摘要
燃烧优化的核心在于建立有效而快速的建模工具及寻优算法,以便于在线应用。为了研究新方法的适用性以及克服常用算法的缺点,本文利用支持向量回归建立了大型四角切圆燃烧电站锅炉NOx排放特性模型。利用大量的热态实炉试验NOx排放数据对模型进行了训练和验证。结果表明,支持向量回归模型能获得较神经网络模型更加准确的预测结果,相对于神经网络,支持向量回归能更好处理大样本量数据的非线性问题。随后,采用一种基于高斯概率密度(GPDD)的分布估计优化算法对NOx排放模型进行了寻优。研究发现,与遗传算法相比,GPDD具有更好的寻优能力与更快的收敛速度。结合支持向量回归与高斯概率密度分布(GPDD)算法能有效降低燃煤锅炉NOx排放量,不到1min的优化时间便于在线应用。研究结论可为该算法在实际电厂中推广应用提供参考依据。
Quick and effective modeling tools and searching algorithms are the critical issues in realizing online 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 NOx 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, 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 NOx 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 Dower Dlants.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2009年第1期223-229,共7页
CIESC Journal
基金
国家自然科学基金项目(60534030,50576081)
新世纪优秀人才支持计划项目(NCET-07-0761)
全国优秀博士学位论文作者专项资金资助项目(200747)
浙江省自然科学基金项目(R107532)~~
关键词
燃烧优化
NOx
支持向量回归
高斯概率密度分布
分布估计算法
combustion optimization
NOx
support vector regression
Gaussian probability densitydistribution
estimation of distribution algorithms
