关键词:

燃烧优化, NO_x, 支持向量回归, 高斯概率密度分布, 分布估计算法

Abstract:

Quick and effective modeling tools and searching algorithms are the critical issues in realizing on-line combustion optimization in coal-fired utility boilers.In order to explore the applicability of novel modeling tools and optimization algorithms and to overcome the drawbacks of the existing methods, in the present study support vector regression (SYR) model was proposed to capture the functional relation between the NO_x emissions and operational parameters of a utility boiler.A large number of thermal field test samples, which were recorded by DCS in the actual power plants, were employed to establish the models.It was found that the predicted NO_x emissions by SVR showed better agreement with the measured than those by neural networks.SVR model was more suitable to non-linear problem with a large number of samples.Subsequently, a Gaussian probability density distribution (GPDD) based optimization algorithm was described and then applied to searching the optimal inputs of SVR model for NO_x reduction.The results showed that GPDD outperformed the existing GA.Less than one minute of optimization time period required for GPDD was suitable for on-line application.The current work will lay a foundation for the further extension of GPDD’s application to actual power plants.

Key words:

燃烧优化, NO_x, 支持向量回归, 高斯概率密度分布, 分布估计算法