【摘要】：煤泥作為煤炭廢料,矸石電廠將其作為燃料在循環(huán)流化床鍋爐內(nèi)摻燒,通過(guò)鋪設(shè)管道進(jìn)行煤泥輸送。煤泥輸送管道的堵塞問(wèn)題一直是影響煤泥輸送系統(tǒng)生產(chǎn)效率和安全的重要因素之一。對(duì)堵塞進(jìn)行故障預(yù)測(cè)和定位將能顯著提高煤泥輸送系統(tǒng)的運(yùn)行性能,也是保障循環(huán)流化床鍋爐正常與安全運(yùn)行的重要課題。本文研究了基于小波變換和極限學(xué)習(xí)機(jī)的煤泥輸送管道堵塞預(yù)測(cè)及定位方法,主要開展了以下研究工作:針對(duì)煤泥輸送系統(tǒng)的監(jiān)測(cè)數(shù)據(jù)易受到干擾影響的問(wèn)題,分析現(xiàn)場(chǎng)監(jiān)測(cè)數(shù)據(jù)的特點(diǎn),對(duì)監(jiān)測(cè)數(shù)據(jù)進(jìn)行缺失數(shù)據(jù)補(bǔ)齊和數(shù)據(jù)去噪預(yù)處理。采用三次指數(shù)平滑法對(duì)數(shù)據(jù)進(jìn)行補(bǔ)齊處理,采用小波變換法進(jìn)行數(shù)據(jù)去噪,比較四種不同閾值方法的去噪效果,并將小波空域相關(guān)法用于信號(hào)去噪,取得了更好的去噪效果,實(shí)現(xiàn)了煤泥輸送管道監(jiān)測(cè)數(shù)據(jù)的預(yù)處理,并為后期的壓力分布建模、堵塞預(yù)測(cè)及定位奠定基礎(chǔ)。針對(duì)復(fù)雜管道煤泥輸送時(shí)阻力損失的問(wèn)題,分析煤泥輸送管道的阻力損失機(jī)理及影響因素,確定阻力損失是剪切應(yīng)力和摩擦阻力共同作用的結(jié)果;建立基于機(jī)理分析法的復(fù)雜管道(水平直管、傾斜管道、垂直管道)的壓力分布模型。對(duì)管道內(nèi)的一段煤泥進(jìn)行受力分析,建立力的平衡方程,并在非線性參數(shù)約束條件下對(duì)其進(jìn)行求解,得出復(fù)雜管道壓力分布的數(shù)學(xué)模型,其服從復(fù)雜的指數(shù)關(guān)系,并在確定模型摩擦阻力系數(shù)中改進(jìn)了常用的的計(jì)算方法;分析壓力分布的多變量影響因素,建立基于量子遺傳(QGA)BP神經(jīng)網(wǎng)絡(luò)的QGA-BP的壓力分布模型。針對(duì)煤泥輸送管道堵塞預(yù)測(cè)問(wèn)題,提出基于粒子群優(yōu)化核函數(shù)極限學(xué)習(xí)機(jī)(PSOKELM)的煤泥輸送管道堵塞預(yù)測(cè)方法,該方法在分析煤泥輸送管道堵塞預(yù)測(cè)機(jī)理的基礎(chǔ)上,確定堵塞預(yù)測(cè)的特征量,將支持向量機(jī)核函數(shù)引入極限學(xué)習(xí)機(jī),并通過(guò)粒子群算法進(jìn)行參數(shù)優(yōu)化。利用黃陵煤矸石熱電廠實(shí)際測(cè)試數(shù)據(jù)進(jìn)行仿真實(shí)驗(yàn),并與粒子群算法優(yōu)化支持向量機(jī)(PSOSVM)預(yù)測(cè)模型和核函數(shù)極限學(xué)習(xí)機(jī)(KELM)預(yù)測(cè)模型進(jìn)行比較,結(jié)果證明基于PSOKELM的預(yù)測(cè)模型在預(yù)測(cè)速度和準(zhǔn)確性方面均優(yōu)于PSOSVM預(yù)測(cè)模型,在預(yù)測(cè)精度上優(yōu)于KELM預(yù)測(cè)模型。針對(duì)煤泥輸送管道堵塞定位問(wèn)題,分析瞬態(tài)正負(fù)壓波法的定位原理,利用小波變換空域相關(guān)法對(duì)正負(fù)壓波信號(hào)去噪,提出基于小波包預(yù)處理經(jīng)驗(yàn)?zāi)B(tài)分解和小波變換模極大值法相結(jié)合(WPEMD-WTM)的正負(fù)壓波突變點(diǎn)檢測(cè)方法,確定正負(fù)壓波時(shí)間差,結(jié)合壓力波波速進(jìn)行堵塞點(diǎn)定位,并通過(guò)仿真驗(yàn)證了該堵塞定位方法的有效性和準(zhǔn)確性。針對(duì)煤泥輸送管道堵塞故障的報(bào)警問(wèn)題,研究堵塞故障異常分析方法。通過(guò)計(jì)算壓力預(yù)測(cè)值及預(yù)測(cè)區(qū)間,結(jié)合壓力樣本數(shù)據(jù)的統(tǒng)計(jì)特征,判斷壓力異常狀況,確定警示閾值,劃分警示等級(jí),根據(jù)不同等級(jí),采取不同的安全控制措施,并驗(yàn)證了堵塞故障安全控制方法的合理性。本文提出的煤泥輸送管道壓力分布模型,可對(duì)管道設(shè)計(jì)和膏體泵選擇提供依據(jù);基于核函數(shù)極限學(xué)習(xí)機(jī)的堵塞預(yù)測(cè)模型和基于小波分析的堵塞定位方法,能對(duì)于煤泥輸送系統(tǒng)的堵塞故障問(wèn)題提供新的安全控制決策和手段。
[Abstract]:Slime is a kind of coal waste, which is burned in a circulating fluidized bed boiler as fuel in a gangue power plant. Slime transportation is carried out by laying pipelines. The blockage of the pipelines has always been one of the important factors affecting the production efficiency and safety of the sludge transportation system. The operation performance of the feeding system is also an important issue to ensure the normal and safe operation of CFB boilers. This paper studies the prediction and location method of slurry pipeline blockage based on wavelet transform and extreme learning machine. The characteristics of field monitoring data are analyzed, and the missing data are complemented and the data are denoised by cubic exponential smoothing method and wavelet transform method. Good denoising effect has realized the pretreatment of monitoring data of slime transportation pipeline, and laid the foundation for modeling the pressure distribution in the later period, predicting and locating the blockage. The pressure distribution model of the complex pipeline (horizontal straight pipe, inclined pipe, vertical pipe) based on the mechanism analysis method is established. The force analysis of a section of slime in the pipeline is carried out, the force balance equation is established, and the mathematical model of the pressure distribution of the complex pipeline is obtained under the condition of nonlinear parameter constraints. The model obeys complex exponential relation and improves the commonly used calculation method in determining the friction coefficient of the model; analyzes the multi-variable influencing factors of pressure distribution, establishes the pressure distribution model of QGA-BP based on the quantum genetic algorithm (QGA) BP neural network. Kernel function extreme learning machine (PSOKELM) is used to predict the blockage of coal slurry pipeline. Based on the analysis of the blockage prediction mechanism of coal slurry pipeline, the characteristic quantity of blockage prediction is determined. The kernel function of support vector machine is introduced into the extreme learning machine, and the parameters are optimized by particle swarm optimization. The test data are simulated and compared with PSOSVM and KELM prediction models. The results show that PSOKELM prediction model is superior to PSOSVM prediction model in prediction speed and accuracy, and is superior to KELM prediction model in prediction accuracy. Based on the analysis of the location principle of the transient positive and negative pressure wave method and the denoising of the positive and negative pressure wave signal by the spatial correlation method of wavelet transform, a detection method of the catastrophe point of the positive and negative pressure wave based on wavelet packet pre-processing empirical mode decomposition and wavelet transform modulus maximum method (WPEMD-WTM) is proposed to determine the time of the positive and negative pressure wave. The validity and accuracy of this method are verified by simulation. Aiming at the alarming problem of the blocking fault of the coal slurry pipeline, the analysis method of abnormal blocking fault is studied. The prediction value and prediction interval are calculated, and the statistical characteristics of the pressure sample data are combined to judge the blocking fault. According to different levels, different safety control measures are adopted, and the rationality of the safety control method for blocking fault is verified. The pressure distribution model of the slurry pipeline proposed in this paper can provide a basis for pipeline design and paste pump selection; based on kernel function limit learning machine The blockage prediction model and the blockage location method based on wavelet analysis can provide a new safety control decision and means for the blockage fault of coal slurry transportation system.
【學(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM621
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本文編號(hào)：2220161