结合仪器参数和采样泵参数,通过采样流量并综合考虑采样气体的温度、压力、湿度因素的影响,计算被测烟气在采样管中的平均流速v;其中采样流量通过CEMS分析仪中采样泵参数确定为Q;
步骤L3:计算被测烟气经过各采样管到达分析仪所需的时间,即需要修正的采样
时延;其中经过气体采样探头到分析装置得到分析结果间的耗时为△t,最终得测量时延为;
(2)预测模型的建立:
步骤T1:对热电厂超低排放智能环保岛中CEMS系统对SCR脱硝前烟气的采样,输入量为锅炉负荷L i ,煤种A i ,给煤量K i ,总风量Q i ,入口原烟气NOx浓度采样值C 1i 、入口原烟气氧量C 2i 、入口温度T i ,输出为入口原烟气NOx浓度采样值C’ i ;
步骤T2:采集数据并进行预处理和修正,确定神经网络预测模型的训练样本大小,工况样本集为D(K i, Q i, C 1i, C 2i, T i, C’ i ) Li,Ai ,先确定锅炉负荷L i ,煤种A i ,在锅炉负荷L i ,煤种A i 为一定量时,再通过考察给煤量K i ,总风量Q i ,入口原烟气NOx浓度采样值C 1i 、入口原烟气氧量C 2i 、入口温度T i ,输出为入口原烟气NOx浓度采样值C’ i 来形成工况样本集,样本大小取20;
步骤T3:取样本数目的90%作为训练集,其余10%作为检验集;
步骤T4:用处理好的数据建立训练模型并评估模型性能,根据模型收敛性调整参数;
(3)针对不同采样断面下的不同污染物浓度进行的预测:
以步骤(1)计算得到的测量时延t作为步进滚动迭代的时间周期,将过去一个时间周期内的历史采样数据作为初始输入量,历史数据的采样时间间隔为t/20,结合步骤(2)中深度神经网络预测输出值进行步进滚动迭代,当完成对下一个时间周期的预测时,最后输出2t时刻的预测值即为经过时延修正后的污染物浓度实时值。
本发明的有益效果在于:
本发明适用于修正超低排放环保岛中不同采样点所有污染物监测时的测量时延。该修正方法不仅结合了不同采样仪器(采样泵、分析仪等)的参数,同时考虑了采样烟气的温度、压力、湿度等其他因素对流速的影响,得到了较为准确的烟气监测时延,解决了目前烟气在线连续监测系统(CEMS)中普遍存在的测量时延问题,对烟气的监测值进行修正,提高了监测的准确性,实现了同步实时监测,能够为后续制定超低排放环保岛不同污染物的脱除方案提供更为可靠的依据。
附图说明
图1是本发明超低排放环保岛污染物测量位点分布图;
图2是本发明获得烟气在线连续监测系统对采样烟气测量时延的方法流程图;
图3是本发明建立神经网络预测模型的方法流程图;
图4是本发明污染物预测方法流程图;
图5是本发明步进滚动的迭代预测方法示意图。
具体实施方式
下面将结合附图对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施案例,而不是全部的实施案例。基于本发明中的实施案例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施案例,都属于本发明保护的范围。
参照图1~5,一种烟气在线连续监测系统(CEMS)的测量时延修正方法,通过神经网络预测模型来修正由于被测样气经过采样装置到达分析仪表消耗的一定时间所带来的监测误差,实现检定装置对被测烟气的实时同步监测,保证在线监测数据的实时性和有效性,实现烟气在线连续监测系统更加准确、客观的现场检定,为后续制定超低排放环保岛污染物脱除方案提供更可靠的数据。具体包括下述步骤:
步骤(1)、基于测量、采样仪器参数以及其他因素的影响,测量各烟气采样管中的烟气流速和采样管实际长度,计算被测烟气通过采样装置到达分析仪表所需的时间,即烟气在线连续监测系统(CEMS)对采样烟气的测量时延:
针对某热电厂超低排放智能环保岛中的CEMS系统,其采样点包括SCR系统脱硝前
烟气采样点,WFGD系统脱硫前采样点,WFGD系统脱硫后采样点,WESP系统除尘前采样点和
WESP系统除尘后采样点,通过采样管在采样点出口刻度L 1 和进入CEMS分析仪时的进口刻度L 2 之差,计算出采样实际长度L=|L 1 -L 2 |;根据采样管型号和参数得到管内径为d;根据采样
泵参数得平均采样流量为Q;通过下式计算出采样管内烟气的平均流速:;
在采样仪器能够测得烟气动压和烟气静动压的情况下,通过以下气体流速计算公式对计算得到的烟气平均流速进行适当修正:
其中,v’为原始流速,K p 为皮托管系数,ρ’为计算气体密度,p 1 为烟气动压,p 2 为烟气静压,p 0 为设定静压(当地大气压,可固化为101.325KPa),1.205为标准大气压下(101.325KPa),20摄氏度时的空气密度;
系统分析仪分析污染物浓度耗时为△t;由此可得烟气在线连续监测系统对采样
烟气的测量时延为:。
步骤(2)、通过被测烟气历史采样值,根据不同污染物选取相应输入量,通过深度学习建立神经网络预测模型:
针对某热电厂超低排放智能环保岛中CEMS系统对SCR脱硝前烟气的采样,采集不同负荷和不同煤种下的特征量,包括锅炉负荷,给煤量,总风量,入口原烟气NOx浓度、入口原烟气氧量、入口温度等。
步骤(3)、利用一种步进滚动的迭代方法来预测被测烟气中污染物的当前实时浓度:
针对某热电厂超低排放智能环保岛中CEMS系统对SCR脱硝前烟气的采样,以步骤(1)计算得到的测量时延t作为步进滚动迭代的时间周期,将过去一个时间周期内的历史采样数据作为初始输入量,历史数据的采样时间间隔为t/20,结合步骤(2)中深度神经网络预测输出值进行步进滚动迭代,当完成对下一个时间周期的预测时,最后输出2t时刻的预测值即为经过时延修正后的污染物浓度实时值。
如图2所示,在步骤(1)中,获得烟气在线连续监测系统(CEMS)对采样烟气测量时延的方法具体包括以下几个步骤:
步骤L1:确定超低排放环保岛各采样位点,结合管路参数测量超低排放环保岛各采样管的实际长度和管径;以SCR系统脱硝前为例,通过观测得到脱硝前采样管在采样点出口刻度L 1 和进入CEMS分析仪时的进口刻度L 2 ,得采样管长度为L,采样管型号并查询到对应的管内径d;
步骤L2:结合仪器参数和采样泵参数,通过采样流量并综合考虑采样气体的温度、压力、湿度因素的影响,计算被测烟气在采样管中的平均流速v;其中采样流量通过CEMS分析仪中采样泵参数确定为Q;
步骤L3:计算被测烟气经过各采样管到达分析仪所需的时间,即需要修正的采样
时延;其中经过气体采样探头到分析装置得到分析结果间的耗时为△t,最终得采样时延为。
如图3所示,在步骤(2)中,建立神经网络预测模型的方法具体包括以下几个步骤:
步骤T1:针对不同污染物进行分析,得到影响其生成和转化的参数,以及系统中各参数的历史采样值,确定神经网络预测模型的输入量和输出量。
对热电厂超低排放智能环保岛中CEMS系统对SCR脱硝前烟气的采样,输入量为锅炉负荷L i ,煤种A i ,给煤量K i ,总风量Q i ,入口原烟气NOx浓度采样值C 1i 、入口原烟气氧量C 2i 、入口温度T i ,输出为入口原烟气NOx浓度采样值C’ i ;
步骤T2:采集数据并进行预处理和修正,确定神经网络预测模型的训练样本大小,在可承受的神经网络结构复杂程度和训练时间长度范围内尽可能的提高模型预测结果精度;工况样本集为D(K i, Q i, C 1i, C 2i, T i, C’ i ) Li,Ai ,先确定锅炉负荷L i ,煤种A i ,在锅炉负荷L i ,煤种A i 为一定量时,再通过考察给煤量K i ,总风量Q i ,入口原烟气NOx浓度采样值C 1i 、入口原烟气氧量C 2i 、入口温度T i ,输出为入口原烟气NOx浓度采样值C’ i 来形成工况样本集,样本大小取20;
步骤T3:划分数据,一部分作为训练集,用于确定神经网络预测模型中的权值;选取一部分作为检验集,用于验证和评价模型预测精度;取样本数目的90%作为训练集,其余10%作为检验集;
步骤T4:用处理好的数据建立训练模型并评估模型性能,根据模型收敛性调整参数。
如图4所示,步骤(3)中,步进滚动的迭代预测方法具体包括以下几个步骤:
步骤S1:采用过去一个时间周期内的烟气历史采样值作为第一次迭代预测过程中的输入量,通过预测模型输出一个预测值;
步骤S2:第二次迭代预测过程中,步骤S1中的预测值与烟气历史采样值的一部分相结合形成新的输入量,通过预测模型输出一个预测值;
步骤S3:之后每一次的输入量形成过程都采用与步骤S2相同的方式完成,根据上一步输出的预测值来更新下一次的输入量;
步骤S4:当完成对下一个时间周期的全部预测时,本次迭代预测过程停止。
本发明通过测量各烟气采样管中的烟气流速和采样管实际长度,综合考虑采样仪器参数以及其他因素的影响计算采样管内的测量时延;通过给定的输入和输出量,建立神经网络预测模型;采用迭代的方法来预测污染物采样值,将上一时刻的输出量作为下一时刻输入量的一部分,与预测过程一同更新;通过选取不同污染物历史采样值作为模型输入量,预测输出当前时刻真实的污染物浓度值。本发明提出的测量时延修正方法能够提高烟气排放连续监测系统对污染物监测的实时性和准确性。
Claims (1)
1.一种烟气在线连续监测系统的测量时延修正方法,其特征在于包括下述步骤:
(1)空间和分析结合的时延计算方法的提出:测量各烟气采样管中的烟气流速和采样管实际长度,计算被测烟气通过采样装置到达分析仪表所需的时间,即烟气在线连续监测系统对采样烟气的测量时延;
对于热电厂超低排放智能环保岛中的烟气在线连续监测系统,其采样点包括SCR系统
脱硝前烟气采样点,WFGD系统脱硫前采样点,WFGD系统脱硫后采样点,WESP系统除尘前采样
点和WESP系统除尘后采样点,通过采样管在采样点出口刻度L 1 和进入CEMS分析仪时的进口
刻度L 2 之差,计算出采样实际长度L=|L 1 -L 2 |;根据采样管型号和参数得到管内径为d;根据
采样泵参数得平均采样流量为Q;通过下式计算出采样管内烟气的平均流速:;
在采样仪器能够测得烟气动压和烟气静压的情况下,通过以下气体流速计算公式对计算得到的烟气平均流速进行适当修正:
其中,v’为原始流速,K p 为皮托管系数,ρ’为计算气体密度,p 1 为烟气动压,p 2 为烟气静压,p 0 为设定静压,1.205为标准大气压下,20摄氏度时的空气密度;
系统分析仪分析污染物浓度耗时为△t;由此可得烟气在线连续监测系统对采样烟气
的测量时延为:;
(2)预测模型的建立:通过被测烟气历史采样值,根据不同污染物选取相应输入量,通过深度学习建立神经网络预测模型;
烟气在线连续监测系统对SCR脱硝前烟气的采样,采集不同负荷和不同煤种下的特征量,包括锅炉负荷、给煤量、总风量、入口原烟气NOx浓度、入口原烟气氧量、入口温度;
(3)针对不同采样断面下的不同污染物浓度进行的预测:利用步进滚动的迭代预测方法来预测被测烟气中污染物的当前实时浓度;
以步骤(1)计算得到的测量时延t作为步进滚动迭代的时间周期,将过去一个时间周期内的历史采样数据作为初始输入量,历史数据的采样时间间隔为t/20,结合步骤(2)中深度神经网络预测输出值进行步进滚动迭代,当完成对下一个时间周期的预测时,最后输出2t时刻的预测值即为经过时延修正后的污染物浓度实时值;
步骤(1)中,获得烟气在线连续监测系统对采样烟气测量时延的方法具体包括以下几个步骤:
步骤L1:SCR系统脱硝前采样位点的确定,通过观测得到脱硝前采样管在采样点出口刻度L 1 和进入CEMS分析仪时的进口刻度L 2 ,得采样管长度为L,采样管型号并查询到对应的管内径d;
步骤L2:结合仪器参数和采样泵参数,通过采样流量并综合考虑采样气体的温度、压力、湿度因素的影响,计算被测烟气在采样管中的平均流速v;其中采样流量通过CEMS分析仪中采样泵参数确定为Q;
步骤L3:计算被测烟气经过各采样管到达分析仪所需的时间,即需要修正的测量时延;
其中系统分析仪分析污染物浓度耗时△t,最终得测量时延为;
步骤(2)中,建立神经网络预测模型的方法具体包括以下几个步骤:
步骤T1:对热电厂超低排放智能环保岛中CEMS系统对SCR脱硝前烟气的采样,输入量为锅炉负荷L i ,煤种A i ,给煤量K i ,总风量Q i ,入口原烟气NOx浓度采样值C 1i 、入口原烟气氧量C 2i 、入口温度T i ,输出为入口原烟气NOx浓度采样值C’ i ;
步骤T2:采集数据并进行预处理和修正,确定神经网络预测模型的训练样本大小,工况样本集为D(K i, Q i, C 1i, C 2i, T i, C’ i ) Li,Ai ,先确定锅炉负荷L i ,煤种A i ,在锅炉负荷L i ,煤种A i 为一定量时,再通过考察给煤量K i ,总风量Q i ,入口原烟气NOx浓度采样值C 1i 、入口原烟气氧量C 2i 、入口温度T i ,输出为入口原烟气NOx浓度采样值C’ i 来形成工况样本集,样本大小取20;
步骤T3:取样本数目的90%作为训练集,其余10%作为检验集;
步骤T4:用处理好的数据建立训练模型并评估模型性能,根据模型收敛性调整参数;
步骤(3)中,步进滚动的迭代预测方法具体包括以下几个步骤:
步骤S1:采用过去一个时间周期内的烟气历史采样值作为第一次迭代预测过程中的输入量,通过预测模型输出一个预测值;
步骤S2:第二次迭代预测过程中,步骤S1中的预测值与烟气历史采样值的一部分相结合形成新的输入量,通过预测模型输出一个预测值;
步骤S3:之后每一次的输入量形成过程都采用与步骤S2相同的方式完成,根据上一步输出的预测值来更新下一次的输入量;
步骤S4:当完成对下一个时间周期的全部预测时,本次迭代预测过程停止。
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