本文選題：神經(jīng)網(wǎng)絡 + 鍋爐燃燒優(yōu)化��； 參考：《北京交通大學》2014年碩士論文

【摘要】：隨著“節(jié)能減排”政策要求加強,在保證安全生產(chǎn)和滿足負荷需求前提下,應努力提高火電廠運行效率、降低煤耗,控制并減少污染排放。本文針對火電廠鍋爐燃燒系統(tǒng)進行了基于數(shù)據(jù)驅(qū)動的建模和優(yōu)化研究。 本文以中國華電一300MW機組為研究對象,給出了電廠鍋爐的數(shù)據(jù)采集、預處理、優(yōu)化指標選取的步驟和神經(jīng)網(wǎng)絡穩(wěn)態(tài)建模過程及其評價。以降低單位相對煤耗和降低氮氧化物(NOx)排放量兩者兼顧做為鍋爐優(yōu)化運行的評價指標。在兩個典型負荷處分負荷點建模。比較了不同的神經(jīng)網(wǎng)絡建模訓練算法的異同并對其泛化能力進行了評價。 對電廠鍋爐運行這一非線性動態(tài)工業(yè)過程,討論和研究了在離散狀態(tài)下一種基于數(shù)據(jù)驅(qū)動的建模方式,通過對鍋爐燃燒過程的穩(wěn)態(tài)建模和動態(tài)建模區(qū)別對待,給出了穩(wěn)態(tài)和動態(tài)相結(jié)合的建模思想。給出了穩(wěn)態(tài)過程和動態(tài)過程的定義、辨識方法、增益計算方法。 在前向神經(jīng)網(wǎng)絡只能構(gòu)建穩(wěn)態(tài)模型和現(xiàn)場采集得到的數(shù)據(jù)存在動態(tài)特性的情況下,給出一種前向神經(jīng)網(wǎng)絡和一階自回歸模型相結(jié)合的混合模型。一階自回歸模型的存在使該混合模型能夠描述動態(tài)特性,并推導出了其訓練算法。將該模型運用到了火電廠的氮氧化物排放量建模中并對其建模能力進行了評價。結(jié)果表明加入的一階自回歸模型能夠使混合模型提高擬合精度,說明在氮氧化物的形成過程中存在動態(tài)特性。 最后采用遺傳算法得到了兩個典型負荷下的以降低煤耗和降低氮氧化物為目標的優(yōu)化結(jié)果,基于神經(jīng)網(wǎng)絡模型得到了優(yōu)化結(jié)果。并在濾除氮氧化物排放量數(shù)據(jù)中存在的擾動后得到了新的優(yōu)化結(jié)果。對基于不同的模型得到的優(yōu)化結(jié)果進行了比較和分析。結(jié)合現(xiàn)場數(shù)據(jù)給出了氮氧化物排放量相對增益計算的分析結(jié)果。利用數(shù)據(jù)庫和動態(tài)網(wǎng)頁技術制作了優(yōu)化結(jié)果發(fā)布的動態(tài)網(wǎng)頁,用以指導電廠機組人員對機組進行優(yōu)化運行。
[Abstract]:With the strengthening of "energy saving and emission reduction" policy, under the premise of ensuring safe production and satisfying load demand, it is necessary to improve the operation efficiency of thermal power plants, reduce coal consumption, control and reduce pollution emissions. In this paper, data-driven modeling and optimization of boiler combustion system in thermal power plant are studied. This paper takes Huadian-300MW unit as the research object, gives the steps of data acquisition, preprocessing, optimization index selection and neural network steady-state modeling process and its evaluation of boiler in power plant. Both reducing the relative coal consumption per unit and reducing the no _ x emissions are considered as the evaluation indexes for the optimal operation of the boiler. Modeling at two typical load disposal points. The similarities and differences of different neural network modeling training algorithms are compared and their generalization ability is evaluated. For the nonlinear dynamic industrial process of power plant boiler, a data-driven modeling method based on discrete state is discussed and studied. The steady state modeling and dynamic modeling of boiler combustion process are treated differently. The idea of combining steady-state and dynamic modeling is presented. The definition, identification method and gain calculation method of steady and dynamic processes are given. Under the condition that the feedforward neural network can only construct the steady model and the data collected in the field have dynamic characteristics, a hybrid model combining the feedforward neural network and the first order autoregressive model is presented. The existence of the first order autoregressive model enables the hybrid model to describe the dynamic characteristics, and its training algorithm is derived. The model is applied to the modeling of NOx emissions in thermal power plants and its modeling ability is evaluated. The results show that the first order autoregressive model can improve the fitting accuracy of the mixed model, which indicates that there are dynamic characteristics in the formation of nitrogen oxides. Finally, genetic algorithm is used to obtain the optimization results with the goal of reducing coal consumption and nitrogen oxides under two typical loads, and the optimization results are obtained based on the neural network model. The new optimization results are obtained after filtering the disturbance in the nitrogen oxide emission data. The optimization results based on different models are compared and analyzed. The results of calculation of the relative gain of NOx emissions are given based on the field data. Using database and dynamic web technology, the dynamic web page of the optimization result is made to guide the crew of the power plant to operate optimally.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM621.2

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