本文選題：煙氣流量 + 軟測(cè)量　； 參考：《華北電力大學(xué)》2017年碩士論文

【摘要】：目前,節(jié)能、環(huán)保是各發(fā)電企業(yè)必須要面臨的問(wèn)題。煙氣流量是節(jié)能環(huán)�？刂苾�(yōu)化過(guò)程中,非常重要的一個(gè)變量。但是煙氣流量的測(cè)量卻面臨各種各樣的問(wèn)題,一方面,機(jī)組容量的擴(kuò)大使得煙道截面較大,出現(xiàn)截面流場(chǎng)分布不均,傳統(tǒng)硬件傳感器測(cè)量出現(xiàn)較大誤差,另一方面,受到測(cè)量傳感器的影響,如目前傳感器在高溫、高塵、高腐蝕的影響,硬件傳感器會(huì)常出現(xiàn)故障,運(yùn)行維修困難等等,這些因素嚴(yán)重制約著發(fā)電企業(yè)節(jié)能環(huán)保的自動(dòng)化運(yùn)行程度,嚴(yán)重制約著發(fā)電企業(yè)的工作效率。本文以燃煤機(jī)組煙氣流量軟測(cè)量技術(shù)為研究對(duì)象,首先,分析影響煙氣流量的各種因素,如影響煙氣量產(chǎn)生與氣體流量等因素,對(duì)這些因素進(jìn)行理論分析與數(shù)據(jù)MATLAB分析,通過(guò)PLS變量投影重要性分析算法與前向搜索算法綜合進(jìn)行變量篩選,解決輔助變量之間存在相關(guān)性和輔助變量對(duì)主導(dǎo)變量影響重要性的問(wèn)題。其次,在建模方面,根據(jù)機(jī)組運(yùn)行情況選擇典型工況數(shù)據(jù)作為靜態(tài)建模的過(guò)程數(shù)據(jù);在數(shù)據(jù)預(yù)處理方面,采用拉依達(dá)準(zhǔn)則和歸一化方法對(duì)奇異點(diǎn)、孤立點(diǎn)進(jìn)行處理,消除對(duì)建模過(guò)程數(shù)據(jù)的影響;在建模方法上,靜態(tài)建模采用改進(jìn)最小二乘支持向量機(jī)算法建模,針對(duì)LSSVM喪失稀疏性問(wèn)題,本文采用相似度函數(shù)法對(duì)建模數(shù)據(jù)進(jìn)行冗余化處理,建模過(guò)程中采用相似度函數(shù)法和剪枝算法來(lái)解決稀疏性問(wèn)題以增加模型的泛化能力。動(dòng)態(tài)建模方面,由于靜態(tài)建模過(guò)程中,無(wú)法選擇全部的工況進(jìn)行建模,在線修正是軟測(cè)量技術(shù)研究必不可少的一步,本文采用自適應(yīng)留一交叉預(yù)報(bào)誤差的滑窗遞推算法進(jìn)行修正模型參數(shù)增加模型在線預(yù)測(cè)能力。本文在以上理論方法的研究基礎(chǔ)上,采集數(shù)據(jù),采用MATLAB編寫程序,完成建模數(shù)據(jù)處理和輔助變量選擇,進(jìn)行煙氣流量的靜態(tài)和動(dòng)態(tài)在線建模。仿真實(shí)驗(yàn)結(jié)果表明所建模型對(duì)各工況下的預(yù)測(cè)結(jié)果能夠達(dá)到預(yù)想效果,為進(jìn)一步完成燃煤機(jī)組節(jié)能環(huán)保優(yōu)化提供依據(jù)。
[Abstract]:At present, energy conservation, environmental protection is the power generation enterprises must face the problem. Flue gas flow is a very important variable in the process of energy saving and environmental protection control optimization. However, the measurement of flue gas flow is faced with various problems. On the one hand, the expansion of unit capacity makes the flue section larger, the cross-section flow field uneven, the traditional hardware sensor measurement error, on the other hand, Affected by the measurement sensors, such as the high temperature, high dust, high corrosion of the sensor, the hardware sensor will often malfunction, operation and maintenance difficulties, etc. These factors seriously restrict the automation operation degree of energy saving and environmental protection of power generation enterprises, and seriously restrict the working efficiency of power generation enterprises. In this paper, the soft measurement technology of flue gas flow in coal-fired units is taken as the research object. Firstly, the factors influencing the flue gas flow, such as the generation of flue gas and the gas flow, are analyzed theoretically and MATLAB. Through the combination of PLS variable projection importance analysis algorithm and forward search algorithm, the problem of correlation between auxiliary variables and the influence of auxiliary variables on dominant variables is solved. Secondly, in the aspect of modeling, according to the operation condition of the unit, the data of typical working condition is selected as the process data of static modeling, and in the aspect of data preprocessing, the singularity and the isolated point are treated by using the Lagrangian criterion and the normalization method. In the modeling method, the improved least squares support vector machine (LS-SVM) algorithm is used to model the static modeling, and the similarity function method is used to deal with the redundancy of the modeling data, aiming at the problem of LSSVM losing sparsity. The similarity function method and pruning algorithm are used to solve the sparse problem in order to increase the generalization ability of the model. In dynamic modeling, due to the static modeling process, it is impossible to choose all the working conditions to model, so online correction is an indispensable step in the research of soft sensing technology. In this paper, a sliding window recursive algorithm with adaptive residual cross prediction error is used to modify the model parameters to increase the on-line prediction ability of the model. On the basis of the research of the above theories and methods, this paper collects the data, writes the program with MATLAB, completes the modeling data processing and the auxiliary variable selection, and carries on the static and dynamic on-line modeling of the flue gas flow. The simulation results show that the predicted results of the model can achieve the desired results, which provides the basis for further optimization of energy saving and environmental protection of coal-fired units.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：X773;X831

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 林艷;;我國(guó)節(jié)能減排政策的優(yōu)化策略研究[J];理論月刊;2016年03期

2 張維;劉吉臻;;超超臨界燃煤機(jī)組直流鍋爐水煤比軟測(cè)量方法研究[J];動(dòng)力工程學(xué)報(bào);2016年02期

3 劉志平;;工業(yè)鍋爐節(jié)能減排探索[J];科技尚品;2016年01期

4 王奇?zhèn)?;某電廠煙氣監(jiān)測(cè)系統(tǒng)與脫硝自動(dòng)控制改造[J];中國(guó)電力;2015年07期

5 周久祥;孫莉;牛金山;張希光;;鍋爐SCR入口NO_X濃度的控制[J];能源研究與利用;2015年03期

6 王金雷;;脫硫煙氣自動(dòng)監(jiān)控設(shè)備的網(wǎng)狀流量測(cè)量方法[J];環(huán)境保護(hù)與循環(huán)經(jīng)濟(jì);2014年08期

7 張振義;鄒磊;;火電廠凈煙氣煙道流場(chǎng)特性分析及流量測(cè)點(diǎn)選擇[J];江蘇電機(jī)工程;2014年04期

8 張英堂;馬超;李志寧;范紅波;;基于快速留一交叉驗(yàn)證的核極限學(xué)習(xí)機(jī)在線建模[J];上海交通大學(xué)學(xué)報(bào);2014年05期

9 王彪;;石灰石濕法煙氣脫硫效率影響因素分析[J];自動(dòng)化應(yīng)用;2014年04期

10 汪麗芬;王恩祿;劉磊;王勐U，

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