【作者】 王佳茗；

【导师】 阎维平；

【作者基本信息】 华北电力大学 ， 流体机械及工程， 2011， 硕士

【摘要】 近年来,随着节能减排越来越受到关注,燃煤电站锅炉燃烧优化课题得到了广泛的研究。电站锅炉在线多目标燃烧优化问题是一个综合了测量装置、大型流体机械电耗、燃烧预测模型建模、可控参数寻优等多方面因素的问题,本文着重研究其中的建模部分,采用混合核函数构造支持向量机建立燃烧预测模型,通过遗传算法实现参数寻优,编写燃烧优化指导系统实现了相应的功能。针对风、粉管内流场不均匀导致难以布置测点的问题提出了提高测量精度的措施;对风速、煤粉浓度、煤粉细度、煤质特性及送引风机的测量装置原理进行了介绍,分析了测量精度对燃烧优化的重要性。采用最小二乘支持向量机建立燃烧预测模型。为了提高支持向量机回归模型的精度,对核函数及其参数的选择进行了研究,将混合核函数应用到了燃烧预测模型的建立中,研究发现对不同的预测目标和训练样本数目,都有各自最佳的核函数,总体上看,对于电站锅炉燃烧优化问题,采用径向基核与多项式核加法混合方式有较理想的拟合与预测精度;在参数选择方面,对于采用加法混合核的支持向量机,分别研究了正则化参数、径向基核宽度、多项式核阶次的作用,总结了参数选择的规律;此外还发现,训练样本应尽可能取到所有输入参数的上下限,否则可能出现较大的预测偏差。介绍了遗传算法的计算步骤,综合考虑了大型流体机械电耗、磨煤机电耗、锅炉热效率及NOx排放浓度等多方面因素,导出了适应度函数“折算供电煤耗”,并实现多目标优化。编程实现了“300MW燃煤锅炉燃烧优化指导系统”。通过燃烧优化指导、切圆中心位置模拟、历史数据查询、模型在线更新等功能,为实现实时在线燃烧优化打下了基础。更多还原

【Abstract】 Energy-saving and emission-reducing in recent years have been gaining increasing attention. As a consequence, the project of Combustion Optimization of Coal-fired utility boiler has been extensively studied. The research of online multi-objective Combustion Optimization of Coal-fired utility boiler is a comprehensive one, which includes the following elements: measuring device, the modeling of large-scaled fluid machinery electricity consumption and combustion prediction model, Controllable parameter optimization and so on. This paper focuses on the modeling part. The corresponding function is achieved by applying mixed kernel functions to establish combustion prediction model; realizing optimization through genetic algorithm, and then authoring combustion optimization guidance system.As to the difficulty in locating the measuring spots which are caused by the ill-distributed state of the flow field inside primary air duct, the paper offers certain measures to enhance the measuring accuracy; it explains the principle of the measuring device of pulverized coal concentration, coal powder fineness, properties of coal quality and blower; and it also analyzes the importance of measuring accuracy to combustion optimization.The least squares support vector machines are applied to establish the combustion prediction model. In order to enhance the accuracy of the regression model, kernel functions and the option of parameters are studied. The mixture of kernel function is applied in the establishing of combustion prediction model. The result is that there are corresponding optimized kernel functions as to different predicting aims and sample numbers.Generally, the application of RBF and Polynominal and additive in optimizing the boiler combustion in power stations achieves the comparatively ideal prediction accuracy. As to the support vector machine in the perspective of the option of parameters, the function of regularization parameter, RBF width and polynominal degree are studied. The regular pattern of the option of parameters is concluded. Besides, the training samples should be widely spread within the upper and lower limits of the parameters, otherwise serious prediction error may appear.This paper introduces the calculating steps of genetic algorithm. The convert net coal consumption of fitness function is also deduced on the basis of considering the following elements: Large-scale fluid machinery, electricity consumption of mills, heat effectiveness of boilers and the emission concentration of NOx, and so on. Multi-objective Combustion Optimization is achieved.Eventually the combustion optimization guidance system of 300MW utility boiler is achieved by programming. The foundation is laid for realizing real-time online combustion optimization through the guidance of combustion optimization, simulation of the central location of the circle, the query of historical data, online updates of the model etc.更多还原