【摘要】：隨著工業(yè)生產(chǎn)和社會經(jīng)濟的飛速發(fā)展,節(jié)能環(huán)保問題日益受到人類的重視�；鹆Πl(fā)電的燃煤鍋爐在燃燒過程中不僅要消耗大量的能源,還會排出大量的廢氣和有害煙塵,對大氣環(huán)境造成嚴重的污染。因此,實現(xiàn)能源的高效利用是火力發(fā)電行業(yè)實現(xiàn)節(jié)能減排的一項有效措施。飛灰含碳量是衡量火力發(fā)電鍋爐燃燒效率高低的一項重要指標,目前,國內(nèi)電力生產(chǎn)企業(yè)大部分都采用人工取樣、制樣和實驗室化學分析的方法進行離線檢測,而對于鍋爐燃燒優(yōu)化工程上通常也只憑借操作經(jīng)驗進行燃料和配風的人工調(diào)整,難以達到理想的運行效果,導致了能源的浪費,因此開展此項課題的研究具有重要的工程意義。本文以發(fā)電燃煤鍋爐為對象,分析了鍋爐生產(chǎn)過程的工藝特點,介紹了飛灰含碳量監(jiān)測和鍋爐燃燒優(yōu)化的研究現(xiàn)狀,并深入分析了影響飛灰含碳量相關因素,在此基礎上,采用改進的BP神經(jīng)網(wǎng)絡算法建立了飛灰含碳量預測模型,并依據(jù)一種新的群體智能算法───狼群算法對鍋爐燃燒工況進行優(yōu)化,最后通過仿真和實測數(shù)據(jù)驗證了預測模型和燃燒優(yōu)化方法的有效性,具有較高的工程應用價值。本文主要研究內(nèi)容如下:1.通過對國內(nèi)外研究現(xiàn)狀的調(diào)研綜述,分析了發(fā)電燃煤鍋爐燃燒過程的工藝特點,并歸納了有關飛灰含碳量影響因素以及降低飛灰含碳量常用方法。2.針對BP神經(jīng)網(wǎng)絡預測飛灰含碳量存在的樣本誤差問題,對神經(jīng)網(wǎng)絡誤差函數(shù)進行改進設計,并驗證了對于輸入樣本中的干擾具有較好的抑制作用。3.采用主元分析方法對神經(jīng)網(wǎng)絡模型進行精簡,針對飛灰含碳量測量輸入變量過多的問題,分析了各輸入變量對輸出變量的貢獻值,篩選了網(wǎng)絡的輸入?yún)?shù);設計了基于主元分析的BP神經(jīng)網(wǎng)絡飛灰含碳量預測模型,并進行了仿真分析和實驗驗證。4.提出了基于狼群算法的鍋爐燃燒優(yōu)化方案。依據(jù)飛灰含碳量的預測數(shù)據(jù),利用狼群算法對燃燒工況進行優(yōu)化,選擇最有利于燃燒的控制方案,并對其進行了仿真研究。
[Abstract]:With the rapid development of industrial production and social economy, the problem of energy saving and environmental protection has been paid more and more attention by human beings. Coal-fired boilers with thermal power not only consume a lot of energy, but also emit a large amount of exhaust gas and harmful smoke and dust, which cause serious pollution to the atmospheric environment. Therefore, the realization of efficient use of energy is an effective measure to achieve energy conservation and emission reduction in thermal power industry. The carbon content of fly ash is an important index to measure the combustion efficiency of thermal power boiler. At present, most domestic power production enterprises use manual sampling, sample making and laboratory chemical analysis to carry out off-line detection. For boiler combustion optimization engineering, it is difficult to achieve ideal operation effect by manual adjustment of fuel and air distribution only by virtue of operation experience, which leads to waste of energy. Therefore, it is of great engineering significance to carry out the research on this subject. Taking coal-fired boiler for power generation as an object, this paper analyzes the technological characteristics of boiler production process, introduces the research status of carbon content monitoring of fly ash and optimization of boiler combustion, and deeply analyzes the related factors affecting carbon content of fly ash, on the basis of which, the paper introduces the research status of carbon content monitoring of fly ash and optimization of boiler combustion. Based on the improved BP neural network algorithm, the prediction model of carbon content in fly ash is established, and the combustion condition of boiler is optimized according to a new swarm intelligent algorithm called wolf swarm algorithm. Finally, the effectiveness of the prediction model and combustion optimization method is verified by simulation and measured data, which has high engineering application value. The main contents of this paper are as follows: 1. Based on the survey of the current research situation at home and abroad, the process characteristics of combustion process in power generation coal-fired boilers are analyzed, and the factors affecting the carbon content of fly ash and the commonly used methods to reduce the carbon content of fly ash are summarized. 2. Aiming at the problem of sample error in prediction of carbon content in fly ash by BP neural network, the error function of neural network is improved, and it is verified that the neural network error function has a better inhibitory effect on the interference in input samples. 3. The neural network model is simplified by principal component analysis. Aiming at the problem that there are too many input variables in carbon measurement of fly ash, the contribution value of each input variable to the output variable is analyzed, and the input parameters of the network are selected. The prediction model of carbon content in fly ash based on BP neural network based on principal component analysis is designed and simulated and verified by experiments. 4. A boiler combustion optimization scheme based on wolf swarm algorithm is proposed. According to the prediction data of carbon content in fly ash, the combustion condition is optimized by using wolf swarm algorithm, and the most favorable control scheme is selected, and the simulation study is carried out.
【學位授予單位】：安徽工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM621.2

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