摘要
根据炉膛温度更能快速反映煤的燃烧热效率,一定的煤量燃烧最充分时其温度也最高的原理,提出了一种由专家控制系统根据不同的燃烧工况提供最佳鼓风量方法,专家系统瓶颈问题的知识获取则由改进遗传算法优化完成。为了避免作为随机搜索的遗传算法在搜索过程中引起鼓风量的混乱,寻优过程离线进行,寻优结果数据提供给专家系统,最后由专家系统提供最佳鼓风量。该系统现场运行正常,通过锅炉燃烧效率测试热效率提高2.6%。
The furnace temperature can fleetly reflect the combustion thermal efficiency of coal and the temperature is highest when coal burns most sufficiently, based on which, a method with optimal air blowing quantity offered by expert system in accordance with different combustion conditions is proposed. The knowledge acquisition, as the bottleneck of expert system, is completed using an improved genetic algorithm optimization. To avoid air-blowing confusion during the random search of genetic algorithm, the optimization seeking is processed off-line and the optimal results are fed to expert system, with which expert system decides the optimal air blowing quantity. The system operations normally on site and enhances the heat efficient by 2.6%.
出处
《电力自动化设备》
EI
CSCD
北大核心
2005年第5期27-29,33,共4页
Electric Power Automation Equipment
基金
北京市教育委员会共建重点实验室资助项目(SYS100070417)
关键词
锅炉燃烧
鼓风量
专家系统
神经网络
遗传算法
boiler combustion
air blowing quantity
expert system
neural network
genetic algorithm
