本文選題：電站鍋爐 + 最小二乘支持向量機��； 參考：《華北電力大學》2014年碩士論文

【摘要】：目前火力發(fā)電在我國占主導地位,電站對煤的需求量持續(xù)增加,燃煤排放的污染物也在不斷增加,因此電站鍋爐燃燒優(yōu)化成為一項重要的研究課題。電站鍋爐燃燒優(yōu)化主要分為兩部分：電站鍋爐燃燒系統(tǒng)模型是鍋爐燃燒優(yōu)化的基礎,優(yōu)化控制算法是鍋爐燃燒優(yōu)化的核心。 鍋爐燃燒系統(tǒng)機理復雜,利用燃燒機理建立的數(shù)值模擬模型運算速度十分緩慢,無法直接應用到鍋爐燃燒優(yōu)化中。機器學習建模運算速度較快,但是無法反應鍋爐內(nèi)部機理,而且模型受訓練樣本數(shù)據(jù)影響較大。結合機理建模與機器學習建模的優(yōu)缺點,提出了一種以數(shù)值機理模型提供數(shù)據(jù),并結合機組試驗數(shù)據(jù)以及實時運行數(shù)據(jù)建立最小二乘支持向量機(LS-SVM)模型的復合建模方法。針對LS-SVM依賴建模樣本數(shù)據(jù)的特性,數(shù)值機理模型提供的數(shù)據(jù)能夠豐富LS-SVM模型的初始樣本的多樣性,提高模型精度。LS-SVM的快速性能夠很好地滿足燃燒優(yōu)化控制的需求。 結合特征選擇提出了SFS-LS-SVM方法,能夠精簡樣本數(shù)據(jù),去除數(shù)據(jù)中冗余成分,提高建模速度。通過具體算例驗證了變量選擇的效果。根據(jù)LS-SVM算法提出了基于LS-SVM的模型更新算法。在復合建模中,模型更新算法用以更新機組的歷史數(shù)據(jù)及實時運行數(shù)據(jù),并且通過算例驗證了建模效果。 本文結合這兩種優(yōu)化方案的優(yōu)缺點提出了一種以鍋爐效率最大為目標的優(yōu)化方案,并驗證了優(yōu)化效果。結合論文對建模與優(yōu)化方案的研究成果,開發(fā)了燃燒優(yōu)化控制系統(tǒng)。
[Abstract]:At present, thermal power generation plays a leading role in our country. The demand for coal in power stations is increasing continuously, and the pollutants emitted from coal combustion are also increasing. Therefore, the combustion optimization of power plant boilers has become an important research topic. The combustion optimization of utility boiler is divided into two parts: the model of boiler combustion system is the basis of boiler combustion optimization, and the optimization control algorithm is the core of boiler combustion optimization. The mechanism of boiler combustion system is complex, and the calculation speed of numerical simulation model established by combustion mechanism is very slow, which can not be directly applied to boiler combustion optimization. Machine learning modeling is fast, but it can not reflect the internal mechanism of boiler, and the model is greatly affected by the training sample data. Combining the advantages and disadvantages of mechanism modeling and machine learning modeling, this paper presents a compound modeling method of least square support vector machine (LS-SVM) model based on numerical mechanism model, unit test data and real-time operation data. According to the characteristics of LS-SVM dependent modeling sample data, the data provided by numerical mechanism model can enrich the diversity of initial samples of LS-SVM model, and improve the rapidity of model precision. LS-SVM can meet the needs of combustion optimization control. Based on feature selection, a SFS-LS-SVM method is proposed, which can simplify the sample data, remove redundant components from the data, and improve the modeling speed. The effect of variable selection is verified by an example. According to LS-SVM algorithm, a model updating algorithm based on LS-SVM is proposed. In the composite modeling, the model updating algorithm is used to update the historical data and real-time operation data of the unit, and the modeling effect is verified by an example. Combining the advantages and disadvantages of these two optimization schemes, this paper presents an optimization scheme aiming at the maximum boiler efficiency, and verifies the optimization effect. Based on the research results of modeling and optimization scheme, the combustion optimization control system is developed.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM621.2;TP273

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