Research On Online Multi-objective Combustion Optimization Of Coal-fired Utility Boiler

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.